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How to Write a Literature Review | Guide, Examples, & Templates

Published on January 2, 2023 by Shona McCombes . Revised on September 11, 2023.

What is a literature review? A literature review is a survey of scholarly sources on a specific topic. It provides an overview of current knowledge, allowing you to identify relevant theories, methods, and gaps in the existing research that you can later apply to your paper, thesis, or dissertation topic .

There are five key steps to writing a literature review:

  • Search for relevant literature
  • Evaluate sources
  • Identify themes, debates, and gaps
  • Outline the structure
  • Write your literature review

A good literature review doesn’t just summarize sources—it analyzes, synthesizes , and critically evaluates to give a clear picture of the state of knowledge on the subject.

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Table of contents

What is the purpose of a literature review, examples of literature reviews, step 1 – search for relevant literature, step 2 – evaluate and select sources, step 3 – identify themes, debates, and gaps, step 4 – outline your literature review’s structure, step 5 – write your literature review, free lecture slides, other interesting articles, frequently asked questions, introduction.

  • Quick Run-through
  • Step 1 & 2

When you write a thesis , dissertation , or research paper , you will likely have to conduct a literature review to situate your research within existing knowledge. The literature review gives you a chance to:

  • Demonstrate your familiarity with the topic and its scholarly context
  • Develop a theoretical framework and methodology for your research
  • Position your work in relation to other researchers and theorists
  • Show how your research addresses a gap or contributes to a debate
  • Evaluate the current state of research and demonstrate your knowledge of the scholarly debates around your topic.

Writing literature reviews is a particularly important skill if you want to apply for graduate school or pursue a career in research. We’ve written a step-by-step guide that you can follow below.

Literature review guide

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Writing literature reviews can be quite challenging! A good starting point could be to look at some examples, depending on what kind of literature review you’d like to write.

  • Example literature review #1: “Why Do People Migrate? A Review of the Theoretical Literature” ( Theoretical literature review about the development of economic migration theory from the 1950s to today.)
  • Example literature review #2: “Literature review as a research methodology: An overview and guidelines” ( Methodological literature review about interdisciplinary knowledge acquisition and production.)
  • Example literature review #3: “The Use of Technology in English Language Learning: A Literature Review” ( Thematic literature review about the effects of technology on language acquisition.)
  • Example literature review #4: “Learners’ Listening Comprehension Difficulties in English Language Learning: A Literature Review” ( Chronological literature review about how the concept of listening skills has changed over time.)

You can also check out our templates with literature review examples and sample outlines at the links below.

Download Word doc Download Google doc

Before you begin searching for literature, you need a clearly defined topic .

If you are writing the literature review section of a dissertation or research paper, you will search for literature related to your research problem and questions .

Make a list of keywords

Start by creating a list of keywords related to your research question. Include each of the key concepts or variables you’re interested in, and list any synonyms and related terms. You can add to this list as you discover new keywords in the process of your literature search.

  • Social media, Facebook, Instagram, Twitter, Snapchat, TikTok
  • Body image, self-perception, self-esteem, mental health
  • Generation Z, teenagers, adolescents, youth

Search for relevant sources

Use your keywords to begin searching for sources. Some useful databases to search for journals and articles include:

  • Your university’s library catalogue
  • Google Scholar
  • Project Muse (humanities and social sciences)
  • Medline (life sciences and biomedicine)
  • EconLit (economics)
  • Inspec (physics, engineering and computer science)

You can also use boolean operators to help narrow down your search.

Make sure to read the abstract to find out whether an article is relevant to your question. When you find a useful book or article, you can check the bibliography to find other relevant sources.

You likely won’t be able to read absolutely everything that has been written on your topic, so it will be necessary to evaluate which sources are most relevant to your research question.

For each publication, ask yourself:

  • What question or problem is the author addressing?
  • What are the key concepts and how are they defined?
  • What are the key theories, models, and methods?
  • Does the research use established frameworks or take an innovative approach?
  • What are the results and conclusions of the study?
  • How does the publication relate to other literature in the field? Does it confirm, add to, or challenge established knowledge?
  • What are the strengths and weaknesses of the research?

Make sure the sources you use are credible , and make sure you read any landmark studies and major theories in your field of research.

You can use our template to summarize and evaluate sources you’re thinking about using. Click on either button below to download.

Take notes and cite your sources

As you read, you should also begin the writing process. Take notes that you can later incorporate into the text of your literature review.

It is important to keep track of your sources with citations to avoid plagiarism . It can be helpful to make an annotated bibliography , where you compile full citation information and write a paragraph of summary and analysis for each source. This helps you remember what you read and saves time later in the process.

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To begin organizing your literature review’s argument and structure, be sure you understand the connections and relationships between the sources you’ve read. Based on your reading and notes, you can look for:

  • Trends and patterns (in theory, method or results): do certain approaches become more or less popular over time?
  • Themes: what questions or concepts recur across the literature?
  • Debates, conflicts and contradictions: where do sources disagree?
  • Pivotal publications: are there any influential theories or studies that changed the direction of the field?
  • Gaps: what is missing from the literature? Are there weaknesses that need to be addressed?

This step will help you work out the structure of your literature review and (if applicable) show how your own research will contribute to existing knowledge.

  • Most research has focused on young women.
  • There is an increasing interest in the visual aspects of social media.
  • But there is still a lack of robust research on highly visual platforms like Instagram and Snapchat—this is a gap that you could address in your own research.

There are various approaches to organizing the body of a literature review. Depending on the length of your literature review, you can combine several of these strategies (for example, your overall structure might be thematic, but each theme is discussed chronologically).

Chronological

The simplest approach is to trace the development of the topic over time. However, if you choose this strategy, be careful to avoid simply listing and summarizing sources in order.

Try to analyze patterns, turning points and key debates that have shaped the direction of the field. Give your interpretation of how and why certain developments occurred.

If you have found some recurring central themes, you can organize your literature review into subsections that address different aspects of the topic.

For example, if you are reviewing literature about inequalities in migrant health outcomes, key themes might include healthcare policy, language barriers, cultural attitudes, legal status, and economic access.

Methodological

If you draw your sources from different disciplines or fields that use a variety of research methods , you might want to compare the results and conclusions that emerge from different approaches. For example:

  • Look at what results have emerged in qualitative versus quantitative research
  • Discuss how the topic has been approached by empirical versus theoretical scholarship
  • Divide the literature into sociological, historical, and cultural sources

Theoretical

A literature review is often the foundation for a theoretical framework . You can use it to discuss various theories, models, and definitions of key concepts.

You might argue for the relevance of a specific theoretical approach, or combine various theoretical concepts to create a framework for your research.

Like any other academic text , your literature review should have an introduction , a main body, and a conclusion . What you include in each depends on the objective of your literature review.

The introduction should clearly establish the focus and purpose of the literature review.

Depending on the length of your literature review, you might want to divide the body into subsections. You can use a subheading for each theme, time period, or methodological approach.

As you write, you can follow these tips:

  • Summarize and synthesize: give an overview of the main points of each source and combine them into a coherent whole
  • Analyze and interpret: don’t just paraphrase other researchers — add your own interpretations where possible, discussing the significance of findings in relation to the literature as a whole
  • Critically evaluate: mention the strengths and weaknesses of your sources
  • Write in well-structured paragraphs: use transition words and topic sentences to draw connections, comparisons and contrasts

In the conclusion, you should summarize the key findings you have taken from the literature and emphasize their significance.

When you’ve finished writing and revising your literature review, don’t forget to proofread thoroughly before submitting. Not a language expert? Check out Scribbr’s professional proofreading services !

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If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

  • Sampling methods
  • Simple random sampling
  • Stratified sampling
  • Cluster sampling
  • Likert scales
  • Reproducibility

 Statistics

  • Null hypothesis
  • Statistical power
  • Probability distribution
  • Effect size
  • Poisson distribution

Research bias

  • Optimism bias
  • Cognitive bias
  • Implicit bias
  • Hawthorne effect
  • Anchoring bias
  • Explicit bias

A literature review is a survey of scholarly sources (such as books, journal articles, and theses) related to a specific topic or research question .

It is often written as part of a thesis, dissertation , or research paper , in order to situate your work in relation to existing knowledge.

There are several reasons to conduct a literature review at the beginning of a research project:

  • To familiarize yourself with the current state of knowledge on your topic
  • To ensure that you’re not just repeating what others have already done
  • To identify gaps in knowledge and unresolved problems that your research can address
  • To develop your theoretical framework and methodology
  • To provide an overview of the key findings and debates on the topic

Writing the literature review shows your reader how your work relates to existing research and what new insights it will contribute.

The literature review usually comes near the beginning of your thesis or dissertation . After the introduction , it grounds your research in a scholarly field and leads directly to your theoretical framework or methodology .

A literature review is a survey of credible sources on a topic, often used in dissertations , theses, and research papers . Literature reviews give an overview of knowledge on a subject, helping you identify relevant theories and methods, as well as gaps in existing research. Literature reviews are set up similarly to other  academic texts , with an introduction , a main body, and a conclusion .

An  annotated bibliography is a list of  source references that has a short description (called an annotation ) for each of the sources. It is often assigned as part of the research process for a  paper .  

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Encyclopedia of Evidence in Pharmaceutical Public Health and Health Services Research in Pharmacy pp 1–15 Cite as

Methodological Approaches to Literature Review

  • Dennis Thomas 2 ,
  • Elida Zairina 3 &
  • Johnson George 4  
  • Living reference work entry
  • First Online: 09 May 2023

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The literature review can serve various functions in the contexts of education and research. It aids in identifying knowledge gaps, informing research methodology, and developing a theoretical framework during the planning stages of a research study or project, as well as reporting of review findings in the context of the existing literature. This chapter discusses the methodological approaches to conducting a literature review and offers an overview of different types of reviews. There are various types of reviews, including narrative reviews, scoping reviews, and systematic reviews with reporting strategies such as meta-analysis and meta-synthesis. Review authors should consider the scope of the literature review when selecting a type and method. Being focused is essential for a successful review; however, this must be balanced against the relevance of the review to a broad audience.

  • Literature review
  • Systematic review
  • Meta-analysis
  • Scoping review
  • Research methodology

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Thomas, D., Zairina, E., George, J. (2023). Methodological Approaches to Literature Review. In: Encyclopedia of Evidence in Pharmaceutical Public Health and Health Services Research in Pharmacy. Springer, Cham. https://doi.org/10.1007/978-3-030-50247-8_57-1

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What is a Literature Review?

A literature or narrative review is a comprehensive review and analysis of the published literature on a specific topic or research question. The literature that is reviewed contains: books, articles, academic articles, conference proceedings, association papers, and dissertations. It contains the most pertinent studies and points to important past and current research and practices. It provides background and context, and shows how your research will contribute to the field. 

A literature review should: 

  • Provide a comprehensive and updated review of the literature;
  • Explain why this review has taken place;
  • Articulate a position or hypothesis;
  • Acknowledge and account for conflicting and corroborating points of view

From  S age Research Methods

Purpose of a Literature Review

A literature review can be written as an introduction to a study to:

  • Demonstrate how a study fills a gap in research
  • Compare a study with other research that's been done

Or it can be a separate work (a research article on its own) which:

  • Organizes or describes a topic
  • Describes variables within a particular issue/problem

Limitations of a Literature Review

Some of the limitations of a literature review are:

  • It's a snapshot in time. Unlike other reviews, this one has beginning, a middle and an end. There may be future developments that could make your work less relevant.
  • It may be too focused. Some niche studies may miss the bigger picture.
  • It can be difficult to be comprehensive. There is no way to make sure all the literature on a topic was considered.
  • It is easy to be biased if you stick to top tier journals. There may be other places where people are publishing exemplary research. Look to open access publications and conferences to reflect a more inclusive collection. Also, make sure to include opposing views (and not just supporting evidence).

Source: Grant, Maria J., and Andrew Booth. “A Typology of Reviews: An Analysis of 14 Review Types and Associated Methodologies.” Health Information & Libraries Journal, vol. 26, no. 2, June 2009, pp. 91–108. Wiley Online Library, doi:10.1111/j.1471-1842.2009.00848.x.

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Definition: A literature review is a systematic examination and synthesis of existing scholarly research on a specific topic or subject.

Purpose: It serves to provide a comprehensive overview of the current state of knowledge within a particular field.

Analysis: Involves critically evaluating and summarizing key findings, methodologies, and debates found in academic literature.

Identifying Gaps: Aims to pinpoint areas where there is a lack of research or unresolved questions, highlighting opportunities for further investigation.

Contextualization: Enables researchers to understand how their work fits into the broader academic conversation and contributes to the existing body of knowledge.

extensive literature review and

tl;dr  A literature review critically examines and synthesizes existing scholarly research and publications on a specific topic to provide a comprehensive understanding of the current state of knowledge in the field.

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  • Published: 30 December 2019

Biomarkers of meat and seafood intake: an extensive literature review

  • Cătălina Cuparencu   ORCID: orcid.org/0000-0003-3889-5498 1   na1 ,
  • Giulia Praticó 1   na1 ,
  • Lieselot Y. Hemeryck 2   na1 ,
  • Pedapati S. C. Sri Harsha 3 ,
  • Stefania Noerman 4 ,
  • Caroline Rombouts 2 ,
  • Muyao Xi 1 ,
  • Lynn Vanhaecke 2 ,
  • Kati Hanhineva 4 ,
  • Lorraine Brennan 3 &
  • Lars O. Dragsted 1  

Genes & Nutrition volume  14 , Article number:  35 ( 2019 ) Cite this article

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Meat, including fish and shellfish, represents a valuable constituent of most balanced diets. Consumption of different types of meat and fish has been associated with both beneficial and adverse health effects. While white meats and fish are generally associated with positive health outcomes, red and especially processed meats have been associated with colorectal cancer and other diseases . The contribution of these foods to the development or prevention of chronic diseases is still not fully elucidated. One of the main problems is the difficulty in properly evaluating meat intake, as the existing self-reporting tools for dietary assessment may be imprecise and therefore affected by systematic and random errors. Dietary biomarkers measured in biological fluids have been proposed as possible objective measurements of the actual intake of specific foods and as a support for classical assessment methods. Good biomarkers for meat intake should reflect total dietary intake of meat, independent of source or processing and should be able to differentiate meat consumption from that of other protein-rich foods; alternatively, meat intake biomarkers should be specific to each of the different meat sources (e.g., red vs. white; fish, bird, or mammal) and/or cooking methods. In this paper, we present a systematic investigation of the scientific literature while providing a comprehensive overview of the possible biomarker(s) for the intake of different types of meat, including fish and shellfish, and processed and heated meats according to published guidelines for biomarker reviews (BFIrev). The most promising biomarkers are further validated for their usefulness for dietary assessment by published validation criteria.

Meat, including fish and shellfish, represents a valuable constituent of a balanced omnivorous diet. The importance of meat from a nutritional point of view is related to its high-quality protein content, as it comprises a balanced source of all essential amino acids for muscle maintenance [ 1 ]. Minerals and vitamins, such as iron and B 12 vitamin, and other micronutrients that are essential for growth and development, are additionally highly bioavailable from meat compared to other sources [ 2 ]. Meat processing, such as curing, smoking, or heating, further improves its organoleptic properties, as well as its microbiological safety and shelf life [ 3 ]. On the other hand, high consumption of processed meat and possibly red meat has been associated with a series of adverse health outcomes, including cardiovascular disease (CVD) [ 4 ], overall mortality [ 5 ], and certain types of cancer, especially colorectal cancer [ 6 , 7 , 8 ]. However, recent large, prospective investigation including almost half a million subjects from ten European countries [ 9 ], only found significant associations with processed meat intake. Poultry was not related to all-cause mortality, whereas red meat was related to higher all-cause mortality only before correcting for potential confounding [ 9 ]. Additionally, high intake of red and processed meat has also been associated with an increased risk of developing type 2 diabetes, although major inconsistencies regarding consumption levels exist between studies [ 10 , 11 , 12 ]. Fish and shellfish intake has been associated with positive health outcomes, such as decreasing CVD risk [ 13 ] and possibly colorectal cancer [ 14 , 15 ], while its role in preventing type 2 diabetes development is still unclear [ 16 , 17 ]. Different outcomes in the studies may also associate with the type of fish consumed and the cooking methods [ 17 ]. Moreover, aquatic meat has proven to be one of the major dietary sources of contaminants that are potentially harmful to human health, such as methylmercury and arsenic [ 18 , 19 ].

Since associations are always debatable in terms of cause and effect and weak associations like those between meat and chronic disease are always at a high risk of being affected by bias or confounding, it is important to find biomarkers able to objectively discriminate between different classes of meat, particularly red meat, white meat from poultry, white meat from fish or shellfish, and processed meats [ 20 ]. The existing self-reporting tools for dietary assessment, such as food frequency questionnaires (FFQ) and dietary records, are imprecise and can be affected by systematic and random errors, especially when a certain food is perceived as healthy or unhealthy [ 21 ]. Dietary biomarkers have been proposed as possible objective measurements of the actual specific food intakes or at least as a support for the classical assessment methods [ 22 ]. A good marker of overall meat intake should reflect total dietary meat intake differentiating any meat consumption from that of other protein-rich foods; additional markers should discriminate between different meat sources (e.g., red vs. white or between different species, e.g., pork vs. beef) and identify cooking methods. In this paper, we present the results of a systematic search of the scientific literature according to the BFIrev methodology [ 23 ] providing a comprehensive overview of the possible marker(s) for the intake of different meat types, including fish and shellfish. The most promising biomarkers are further validated according to a validation scheme previously proposed for biomarkers of food intake [ 24 ]. This review represents the continuation of the reviewing process for candidate intake biomarkers for various foods of animal origin initiated in a previous paper on dairy and egg biomarkers, as part of the FoodBAll project [ 25 ].

Selection of food groups

In order to obtain a good coverage of the different meat sources, meat was subdivided into fresh meat (e.g., overall meat intake, red meat, white meat), fish and fish oil, and other aquatic meat, e.g., various shellfish, processed meat products (cured and smoked), and offal or organ meats. Two particular groups, strongly heated (e.g., grilled) meat products and biomarkers for fish contaminants, were also investigated. A total of nine food groups were thus selected for reviewing their respective markers of intake. A systematic literature search was carried out separately for each food group as detailed below.

Primary literature search

The reviewing process, including article search and selection, reviewing and reporting of the results, follows the guidelines previously proposed by the FoodBAll consortium to carry out an extensive literature search and evaluation of biomarkers for food intake (BFIs) [ 23 ].

In brief, original research papers and reviews were searched in at least two databases, among which PubMed, Scopus, and ISI Web of Knowledge, using combinations of the grouped search terms (biomarker* OR marker* OR metabolite* OR biokinetics OR biotransformation) AND (trial OR experiment OR study OR intervention OR cohort) AND (human* OR men OR women OR patient* OR volunteer* OR participant*) AND (urine OR plasma OR serum OR blood OR excretion) AND (intake OR meal OR diet OR ingestion OR consumption OR eating OR drink* OR administration), as reported in Additional file 1 : Table S1, together with specific keywords related to each animal-derived food group (Additional file 1 : Table S2). The fields used as default for each of the databases were [All Fields] for PubMed, [Article Title/ Abstract/ Keywords] for Scopus, and [Topic] for ISI Web of Science, respectively. For almost all food groups, the three databases were consulted. For “processed meat” and “offal meat,” the search was carried out only in PubMed and ISI Web of Knowledge. The literature search process was carried out between November and December 2015 and updated at the end of December 2018 for all food groups.

The search was limited to papers written in English, while no restriction was applied regarding publication date. The research papers identifying or using potential biomarkers of intake for the different kinds of meat were selected from the list of retrieved references by one or more skilled researchers in a process outlined in Additional file 1 : Figure S1. The papers obtained from the search in different databases were merged and filtered for duplicates. Subsequently, papers were screened based on title and abstract. The selected papers were then retrieved and assessed for eligibility based on the contents of the whole manuscript. Additional papers were identified from reference lists in these papers and from reviews or book chapters identified through the search. The result was a list of compounds potentially relevant as biomarkers for the food group and corresponding references.

Secondary search, marker identification, and classification

A second search step was used to evaluate the apparent specificity of the markers in the list. The compound databases HMDB [ 26 ] and FooDB ( http://foodb.ca/ )) were used for a first evaluation of marker specificity. If the marker was not specific for a single food group, it was noted whether it was related to any food outside the meat food groups. In the latter case, unless levels were reported to be very low elsewhere, the compound was omitted from the list for the next search. The remaining list of putative biomarkers was used for a second literature search in the three bibliographic databases used also for the primary search. This was done to potentially identify other foods containing the putative biomarkers or their precursors as well as foods otherwise associated with these compounds. For the second web-search, we used the chemical name(s) of the marker as keyword, together with AND (biomarker* OR marker* OR metabolite* OR biokinetics OR biotransformation). Further filters, such as (urine OR plasma OR serum OR blood OR excretion) AND (intake OR meal OR diet OR ingestion OR consumption OR eating OR drink* OR administration) AND (human* OR men OR women OR patient* OR volunteer* OR participant* OR subject*), were added based on the results obtained. Again, markers not related to meat intake were deleted from the list. The remaining putative markers with potential specificity for each food group or for several combined animal food groups were finally listed in Additional file 2 : Table S3, together with information about the study designs of the papers reporting their use. Due to the high number of results on marine fatty acids from the search on “fish and fish oil,” only the most representative among the observational studies having the highest number of participants were kept in Additional file 2 : Table S3. A summary of the markers considered for further validation according to the validation guidelines [ 24 ] is reported in Additional file 3 : Table S4.

Marker validation

In order to further assess the validity of the candidate biomarker, the scoring system for biomarkers of food intake was used as described previously [ 24 ]. Briefly, the usefulness of each selected marker in Additional file 3 : Table S4 has been established by answering a set of simple questions reflecting the biological and analytical criteria that a biomarker should fulfill in order to be considered valid. The questions have been answered for the most promising biomarkers. Possible answers were Y (yes), N (no), or U (unknown or uncertain). Each candidate marker was evaluated for plausibility (question 1), meaning that it is considered a plausible BFI for the food or food group based on food chemistry; dose-response relationship between quantity of food ingested and biomarker response (question 2); kinetics of immediate postprandial response (question 3a) and/or of repeated intakes (question 3b); robustness in complex diets or real-life exposure situations (question 4); reliability, i.e., concordance with other established measures of intake for the food or food group in question (question 5); analytical aspects, including the chemical stability of the marker (question 6), its analytical performance (question 7); and reproducibility in different labs (question 8) [ 24 ].

General biomarkers of meat intake

General biomarkers of meat intake are common to all or a large number of foods investigated in this review. The search for biomarkers of meat intake provided 953 hits after removal of duplicates, resulting in the final selection of 20 papers from the web-search (Additional file 1 : Figure S1). From the analysis of the reference lists and from the secondary search, another six papers were included in the review, resulting in a total of 26 papers; several relevant papers were dealing with specific meat subgroups and were therefore moved to this heading after reading the full text papers (Additional file 2 : Table S3). The main markers associated with the intake of animal protein from meats were various isotope ratios, anserine, carnosine, 1- and 3-methylhistidine (MH), creatine, creatinine, carnitine and acylcarnitines, taurine, trimethylamine oxide (TMAO), and several unidentified features specified by their m/z ratio. In addition, urinary nitrogen was used for total protein intake assessment in many studies but the marker is not a specific meat intake biomarker and therefore not included. The main human sample investigated in the studies was urine, followed by plasma, although feces and hair were also used occasionally. Both interventional and observational studies were considered for the evaluation of the putative BFIs for meat intake.

Histidine-related compounds

Vertebrate muscles contain a large amount of dipeptides containing histidine, such as carnosine ( β -Alanyl- l -histidine), anserine ( β -Alanyl-3-methyl- l -histidine), and balenine ( β -Alanyl-1-methyl-histidine); therefore, the quantification in human bio-specimens of such compounds or products of their catabolism, such as β -alanine, 1-MH (N τ -MH) and 3-MH (N π -MH), may support the assessment of meat intake [ 20 ]. Some degree of confusion exists regarding the nomenclature of 1-MH and 3-MH in regards to the numbering of the nitrogen in the imidazole ring of the histidine moiety because chemists and biochemists have used opposite designations for the two molecules; for instance, PubChem lists both compounds with both names and with both synonyms. We use here the chemical nomenclature, where N π -MH (π for nearest to the side chain) is termed 3-methylhistidine, while the common form in human muscle, N τ -MH (t for far from the side chain) is termed, 1-methylhistidine, as recommended in the IUPAC definition and in the major compound databases [ 26 , 27 ]. The synonym of the dipeptide anserine is thus β -Alanyl-3-methyl- l -histidine (Mora et al. 2007; HMDB metabocard for anserine (HMDB00194)); however, while some older papers have termed it β -Alanyl-1-methyl- l -histidine, in this review we have reported the results from these papers with the IUPAC name [ 28 , 29 ]. The dipeptide anserine, common in poultry, undergoes cleavage to give rise to 3-MH. The dipeptide balenine, common in some whales, cleaves to form 1-MH [ 30 ].

The content of histidine-containing dipeptides in food is highly variable [ 31 , 32 , 33 ] and their use as markers for meat intake may therefore be better at the group level than at the individual level. Carnosine contents are higher in beef and pork, while they are slightly lower in poultry [ 31 , 33 ] and almost absent in fish, with the exception of some species of Anguilloidei (eel) [ 32 ]. Moreover, this compound was not detected in other foods of animal origin, such as milk [ 34 ] and may only be found in some sources of liver [ 35 , 36 ], suggesting that the compound could represent a putative marker to assess the intake of terrestrial meat. Carnosine levels were found to be markedly increased in urine after the ingestion of beef, pork, chicken, and eel [ 31 , 34 , 37 , 38 ], showing also dose-response [ 34 ]. Cheung et al. [ 38 ] further compared several meat sources and reported that urinary carnosine associates with meat intake in a selected group of subjects from the European Prospective Investigation into Cancer and Nutrition (EPIC). Carnosine levels were significantly higher in the urine samples from subjects who consumed chicken, red meat, and processed meat compared to those consuming fish, with no significant differences observed between subjects consuming similar amounts of the three types of meat. Carnosine is completely excreted in urine within 20–25 h following the meal, reaching a peak after 5 h [ 31 ]. This kinetic behavior makes the compound suitable for quantification in 24 h urines to estimate total intake of terrestrial muscle meat.

The results regarding the detection of carnosine in plasma are contradictory. Parker et al. [ 39 ] observed a peak in plasma at 2.5 h after beef consumption returning to background levels after 5.5 h, while other studies failed to confirm this [ 37 , 38 ]. In a cross-sectional study of 294 Bavarian men and women completing 24-h recalls and providing non-fasting samples, plasma carnosine was associated with intake levels for all meat, red meat, and beef + pork but not for poultry, fish, or dairy [ 40 ] with approximately similar intake levels. These discrepancies could therefore be ascribed to a possibly weak performance of this marker, even at the group level. Plasma carnosine needs to be further investigated in carefully controlled trials to evaluate its usefulness as a BFI. With the current evidence, plasma carnosine is not a likely candidate marker for meat intake.

Anserine is present in the muscles of different non-human vertebrates, with poultry, rabbit, tuna, plaice, and salmon having generally higher contents than other marine foods, beef, or pork [ 31 , 32 , 33 , 41 ]. An increase of urinary anserine excretion was found in humans after the consumption of chicken [ 31 , 37 , 38 ], rabbit [ 41 ], and tuna [ 31 ] and has been associated with intake of chicken [ 38 ], salmon [ 42 ], and, to a lesser extent, beef [ 34 ], while other observational evidence indicates no increase with fish intake [ 38 ]. In particular, anserine in urine was a good marker for chicken intake based on FFQ data in a selected sample of high vs. non-consumers from the EPIC study [ 38 ].

In a cross-sectional study from Bavaria, plasma anserine was associated in a dose-response manner with 24-h recall information for total meat, beef + pork, turkey, processed meat, and total dairy but not with chicken, total poultry, “seafood,” or beef or pork, individually [ 40 ].

In the human body, anserine is a substrate for carnosinase and is mainly degraded and excreted as 3-MH [ 31 ]. Endogenous formation of 3-MH is minimal in humans; therefore, plasma and urinary 3-MH are primarily associated with food intake. A significant increase in the level of urinary 3-MH was observed after consumption of red meat [ 34 , 43 , 44 ], chicken or poultry [ 31 , 34 , 38 , 44 , 45 , 46 ], and fish [ 31 , 38 , 42 , 44 , 47 ], with a clear dose- or time-response relationship between urinary 3-MH excretion and the intake of the different animal proteins [ 38 , 43 , 44 , 46 ]. Urinary 3-MH was able to discriminate subjects with vegetarian or omnivorous diets in controlled settings [ 43 ] as well as in free living subjects [ 48 ], suggesting that this marker could provide evidence of overall habitual meat intake. Importantly, the excretion of 3-MH depends on meat source. In studies reflecting recent intake, poultry intake gave higher levels of 3-MH when compared to those following consumption of pork and/or beef, most likely due to anserine breakdown [ 34 , 38 , 44 ]. The discrimination between poultry and fish intake is more uncertain, as fish species vary significantly in anserine and 3-MH content [ 32 , 44 ], but chicken meat seems to have generally higher levels than fish. An increased level of 3-MH was reported after consumption of chicken compared to that of cod [ 38 ]. In one observational study from the Adventist Health Study-2 cohort ( n = 1011 subjects), urinary 3-MH was positively associated with the intake of animal protein, red meat, and poultry, assessed both by FFQ and 24-h recall, while the correlation tended to be lower with fish, probably due to the preference for meat consumption in the population investigated [ 49 ]. On the other hand, Cheung et al. [ 38 ] observed higher concentration of 3-MH in the urine of a subset of EPIC subjects who consumed fish (mainly cod and haddock) compared to those consuming red and processed meat. Also in this case, poultry intake gave the highest amount of 3-MH excretion. 3-MH peaks in urine around 5 h after intake and decreases to baseline levels after ~ 40 h [ 31 ] or 48 h [ 50 ]. Sjolin et al. [ 44 ] reported an excretion half-life of 11.7 h for 3-MH, indicating that excretion would not be complete in a 24-h urine collection. In a recent study, ten healthy subjects ingested increasing amounts of chicken daily for 3 days in three consecutive weeks. Peak postprandial plasma levels on day 3 and fasting levels in urine and plasma on day 4 in each week increased with dose [ 46 ] corroborating accumulation of 3-MH with daily intakes. 3-MH was also detected in plasma samples after the intake of chicken and to a lesser extent after the consumption of fish in another controlled intervention study [ 38 ]. A much smaller increase was also observed after intake of beef and processed meat. Urinary and plasma 3-MH therefore represent possible markers to assess meat intake, but due to the differences between meat sources with higher levels observed after intake of chicken and some species of fish, the marker should be carefully evaluated for different study populations, based on their food preferences.

1-MH is a constituent of actin and myosin, the contractile proteins of skeletal muscles, and its urinary excretion is rapid following muscle protein breakdown [ 51 ]. This compound has been observed to significantly increase in urine after meat consumption [ 43 , 52 , 53 , 54 ]. A single meal with 15 N-labeled beef increased 15 N-labeled 1-MH in urine at 0–8 h, confirming that food-derived 1-MH is excreted in urine [ 55 ]. Unlike 3-MH, the excretion of 1-MH increased during 72 h of fasting indicating catabolism of muscle tissue [ 56 ]. Daily meat intake in omnivores associates with 1-MH excretion, however with much more variation compared to 3-MH [ 34 , 44 ], although with a similar postprandial kinetics and a half-life of 12.6 h [ 44 ]. Endogenous production causes a high inter-individual variability making this marker less suitable to asses meat intake by itself [ 43 ]. In one observational study, urinary 1-MH after meat and fish intake was not different between high and non-consumers [ 38 ]. Plasma 1-MH was not associated with any group of meat, poultry, or fish intake in a study based on 24-h recalls [ 40 ] and did not increase after a meal intervention with chicken breast [ 50 ].

As a consequence of the breakdown of histidine-containing dipeptides, β-alanine can also be observed in body fluids [ 41 ]. So far, only one metabolomics study reported an increase in plasma levels of this compound after beef consumption [ 57 ]. In an early study, β-alanine appeared to increase in human urine only after an anserine containing meal [ 41 ], but since β-alanine can also be formed endogenously by catabolism of pyrimidines [ 26 ], it may not be a robust marker to assess meat intake; further investigations are necessary to evaluate its specificity and sensitivity in a cross-sectional study.

Carnitine and acylcarnitines

Carnitine represents an essential cofactor in fatty acid metabolism in mammals, as it transports fatty acids under the form of acylcarnitines into the mitochondria of muscle cells for cellular energy production by lipid β-oxidation [ 58 ]. Carnitine may be synthetized in the body from the essential amino acids lysine and methionine. Most of the carnitine in omnivores comes from dietary sources, particularly beef and lamb [ 20 ]. Carnitine and acetylcarnitine were higher in the urine [ 59 ] and serum [ 60 ] of subjects consuming high-meat diets compared to vegetarians, and their urinary values were representative of habitual intake of red and processed meat in free-living subjects [ 61 , 62 ]. Three acylcarnitines (acetylcarnitine, propionylcarnitine and 2-methylbutylcarnitine) were significantly higher in the urine of subjects from the EPIC cohort after the intake of meat and fish compared to control subjects with no meat intake, with no distinction among the different animal protein sources [ 38 ]. Propionylcarnitine and acetylcarnitine were also observed in plasma after a dietary intervention with meat and fish from the same research group [ 38 ]. Acetylcarnitine had slower kinetics than propionylcarnitine, reaching the highest value after 24 h when propionylcarnitine had already returned to the baseline level. In the EPIC-Oxford cohort, a series of acylcarnitines were also characteristic of the dietary pattern associated with high meat intake [ 63 ]. In particular, the concentrations of carnitine and acylcarnitines C-4 and C-5 were highest in meat eaters, followed by fish eaters, vegetarians, and vegans. Furthermore, C-3 and C-16 were higher in meat eaters and lower in vegans. Carnitine and acylcarnitines may reflect the intake of highly accessible amino acids and fatty acids contained in meat and fish and may be considered generic markers of intake of foods of animal origin. However, given that physiological conditions such as age, gender, and health status, as well as the intake of other foods with highly accessible amino acids or fatty acids may affect plasma acylcarnitine levels and their excretion into urine [ 64 , 65 ], these metabolites may not be suitable markers per se for specific and quantitative assessment of meat intake and are therefore not considered for further validation as single markers.

Creatine and creatinine

Creatine is present in animal muscles, mainly in the form of phosphocreatine, and can be synthesized endogenously from arginine, glycine, and methionine. The main dietary source is meat, including red meat, fish, and poultry [ 66 ]. Several studies reported an increase in creatine levels after meat intake in urine [ 59 , 62 , 67 , 68 ], erythrocytes [ 60 ], plasma, and serum [ 60 , 69 , 70 ]. Even a single meal containing meat has been shown to increase creatine in urine [ 71 ]. Associations between urinary creatine and habitual intake of shellfish [ 62 ] and oily fish such as salmon [ 42 ] have also been reported. Pallister et al. [ 69 ] proposed circulating levels of creatine as a possible marker of habitual intake of red meat and poultry, as observed in the UK Twin cohort study ( n = 3559 subjects). However, experimental studies supporting this observation are lacking. Creatine in urine and plasma/serum are therefore candidate markers of total meat intake.

Creatinine can originate both from spontaneous decomposition of creatine and creatine phosphate in the human body and to some extent also during cooking of meat [ 72 ]. An effect of diet on urinary creatinine was observed in a study comparing omnivores and vegetarians, being higher in the former group [ 60 ]. Cross et al. [ 43 ] showed that urinary creatinine performed as a food intake biomarker only for extreme levels of meat intake. Creatinine excretion mainly reflects endogenous production. The excretion into urine is highly regulated in the body and is proportional to the total content of creatine and creatine phosphate, i.e., the total muscle mass, in individuals with normal kidney function [ 20 ]. Therefore, creatinine cannot be used to estimate intake of meat without correcting for muscle mass and the compound is consequently not generally useful as a meat intake biomarker.

Taurine is the most abundant free amino acid in animal tissues and mainly derives from the ingestion of many different foods of animal origin including eggs and dairy, while it can also be synthetized endogenously [ 73 ]. Metabolomics studies reported higher urinary taurine in omnivorous subjects compared to vegetarians [ 59 , 74 ]. In two intervention studies, taurine excretion increased with animal protein intake but the change was not significant for low levels of meat intake [ 43 , 75 ]. Taurine also associated with shellfish intake in an observational study ( n = 253 subjects), but not with general meat intake [ 62 ]. The marker does not appear robust enough to assess total meat intake.

Trans-4-hydroxyproline

Hydroxyproline (Hyp) is a post-translationally modified amino acid that represents a major component in protein collagen and elastin, and its main dietary source comes from animal foods rich in connective tissue [ 76 ]. This compound was found in plasma after the intake of beef [ 57 ], while Pallister et al. [ 69 , 70 ] observed a significant correlation between the level of this compound in fasting blood and the frequency of meat and processed meat consumption in the UK Twin cohort. High levels of collagen have also been reported in fish [ 77 ], therefore it can be speculated that this marker may better reflect the overall intake of meat rich in collagen, rather than that of red or processed meat. The dipeptide prolyl-hydroxyproline (Pro-Hyp) has been reported as a marker of a healthy diet rich in fish [ 78 ]. Since there is also collagen in offal meat, HyP in plasma is a potential intake marker of total meat intake. Further studies of the contribution of endogenously formed HyP to its urinary excretion is needed as well as studies on HyP containing dipeptides in plasma and urine to potentially discriminate between endogenous and dietary sources.

δ15N and δ13C

Recently, the ratios of naturally occurring stable isotopes of carbon ( 13 C/ 12 C ratio, expressed as δ13C) and of nitrogen ( 15 N/ 14 N ratio, expressed as δ15N) in biological fluids and tissues have been proposed as novel nutritional biomarkers of meat and fish intake. Such an approach was originally used in archeology and recently introduced also in nutritional science to support dietary assessment of animal proteins [ 79 ]. When animals consume plants, they incorporate carbon and nitrogen from plants into their own tissues. The abundance of δ13C depends on the photosynthetic process utilized by the plants consumed by the animals; hence, animal feeding represents the primary determinant of variation in animal δ13C values [ 79 ]. In particular, C4 plants (crabgrass, sugarcane, and corn) have δ13C values 12–13‰ higher than C3 plants (wheat, rice, and the majority of fruit and vegetables). Incorporation of 15 N depends also on dietary sources. In particular, animals preferentially excrete 14 N as waste nitrogen, leading to δ15N values that are 3‰ to 4‰ higher than in their diet [ 79 ]. Moreover, 15 N abundance of tissue proteins increases in the food chain. As a consequence, animal-derived food proteins are expected to have a higher amount of 15 N compared to plant-derived protein food. In particular, the δ15N value has been suggested as a particularly good marker for aquatic meat consumption, as commonly consumed fish, such as tuna, salmon, and cod, are predators and reside at the higher levels of the trophic chain [ 80 ].

In a randomized 4 × 8 days cross-over study, Kuhnle and coworkers [ 81 ] observed significantly different isotope ratios among different diets (meat, fish, half-meat–half-fish, and vegetarian) in feces and urine samples, but not in blood samples, possibly due to the slower protein turnover. Higher δ13C and δ15N were observed in urine and fecal samples following a fish diet, and lower following a vegetarian diet, where protein had been substituted by carbohydrates. It was not possible to distinguish between meat and half-meat–half-fish diets in any of the biofluids. The higher level of δ13C and δ15N after the fish diet may be ascribed to the high level of fish that the subjects consumed during the intervention period. These results suggest that urinary and fecal δ13C and δ15N may be suitable biomarkers to assess short-term meat and fish intake, while blood levels may reflect longer-term intake. This interpretation is supported by data from Patel et al. [ 82 ], who were able to distinguish vegetarian from non-vegetarian subjects by serum δ13C and δ15N abundance in a sub-cohort within the EPIC-Norfolk study ( n = 1254). Serum δ13C and δ15N were positively associated ( P < 0.001) with fish protein intake (assessed by FFQ) and increased with increased fish consumption, while animal proteins, including dairy and meat proteins, were significantly associated with δ15N. Serum δ15N increased also with total animal protein consumption. Overall fish intake is more clearly associated with the isotope ratios in populations with high fish intakes while the ratios cannot distinguish different sources of intake in populations with mixed diets of aquatic and terrestrial meats [ 80 , 83 , 84 , 85 ].

To monitor the habitual intake of animal proteins, δ15N and δ13C were measured in hair and compared with the data collected from FFQ and dietary records in observational studies [ 86 , 87 , 88 , 89 ]. This is possible as hair keratin is not recycled in the body, and allows a reliable recording of dietary habits during the past months [ 86 ]. Overall δ15N and δ13C in hair are positively associated with total meat intake and no specific meat group seems to be dominant, except when either fish or terrestrial meats dominate the diet [ 86 , 90 ].

In conclusion, δ15N alone or in combination with δ13C represent promising markers to assess animal protein as well as meat intake, both for short-term based on urine or feces and habitual intake based on blood and hair. As these ratios are affected by local diets and agriculture, they should be validated for each population before being applied.

The intake of red meat has been mentioned as a cause of increased plasma or urine levels of TMAO in some observational studies [ 91 , 92 ], while increased levels are characteristic of fish or shellfish in intervention studies. In cross-over randomized controlled trials (RCTs) comparing meals with beef, egg, fruit, and cod, urine or plasma levels increased only with cod [ 38 , 93 , 94 , 95 ]. Plasma TMAO, trimethylamine (TMA), and dimethylamine increased quickly after cod with a peak at 2 h. Beef and egg slightly increased postprandial plasma TMAO and the volunteers segregated into TMAO producers and non-producers, based on their microbiota [ 94 ]. In an intervention study comparing 4-week intakes of red meat, poultry, or non-meat proteins with levels of TMAO in plasma and urine, TMAO excretion as well as plasma levels increased only with red meat. A subset of 13 volunteers received isotope-labeled carnitine and betaine but only carnitine increased circulating isotope-labeled TMAO [ 96 ]. In conclusion, TMAO in urine does not seem to be a marker of recent red meat intake but is increased postprandially with certain types of fish (see section below). The microbial formation of TMA and its hepatic oxidation to TMAO increase plasma levels by 2–4-fold in subjects with a pre-disposing microbiota after diets rich in TMA precursors, including carnitine. However, plasma TMAO cannot serve as a marker of red meat intake because the response varies between individuals and may also depend on the efficiency of the kidney in removing it from the circulation.

Mammalian (red) meat

Red meat includes muscle meat from mammals, although the amount of color (heme) varies considerably between muscle tissues from different species. Sometimes, the term pink meat is used to include pork, which has lower contents of heme iron compared to beef, veal, and mutton. However, we have included pork with the mammalian red meats here. Although some non-muscle mammalian meats (e.g., liver) and some fish (e.g., tuna) and poultry (e.g., duck) have red meat, they are usually not included with the term and also not included in this category here. The primary literature search for mammalian red meat in the online databases (Web Of Science, Pubmed and Scopus) resulted in a total of 1516 articles after the removal of duplicates. The exclusion of animal studies, studies on nutrition, physiology, and food composition based on title, abstract, or full text reduced the number of articles to 49. After the evaluation of marker specificity in online databases (HMDB and FooDB) and from additional literature, 26 putative biomarkers (lead, indole propionate, xylitol, ethyl glucuronide, methyl-α-glucopyranoside, sorbitol, cinnamoylglycine, and a range of lipid species) were excluded from entering the candidate biomarker list (Additional file 2 : Table S3) because they can also be retrieved in foodstuffs other than mammalian meat. Based on references retrieved in full text or secondary sources, three additional articles were added. As a result, 19 papers were included containing information on 31 putative biomarkers (including ten unknown metabolites) related to red meat intake.

Acylcarnitines, carnitine, 3-dehydrocarnitine, anserine, β-alanine, 4-hydroxyproline, histidine, 13 C/ 12 C, 15 N/ 14 N, carnosine, creatine, 1-MH, and 3-MH have been found to increase after red meat consumption (Additional file 2 : Table S3), but these compounds or their precursors are also associated with the overall intake of any meat and they are therefore not necessarily resulting from intake of red meat. The products of cooking of meat mentioned in this review are also unspecific with respect to the type of meat that is cooked. Therefore, these compounds are not suited as red meat-specific biomarkers and have been discussed in the appropriate sections on biomarkers of general, cooked, and processed meat intake.

Compounds potentially specific to red meat intake

Only three candidate markers, i.e., ferritin, apparent total N-nitroso compounds (ATNCs) (a term that encompasses nitrosothiols, nitrosyl iron, and other N-nitroso compounds (NOCs)), and 1,4-dihydroxynonane mercapturic acid (DHN-MA), were found as potentially red meat specific intake biomarkers. This is because these compounds may be formed at a significantly increased level as a consequence of the heme iron content of consumed meat.

Ferritin is a protein that carries and stores iron in the body, and its abundance in serum is thus related to the dietary intake of iron. Serum ferritin is more strongly associated with heme iron than non-heme iron or total iron intake [ 97 ]. However, the influence of other dietary constituents on iron absorption and serum ferritin concentration makes it difficult to use this protein as a biomarker for an individual’s red meat consumption and associations are not consistent in all studies [ 98 , 99 , 100 ]. Serum ferritin, therefore, does not seem to be a useful marker of individual red meat intake.

Heme iron is known to catalyze the formation of nitrite-derived NOCs associated with the formation of ATNCs. Heme iron also catalyzes formation of lipid peroxidation products (e.g., 4-hydroxy-2-nonenal (4-HNE) [ 101 , 102 ]. The electrophilic nature of 4-HNE causes its conjugation with glutathione and this adduct is further metabolized into DHN-MA. Several intervention studies confirm the positive association between ATNCs and DHN-MA on the one hand, and heme iron content and/or increasing doses of red meat on the other hand [ 101 , 103 , 104 , 105 ]. However, validation of DHN-MA as biomarker for red meat intake remains insufficient since only eight male volunteers were included in an intervention study for DHN-MA [ 105 ]. Additional information concerning the presence of DHN-MA in a larger and more representative study (e.g., including female test subjects as well) is needed. Furthermore, the DHN-MA precursor, 4-HNE, has previously been detected in foodstuffs other than red meat (e.g., in fish, edible oils, and fried foods [ 106 ]), limiting the specificity of DHN-MA as a red meat intake biomarker. For ATNCs, three independent intervention studies have been performed (encompassing a larger group of volunteers, including female test subjects), confirming the link between red meat intake and higher ATNCs levels in stool [ 101 , 103 , 104 ]. However, ATNCs have also been detected in nitrate or nitrite (as precursors of ATNCs) containing foodstuffs, e.g., cured meat (see section on “processed meat”), some cheeses, and beer [ 107 , 108 ], questioning the use of ATNCs as a qualitative biomarker for red meat consumption. In addition, the analysis of ATNCs has been performed by a method involving thermal release of nitrosamines. In an earlier publication, this heating process has been shown to lead to artefactual formation of nitrosamines while no formation of any simple volatile nitrosamines was detectable [ 109 ]. In a study examining the factors affecting ATNCs, all meats, meat iron, and nitrate in the food were directly associated with their formation while vitamin C and total energy intake were inversely associated [ 110 ]. While ATNCs apparently associate strongly with red meat intake, the marker still needs validation by another independent and direct analysis of nitrosamines in human feces without the use of thermal sample treatments. In conclusion, there are currently no reliable candidate biomarkers of red meat intake.

As reported in Additional file 1 : Table S2, the search for offal meat biomarkers was restricted by the use of “NOT”-terms to limit the number of irrelevant papers (the search without these NOT-terms yielded a total number of > 100,000 papers). With the use of the selected NOT-terms, the search for offal meat biomarker papers yielded 647 papers in PubMed and 265 in Web of Science. After combination of the results and elimination of duplicates, the remaining number of papers was 671. Only a single relevant paper [ 105 ] includes two putative biomarkers for the intake of blood sausage (and liver paté) in rats and humans: DHN-MA and 8-iso-prostaglandin-F2alpha (8-iso-PGF2A), which are both known end products of lipid peroxidation.

Pierre et al. [ 105 ] report that urinary levels of 8-iso-PGF2A and DHN-MA increased in rats after the consumption of blood sausage. An increased excretion of DHN-MA, but not 8-iso-PGF2A, was subsequently observed in humans [ 105 ]. The results of this small-scale human study also show a trend toward an increase in DHN-MA after the consumption of liver paté, although this effect did not appear to be significant; both blood sausage and liver pate contain pre-formed 4-hydroxynonenal, which may have further complicated the interpretation. The authors ascribed the observed increase of 8-iso-PGF2A and DHN-MA to the high heme content of blood sausage, corroborating the potential use of the latter as a candidate heme intake biomarker. Consequently, any heme-related BFI would be likely to rise after the consumption of all meat products rich in heme-iron, including red meat, blood products, and liver. Neither 8-iso-PGF2A nor DHN-MA is therefore specifically related to the consumption of offal meat, although the latter may be a lipid peroxidation product more abundant after heme-rich food consumption, as described above. In conclusion, no candidate biomarkers of offal meat intake were identified in the literature search.

Poultry meat intake includes mainly farmed chicken, duck, goose, and turkey; however, wild fowl is expected to have similar markers. The term “white meat” is often used interchangeably to cover either poultry only, or to include poultry as well as fish and shellfish. As already mentioned, some poultry (e.g., duck and fowl) has reddish muscle and this is also the case for several species of larger fish, including tuna. In this review, we have included the intake markers for the different meats with their biological class of origin and “white” meats in this section include only poultry.

The literature search performed identified a total of 2311 articles from the three databases. This was reduced to 2055 articles after removal of duplicates. Sixteen articles were identified after screening on the basis of title and abstract. Exclusion criteria for the remaining 2026 articles included effects on physiology; effects on drug metabolism; articles related to antioxidant markers, disease/health markers, or oxidative stress markers; and other articles not relevant to biomarkers of poultry intake by humans, such as animal studies. Full texts of the 16 papers were downloaded and assessed further for exclusion/inclusion criteria. Exclusion criteria at this stage included inappropriate study design, studies on whole diets or on food preparation, and articles not specific to poultry meat intake. Eight articles were retained, focused on poultry products. Additional file 2 : Table S3 provides a summary of remaining candidate biomarkers for poultry meat. Only studies on chicken or turkey intake were identified through this search process.

Anserine and methylhistidines

As previously mentioned, we identified a certain degree of confusion surrounding the nomenclature of 1-MH and 3-MH. Based on IUPAC nomenclature, 3-MH and not 1-MH is of interest with respect to poultry consumption [ 46 ]. Dietary intake of poultry meat is consistently associated with urinary excretion of 3-MH [ 44 , 111 ], although a low endogenous source of this compound (3-MH in the correct nomenclature, 1-MH in the article by Giesecke et al.) may also exist in mammals, including humans [ 56 ], leading to a low level of background 3-MH excretion in all studies. Excretion of 3-MH in urine [ 38 , 40 ] as well as peak postprandial plasma levels show dose-response within normal levels of chicken intake and plasma 3-MH is still measurable after overnight fasting [ 46 ]. The reported levels vary between quantitative studies [ 40 , 46 ] indicating analytical issues or instability, and inter-laboratory comparisons are therefore needed.

The dipeptide, anserine, was reported to increase in both plasma ( C max = 2.72 ± 1.08 μM at 100 min) and urine samples after ingestion of chicken or chicken broth in healthy women ( n = 4) [ 37 ]. Plasma anserine has been associated with poultry intake in a dose-response fashion and high levels were especially associated with self-reported ingestion of turkey in a cross-sectional study from Bavaria [ 37 ]. Urinary anserine was particularly associated with chicken intake in high versus non-consumers in a small subset of EPIC participants [ 38 ]; however, no information exists on its excretion kinetics.

Guanidinoacetate, a precursor compound in the biosynthesis of creatine found to increase muscle creatine and muscle growth in chicken [ 112 ], has been found by NMR analysis in urine as a result of chicken consumption [ 46 ]. The compound was found to associate strongly with chicken intake in an intervention study in the UK and this finding was confirmed in a cross-sectional study from Ireland [ 46 ]. Guanidinoacetate in urine discriminated intake of chicken from intake of other meat sources and reflected chicken intake in a dose-response fashion. In a dietary study with mixed dairy and meat proteins, including poultry, provided to Danish boys, guanidinoacetate was also observed by NMR analysis [ 113 ]. Further validation of the marker is needed from other countries and for other sources of poultry, in particular to determine if the excretion of this metabolite is in any way a consequence of animal feeds [ 114 ]. Up until then, this marker seems particularly promising as a unique chicken intake marker.

Several markers were also reported as being associated with cooking methods such as fried and grilled chicken. The putative markers PhIP (2-amino-1-methyl-6-phenylimidazo[4,5- b ]pyridine), MeIQx (2-amino-3,8-dimethylimidazol4,5- f ]quinoxaline), and their metabolites are extensively covered in the next section of this review. Similarly, metabolites related to polycyclic aromatic hydrocarbons (PAHs) detected after consumption of grilled chicken are also reported there. Although levels of these metabolites are increased following consumption of cooked chicken, they are also found in other cooked foods such as fish and beef [ 115 , 116 ], and consequently lack specificity. In conclusion, 3-MH and anserine in urine and plasma should be considered as candidate biomarkers of poultry intake and guanidinoacetate in urine as a highly promising marker of chicken intake. Anserine, in particular, will largely benefit from information on its kinetic profile and dose-response. Overall, additional studies with other poultry meats are needed for further confirmation and validation of all three markers.

Intake biomarkers for heated meat

Most meat is ingested after cooking; frying, broiling, baking, and grilling are commonly reported as the most popular cooking methods for meat products [ 117 ]; however, actual frequencies of different cooking practices are not often reported. In the search for dietary exposure markers for fried, broiled, or grilled meat, 391 unique papers were identified, resulting in 26 selected papers after the screening process and additionally 13 from the secondary search and reference lists (Additional file 1 : Figure S1). A total of 39 putative markers for cooked meet have been identified. All compounds belong to the families of heterocyclic aromatic amines (HAAs) and PAHs. One study, based on untargeted metabolomics, reported a series of pyrraline derivatives which were positively associated with the heating process, but the contribution of heated meat could not be separated from those of other cooked foods [ 118 ]. Therefore, such compounds were disregarded in the final table. The final putative biomarkers are reported in Additional file 2 : Table S3.

Heterocyclic aromatic amines

HAAs may represent specific markers for the intake of intensively cooked meat, as they are produced during high-temperature cooking of foods containing creatinine, creatine, and amino acids, which is the case only for muscle tissue. So far, more than 25 different heterocyclic aromatic amines have been found in roasted and fried meat and fish [ 119 , 120 ]. Their content depends on the cooking method, but it has been shown that in real-life conditions the most abundant HAAs are PhIP and MeIQx [ 120 , 121 ]. Only a small fraction of the HAAs can be found intact in urine, while the rest undergo biotransformation, including phase I metabolism by CYP1A2 and phase II metabolism by acetylation, glucuronidation, and sulphation. As a result, bio-fluids and tissues (e.g., urine, stool, blood, or hair) may contain a complex metabolic pattern reflecting the exposure to specific HAAs [ 122 , 123 ]. The most reactive metabolites can also covalently bind DNA or proteins, producing adducts, which can be detected in DNA or tissue as well as in urine after DNA-repair or protein degradation [ 122 , 124 ]. The use of adduct markers is limited due to the low abundance of these compounds and the invasive techniques sometimes required to collect the relevant tissue. Indeed, the majority of these investigations have been conducted on biopsy samples of patients that were obtained during clinical diagnosis of cancer [ 122 ]. However, some studies also used blood protein adducts to albumin or erythrocyte globin [ 124 ].

Several studies have examined possible markers related to acute HAA exposure in humans after the intake of roasted, fried, or grilled beef, chicken, and/or fish, focusing on the analysis of both free and conjugated HAAs in urine. The most investigated compounds so far are PhIP and MeIQx, which have been quantified before and after enzymatic hydrolysis [ 121 , 125 , 126 , 127 ] and as their phase I and II metabolites [ 128 , 129 , 130 ] (Additional file 2 : Table S3). These studies show that PhIP and MeIQx are mainly transformed through hydroxylation followed by conjugation, with glucuronidation as the major urinary excretion pathway in humans. The biotransformation products are completely excreted within 24 h after single intakes of roasted meat [ 128 , 130 ]. After volunteers had consumed identical amounts of charbroiled beef for four consecutive days, the excretion of PhIP in enzymatically hydrolyzed urine decreased almost to baseline levels 48–72 h later, with a significant correlation between the urinary levels of PhIP and the amount of charbroiled meat consumed [ 125 ].

The levels of MeIQx in hydrolyzed urine were higher than those of PhIP [ 121 , 126 ], suggesting a higher degree of direct conjugation of MeIQx, or a relatively greater excretion of metabolized PhIP through feces, as shown by Vanhaecke et al. [ 131 ]. When urine was not hydrolyzed, only PhIP was detected in urine after the intake of a single meal of roasted chicken, whereas unconjugated MeIQx and 4,8-DiMeIQx, which were found in the fried chicken, remained undetected [ 132 ]. Although the amounts of HAA metabolites measured in urine were highly associated with the ingested dose of MeIQx and PhIP, high inter- [ 116 , 133 ] and intra-individual [ 128 ] variability in metabolism of the HAAs was observed. Notably, differences between “fast” and “slow” excretors as well as between men and women were observed [ 128 , 130 ].

In observational studies, MeIQx has been found to be a more reliable urinary marker to assess consumption of highly cooked or processed meat. The urinary levels of MeIQx [ 134 ] and PhIP [ 135 ] have been compared among black, Asian (Chinese or Japanese), and white men, and correlated with the results of a food frequency questionnaire. MeIQx levels were positively associated with the intake frequency of bacon, pork/ham, and sausage/luncheon meats [ 134 ] while urinary PhIP was not associated with intake frequencies of any cooked meat, suggesting that self-administered dietary questionnaires may not properly describe cooked meat preferences, frequency of intake, and cooking methods [ 135 ], or alternatively that unknown sources of PhIP affect total excretion.

Further markers related with dietary HAA exposure are the PhIP hydroxylation metabolites. 4′-OH-PhIP (2-amino-1-methyl-6-(4′-hydroxyphenyl)imidazo[4,5-b]pyridine) has been proposed as detoxification product, derived from the oxidation by several CYPs of the aromatic ring system. The metabolite, 5-OH-PhIP (2-amino-1-methyl-6-(5-hydroxy)phenylimidazo[4,5- b ]pyridine), has been suggested as a marker of activation of PhIP [ 136 ] since it represents a rearranged product of N-hydroxylation of the exocyclic amino group by cytochrome P450 (CYP1A2). In several studies [ 121 , 132 , 137 ], 4′-OH-PhIP was found to be the most abundant urinary hydroxylated metabolite after the consumption of cooked chicken. This compound is already abundantly present in the heated chicken meat and only a small amount (11% after enzymatic hydrolysis and 0.66–1.3% in untreated urine samples) is derived from human PhIP metabolism [ 132 , 137 ]. This suggests that 4′-OH-PhIP may represent a good marker for heated meat intake, although studies showing its presence and kinetics of formation in other meats than chicken are needed in order to validate its plausibility as a general marker of any fried or grilled meat.

High inter-individual differences were observed for fecal excretion with no differences in the percentage of the PhIP dose excreted [ 131 ]. Vanhaecke et al. [ 131 ] examined the urinary and fecal excretion in humans of a newly identified microbial PhIP metabolite, PhIP-M1 (7-hydroxy-5-methyl-3-phenyl-6,7,8,9-tetrahydropyrido[3′,2′:4,5]imidazo[1,2- a ]pyrimidin-5-ium chloride) after the consumption of cooked chicken, containing a known amount of PhIP. Urinary PhIP-M1 increased over time with a peak between 48 and 72 h, showing that microbial metabolism appears later in the excretion profile of body fluids compared to the parent compound. The percentage of recovered PhIP-M1 was quite high in feces, with high variability among subjects, probably associated with differences in diet, digestion, metabolism, and microbial composition. There was no difference in the percentage of the PhIP dose excreted as PhIP-M1, indicating apparent dose-response. In conclusion, this additional microbial PhIP metabolite, PhIP-M1, adds to the complex metabolism of PhIP and may further complicate the use of any PhIP metabolite for monitoring exposures to heated meats.

Other compounds besides PhIP and MeIQx have been identified in urine after the intake of roasted meat (Additional file 2 : Table S3). These include 9 H -pyrido[3,4- b ]indole (norharman) [ 132 , 138 ], IQ (2-Amino-3-methylimidazo[4,5-f]quinoline), Trp-P-2 (3-amino-1-methyl-5 H -pyrido[3,4- b ]indole), Trp-P-1 (3-amino-1,4-dimethyl-5 H -pyrido[3,4- b ]indole), and AαC (2-amino-9 H -pyrido[2,3- b ]indole) and harman [ 138 ]. However, these molecules were found to be less reliable as markers of exposure to heated meat, when compared to PhIP and MeIQx. Their presence in body fluids or tissue may not be entirely due to meat consumption, but also to smoking habits [ 139 , 140 , 141 ], endogenous production, and the consumption of other foods derived from heating or fermentation, such as alcoholic drinks and coffee [ 142 ].

Markers related to the intake of high-intensity cooked meat have been investigated also in human hair, providing an average of the exposure over a period of several months [ 143 , 144 , 145 , 146 , 147 , 148 , 149 , 150 ]. Analysis of hair samples showing dose-response, a known dependence on melanin levels and independence of hair dying, and CYP1A2 phenotype, has been shown for measurement of PhIP after fried beef intake for 3–4 weeks [ 143 , 144 , 145 ]. The lack of dependence on metabolism is in accordance with findings showing that CYP1A2 phenotype over a 20-fold activity span is a poor predictor of HAA metabolite excretion in urine [ 150 ]. Turesky et al. [ 149 ] examined the levels of PhIP accumulating in the hair closest to the scalp of 44 volunteers after a 4-week semi-controlled diet of cooked meat containing known quantities of PhIP. The study showed a good correlation between the level of PhIP in hair and the dose of PhIP ingested. Kobayashi et al. [ 144 ] compared PhIP levels in the hair of seven volunteers with the results from dietary records over 28 consecutive days. PhIP levels in hair were found to be highly associated with the grilled/stir-fried meat intake but not with the grilled/stir-fried fish intake. The marker, therefore, seems to be useful in very different populations in terms of food culture and genetics. No other HAAs can be measured in hair and no, or only marginal, PhIP could be measured in vegetarians. These studies point to PhIP in hair as a promising biomarker for assessment of average intakes of heated terrestrial meats but further validation studies are needed to assess stability of the samples and influence of hair structures.

In conclusion, the studies reported so far identified and quantified specific carcinogenic HAAs in urine, feces, and hair after consumption of well-done, heated meat, showing that there is a significant correlation between the intake and the excretion of these compounds. In particular, the most promising exposure markers seem to be total PhIP and MeIQx in urine and PhIP in hair. The complexity of absorption, metabolism, and distribution of these compounds leads to very high inter- and intra-individual variation for some of the blood and urine metabolites and these compounds may therefore only work reliably as markers for comparison of groups rather than individuals. PhIP in hair seems a consistent candidate biomarker to estimate intakes of fried or grilled terrestrial meat.

Polycyclic aromatic hydrocarbons

PAHs are mainly produced from the incomplete combustion of organic matter. Therefore, the main human exposure sources are associated with environmental factors, such as tobacco smokes, emission of vehicles, and occupational exposure [ 151 ]. Anyway, it has been observed that in non-smoking subjects without occupational exposure, the main source of PAHs is represented by food [ 152 , 153 ]. PAHs are present both in uncooked and cooked food. In the former, these compounds may originate from environmental contamination from soil, air, or water [ 154 ]. Cooked food, particularly meat grilled directly over an open flame, grilled over charcoal, or smoked, may contain measurable amounts of PAHs arising from fat pyrolysis or from the adsorption onto foods of PAHs emitted from the combustion process [ 155 ]. The most abundant PAHs reported in barbequed or grilled meat were naphthalene (NAP), phenanthrene (PHE), fluorine, and pyrene [ 152 , 156 ].

Urinary 1-hydroxypyrene (1-OHP) and 1-hydroxypyrene-O-glucuronide (1-OHPG), two metabolites of pyrene, are considered the most relevant biomarkers for assessing individual exposure to PAHs [ 157 , 158 , 159 , 160 ]. Even though they have been observed to increase following the consumption of barbecued meat, a very high inter-individual variability has been observed, suggesting that these metabolites may not be suitable markers for individual assessment of the intakes of grilled, broiled, or roasted meat [ 158 , 159 , 160 ]. Furthermore, it has been reported that the main source of urinary 1-OHPG is cigarette smoke, while car vehicle exhaust contributed almost to the same extent as the diet in non-smoking subjects [ 161 ]. A considerable amount of 1-OHPG excretion is also attributed in populations using mate tea while fried or grilled meat was not significantly associated with 1-OHPG [ 162 ]. Cooking methods (baking bread at home and the intensity of food frying), passive smoking, and genetic polymorphisms affecting hydroxylation and conjugation, additionally highly contributed to the excretion of 1-OHPG [ 163 ]. Therefore, the background levels for 1-OHPG would be too variable in a real exposure setting and the contribution of dietary intake of high-intensity heated meat could not be distinguished from environmental factors, even in subjects who never smoked.

Further studies identified monohydroxylated metabolites of NAP and PHE in urine as possible markers of short-term dietary intake of PAHs after consumption of fried beef and chicken, particularly 2-hydroxy-NAP and 1-, 2-, 3-, 4-, and 9-hydroxy-PHE [ 152 , 156 ]. These metabolites demonstrated a high correlation with the parent compound present in food in a highly controlled setting where the environmental confounders were minimized. Even though these metabolites show a higher robustness in relation to the other environmental factors, the existence of other background source acting as potential confounders cannot be overruled. NAP and PHE are well-known environmental contaminants causing aquatic toxicity and they are therefore likely to show environmental bias similar to other PAH related markers [ 164 ].

In conclusion, none of the markers of PAH intake are currently promising markers of charcoal-grilled meat intake.

  • Processed meat

The search for papers on processed (not including heat processing) meat biomarkers delivered 2522 papers via PubMed and 2560 papers from Web of Science. This rendered 3772 unique papers, of which 38 were included after screening of the titles and abstracts, whereas only six relevant papers remained after assessment of the full text. Exclusion was based on a lack of focus on the assessment of dietary intake in general, and processed meat intake in particular. Four additional papers were retrieved after the secondary web search. In total, 12 relevant papers were found, reporting putative markers such as MeIQx, 1-OHP, thiobarbituric acid reactive substances (TBARS), ATNCs, nitrosoproline, glycerophosphatidylcholine (PC) C38:4 (PC38:4), acetylcarnitine, carnitine, and lathosterol. Out of these, MeIQx and 1-OHP are also markers of grilled and fried meat; ATCN and DHN-MA are potential markers of heme-containing foods; carnitine and acyl-carnitines are candidate markers of meat intake in general; and lathosterol, TBARS, and PC38:4 are unspecific to processed meat intake. These compounds were therefore disregarded as candidate markers of processed meat. However, they may still be useful as part of combined markers for processed meat intake assessment. This leaves N-nitrosoproline as the only candidate biomarker for this specific meat subgroup.

N-nitrosoproline

Decades ago, Stich and co-workers [ 165 ] investigated the interfering role of the consumption of nitrite-preserved meats on urinary N-nitrosoproline concentrations. The results demonstrated that N-nitrosoproline increased significantly after the consumption of, e.g., tinned ham, pork luncheon meat, and different types of sausages, although N-nitrosoproline can also be formed endogenously in the gastro-intestinal tract. The authors were able to directly link N-nitrosoproline levels in different processed meat types to higher levels of N-nitrosoproline in human urine. Moreover, endogenous N-nitrosoproline formation appeared to be negligible in comparison with the levels formed after intake of cured meats. N-nitrosoproline therefore demonstrated potential to serve as a candidate quantitative biomarker for the intake of nitrite-preserved meat. Later on, other investigations showed that nitrite-preserved meat is not the sole or even the main potential contributor to elevated urinary N-nitrosoproline [ 166 , 167 ] since tobacco use is another important source, eliminating N-nitrosoproline as a robust, specific biomarker for nitrite-preserved meat intake. However, other sources may be identified with other biomarkers and combinations of markers may therefore be developed to assess intake of cured meat besides other environmental or food sources. Further studies of N-nitrosoproline in complex exposure scenarios are therefore warranted.

Unidentified potential markers of processed meats

In studies by Playdon et al. (2016) and Cheung et al. (2017), the occurrence of five unidentified compounds was associated with the intake of processed meat [ 38 , 62 ]. Additional research is required to identify these molecules and assess their relevance as markers of processed meat intake.

In conclusion, only a limited amount of original research papers document the search for a processed meat biomarker. More importantly, up until now, none were able to identify a biomarker that is specific for processed meat intake.

Biomarkers of fish, fish oil, and other seafood intake

Three parallel searches of the scientific literature have been carried out for markers of aquatic meats and oils: one search addressed the identification of markers related to fish and fish oil intake, another on seafood, and a third search was focused on naturally occurring environmental toxicants related to seafood consumption (Additional file 1 : Table S2).

A number of 1340 unique articles were identified for the search on fish and fish oil biomarkers of which 1115 articles were discarded in the first screening based on title and abstract. The main reasons for exclusion were effects on physiology, bone health and calcium metabolism, macular degeneration, effects on drug metabolism, as well as studies on effects in fish. Out of the 225 papers included in the second screening for which the full-text was assessed, 55 were kept and are included in Additional file 2 : Table S3 together with 19 papers identified from the secondary searches. Papers that were discarded dealt with irrelevant diets (pure n-3 fatty acids, not fish oil), arachidonic acids, seal, astaxanthin from microbes, etc.), outcomes (effects on cholesterol levels, 1-carbon metabolism, immunology, fatty acid levels in breast milk, neurological development of newborns, urinary iodine excretion), or were excluded due to poorly reported methodology or inappropriate study designs for BFI discovery. One proteomics study unrelated to biomarker discovery and four studies for which the access links were unavailable were also excluded. Several additional studies arose from the literature search within the general meat section, while one cross-sectional and one intervention study were found through the targeted searches on TMAO and CMPF (3-carboxy-4-methyl-5-propyl-2-furanpropionic acid), adding a total of 15 papers to the final list ( n = 86). Among the papers included in Additional file 2 : Table S3, 45 are relevant for fish consumption, 34 for fish oil supplementation, and five investigate both fish and fish oil mixed intakes.

The literature search for shellfish resulted in 443 papers, of which 33 were kept after screening abstracts and titles. The reasons for exclusion were investigation of contaminants in seafood and their effects on health, effects on physiology of aquatic food consumption, and food allergies. After accurate examination of the text, the remaining papers included 19 mentioning shellfish, 13 on mixed intakes together with fish, and six only on shellfish, see Additional file 1 : Figure S1.

For the search on natural environmental contaminants associated with the intake of aquatic meats, only arseno-compounds have been considered. The literature search resulted in 100 papers, which were reduced to 14 after screening abstracts and titles. Generic markers for inorganic arsenic exposure were excluded. Other reasons for exclusion within this category were irrelevant dietary intervention (e.g., arseno-sugar and cod liver) and a lack of firm conclusions in associating specific foods to the markers of interest. Only eight papers measuring organo-arsenic species in body fluids and tissues in relation to fish, mixed fish, or shellfish intakes were included and these papers are therefore listed under the fish, or mixed fish and shellfish categories.

In Additional file 2 : Table S3, the studies on intake biomarkers of fish, fish oil, shellfish, and mixed exposures of these are listed in separate sections; however, due to the large overlap in the compounds identified as markers, the foods are all discussed under one heading here and subdivided by chemical class.

Several biomarkers have been found to be associated with intake of fish and shellfish as reported in Additional file 2 : Table S3. The majority of the studies concern consumption of marine foods and derived products aimed to increase the levels of omega-3 (n-3) long-chain polyunsaturated fatty acids (n-3 LCPUFAs) in biofluids and tissues, while more recently suggested markers include furan fatty acids, e.g., CMPF, TMAO, 1,2,3,4-tetrahydro- β -carboline-3-carboxylic acid (THCC), cetoleic acid, astaxanthin, and organoarsenic compounds, such as arsenobetaine. Fish and shellfish are reasonably high in the trophic chain and tend to accumulate compounds present in their local pray or foods. This accumulation depends on the biotope, e.g., marine or fresh waters, geographical differences, and also on the fat content of the fish or shellfish. A high abundance of LCPUFAs, including docosahexaenoic acid (DHA), exists in marine plankton [ 168 ] while freshwater microalgae are generally forming other fats and only little eicosapentaenoic acid (EPA) [ 169 ]. Most fatty acids and xanthophylls are deposited in fats and accumulate more in fatty fish while TMAO is produced in some species to regulate osmotic pressure in deeper marine waters. Biomarkers of fish and shellfish may therefore depend on the local environments where these foods come from.

n-3 LCPUFAs

The n-3 LCPUFAs, EPA and DHA, as well as their intermediate product, docosapentaenoic acid (DPA), are high in marine fatty fish and in cod liver. Two to 20 times lower levels are found in most other aquatic meats, including shellfish, lean fish, some freshwater fish, and farmed fish from coastal waters or ponds, while terrestrial meat are 20–100 times lower, depending on their feed [ 170 ]. The n-3 LCPUFAs in blood are well-established fish-related biomarkers, known for their high specificity to fish and shellfish. In particular, fatty fish such as salmon, tuna, herring, and mackerel are affecting blood n-3 LCPUFAs [ 171 ]. Although eggs and meats originating from livestock fed with diets high in n-3 may also provide some quantities of EPA and DHA (e.g., through their higher consumption rates in many populations), marine and freshwater foods represent by far the main dietary source of n-3 LCPUFAs [ 171 , 172 ]. LCPUFAs can additionally be synthesized in humans by converting α -linolenic acid (ALA) through a well-documented series of elongations and desaturations [ 173 ]. However, the conversion rate of ALA from plant sources is limited [ 174 ] and should therefore not interfere with the net content provided by marine sources.

EPA and DHA have often been used to assess compliance to fish and fish oil intake in intervention studies [ 175 , 176 , 177 , 178 , 179 ], and have been extensively associated with self-reported fish intake, measured either through 24-h dietary recalls or FFQs in a wide range of cohort and cross-sectional studies carried out in different populations [ 45 , 180 , 181 , 182 , 183 ]. Furthermore, EPA and DHA have been widely used to validate proposed FFQs for assessment of fish intake [ 83 , 184 , 185 , 186 , 187 ]. DHA, DPA, and EPA were also shown to increase in a 12w parallel RCT in adolescents comparing fish meals (herring, salmon, mackerel) with meat (chicken, turkey, beef, lamb) and cheese [ 188 ].

Blood is the most extensively explored matrix for measuring PUFAs; however, blood components differ in their metabolism and incorporation of LCPUFAs [ 189 ], thereby greatly influencing the duration of consumption reflected and the selection of the adequate blood fraction to match the intended outcome.

Plasma and serum have been suggested to reflect shorter-term intakes, whereas erythrocyte membranes and adipose tissue are more reflective of long-term (habitual) consumption of fish [ 190 ]. In serum, EPA, DHA, and total n-3 fatty acids increase within 4 h after intake [ 191 ], whereas their levels in cholesteryl esters (CE) and red blood cells (RBC) were only apparent after 3 days of supplementation. Incorporation of EPA and DHA was also shown in PC after 1–2 weeks, whereas levels in platelets were affected after 3–4 weeks and 1–2 months, respectively [ 192 ]. The incorporation of DHA was further reported to be slower than that of EPA in CE, RBC membranes, and the adipose tissue of healthy adults [ 193 ].

Plasma phospholipids act as transporters and are therefore a more transient compartment than RBC membranes [ 190 , 194 ], with equilibrium reached after 1 and 6 months, respectively, at fixed dietary intakes [ 195 ]. The clearance of EPA and DHA from plasma phospholipids was also shown to occur more rapidly than from RBC [ 196 ], emphasizing on the ability of plasma phospholipids to adequately reflect only short-term changes in intake (weeks). Esterified LCPUFAs in whole blood were associated as strongly as LCPUFAs in erythrocytes in a fish oil intervention with 0.25–1 g/day of EPA and DHA over 4 weeks [ 197 ], and were shown to have similar kinetics within RBCs and in plasma, while EPA has faster kinetics than DHA [ 198 ]. The sum of EPA + DHA in plasma PCs was suggested as a suitable biomarker for recent EPA + DHA intake, while platelet and mononuclear cell EPA + DHA may reflect habitual intakes, showing reasonable dose-response relationship [ 192 ]. PC-EPA was also reported in an untargeted metabolomics investigation with fish oil [ 199 ] as well as in a cross-sectional association with aquatic meats [ 200 ], both measured in serum. Fish studies targeting PC-EPA and PC-EPA + PC-DHA may thus potentially shed light upon more accurate assessment of short-term fish intake.

Adipose tissue is less sensitive to changes in dietary n-3 PUFA than other lipid pools, thus fatty acids content in adipose tissue reflects only longer-term intake of fish oil [ 193 , 201 ].

In studies where similar content of EPA and DHA was provided as either fish oil capsules or fatty fish, differentiation by plasma markers between these intakes is not possible [ 189 , 202 ], nor is it possible to reflect different composition of the fish and fish oil [ 203 ].

Only one intervention study investigated the potential dose-response relationship between LCPUFAs after fish consumption [ 204 ]. EPA measured in plasma phospholipids showed dose-response at doses of 180 g and 270 g of salmon consumed twice a week for 4 weeks, but no significant difference from control was observed at 90 g of salmon. In contrast, DHA seemed to reach a plateau in this study even after 90 g salmon consumed, without a subsequent increase at higher doses [ 204 ]. When added together, EPA + DHA showed a dose-response starting with the lowest level of fish intake [ 204 ]. Other studies that investigated dose-response only used fish oils. In blood samples, EPA and DHA displayed dose-response in some studies with fish oil [ 193 , 205 , 206 , 207 , 209 , 210 ]. Ulven and colleagues compared the efficacy of fish or krill ( Euphausia superba , a crustacean) oils on n-3 LCPUFA contents in fasting plasma samples of healthy volunteers. Despite lower n-3 contents in krill oil, effects comparable to fish oil were observed for EPA, DPA, and DHA, suggesting that bioavailability of n-3 PUFA from krill oil is equal or even more efficient than that of fish oil [ 211 ]. The different efficiency may be attributed to different n-3 containing fats in krill oil, mainly phospholipids, compared with fish oil, mainly triglycerides. In a 14-day dose-response study, Hudson et al. have shown that incorporation of EPA, DPA, and DHA in PC shows dose-response in the range from 0.3–4.5 g EPA + DHA/day so that contents in PC is a sensitive and dynamic measure of recent LCPUFA intakes [ 212 ].

The use of DHA and EPA as BFIs for fish are likely to reflect mainly marine fish and should therefore be validated separately for different populations, as always recommended for BFIs [ 24 ]. In experimental studies, lean fish only affect n-3 LCPUFAs to a very limited extent [ 213 ] while these markers in observational studies have less power to discriminate lean and fatty fish intakes [ 180 , 214 , 215 , 216 ]. Shellfish have a composition similar to lean fish but most observational studies on EPA and DHA did not differentiate between fish and other shellfish [ 184 , 216 , 217 ]. When shellfish intake was evaluated separately from non-fried fish intake in 900 participants from the Multi-Ethnic Study of Atherosclerosis (MESA), no association was observed between the concentrations of EPA and DHA in plasma phospholipids and shellfish intake [ 218 ]. When looking into intakes of more specific shellfish such as oysters, clams, crabs, squid, mussels, and shrimp in normolipidemic men, all shellfish increased n-3 PUFAs in fasting plasma and erythrocytes, except for clams that only elevated them in plasma, and shrimps that only showed an effect on RBC [ 219 ]. The plasma levels increased five times more than those in RBC, confirming that the turnover rate in different compartments is affected similarly by fish and shellfish. Tropical shellfish, unlike those from temperate waters, are a source of both n-3 and n-6 PUFAs, as shown by increased plasma levels of arachidonic acid reported together with the regular EPA, DPA, and DHA [ 220 ]; this underlines once again that BFIs should always be validated for each specific population taking their food sources into consideration.

Ratios of n-3/n-6 fatty acids

Fish intake negatively affects the plasma levels of certain omega-6 PUFAs. Linoleic acid (LA), gamma-linolenic acid (GLA), dihomogammalinolenic acid (DGLA), and arachidonic acid (AA), all n-6 PUFAs, have often been reported in studies on fish intake, but are of limited interest as fish intake biomarkers since they do not reflect fish intake per se but rather a change in their equilibrium with the n-3 FAs. Consequently, FA ratios such as n-3/n-6, n-6/n-3, the omega-3 index, and EPA/AA may be of potential relevance for health outcomes related to fish consumption, but are not robust as BFIs, as they are highly influenced by other food sources, although they may be relevant in some specific studies [ 204 , 214 , 221 , 222 , 223 , 224 ]. The same applies to the reported sum of n-3 and sum of n-6 PUFAs, measured in a handful of studies reported in Additional file 2 : Table S3 [ 176 , 191 , 201 , 204 , 213 , 214 , 225 , 226 , 227 , 228 , 229 , 230 , 231 , 232 , 233 ], as they can easily be affected by the intake of other food groups than aquatic or terrestrial meat, such as vegetable oils and nuts.

In conclusion, n-3 LCPUFA in different blood compartments seem to be valid biomarkers of fatty fish intake and also of total fish, fish oil, and shellfish intake in populations consuming a mixed diet. The ratios, EPA/AA, (EPA + DHA)/AA, and (n-6)/LCPUFA may only be useful in controlled studies comparing fish and meat since they are too sensitive to other n-6 FA sources to be useful as general fish intake biomarkers.

Furan fatty acids

In addition to DHA and EPA, furan fatty acids have been recently suggested as biomarkers for fish/fish oil intake. CMPF was identified as a biomarker for fish consumption in fasting plasma, as its level was associated with dietary fish intake after a 12-week intervention trial with a Nordic diet [ 234 ]. In addition, another dietary intervention study has reported increased furan fatty acids after increased protein intake from fish and meat, including circulating levels of CMPF following consumption of a Mediterranean-type diet [ 235 ]. Plasma, serum, and urine CMPF has been observed also in free living populations across the Northern Hemisphere, where levels were associated with habitual dietary intake of fish and shellfish in several populations in Europe, China, and the USA [ 62 , 236 , 237 ]. Plasma as well as urine CMPF has also been observed to increase in hyperlipidemic patients after 600 mg/day of fish oil for 4 weeks and levels were still increased after 4 weeks’ wash-out [ 238 ]. Besides CMPF, four other compounds putatively identified as furan fatty acid metabolites were increased in blood after a diet high in fish [ 235 , 239 ].

Trimethylamine- N -oxide

TMAO is a naturally occurring metabolite abundant in certain fish or generated from nutritional compounds including choline and carnitine by a microbiota mediated conversion of betaines to trimethylamine and further oxidation by hepatic flavin monooxygenases to TMAO [ 240 , 241 ]. Several studies have shown increased urinary excretion of TMAO related to consumption of fish: a high level of TMAO in morning urine was associated with intake of fish containing PUFA and n-3 PUFA on the previous day [ 242 ]. Increased urinary TMAO was also observed within 4.5 h after a salmon-containing breakfast [ 47 ]. In an intervention with fish or meat dishes, urinary TMAO increased substantially after fish intake but not after standardized meals containing chicken, red meat, or processed meat [ 38 ]. In another crossover meal study with three fish dishes, three meat dishes and three lacto-ovo vegetarian dishes, TMAO increased in urine only after fish intake, peaking at 3 h [ 93 ]. TMAO in urine seems to show good specificity as a potential biomarker of fish and shellfish intake, because only fish and other aquatic meat products contributed to elevated urinary excretion of trimethylamine and TMAO, among 46 investigated food groups [ 240 ]. TMAO levels in plasma showed a dose-response with increased intakes [ 38 ], but also increased postprandially after intake of a fish meal, thus proving that the TMAO present in fish can be readily absorbed without the involvement of gut microbiota, while TMAO is elevated much later when metabolic precursors in meat are converted to TMAO by the gut-host co-metabolism [ 94 ]. TMAO is particularly high in sharks, skates, cods, and other fish diving in temperate or cold sea waters, serving as a muscle osmolyte, and it is also found in crustaceans from the same biotopes [ 243 ]. It may therefore be a better biomarker for intake of this group of fish and shellfish, e.g., from the North Atlantic Ocean. Plasma TMAO has recently evoked attention as potential risk factor for cardiovascular diseases [ 244 ]. However, this risk seems to be partially related to reduced kidney function [ 245 ] and also depends on the composition of the gut microbiota [ 94 ]. Therefore, the potential implication of TMAO from aquatic meat in disease development still needs scrutiny. In conclusion, TMAO in urine is a candidate short-term biomarker of intake for fish and shellfish from deeper water layers, at least in the North Atlantic Ocean, while studies of fish and shellfish from most other biotopes are still missing.

1,2,3,4-Tetrahydro- β -carboline-3-carboxylic acid

In a randomized, single-blinded, crossover meal trial, metabolomics analysis was used to investigate plasma and urinary excretion following consumption of cod protein compared to other protein sources, such as whey, casein, or gluten. The tryptophan-derivative THCC was elevated in plasma and urine after cod protein intake [ 95 ]. Further studies to evaluate its specificity are needed, given the potential presence of THCC at considerable levels in cocoa products and in some fruit juices, jams, and fermented food products [ 246 ]. THCC is therefore not considered further as a fish intake biomarker.

Amino acids

Increased urinary excretion of taurine and 3-methylhistidine have been observed after fish and shellfish intake but have also been associated with animal protein consumption [ 247 ]. Though being less specific than TMAO, plasma levels of taurine, methylhistidines, and N6,N6,N6-trimethyl-lysine were also elevated following the consumption of cod protein [ 95 ]. Anserine, 1- and 3-methylhistidine were also reported in urine after a smoked salmon-containing breakfast [ 47 ]. As methylhistidines and taurine may be derived from several different sources of animal muscle, they are not specific to fish or shellfish and have therefore been discussed in the section on general markers of meat intake. However, in combined biomarker approaches, they may serve to distinguish between intake of fish (as part of “white meat”) from intake of mammalian red meats but this approach will need additional study.

Other lipids

Other lipids have been suggested as possible biomarkers of fish intake. In particular, a lipidomics investigation from O’Gorman et al. [ 248 ] demonstrated that a combination of specific lipid patterns had a reasonably good ability to discriminate between low and high dietary fish intake (AUC = 0.76) in 34 volunteers from the Metabolic Challenge Study (MECHE), with lysophosphatidylethanolamine (LPE) acyl C18:2 (LPE (18:2)) and phosphatidylethanolamine diacyl C38:4 (PE (C38:4)) identified as potential biomarkers in serum. Since C18:2 (LA) is an (n-6) fatty acids unrelated to fish intake, the association may be caused by confounding with, e.g., oils used for frying the fish. In another randomized controlled trial, a fatty fish-based diet produced an increase in the levels of lysoPC (20:5) and LPEs (20:5) and (20:6), which are very likely derived directly from the fatty fish [ 234 ]. Plasma phospholipid-LCPUFAs may serve as BFIs for shorter term or recent fatty fish intake in analogy with free LCPUFAs as already discussed.

In a study on plasma lipids in overweight men following meals with baked herring or baked beef, cetoleic acid (22:1) increased after herring intake, while 2-aminoadipic acid, leucine, β -alanine, and 4-hydroxyproline reflected beef intake [ 57 ]. Additional studies of cetoleic acid as potentially specific to intake of herring or of fish in general is needed for confirmation.

Astaxanthin

Astaxanthin is a natural lipophilic compound, which belongs to the xanthophylls and its bioavailability is enhanced by dietary lipids [ 249 ]. While it is found in a variety of aquatic organisms, including algae, krill, crayfish, salmon, salmon roe, shrimp, and crab, particularly high amounts are found in the flesh of salmon and in the carapace of crustaceans [ 250 , 251 ]. No studies exist to evaluate the main food sources of plasma astaxanthin in different populations but in the Western World, salmon and shellfish are likely to be the major sources. Astaxanthin cannot be synthesized by animals or humans, but it is essential for the growth of Atlantic salmon alevins [ 252 ] and its presence therefore indicates accumulation from food. Astaxanthin isomers, preferentially absorbed and concentrated into VLDL in humans, contain cis-double bonds, whereas the all-trans astaxanthin is most accumulated in salmonoids [ 253 , 254 ]. The biokinetics of astaxanthin have only been studied after supplementation. The maximal concentration of astaxanthin in serum is found at 12 h reaching C max of 50–280 μg/L after intake of doses of 10–100 mg given as supplements and is then declining with a half-life of around 12–21 h [ 253 , 254 , 255 ]. Accumulation of astaxanthin has been indicated from doses of 1–3 mg over 4–12 weeks showing dose- and time-dependent increases up to final levels of 11 μg/L after 1 mg/day and 35 μg/L after 3 mg/day at 12 weeks [ 256 ]. Daily doses of 4 mg astaxanthin over 3 months were found to provide final plasma levels of around 30 μg/L in reasonable agreement with other data [ 257 ]. Feeding of hens with algae, specific yeasts, shellfish waste, or even synthetic astaxanthin has been shown to increase its presence in egg yolk, providing the basis for commercial “golden” eggs [ 258 ]. Other food sources in the human diet besides supplements are food colorants, e.g., E161. However, they are likely to be minor in populations ingesting aquatic meats. Background levels, presumably from aquatic meat intake, in middle-aged Japanese males were found to be 0.1–0.5 μg/L while background levels in Norwegian, German, or Dutch men and in Korean women were reported as non-detectable [ 254 , 256 , 259 , 260 , 261 ]. In groups of English and Russian subjects abstaining from aquatic meat for 10 days, levels were 0.25 μg/L [ 262 ]. In a study with 28 German men, recruited to consume 250 g wild or aqua-cultured salmon providing 5 μg astaxanthin/g salmon flesh in a randomized and double-blind trial over 4 weeks, plasma astaxanthin concentrations of around 25 μg/L were detected [ 261 ]. The bioavailability from salmon seems therefore comparable to or higher than what was observed with the supplements.

In conclusion, astaxanthin may not be specific to fish and shellfish but in populations with low intakes of other sources of astaxanthin, it can be considered a candidate biomarker of these foods. Since there is good concordance between plasma levels measured in different laboratories, astaxanthin is likely to have good reproducibility. However, inter-individual response to the same dose of astaxanthin was quite large in some of the studies [ 256 , 261 ].

Organoarsenic compounds

It has been observed that many persistent organic pollutants (e.g., dioxin and organochlorine pesticides) and inorganic pollutants (e.g., mercury) are highly associated with fish consumption [ 263 ]. However, the presence of such compounds and elements in several other food matrices, as well as their differential distribution worldwide, create highly variable background levels, which do not allow the use of such compounds or their metabolites as reliable fish intake biomarkers [ 264 ]. Thus, for the search on environmental contaminants associated with the intake of aquatic meat, only arseno-compounds have been considered, given their high specificity to marine foods.

Due to the bioaccumulation of arseno-compounds along the aquatic food chain, fish and shellfish contain significant amounts of arsenic (As). In marine organisms, inorganic As present in ocean water is transformed into unique organic compounds by methylation or esterification with sugar (arsenosugars), lipid (arsenolipids), betaine (arsenobetaine, AsB), or choline (arsenocholine) [ 265 ]. AsB and methylated As (dimethylarsinate or DMAs, monomethylarsonate or MMAs) constitute the biggest proportion of As in fish and shellfish, while inorganic As(III) and As(V) are the least abundant metabolites [ 266 ]. Urinary profiles of As metabolites thus showed a positive dose-response relationship with recent fish or shellfish intake, despite differences based on species [ 267 ] and preparation method, since the water-soluble As metabolites are easily leaking from the meat when boiled [ 268 ].

Urinary excretion of DMAs 13–24 h after aquatic meat intake [ 266 , 269 ] shows that As methylation is one of the As detoxification pathways in the human body [ 267 , 268 ]. Urinary level of DMAs can thus also reflect As exposure from any other sources, such as seaweed [ 270 ], rice, mushroom [ 271 ], drinking water [ 269 ], or inhalation [ 272 ]. Consequently, DMAs and MMAs have low specificity as potential biomarkers of fish intake and are not considered further here.

Another major As metabolite, AsB, was rapidly excreted after fish and shellfish intake [ 95 , 266 , 268 ], showing limited biotransformation and accumulation. Formation of AsB from other As metabolites takes place in humans and AsB also influences urinary DMAs excretion [ 267 , 269 ]. The extent of these processes may be limited since AsB was not associated with As content in drinking water [ 269 ] or inhaled air [ 272 ], and was only weakly associated with rice intake [ 269 ]. In addition, urinary AsB is less susceptible to inter-individual variation than other organoarsenic species, especially when expressed as concentration rather than as μg/g creatinine [ 266 ]. Adjustment for AsB or stratified analysis in some subjects with undetectable urinary AsB nullified the association between aquatic meat intake and urinary concentrations of total As or DMAs [ 270 ]. This finding supports the specificity of AsB as a biomarker of total fish and shellfish intake and AsB seems to be a promising candidate biomarker for these foods.

The most commonly investigated biospecimen for AsB quantification is urine [ 266 , 269 , 270 ]. We only found one intervention study by Stanstrup and colleagues [ 95 ] that examined the biomarkers of acute fish intake in both plasma and urine samples, however without comparing the performance of the two. We did not find any article discussing the measurement of plasma AsB as a potential indicator of habitual fish intake. Hence, current evidence support the usage of plasma AsB to reflect only recent intakes, and urinary AsB as an indicator of both recent (within 3 days) [ 269 ] and habitual fish and shellfish intake [ 270 ]. The kinetics of AsB varied across intervention studies, peaking in plasma within 4–14 h and returning to baseline after 8–50 h for intakes of cod [ 95 , 267 ] and for urinary excretion after intake of crabs, wolfish, and lemon soles [ 272 ]. An observational study showed an association between urinary AsB and fish and shellfish intake within the last 72 h [ 266 ]. Association between urinary AsB and habitual aquatic meat consumption in the past year [ 270 ] was also observed at the group level but suggests no retention or tissue accumulation of AsB. Furthermore, as a natural, widespread water pollutant metabolite, AsB levels in fish or shellfish may vary across species and location [ 267 ]. Current evidence indicates that AsB may serve as a promising qualitative biomarker of fish and shellfish intake but its use for quantitative purposes needs further investigation. Studies so far were conducted in Caucasian populations in Europe [ 95 , 266 , 267 , 272 ] and the US [ 270 ]; studies in other populations are therefore warranted. Other As-compounds such as arsenolipids and arsenosugars did not show enough potential as fish intake biomarkers because the biotransformation to larger organoarsenic metabolites does not always occur in the aquatic food chain. Moreover, in the human body, arsenosugars and arsenolipids are metabolized by the liver to thioarsenic [ 273 ] and As-containing fatty acids [ 274 ], respectively, rather than to DMAs or other smaller As-metabolites [ 267 ]. Inter-individual variability in arsenosugar metabolism has also been previously reported [ 273 ]. These As compounds are therefore unlikely to be good biomarker with specificity to fish or shellfish intake.

In conclusion, n-3 LCPUFA in blood are valid BFI for the intake of fatty fish and fish oil at the individual level and would therefore reflect compliance to these foods in experimental studies, with measurements in different blood compartments reflecting the average intakes over different time periods. Marine lean fish and shellfish also contribute to their levels, while the contribution from freshwater fish depends on their feed. Total n-3 LCPUFA in most blood compartments should therefore reflect overall fish and shellfish intakes in populations with mixed diets. CMPF and related furan fatty acid metabolites in blood or urine are not sufficiently investigated; they may reflect more recent intakes of fish, fish oil, and shellfish but might also reflect habitual intakes over the last 4 weeks or more, and may depend on the origins of these foods. TMAO in urine is a shorter-term marker of intake for certain marine fish and shellfish, especially those from cold-temperate, deeper sea levels. Additional biomarkers of mainly marine fish and shellfish may include arseno-betaines in urine and possibly plasma, but further validation is needed for these markers, as well as for CMPF and TMAO. A biomarker reflecting intake of lean freshwater fish has not been found in this literature review. No specific biomarkers arose from our separate search on shellfish.

Validation of candidate biomarkers

The literature search outlined in the previous sections points to several putative biomarkers which may potentially be used to assess intake of animal protein, meat or fish and shellfish. However, several of these putative markers were not considered suitable as candidate BFIs for any of these foods. The reason for inclusion or exclusion of each compound from the list of putative markers is reported in Additional file 3 : Table S4. The resulting list of candidate biomarkers have been further evaluated to assess their usefulness as BFIs through the eight validation criteria, previously proposed by the FoodBAll consortium [ 24 ] as reported in Table 1 .

All the candidate markers in Table 1 are plausible (question 1) in the sense that their origin or presence in the food is well known or explained and also reasonably unique for the food group. Regarding the ability to quantitatively estimate the intake of a certain food, most of the candidate BFIs seem valid at the population level; even though a good dose-response relationship has been reported for several of the BFIs at the individual or group level (question 2), we still have little evidence for exact quantitation of individual food intake in samples from observational studies. Variation may be high due to the high variability in the concentration of precursors in different foods, to differences in absorption and metabolism, and to matrix effects caused by other dietary factors. The use of BFIs for quantitative assessment of food intakes should therefore be carefully considered for each specific study population, sample type, and for each specific intake assessment. For the remaining questions, the available evidence depends on the marker and its application as outlined below.

None of the meat exposures can be assessed quantitatively and without limitations by any candidate BFI. In general, the development of meat biomarkers faces significant challenges, such as (i) the overlap between meat-related metabolites and products of human muscle catabolism, (ii) variations in raw meat caused by variation between and possibly also within species, and (iii) the influence of production systems and feeding regimes. In spite of this, some BFIs are promising candidates and can be used under some circumstances for each of the exposures; these are outlined and discussed below, in light of the eight validation criteria. Table 1 should be used as an indicator of lacking evidence for each candidate BFI that needs to be addressed in further research.

Animal protein intake may be determined by the changes in isotope ratios, i.e., δ15N and/or δ13C in urine, stool, hair or blood. Dose-response for both ratios has been observed for hair and whole blood. The repeated-dose time-response was established for hair samples for a single individual indicating that more than 5 months may be needed to change levels in hair close to the scalp. The time-course for blood has not been established, except that 1–4 weeks of dietary change seems insufficient. Time-response for urine and stool has not been established for single meals but for repeated intakes of diets based on animal or plant protein, and it was shown that 8 days of change was enough to alter the isotope ratios. The ratios in hair and blood are robust since they have been successfully applied in experimental as well as observational settings and they are reliable since they have repeatedly been validated against other intake measures, including dietary records and markers of aquatic meat intake. Robustness and reliability have not yet been established for the shorter-term markers in urine or stool. The ratios are chemically and biologically stable since stable isotopes cannot change with storage or handling and since they are known to work also in archeology. Moreover, their analytical performance is well established, their precision and accuracy is high, and inter-individual variability low, which has been observed in several laboratories, indicating also good reproducibility in terms of absolute values. The major weakness is the variation between populations due to food matrix effects and the lack of knowledge about the ability of the marker to pick up smaller differences in the average intakes of animal protein in individuals. Overall, the isotope ratios are excellent for comparing longer-term overall animal protein intakes within a population but need further refinement for measurements of shorter-term intakes or for estimating intakes accurately.

Total meat intake, including terrestrial and aquatic meats, may be assessed at the group level by 3-MH in blood or urine and potentially by anserine due to their presence in poultry and/or fish; the most promising markers of total meat intake at the individual level may be creatine in blood or urine or Hyp in blood . The latter (Hyp) may be a general marker of all meat since it reflects intake of cartilage, present also in offal. Dose-response has been shown only for the markers in blood. The measurements in blood seem robust and have been shown so far to associate with habitual intakes at the group level. Plasma Hyp has also been associated with meat intake in a few observational studies. Analytical performance seems adequate for creatine, while Hyp exists both free and as part of dipeptides and better validation is needed for each of these or for their combination. There are sparse data for validation of the individual variability, shorter- and longer-term time-response, reliability, and reproducibility of these markers. Urinary creatine has been proposed as a marker of habitual meat intake, with good robustness shown also in observational studies. Further validation studies are needed to evaluate reliability of these markers when different sources of meat are consumed.

Terrestrial meat (muscle) intake, including meat from mammals and birds, may be assessed by quantifying carnosine in urine . Carnosine seems to reflect only terrestrial meat intake, as it is absent in almost all aquatic meats. The marker has been reported to show dose-response and single meal time-response with no apparent accumulation upon repeated daily intakes; thus it is only reflecting recent meat intakes. The marker also seems robust in studies with a complex diet and it is strongly associated with other dietary assessment, i.e., FFQs at the group level, although there is still a paucity regarding the reliability at the individual level. Chemical and biological stability in urine and inter-laboratory reproducibility studies are also lacking while analytical performance seems adequate with LC-MS-based analytical platforms available for accurate measurements. Carnosine in combination with 1- and 3-MH, provided a better estimate of meat intake than each of the compounds alone [ 275 ]. Even though this model needs to be validated for reliability in observational studies, it underlines that measuring a combination of markers could optimize dietary assessment of meat intake.

Intake of white meat, i.e., poultry, but also some species of fish, may be assessed by the urinary excretion of 3 - MH or anserine , or by 3 - MH in plasma . Anserine is present in hen and turkey muscle and to a lesser extent in meat from several species of fish, including cod, salmon and tuna; excretion in humans may therefore mainly reflect poultry intake, while more studies are needed to assess the influence of fish on this marker. Human excretion of anserine has been shown to be strongly associated only with chicken intake and not with fish at the group level (high vs. non-consumers) in a small European sub-study [ 38 ]. Anserine excretion after dietary intake has acceptable analytical performance in several analytical systems, stability after longer-term storage, and robustness; but experimental evidence for dose-response and time-response is still missing, including for experimental studies with fish intake. On the other hand, the validation of 3 - MH in urine shows reproducible dose-, and time-response in experimental studies, although it is strongly associated with the intake of both bird and fish meat in observational studies. Increased excretion is also observed after red meat intake, albeit less strongly [ 276 ]; even so, experimental studies showing time- and dose-response of 3-MH excretion have been observed after intake of beef [ 43 ]. Inter-individual variation seems limited and background excretion rates with lacto-ovo-vegetarian diets are low. Stability is excellent and analytical performance is good in systems able to discriminate 1- and 3-MH [ 277 , 278 ], but more data are needed regarding relative excretion levels after different sources of meat. Postprandial plasma 3 - MH has been shown to respond stronger to chicken than to fish (haddock) with clear dose-response but with only marginal increases after high intakes of beef compared with other sources [ 38 ]. Plasma or serum 3-MH has not been extensively used as a marker in observational studies and most aspects of validation are therefore still missing, needing further research. From the scoring pattern, 3-MH in urine can be considered a valid compliance marker of poultry intake but may reflect general meat intake more broadly in samples from observational studies. Further validation studies are needed to study the relationship of 3-MH excretion with different kinds of aquatic meats.

Intake of cooked chicken may be quantified reliably by urinary levels of guanidinoacetate , which shows good dose-response, robustness, and analytical performance in a recent study from UK and Ireland [ 46 ] using NMR; guanidinoacetate in urine has also been observed in Denmark after a diet with mixed foods, including chicken [ 113 ]; however, further validation is needed in relation to other poultry meats and to potential geographical variation in the contents of this compound in chicken meat.

Average intake of heated muscle food may be assessed by the level of PhIP in hair while urinary excretion of PhIP , 4 - OH - PhIP or MeIQx after hydrolysis of conjugates may be markers of recent intakes. All markers can be quantified reliably, while sample pretreatment (hydrolysis) strongly affects the measured concentrations. Dose-response was shown experimentally for PhIP in urine during a 4-day intervention showing little tendency for metabolite accumulation. However, urinary markers show large inter-individual variation. Only measurements of MeIQx in urine showed some agreement with FFQ-based estimations of cooked meat intakes in an observational study and MeIQx therefore seems as the more robust and reliable of the two. 4′-OH-PhIP may also be useful as a urinary marker of fried meat but lacks most aspects of validation, thus needing further research. Hair levels of PhIP show promise for estimating average intakes of fried and grilled meat over several months confirming levels in experimental diets with evidence of dose-response; however, robustness and reliability in observational studies have not yet been documented.

Habitual intake of aquatic meats, including fish and shellfish, or of fish oil, may be assessed by n - 3 LCPUFA in whole blood , RBC , plasma or plasma phospholipids . Among the metabolites associated with repeated or habitual fish intake, EPA, DHA, and total n-3 LCPUFA have shown reasonable ability to discriminate between terrestrial meat and fish intake or to estimate consumption of fish, shellfish, and fish oil supplements. EPA and DHA levels in various fractions in blood have mainly been investigated after repetitive intakes, showing clear dose-response with reported intakes; DHA was shown also to increase in plasma after a single meal in studies with cod or fish oil [ 95 , 189 ], peaking at 2–4 h. In experimental dietary studies, the time to reach steady state of LCPUFAs after fish, shellfish, or fish oil was 2–6 weeks for whole blood, plasma, and plasma lipids; 8 weeks for serum; and 4 weeks to 6 months for RBC. Total plasma free or esterified EPA and DHA were proved to be sufficiently robust and all analytical criteria are fulfilled.

Recent intake of aquatic meats, including fish and shellfish, may be assessed by several markers, including CMPF , AsB , or TMAO in urine , and CMPF or LCPUFA in plasma . CMPF and TMAO respond postprandially to meals with fish, showing dose-response and time-response. Complete excretion of TMAO within 24 h is observed in subjects with normal renal function. CMPF has a longer half-life in blood and is not completely excreted in 24 h; in some groups of patients, the half-life may reach 4 weeks but better kinetic data are needed from experiments in healthy subjects. CMPF in blood seems to be observed in most cross-sectional studies of fish and shellfish intake while TMAO is not always associated with these intakes when cod or crustaceans are not an important part of the diets. CMPF seems therefore more reliable as a general fish marker in areas where furan fatty acids are present in the food chain but studies from additional areas of the world are needed to explore whether this is common everywhere. Both CMPF and TMAO are stable during storage, easily quantified but excretion is affected by kidney function, potentially making them reliable only in healthy subjects. Inter-laboratory comparison studies are not yet published but reported levels are comparable between laboratories, indicating fair reproducibility. AsB has known short-term kinetics in plasma and urine but repeated-dose experimental studies are still lacking. The compound is likely to vary with geographical region and cooking practices but has been shown to be reliable in some regions, reflecting habitual intakes at the group level measured by dietary records. The analytical performance is sufficiently good to show clear kinetics by untargeted measurements but formal analytical validation is lacking for urine as well as for plasma. Plasma AsB has been determined in a single experimental study. Much additional validation is therefore required for the use of AsB as a biomarker of aquatic meat intake.

A range of biomarkers have potential to estimate meat and fish intake. The δ15N and/or δ13C ratios in hair are good determinants of long-term animal protein. Blood levels of creatine, hydroxyproline, and 3-MH are promising markers of total meat while carnosine works for terrestrial meats. However, more studies are needed to validate these markers, both alone and in combinations. The best validated markers are DHA and EPA in blood, serum, and blood lipids, clearly recognized as markers of habitual oily fish consumption. CMPF in blood and urine and TMAO in urine may also be useful markers to measure fish intake but depend on dietary habits. Overall, fish markers need validation in different populations. 3-MH seems to be a good biomarker of poultry intake showing dose-response, time-response, and good analytical performance but its robustness may be questioned because intake of some fish and possibly other meats are likely to interfere. Anserine is particularly good at characterizing poultry in both intervention and observational settings, but studies on time-response and dose-response are lacking. Guanidinoacetate excretion in urine is a promising new biomarker of chicken intake showing dose-response and robustness. No robust markers have been recognized so far to specifically assess red meat intake. BFIs able to discriminate between red and processed meats are also lacking. For fried and grilled meats, MeIQx in hydrolyzed urine and PhIP in hair are candidate biomarkers but need further work for their full validation. Additional studies on BFI discovery and validation are therefore needed before we can reliably assess or quantify intakes of all meat and most meat subgroups, except for aquatic n-3 LCPUFA.

Availability of data and materials

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Abbreviations

1-Hydroxypyrene

1-Hydroxypyrene-O-glucuronide

2-Amino-1-methyl-6-(4′-hydroxyphenyl)imidazo[4,5-b]pyridine

4-Hydroxy-2-nonenal

2-Amino-1-methyl-6-(5-hydroxy)phenylimidazo[4,5-b]pyridine

8-Iso-prostaglandin-F2alpha

Arachidonic acid

α-Linolenic acid

Arsenobetaine

Apparent total N-nitroso compounds

Biomarker for food intake

Cholesteryl esters

3-Carboxy-4-methyl-5-propyl-2-furanpropionic acid

Cardiovascular disease

Docosahexaenoic acid

1,4-Dihydroxynonane mercapturic acid

Dimethylarsenate

Docosapentaenoic acid

Eicosapentaenoic acid

European Prospective Investigation into Cancer and Nutrition

Food frequency questionnaires

Hydroxyproline

Linoleic acid

Long-chain polyunsaturated fatty acids

Lysophosphatidylethanolamine

2-Amino-3,8-dimethylimidazol4,5-f]quinoxaline

Methylhistidine

Monomethylarsenate

Naphthalene

N-nitroso compounds

Glycerophosphatidylcholine

Phosphatidylcholine

Phenanthrene

2-Amino-1-methyl-6-phenylimidazo[4,5-b]pyridine

7-Hydroxy-5-methyl-3-phenyl-6,7,8,9-tetrahydropyrido[3′,2′:4,5]imidazo[1,2-a]pyrimidin-5-ium chloride

Prolyl-hydroxyproline

Polyunsaturated fatty acids

Red blood cells

Randomized controlled trial

Thiobarbituric acid reactive substances

1,2,3,4-Tetrahydro-β-carboline-3-carboxylic acid

Trimethylamine

Trimethylamine oxide

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Acknowledgments

FoodBAll is a project funded by the BioNH call (grant number 529051002) under the Joint Programming Initiative, “A Healthy Diet for a Healthy Life.” The project is funded nationally by the respective Research Councils; the work was funded in part by a grant from the Danish Innovation Foundation (#4203-00002B) and a Semper Ardens grant from the Carlsberg Foundation to LOD; a postdoc grant from the University of Rome La Sapienza (“Borsa di studio per la frequenza di corsi o attività di perfezionamento all’estero” erogata ai sensi della legge 398/89) to GP; nationally funded by a grant from Science Foundation Ireland (SFI 14/JPI-HDHL/B3075) to SHP and LB; working grant from Juho Vainio Foundation, Saara Kuusisto and Salme Pennanen Grant for Cardiovascular Research and salary from Kuopio University Hospital (project number VTR 510RA17) to SN.

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Cătălina Cuparencu, Giulia Praticó, Lieselot Y. Hemeryck, and Pedapati S.C. Sri Harsha shared first co-authorship.

Authors and Affiliations

Department of Nutrition, Exercise and Sports, University of Copenhagen, Rolighedsvej 30, 1958, Frederiksberg C, Denmark

Cătălina Cuparencu, Giulia Praticó, Muyao Xi & Lars O. Dragsted

Department of Veterinary Public Health & Food Safety, Ghent University, Salisburylaan 133, 9820, Merelbeke, Belgium

Lieselot Y. Hemeryck, Caroline Rombouts & Lynn Vanhaecke

School of Agriculture and Food Science, Institute of Food & Health, University College Dublin, Belfield 4, Dublin, Ireland

Pedapati S. C. Sri Harsha & Lorraine Brennan

Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Yliopistonranta 1, 70210, Kuopio, Finland

Stefania Noerman & Kati Hanhineva

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Contributions

CC and GP coordinated the work. GP performed the literature search for the general biomarkers of meat intake and heated meat and wrote the related sections; CC, SN, and KH performed the literature search for all the sections relative to fish and shellfish and wrote the related sections, except for astaxanthin that was written by MX; LH, CR, and LV carried out the literature search on biomarker for red, processed and offal meat, and wrote the related sections. SHP and LB performed the literature search on poultry and wrote that section. LOD revised and critically commented the manuscript and updated the searches and validation section until December 2018. CC and LOD thoroughly edited the manuscript. All the authors accepted the final version of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Cătălina Cuparencu .

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The authors declare no competing interests; however, LOD received funding in 2012 from the Danish meat industry to support work on meat intake biomarker development.

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Supplementary information

Additional file 1: tables1..

Common keywords. TableS2. Specific keywords for each food group. Figure S1. Flow diagram of study selections describing each individual food group

Additional file 2: Table S3.

Summary of the human studies reporting biomarkers positively related to intake of different kinds of meat, found in the systematic literature search. The studies are grouped and classified according to the category for which the biomarkers have been discussed in the main text.

Additional file 3: Table S4.

Summary of the selected candidate biomarkers of meat and seafood intake and the discarded markers. For each marker, a brief explanation for inclusion or exclusion from the list of candidate FIBs is reported.

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Cuparencu, C., Praticó, G., Hemeryck, L.Y. et al. Biomarkers of meat and seafood intake: an extensive literature review. Genes Nutr 14 , 35 (2019). https://doi.org/10.1186/s12263-019-0656-4

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How to write a literature review introduction (+ examples)

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The introduction to a literature review serves as your reader’s guide through your academic work and thought process. Explore the significance of literature review introductions in review papers, academic papers, essays, theses, and dissertations. We delve into the purpose and necessity of these introductions, explore the essential components of literature review introductions, and provide step-by-step guidance on how to craft your own, along with examples.

Why you need an introduction for a literature review

When you need an introduction for a literature review, what to include in a literature review introduction, examples of literature review introductions, steps to write your own literature review introduction.

A literature review is a comprehensive examination of the international academic literature concerning a particular topic. It involves summarizing published works, theories, and concepts while also highlighting gaps and offering critical reflections.

In academic writing , the introduction for a literature review is an indispensable component. Effective academic writing requires proper paragraph structuring to guide your reader through your argumentation. This includes providing an introduction to your literature review.

It is imperative to remember that you should never start sharing your findings abruptly. Even if there isn’t a dedicated introduction section .

Instead, you should always offer some form of introduction to orient the reader and clarify what they can expect.

There are three main scenarios in which you need an introduction for a literature review:

  • Academic literature review papers: When your literature review constitutes the entirety of an academic review paper, a more substantial introduction is necessary. This introduction should resemble the standard introduction found in regular academic papers.
  • Literature review section in an academic paper or essay: While this section tends to be brief, it’s important to precede the detailed literature review with a few introductory sentences. This helps orient the reader before delving into the literature itself.
  • Literature review chapter or section in your thesis/dissertation: Every thesis and dissertation includes a literature review component, which also requires a concise introduction to set the stage for the subsequent review.

You may also like: How to write a fantastic thesis introduction (+15 examples)

It is crucial to customize the content and depth of your literature review introduction according to the specific format of your academic work.

In practical terms, this implies, for instance, that the introduction in an academic literature review paper, especially one derived from a systematic literature review , is quite comprehensive. Particularly compared to the rather brief one or two introductory sentences that are often found at the beginning of a literature review section in a standard academic paper. The introduction to the literature review chapter in a thesis or dissertation again adheres to different standards.

Here’s a structured breakdown based on length and the necessary information:

Academic literature review paper

The introduction of an academic literature review paper, which does not rely on empirical data, often necessitates a more extensive introduction than the brief literature review introductions typically found in empirical papers. It should encompass:

  • The research problem: Clearly articulate the problem or question that your literature review aims to address.
  • The research gap: Highlight the existing gaps, limitations, or unresolved aspects within the current body of literature related to the research problem.
  • The research relevance: Explain why the chosen research problem and its subsequent investigation through a literature review are significant and relevant in your academic field.
  • The literature review method: If applicable, describe the methodology employed in your literature review, especially if it is a systematic review or follows a specific research framework.
  • The main findings or insights of the literature review: Summarize the key discoveries, insights, or trends that have emerged from your comprehensive review of the literature.
  • The main argument of the literature review: Conclude the introduction by outlining the primary argument or statement that your literature review will substantiate, linking it to the research problem and relevance you’ve established.
  • Preview of the literature review’s structure: Offer a glimpse into the organization of the literature review paper, acting as a guide for the reader. This overview outlines the subsequent sections of the paper and provides an understanding of what to anticipate.

By addressing these elements, your introduction will provide a clear and structured overview of what readers can expect in your literature review paper.

Regular literature review section in an academic article or essay

Most academic articles or essays incorporate regular literature review sections, often placed after the introduction. These sections serve to establish a scholarly basis for the research or discussion within the paper.

In a standard 8000-word journal article, the literature review section typically spans between 750 and 1250 words. The first few sentences or the first paragraph within this section often serve as an introduction. It should encompass:

  • An introduction to the topic: When delving into the academic literature on a specific topic, it’s important to provide a smooth transition that aids the reader in comprehending why certain aspects will be discussed within your literature review.
  • The core argument: While literature review sections primarily synthesize the work of other scholars, they should consistently connect to your central argument. This central argument serves as the crux of your message or the key takeaway you want your readers to retain. By positioning it at the outset of the literature review section and systematically substantiating it with evidence, you not only enhance reader comprehension but also elevate overall readability. This primary argument can typically be distilled into 1-2 succinct sentences.

In some cases, you might include:

  • Methodology: Details about the methodology used, but only if your literature review employed a specialized method. If your approach involved a broader overview without a systematic methodology, you can omit this section, thereby conserving word count.

By addressing these elements, your introduction will effectively integrate your literature review into the broader context of your academic paper or essay. This will, in turn, assist your reader in seamlessly following your overarching line of argumentation.

Introduction to a literature review chapter in thesis or dissertation

The literature review typically constitutes a distinct chapter within a thesis or dissertation. Often, it is Chapter 2 of a thesis or dissertation.

Some students choose to incorporate a brief introductory section at the beginning of each chapter, including the literature review chapter. Alternatively, others opt to seamlessly integrate the introduction into the initial sentences of the literature review itself. Both approaches are acceptable, provided that you incorporate the following elements:

  • Purpose of the literature review and its relevance to the thesis/dissertation research: Explain the broader objectives of the literature review within the context of your research and how it contributes to your thesis or dissertation. Essentially, you’re telling the reader why this literature review is important and how it fits into the larger scope of your academic work.
  • Primary argument: Succinctly communicate what you aim to prove, explain, or explore through the review of existing literature. This statement helps guide the reader’s understanding of the review’s purpose and what to expect from it.
  • Preview of the literature review’s content: Provide a brief overview of the topics or themes that your literature review will cover. It’s like a roadmap for the reader, outlining the main areas of focus within the review. This preview can help the reader anticipate the structure and organization of your literature review.
  • Methodology: If your literature review involved a specific research method, such as a systematic review or meta-analysis, you should briefly describe that methodology. However, this is not always necessary, especially if your literature review is more of a narrative synthesis without a distinct research method.

By addressing these elements, your introduction will empower your literature review to play a pivotal role in your thesis or dissertation research. It will accomplish this by integrating your research into the broader academic literature and providing a solid theoretical foundation for your work.

Comprehending the art of crafting your own literature review introduction becomes significantly more accessible when you have concrete examples to examine. Here, you will find several examples that meet, or in most cases, adhere to the criteria described earlier.

Example 1: An effective introduction for an academic literature review paper

To begin, let’s delve into the introduction of an academic literature review paper. We will examine the paper “How does culture influence innovation? A systematic literature review”, which was published in 2018 in the journal Management Decision.

extensive literature review and

The entire introduction spans 611 words and is divided into five paragraphs. In this introduction, the authors accomplish the following:

  • In the first paragraph, the authors introduce the broader topic of the literature review, which focuses on innovation and its significance in the context of economic competition. They underscore the importance of this topic, highlighting its relevance for both researchers and policymakers.
  • In the second paragraph, the authors narrow down their focus to emphasize the specific role of culture in relation to innovation.
  • In the third paragraph, the authors identify research gaps, noting that existing studies are often fragmented and disconnected. They then emphasize the value of conducting a systematic literature review to enhance our understanding of the topic.
  • In the fourth paragraph, the authors introduce their specific objectives and explain how their insights can benefit other researchers and business practitioners.
  • In the fifth and final paragraph, the authors provide an overview of the paper’s organization and structure.

In summary, this introduction stands as a solid example. While the authors deviate from previewing their key findings (which is a common practice at least in the social sciences), they do effectively cover all the other previously mentioned points.

Example 2: An effective introduction to a literature review section in an academic paper

The second example represents a typical academic paper, encompassing not only a literature review section but also empirical data, a case study, and other elements. We will closely examine the introduction to the literature review section in the paper “The environmentalism of the subalterns: a case study of environmental activism in Eastern Kurdistan/Rojhelat”, which was published in 2021 in the journal Local Environment.

extensive literature review and

The paper begins with a general introduction and then proceeds to the literature review, designated by the authors as their conceptual framework. Of particular interest is the first paragraph of this conceptual framework, comprising 142 words across five sentences:

“ A peripheral and marginalised nationality within a multinational though-Persian dominated Iranian society, the Kurdish people of Iranian Kurdistan (a region referred by the Kurds as Rojhelat/Eastern Kurdi-stan) have since the early twentieth century been subject to multifaceted and systematic discriminatory and exclusionary state policy in Iran. This condition has left a population of 12–15 million Kurds in Iran suffering from structural inequalities, disenfranchisement and deprivation. Mismanagement of Kurdistan’s natural resources and the degradation of its natural environmental are among examples of this disenfranchisement. As asserted by Julian Agyeman (2005), structural inequalities that sustain the domination of political and economic elites often simultaneously result in environmental degradation, injustice and discrimination against subaltern communities. This study argues that the environmental struggle in Eastern Kurdistan can be asserted as a (sub)element of the Kurdish liberation movement in Iran. Conceptually this research is inspired by and has been conducted through the lens of ‘subalternity’ ” ( Hassaniyan, 2021, p. 931 ).

In this first paragraph, the author is doing the following:

  • The author contextualises the research
  • The author links the research focus to the international literature on structural inequalities
  • The author clearly presents the argument of the research
  • The author clarifies how the research is inspired by and uses the concept of ‘subalternity’.

Thus, the author successfully introduces the literature review, from which point onward it dives into the main concept (‘subalternity’) of the research, and reviews the literature on socio-economic justice and environmental degradation.

While introductions to a literature review section aren’t always required to offer the same level of study context detail as demonstrated here, this introduction serves as a commendable model for orienting the reader within the literature review. It effectively underscores the literature review’s significance within the context of the study being conducted.

Examples 3-5: Effective introductions to literature review chapters

The introduction to a literature review chapter can vary in length, depending largely on the overall length of the literature review chapter itself. For example, a master’s thesis typically features a more concise literature review, thus necessitating a shorter introduction. In contrast, a Ph.D. thesis, with its more extensive literature review, often includes a more detailed introduction.

Numerous universities offer online repositories where you can access theses and dissertations from previous years, serving as valuable sources of reference. Many of these repositories, however, may require you to log in through your university account. Nevertheless, a few open-access repositories are accessible to anyone, such as the one by the University of Manchester . It’s important to note though that copyright restrictions apply to these resources, just as they would with published papers.

Master’s thesis literature review introduction

The first example is “Benchmarking Asymmetrical Heating Models of Spider Pulsar Companions” by P. Sun, a master’s thesis completed at the University of Manchester on January 9, 2024. The author, P. Sun, introduces the literature review chapter very briefly but effectively:

extensive literature review and

PhD thesis literature review chapter introduction

The second example is Deep Learning on Semi-Structured Data and its Applications to Video-Game AI, Woof, W. (Author). 31 Dec 2020, a PhD thesis completed at the University of Manchester . In Chapter 2, the author offers a comprehensive introduction to the topic in four paragraphs, with the final paragraph serving as an overview of the chapter’s structure:

extensive literature review and

PhD thesis literature review introduction

The last example is the doctoral thesis Metacognitive strategies and beliefs: Child correlates and early experiences Chan, K. Y. M. (Author). 31 Dec 2020 . The author clearly conducted a systematic literature review, commencing the review section with a discussion of the methodology and approach employed in locating and analyzing the selected records.

extensive literature review and

Having absorbed all of this information, let’s recap the essential steps and offer a succinct guide on how to proceed with creating your literature review introduction:

  • Contextualize your review : Begin by clearly identifying the academic context in which your literature review resides and determining the necessary information to include.
  • Outline your structure : Develop a structured outline for your literature review, highlighting the essential information you plan to incorporate in your introduction.
  • Literature review process : Conduct a rigorous literature review, reviewing and analyzing relevant sources.
  • Summarize and abstract : After completing the review, synthesize the findings and abstract key insights, trends, and knowledge gaps from the literature.
  • Craft the introduction : Write your literature review introduction with meticulous attention to the seamless integration of your review into the larger context of your work. Ensure that your introduction effectively elucidates your rationale for the chosen review topics and the underlying reasons guiding your selection.

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  • Published: 13 November 2021

Data-driven smart eco-cities and sustainable integrated districts: A best-evidence synthesis approach to an extensive literature review

  • Simon Elias Bibri 1 , 2  

European Journal of Futures Research volume  9 , Article number:  16 ( 2021 ) Cite this article

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As materializations of trends toward developing and implementing urban socio-technical and enviro-economic experiments for transition, eco-cities have recently received strong government and institutional support in many countries around the world due to their ability to function as an innovative strategic niche where to test and introduce various  reforms. There are many models of the eco-city based mainly on either following the principles of urban ecology or combining the strategies of sustainable cities and the solutions of smart cities. The most prominent among these models are sustainable integrated districts and data-driven smart eco-cities. The latter model represents the unprecedented transformative changes the eco-city is currently undergoing in light of the recent paradigm shift in science and technology brought on by big data science and analytics.  This is motivated by the growing need to tackle the problematicity surrounding eco-cities in terms of their planning, development, and governance approaches and practices. Employing a combination of both best-evidence synthesis and narrative approaches, this paper provides a comprehensive state-of-the-art and thematic literature review on sustainable integrated districts and data-driven smart eco-cities. The latter new area is a significant gap in and of itself that this paper seeks to fill together with to what extent the integration of eco-urbanism and smart urbanism is addressed in the era of big data, what driving factors are behind it, and what forms and directions it takes. This study reveals that eco-city district developments are increasingly embracing compact city strategies and becoming a common expansion route for growing cities to achieve urban ecology or urban sustainability. It also shows that the new eco-city projects are increasingly capitalizing on data-driven smart technologies to implement environmental, economic, and social reforms. This is being accomplished by combining the strengths of eco-cities and smart cities and harnessing the synergies of their strategies and solutions in ways that enable eco-cities to improve their performance with respect to sustainability as to its tripartite composition. This in turn means that big data technologies will change eco-urbanism in fundamental and irreversible ways in terms of how eco-cities will be monitored, understood, analyzed, planned, designed, and governed. However, smart urbanism poses significant risks and drawbacks that need to be addressed and overcome in order to achieve the desired outcomes of ecological sustainability in its broader sense. One of the key critical questions raised in this regard pertains to the very potentiality of the technocratic governance of data-driven smart eco-cities and the associated negative implications and hidden pitfalls. In addition, by shedding light on the increasing adoption and uptake of big data technologies in eco-urbanism, this study seeks to assist policymakers and planners in assessing the pros and cons of smart urbanism when effectuating ecologically sustainable urban transformations in the era of big data, as well as to stimulate prospective research and further critical debates on this topic.

Introduction

Urbanization is creating greater pressure on limited resources. Already, cities consume more than 75% of the natural resources available globally. This is estimated to increase by 90 billion tons by 2050 compared to 40 billion tons in 2010 [ 1 ]. As an irreversible global trend, urbanization involves a multitude of environmental, economic, social, and spatial conditions, which pose unprecedented challenges to politicians, policy makers, and planners. Nonetheless, it can have many positive outcomes by creating changes out of these challenges and as a response to external factors. These changes provide great opportunities for advancing sustainability in terms of applying innovative technologies to use resources more efficiently and control them more safely, to promote more sustainable land use, and to preserve the biodiversity of natural ecosystems and reduce pressure on their services, with the aim to improve economic and societal outcomes. In a nutshell, cities across the globe hold the potential to maximize the benefits of urbanization and offset its negative consequences by relying on emerging and future information and communication technology (ICT). In fact, urbanization has become a popular discourse in urban policy and academic circles across the world due to the rising popularity of smart urbanism and its potential role in advancing urban sustainability.

As cities represent part of the problems and solutions in the quest for sustainability, they can function as one of the keys in the required transition toward sustainability thanks to their configuration and innovative potential. Therefore, numerous stakeholders and institutions have devoted much attention to sustainable development and allocated tremendous resources in an attempt to incorporate the ideas and visions of sustainability into the reality of cities. In fact, the increased pressure on cities has led to a stronger need to build sustainable cities that last. Designing sustainable cities of the future, educated by the lessons of the past and anticipating the challenges of the future, entails articulating a multi-scalar vision and these key principles—energy, ecology, infrastructure, waste, water, livability, mobility, accessibility, economy, and culture—while responding to major societal and intellectual trends and paradigm shifts in science and technology [ 2 ]. Sustainable cities have been the leading global paradigm of urbanism, and eco-cities are one of the most advocated models for sustainable urban development [ 3 ]. To totally change the pattern of urban development, and to implement the development strategy of the eco-city, is the most effective way to create sustainable development [ 4 ]. Therefore, eco-city projects are becoming increasingly prevalent in policy, environmental, and political-economic discourses worldwide and thus gaining strong momentum in research agendas in various disciplines (e.g., [ 5 , 6 , 7 ]; Simon and Molella [ 8 , 9 , 10 ]). It is important to take modern ecological civilization and ecological restoration to guide and manage all economic and social activities of the city. Especially, cities have become a focal point for efforts to transition toward a more sustainable, low-carbon society, with many city government or municipal agencies championing eco-city or eco-district initiatives of one kind or another.

In recent years, the development of sustainable urban districts, especially eco-city districts, has attracted increased interest and become an expansion route for many growing cities (e.g., Joss 2015 [ 11 , 12 , 13 ];) as well as a common way to address and implement sustainability in the built environment (Joss 2015 [ 11 , 14 ]). However, the development of new sustainable urban districts is often subject to more rigorous sustainability objectives with respect to their evaluation in order to meet future challenges [ 13 ]. As a result, a recent wave of research has started to focus on improving the performance of eco-cities by partly or completely integrating their green design principles and environmental technology solutions with the design strategies of compact cities (e.g., [ 3 , 15 , 16 , 17 ]). This relates to the mission of Urban Ecology as to creating eco-cities based on a number of principles [ 18 ] that are intended to achieve the goals of sustainability. This is at the core of emerging Eco-Compact or Eco-Density initiatives, which are seen as an unprecedented planning effort and a response to the deconcentration of land use due to urban sprawl. Accordingly, these initiatives use density, mixed-land use, and sustainable transportation as catalysts toward livability, affordability, and environmental sustainability. They aim to deliver more efficient land use, improve green energy systems, and build resilient and adaptable urban communities. Recent research within eco-urbanism tends to focus on the three dimensions of sustainability in terms of benefits and shortcomings (e.g., [ 19 , 20 ]; Khan et al. 2020 [ 21 , 22 ]). This relates to the integrated models for urban development, which explore the development of sustainable integrated districts (SIDs) as a model for high-density, high-liveability cities. 

With the above in mind, the eco-city continues to strive toward reaching the status of urban sustainability by reducing material use, lowering energy consumption, mitigating pollution, and minimizing waste, as well as improving social equity, well-being, and the quality of life. Eco-urbanism has taken on a salient position in policy and political discourses focused not only on the environment, but also on economic, social, and technological transitions (e.g., [ 6 , 7 , 19 , 23 ]; Khan et al. 2020 [ 21 , 24 ]). The motivation for achieving the Sustainable Development Goal (SGD) 11 of the United Nations’ 2030 Agenda—Sustainable Cities and Communities in terms of making cities sustainable, resilient, inclusive, and safe [ 25 ] has increased the need to understand, plan, and manage eco-cities in new and innovative ways. These are increasingly based on more advanced forms of ICT, especially the Internet of things (IoT) and big data technologies. The United Nations’s 2030 Agenda regards advanced ICT as a means to promote socio-economic development, restore and protect the environment, increase resource efficiency, upgrade legacy infrastructure, and retrofit industries based on sustainable design principles [ 26 ]. This relates to the multifaceted potential of smart cities with respect to the growing role of big data technologies and their novel applications in strategic sustainable development. The main objective of the smart city is achieving heightened economic development, the quality of life, and different sustainability targets through the use of data and technology (e.g., Ahvenniemi et al. [ 27 , 28 , 29 , 30 , 31 , 32 ]). The explosive growth of urban data, coupled with their analytical power, opens up new windows of opportunity for innovation in eco-cities. This means finding more effective ways of incorporating sustainability into the physical, spatial, environmental, economic, and social forms of eco-cities.

The conscious push for eco-cities to become smarter and thus more sustainable in the era of big data is due to the problematicity surrounding their development planning approaches and operational management mechanisms, as well as their governance models. This has had a clear bearing on their performance with respect to sustainability. In order to deal with these problems, issues, and challenges, advanced forms of ICT are required. The underlying argument is that more innovative smart solutions are needed to enable eco-cities to tackle the kinds of complexities and conundrums they embody. In fact, smart technologies are socially constructed as the response to almost every facet of the contemporary urban questions [ 33 ], and smart urbanism is being represented as a panacea to the majority of problems facing contemporary cities. As enacted by national governments, supranational agencies, and technology companies, the discourse of smart urbanism claims a supremacy of urban digital technologies for managing and controlling infrastructures, achieving greater effectiveness in managing service demand and reducing carbon emissions, developing greater social interaction and community networks, providing new services around health and social care, and so on [ 34 ].

Therefore, eco-cities across the globe are increasingly embracing and leveraging what smart cities have to offer in terms of advanced ICT, especially big data technologies and their novel applications, in an effort to monitor, evaluate, and improve their performance with respect to sustainability, especially its environmental and economic dimensions (e.g., [ 23 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 ])—under what has been termed “smart eco-cities” or “data-driven smart eco-cities.” It has become increasingly feasible to attain important improvements of sustainability by integrating eco-urbanism and smart urbanism thanks to the proven role and untapped potential of data-driven technologies for maximizing the benefits of sustainability. This pertains to the question involving the weak connection between sustainable cities and smart cities as prevalent models of urbanism and their extreme fragmentation as landscapes, both at the technical and policy levels, adding to their opposite conceptual characteristics and existing tensions (e.g., [ 3 , 29 , 45 , 46 , 47 , 48 , 49 ]). An “either/or” approach will hamper progress toward urban sustainability, as the huge challenges facing eco-cities within many of their administration spheres (transport, traffic, mobility, energy, environment, waste, healthcare, public safety, etc.) require an integrated approach to urbanism.

The emerging area of data-driven smart eco-cities is a significant gap in and of itself that this paper seeks to fill together with to what extent the integration of eco-urbanism and smart urbanism is addressed in the era of big data, what driving factors are behind it, and what forms and directions it takes. Employing a combination of both best-evidence synthesis and narrative approaches, this paper provides a comprehensive state-of-the-art and thematic literature review on sustainable integrated districts (SIDs) and data-driven smart eco-cities. The value of this review resides in its topicality, thoroughness, substantive nature, as well as original contribution in the form of new insights and perspectives as a result of synthesizing a broad range of literature characterized by various disciplinarities.

The remainder of this paper is structured as follows: “Research Methodology” section details and justifies the literature review methodology. “Conceptual, theoretical, discursive, and practical foundations of eco-city/eco-urbanism” section describes and discusses the conceptual, theoretical, discursive, and practical foundations of eco-city/eco-urbanism. “Analysis, evaluation, synthesis, and discussion” section provides a thorough analysis, evaluation, synthesis, and discussion of the emerging phenomena of SIDs and data-driven smart eco-cities. “Knowledge gaps in the area of data-driven smart eco-cities” section identifies and enumerates the relevant topics related to the key knowledge gaps in the area of data-driven smart eco-cities. Finally, this paper concludes, in “Conclusions” section, by providing a summary of the key findings, highlighting the main contributions, and suggesting some future research directions.

Research Methodology

Interdisciplinarity  and transdisciplinarity   .

Research review has long been one of the most important scholarly activities in all academic disciplines or branches of science. This literature review analyzes, evaluates, synthesizes, and discusses a large body of research done on the burgeoning field of data-driven smart eco-urbanism. In doing so, it draws on a number of city-academic or scientific disciplines and their integration and fusion, as well as on practical insights from numerous case studies.

Interdisciplinarity and transdisciplinarity have become a widespread mantra for research within diverse fields, accompanied by a growing body of scholarly publications. The research field of data-driven smart eco-urbanism is profoundly interdisciplinary and transdisciplinary in nature. It operates out of the understanding that advances in knowledge necessitate pursuing multifaceted questions that can only be resolved from the vantage point of interdisciplinarity and transdisciplinarity. This in turn implies that the research problems within this field are inherently too complex and dynamic to be addressed by single disciplines. This is clearly reflected in the literature on eco-cities and smart eco-cities in the era of big data.

Accordingly, the interdisciplinary and transdisciplinary approaches to scholarly research apply by extension to the review of this literature. The former insists on mixing disciplines, and crosses boundaries between different disciplines to create new perspectives and insights on the basis of interactional knowledge beyond these disciplines. Its strength lies in the ability of interlinking different analyzes using insights and methods from different disciplines in parallel and spilling over disciplinary boundaries. The latter insists on fusing different disciplines and thus using insights and methods from these disciplines in conjunction—with a result that exceeds the simple sum of each of them. Transdiciplinarity concerns that which is at once between, across, and beyond single disciplines.

Approaches and objectives 

Reviews are generally categorized into different types, each with own qualities and perspectives on reviewing a topic, namely systematic review, best-evidence synthesis, narrative review, meta-analysis, scoping review, umbrella review, and rapid review. As a rigorous approach, a systematic review is best suitable for focused topics, which is not the case for data-driven smart eco-cities and SIDs. Indeed, this emerging area of research involves multifaceted questions and remarkably heterogeneous research programs. As regards the narrative approach to literature review, which is applied in this study, it is of a wide scope and non-standardized nature and does not follow an established procedure. A narrative review summarizes different primary studies from which conclusions may be qualitatively drawn into a holistic interpretation contributed by the reviewers’ own experiences, existing theories, and models [ 50 ]. It also proposes to comprehend the diversities and pluralities of understanding around scholarly research [ 51 , 52 ]. Narrative reviews are best suitable for comprehensive topics [ 53 ].

As regards the best-evidence synthesis approach to literature review, which complements the narrative approach in this study, it draws on a wide range of evidence and explores the impact of context. As such, it brings together all relevant information on a research topic. This can be useful to identify gaps in knowledge, establish an evidence base for best-practice guidance, or help inform policymakers and practitioners. Furthermore, the best-evidence synthesis approach offers an alternative to a narrative review, giving attention to substantive issues of a narrative point of view, adding a rational for study-selection and effectiveness of treatment, and emphasizing the importance of well-justified inclusion criteria [ 2 ]. By providing the reader enough information about the primary research, they must be able to reach independent conclusions, as far more information is extracted from a large body of literature by clearly describing the best evidence on a topic. Overall, this study intends to demonstrate the usefulness of combining the two substantive categories of literature review.

Generally, there are different objectives of literature review, including methodological, theoretical, thematic, and state-of-the-art. This literature review is concerned with the state-of-the-art and thematic objectives. The former considers mainly the most current research and summarizes emerging research priorities and academic trends in the field. It provides a critical survey of the extensive literature produced in the past decade or so, a synthesis of current thinking in the field, and also offers new perspectives. The latter describes particular areas of the literature (e.g., emerging models of urbanism), where the intent of the outcome is to identify weaknesses and disseminate the path toward improvements. It also provides an in-depth examination of the principles underlying the phenomenon under study through the evaluation of its objectives.

Hierarchical search strategy and scholarly sources

A literature search is the process of querying quality scholarly literature databases to gather the research publications pertaining to the topic under review. A search strategy was used, covering several electronic search databases, including Scopus, ScienceDirect, SpringerLink, and SageJournals, in addition to Google Scholar. The main contributions came from the leading journal articles. The hierarchical search approach adopted consists of:

searching databases of reviewed high quality literature;

searching evidence-based journals for review articles; and

routine searches and other search engines.

In addition, the collection process is based on Scott’s (1990) four criteria for assessing the quality of the targeted material, namely:

Authenticity: the evidence gathered is genuine and of unquestionable origin.

Credibility: the evidence gathered is free from error and distortion.

Representation: the evidence obtained is typical.

Meaning: the evidence gathered is clear and comprehensible.

Selection criteria: inclusion and exclusion

To find out what is known about the field of data-driven smart eco-urbanism, the above search approach was adopted with the objective to identify the relevant studies addressing the various strands of research within this field that cover the questions being addressed. Accordingly, the preliminary selection of the available material was done in accordance with the problems under study. In this respect, it is feasible to refine and narrow down the scope of reading, although there may seem to be a number of information sources that appear to be pertinent to the topic on focus. With that in mind, for a document to be considered as to its potential to provide any information of relevance, it should relate to one of the conceptual subjects and thematic categories specified in regard to the questions being addressed. The focus was on the documents that provided definitive primary information, typically from the following scholarly perspectives:

Cross-disciplinary: viewing one discipline from the perspective of another

Interdisciplinary: integrating knowledge and methods from different disciplines based on a synthesis of approaches

Transdisciplinary: creating a unity of intellectual frameworks beyond the disciplinary boundaries

Overall, scoring the documents to be selected was based on the inclusion of the problems being addressed, though to varying degrees with respect to the conceptual subjects and thematic categories decided on. The latter was meant to emphasize the quintessential aspects of data-driven smart eco-cities and SIDs. Conversely, the documents excluded were those that did not meet the specific criteria as regards their relevance to the questions being addressed. Nonetheless, a few of these documents provided some insights related to the paradigms of urbanism underpinning data-driven smart eco-cities as an integrated model. Furthermore, the abstracts were reviewed to assess their pertinence to the topic on focus, as well as to ensure a reliable application of the inclusion and exclusion criteria. Inclusionary discrepancies were resolved by the re-review of the abstracts. The process allowed to further refine and narrow down the scope of reading.

The keywords searched for included “eco-cities,” “compact cities,” “sustainable cities,” “smart eco-cities,” “data-driven smart cities,” “data-driven smart sustainable cities,” “sustainable urbanism,” “eco-urbanism,” “smart urbanism,” “data-driven urbanism AND sustainability,” “sustainable urban districts,” “urban planning AND big data technology,” “data-driven solutions AND eco-city,” “smart planning AND eco-city,” “data-driven management AND eco-city,” “smart city governance,” “smart governance AND eco-city,” and “eco-modernization AND eco-city.” These were used to search against such categories as the articles’ keywords, title, and abstract to produce some initial insights. Due to the limitations associated with relying on the keyword approach, backward literature search (backward authors and backward references) and forward literature search (forward authors and forward references) were used, when appropriate, to enhance the search approach.

Purposes and organizational approaches

The literature review is typically performed for various purposes. This depends on whether it is motivated by, or an integral part of, a research study and thus its scope or area of focus. This explains the extent to which the research area will be explored in the study, which basically means defining what the study covers and what it focuses on. This literature review is for publication and carried out to:

describe and discuss the conceptual foundation of data-driven smart sustainable eco-urbanism;

analyze, evaluate, and synthesize the existing knowledge in the field;

highlight the strengths, weaknesses, and contradictions of the existing knowledge in the field, thereby providing a critique of the research that has been conducted so far;

discuss the identified strengths and weaknesses with respect to sustainability and data-driven smart technologies and their relationships;

identify the opportunities, potentials, and prospects offered by data-driven smart technologies in terms of improving and advancing the goals of sustainability; and

identify the key relationships between the relevant studies addressing the different aspects of the topic on focus by comparing, linking, and enhancing their results, as well as reinterpreting their conclusions.

This review is structured using a combination of two organizational approaches: thematic and inverted pyramid. That is to say, it is divided into a number of sections representing the conceptual subjects and thematic categories for the topic of data-driven smart eco-cities and SIDs. The analysis, evaluation, synthesis, and discussion of the relevant issues are organized accordingly while, when appropriate, starting from a broad perspective and then dealing with a more and more specific perspective with respect to the selected studies.

Conceptual, theoretical, discursive, and practical foundations of the eco-city/eco-urbanism

Definitional issues, initiatives, and categories.

Over the past two decades, eco-urbanism has gained significant traction and its scope has been expanded to cover multiple aspects of sustainability. Generally, eco-urbanism focuses on developing urban environments based on the principles of ecological sustainability, i.e., multi-dimensional sustainable human communities within harmonious and balanced built environments. According to Spirn [ 54 ], eco-urbanism weds the theory and practice of urban design and planning, as a means of adaptation, with the insights of ecology and other environmental disciplines. The author describes the roots of eco-urbanism and identifies fundamental concepts and principles, as well as  provides a framework to guide more comprehensive reviews of the literature and to advance the practice of eco-urbanism, i.e., eco-city planning and development.

The term “eco-city” can be traced back to the mid-1970s, when it was first coined by Richard Register through his Urban Ecology Initiative. Since the late 1980s, the eco-city concept has gained worldwide recognition thanks to the advocacy of interdisciplinary cooperation. The practice of eco-city planning and development has been carried out in many countries across the globe (see, e.g., [ 9 , 55 , 56 ]). The eco-city has recently built great momentum in international research areas in response to the rising concerns about the environment due to the escalating rate and scale of urbanization. The idea of the eco–city is widely varied in conceptualization and operationalization. In particular, there are multiple definitions of the eco-city, depending on the context where it is embedded in the form of urban projects and initiatives in terms of the practices and strategies adopted to achieve the goals of the eco-city. Roseland [ 18 ] argues that there is no single accepted definition of the eco–city, but more a collection of ideas about concepts. Joss [ 55 ] substantiates the conceptual diversity and plurality of the initiatives and projects using the term across the globe. Bibri [ 57 ] provides a detailed discussion of the definitional issues of the eco-city. Nonetheless, there is some consensus on the basic features of the eco-city among common definitions (Table 1 ). Furthermore, Joss [ 56 ] provides a full discussion of the history and recent international development of eco-cities. Rapoport [ 10 ] traces the evolution of the eco-city as a concept and an urban planning model over the last 40 years, outlining the various definitions, applications, and critiques of the term. In this regard, Caprotti [ 6 ] argues for the need to interrogate the multiple definitions of the eco-city, evaluation methods, and performance frameworks as a tool for critically analyzing the marketing, presentation, and actual built urban environments in eco-city projects.

The concept of the eco-city has been used to describe a wide range of urban projects and initiatives, mostly large-scale new districts, encapsulating a diversity of conceptualizations and pulling together an ensemble of normative values and prescriptive principles supported by various policies about how to develop and design sustainable urban areas. There is a need to engage with the issue of defining the eco-city, an emphasis which can be, at the most basic level, based on performance indicators.

In recent years, the world has witnessed the emergence of numerous eco-districts and eco-cities, which are still under the banner of experimentation, or seen as sites of innovation. The rise of eco-city initiatives serves as a sign or evidence of renewed attempts to experiment in designing urban futures [ 5 ] or engineering urban zones. These initiatives have promoted a number of alternative models of eco-city development capable of, in some instances, creating sustainable urban districts. There is a wide range of eco-districts or cities across the globe, with high-profile examples being Western Harbour in Malmö, Sweden; Hammarby Sjöstad and Royal Seaport in Stockholm, Sweden; and Masdar City in Abu Dhabi, United Arab Emirates (UAE). In a global survey of eco-cities conducted by Joss [ 55 ], 178 eco-cities are spread around the globe, with a concentration in Europe and Asia in terms of higher project intensity. China has plenty of eco-city projects (see, e.g., [ 62 , 63 ]) as a result of embarking on an ambitious program to build new eco-cities for quite sometime. In Europe, according to Joss [ 55 ], only in 2009, it was announced that Paris would become the first “post-Kyoto eco-city,” and later that year, the British government decided to build four new “eco-towns” across England. The eco-city stands out in terms of geographical diffusion among the existing models of sustainable urbanism, and its advocates hails it as the mark of urban innovation. However, Joss [ 55 ] identifies three categories through which eco-cities can be analytically approached and segmented, namely type of development, the development phase, and the implementation focus. The first category is inherently scalar: (1) a city built from scratch; (2) the expansion of an existing urban area; and (3) retro-fitting existing urban structures and environments through sustainability-focused innovations and adaptations. Also an eco-city is based on three analytical categories: (1) a development on a substantial scale, (2) occurring across multiple domains, and (3) supported by policy processes [ 64 ].

In view of the above, what exactly constitutes the eco-city as an overarching approach to sustainable urbanism seems to be even more unclear. Today, an ever-increasing range of existing cities, districts, and new or planned urban projects, from minor retrofits to large-scale new-towns, are labeled eco-cities. Conceptualizing an eco-city is a key question in any study focused on eco-urbanism and on tracking its impact and performance across a range of sustainability indicators. The way eco-city projects conceive of the eco–city status reflects more divergences than convergences. In other words, the guiding planning documents related to these projects tend to be largely developed as independent islands of locally oriented ecological sustainability. Still, holding eco-city projects up to scrutiny according to existing norms and standards is crucial for the critical analysis of these projects given their flagship nature. In addition, it is more appropriate to think of the eco-city as an ambition that can be achieved through multiple ways, or adapted to different urban contexts. Overall, with the label of the eco-city being used to describe a wide range of urban initiatives and projects, the eco-city is best to be viewed as an umbrella term so as to encapsulate the underlying diversity, bringing together an amalgam of procedural principles, scientific approaches, and normative visions about how to build more sustainable urban environments. Regardless, failing to consider a holistic approach into the local or national planning of the eco-city is likely to increase the risk potentiality of future lock-ins and targets being missed on a higher level. Also, analyzing the eco-city according to its marketed performance targets in terms of environmental sustainability stimulates further discussion and helps critical analysis by highlighting the actual performance behind the often glitzy branding and marketing characterizing eco-city projects.

Principles and strategies

The principles and strategies of the eco-city have been approached from different perspectives based on the multidimensional context this model of sustainable urbanism is embedded. Register (1996) puts forward several principles of the eco-city, including land development, transportation, natural environment, resource utilization technology, production and consumption patterns, ecological awareness and social equity, and government management. Kenworthy [ 17 ] argues that the eco-city should incorporate 10 key principles, with sustainable urban form and transport at the core of the model (Fig. 1 ). This relates to the emerging integrated models for  sustainable urban development, which is to be discussed further in the next section.

figure 1

A conceptual model for the eco-city based on 10 key principles in urban planning, urban transport, urban design, and planning process considerations. Source: Kenworthy [ 65 ]

Based on a recent case study conducted on two Swedish eco-city districts in Malmö and Stockholm, Bibri and Krogstie [ 15 ] distill, integrate, and enumerate their key strategies in accordance with the three dimensions of sustainability (Table 2 ). This is linked to what is known as sustainable urban districts or SIDs.

Models and practices

The eco-city focuses more on the environmental dimension of sustainability in terms of the natural environment and ecosystems than on the economic and social dimensions of sustainability (e.g., [ 20 , 24 , 66 ]). While the natural environment has been a common concern throughout the history of urban planning, eco-cities bring this concern to the forefront of planning, engineering, and design. In addition, the increasing, global proliferation of eco-cities can be placed in a variety of theoretical and interpretive contexts. On the one hand, eco-cities can be seen as a continuation of planning, architecture, and design trends which have sought to reconcile nature and the city from Garden City movement, to New Towns, to the “techno-cities” of the twentieth century [ 67 ], to the more dystopian “emerald enclaves” conceptualized as green and sustainable islands in a broader global scenario characterized by environmental degradation and contamination ([ 68 ]; Pow and Neo 2010 [ 69 ]).

There are many models of the eco-city, which can be caterogarized into three types: (1) emphasizes passive solar design, (2) combines passive solar design and greening, and (3) focuses on green energy technologies and/or smart energy and environmental technologies (Table 3 ) [ 2 ]. Type 3 relates to the concept of the smart eco-city, which captures the recent trend of future-oriented urban development schemes that display both green and smart ambitions.

With the above in mind, the eco-city goes beyond the iteration of the nature-city relationship to contain novel and innovative components. The practice of eco-city development depends on the strategies and solutions that the cities badging or regenerating themselves as ecological or smart ecological prioritize with respect to both ecological sustainability and applied smart technology in response to the kind of the challenges they deal with within a particular context in a given period of time. For example, Cai and Tang (120) review the development of eco-cities in China, and take several typical eco-cities as case studies to illustrate the practices of the eco-city construction. The authors show that there are three periods of eco-city practice development in China: eco-cities (1990s), low-carbon cities (2000s), and smart-eco cities (2010s). During 1990s, the focus was on ecological and environmental issues, and the concept of “harmony between humans and nature” was widely applied to guide eco-city construction. During 2000s, the focus shifted to cutting greenhouse gases (GHG) amid the severe domestic environmental situation and international societal pressure. During 2010s, great importance was attached to the city with green and ecological concepts in an attempt to integrate eco-cities and smart cities.

Design principles

As mentioned previously in relation to type 1 and type 2, there are two key distinctive design principles associated with the ecological agenda promoted by the early models of the eco-city along with the associated policy instruments related to environmental planning and management.

Ecological design—greening

Ecological design is a form of design which integrates itself with living processes to minimize environmentally negative or destructive impacts. As an integrative, ecologically responsible approach, ecological design involves greening as an important design concept for sustainable urban forms. Green space has the ability to contribute positively to the key agendas of sustainability in urban areas [ 70 ]. It refers to the areas of nature found in the urban landscape, including trees, grassy patches, flowerbeds, rock gardens, sports fields, woods, lakesides, and water features. Green space has numerous benefits, including improving health and wellbeing, ameliorating the physical urban environment by removing CO 2 emissions and other toxins from the air, enhancing the esthetics of urban areas and thus making them more pleasant, increasing the urban image and economic attractiveness, as well as controlling storm runoff. In particular, the research in this area tends to focus on the health advantages of urban green space (see, e.g., De Vries et al. 2003 [ 71 ]).

At the core of ecological design is green structure, a strategically planned network of natural and semi-natural areas with other environmental features that are designed and managed to deliver a wide range of ecosystem services, including water purification, air quality, space for recreation, climate mitigation and adaptation, flood protection, temperature regulation, biodiversity, and local stormwater management. Green structure encompasses large green spaces, waterways and streams, shorelines, parks, natural land, and forests as one common structure. Swedish cities, for example, operate with the concept of “green structure” when it comes to sustainable (green and compact) urbanism (e.g., [ 57 , 60 , 72 , 73 , 74 ]). As an ecological strategy, green structure emphasizes the benefits and losses of natural environmental and map green resources by assessing the associated natural and recreational qualities. This strategy can be broken into the following substrategies [ 15 ]:

Rainwater harvesting

Biodiversity

Green parks

Green streets and alleys

Green factor and green points

Green roofs and rain garden

Bioswales and permeable pavements

The green structure strategy relates to the idea of letting nature do the work by designing multifunctional green infrastructure to provide important ecosystem services of various categories, including provisioning, regulating, cultural, and supporting services. This idea involves ensuring that greenery and water are used as active components in urban design. Green structure replaces and complements technical systems, creates a richer plant and animal life, and contributes to human health and well-being. On the whole, ecological design has a tremendous potential for reducing the environmental impacts of the built environment.

Passive solar design

Passive solar design is one of the key design concepts for achieving sustainable urban forms. It is about reducing the demand for energy and using the solar energy through particular design measures. The orientation of buildings and urban densities as a design feature affects the form of the built environment [ 75 ]. The environmental impacts and contextual implications of the building in relation to the site are key criteria for the urban designer to look at, in addition to searching for different alternatives to orient the building according to the sun path for passive solar gain and daylighting [ 76 ]. By means of design, orientation, layout, and landscaping, solar gain and microclimatic conditions can be used in an optimal way to minimize the need for buildings’ space heating or cooling by conventional energy sources [ 77 ]. Orientation and clustering of buildings determined by the settlement formation of a city affects the microclimatic conditions [ 59 ]. The built form, coupled with the street widths and orientation, largely determine urban surfaces’exposure to the sun. Yannas ([ 78 ], cited in [ 59 ]) summarizes six design parameters for achieving environmentally sustainable urban forms and improving urban microclimate:

Built form—density and type, to influence airflow, view of sun and sky, and exposed surface area

Street canyon—width-to-height ratio and orientation, to influence warming and cooling processes, thermal and visual comfort conditions, and pollution dispersal

Building design—to influence building heat gains and losses, albedo and thermal capacity of external surfaces, and use of transitional spaces

Urban materials and surfaces finish—to influence absorption, heat storage, and emissivity

Vegetation and bodies of water—to influence evaporative cooling processes on building surfaces and/or in open spaces

Traffic—reduction, diversion, and rerouting to reduce air and noise pollution and heat discharge.

Moreover, the interaction between energy systems and urban structures occurs at different spatial scales, ranging from the region, city, and neighborhood to the building. Also, passive solar design techniques can be applied to both new buildings as well as existing buildings through retrofitting. The major themes evident in the current debates on passive solar design include [ 59 ]:

Influencing building heat gains and losses

Influencing warming and cooling processes

Influencing evaporative cooling processes on building surfaces and/or in open spaces.

Influencing absorption, heat storage, and emissivity

Influencing airflow, view of sun and sky, and exposed surface area

Reducing and rerouting traffic to reduce air and noise pollution and heat discharge.

Analysis, evaluation, synthesis, and discussion

Eco-city ideals, benefits, and guiding principles.

The image of the eco-city has proven to be a highly influential translation of what a sustainable city should be. Ideally, an eco-city secures environmentally sound, economically viable, and socially beneficial development that is supported by sustainable transportation and advanced applied smart technology. A well-designed eco-city should be able to achieve the benefits of sustainability in terms of its tripartite composition. In this respect, the eco-city becomes an all-encompassing concept for urban policymaking and planning practices. Indeed, the mission of Urban Ecology (1996) is to create eco-cities by following these 10 principles [ 18 ]:

Revise land-use priorities to create compact, diverse, green, safe, pleasant and vital mixed- use communities near transit nodes and other transportation facilities

Revise transportation priorities to favor foot, bicycle, cart, and transit over autos, and to emphasize 'access by proximity;

Restore damaged urban environments, especially creeks, shore lines, ridgelines and wetlands

Create decent, affordable, safe, convenient, and racially and economically mixed housing

Nurture social justice and create improved opportunities for women, people of color, and the disabled

Support local agriculture, urban greening projects and community gardening

Promote recycling, innovative appropriate technology, and resource conservation while reducing pollution and hazardous wastes

Work with businesses to support ecologically sound economic activity while discouraging pollution, waste, and the use and production of hazardous materials

Promote voluntary simplicity and discourage excessive consumption of material goods

Increase awareness of the local environment and bioregion through activist and educational projects that increase public awareness of ecological sustainability issues

Irrespective of the way the idea of the eco-city has been conceptualized and operationalized, there are still criteria that have been proposed to identify what an ideal eco-city looks like, comprising the environmental, social, and economic dimensions of sustainability (Table 4 ):

As added by Graedel [ 80 ], the eco-city is scalable and evolvable in design in response to urban growth and socio-economic need changes. Achieving urban sustainability requires all its dimensions be in balance. Whether this is actually the case in existing or ongoing eco-city projects varies from one eco-city to another based on the multidimensional context these projects are embedded. A large body of work has investigated the presumed outcomes of the eco-city. More specifically, scholars and practitioners have discussed to what extent the model of the eco-city produces the expected environmental, economic, and social benefits of sustainability in different urban contexts (see, e.g., [ 6 , 20 ]; Khan et al. 2020 [ 9 , 13 , 21 , 66 , 81 ]).

Sustainable integrated cities or districts

Integrating eco-cities and compact cities, shortcomings and deficiencies.

Sustainable development has undoubtedly inspired a whole generation of urban scholars and practitioners into a quest for the fascinating opportunities that could be explored by, and the enormous benefits that could be realized from, the planning and design of the existing models of sustainable cities, notably eco-cities. Sustainable urban development is seen as one of the keys toward unlocking the quest for a sustainable society. Therefore, it is promoted by global, national, and local policies alike as the most preferred response to the challenges of sustainable development. The eco-city is one of the central paradigms of sustainable urban development and the most prevalent and advocated models of sustainable urban forms. Numerous recent national and international policy reports and papers state that this model contributes to resource efficiency and reliability, environmental protection, socio-economic development, social cohesion and inclusion, life quality and well-being, and cultural enhancement. It is argued that the eco-city model is able to achieve the key objectives of environmental sustainability and to produce some economic and social benefits of sustainability (e.g., [ 9 , 15 , 17 , 24 , 81 ]). The environmental goals of sustainability dominate in the discourse of the eco-city (e.g., [ 13 , 20 , 66 , 82 ]) compared to the economic and social goals of sustainability [ 15 ]. Furthermore, Jabareen [ 59 ] addresses the question of whether certain urban forms contribute more than others to sustainability, and subsequently proposes a matrix of sustainable urban forms that aims to help practitioners and policy makers in analyzing and assessing the contribution of these forms to sustainability according to their design strategies. The eco-city is ranked in a second position after the compact city. However, debating the most desirable sustainable urban form has been a long-standing scholastic question. Indeed, while the compact city has more economic benefits than the eco-city, it is far from certain that it provides the expected benefits of environmental and social sustainability. Indeed, the economic goals of sustainability seem to dominate over the environmental and social goals of sustainability with respect to the compact city model [ 2 , 83 ]. This is also in line with the conclusion drawn by Hofstad [ 74 ], in a case study performed on two Swedish cities and two Norwegian cities, that the economic goals remain at the core of planning, while the environmental and social goals play second fiddle. The eco-city is not immune to such criticism either.

In the eco-city, the discourse of environmental sustainability clearly dominates over that of economic sustainability and that of social sustainability. Indeed, the eco-city brings the concern for various environmental foci to the forefront of sustainable urbanism practices. Accordingly, the contours of a goal hierarchy are evident in eco-urbanism. Bibri and Krogstie [ 15 ] conclude that the environmental and some economic concerns are at the top of the goal hierarchy supporting the eco-city district strategies in Stockholm and Malmö, Sweden, notwithstanding the claim about the three dimensions of sustainability being equally important at the discursive level. With reference to the eco-district of Stockholm Royal Seaport (SRS), Holmstedt et al. [ 20 ] point out that implementing sustainable solutions in the context of the eco-city is more difficult because no unified practical definition is still accepted, although the subject of sustainability has been hotly debated over the last four decades. The authors add that most of the eco-city projects act dishonesty in order to gain an advantage by not defining what is meant by sustainability and not meeting all its requirements. The concept of the eco-city has, in policy and planning, tended to focus mainly on the underlying structure of urban metabolism—sewage, water, energy, and waste [ 84 ], thereby falling short in considering economic and social issues. This is considered as a shortcoming when it comes to sustainability because the social and economic aspects are highly and equally important in the context of sustainable cities.

The hierarchy of sustainability goals in the context of the eco-city reflects particularly the challenge of incorporating the social issues into a design- and technology-led approach. Efforts to handle ecological challenges risk having negative impacts on equality and social welfare (Khan et al. 2020 [ 21 ]). The planners of the leading eco-city districts are perhaps more knowledgeable about and experienced with how to tackle the environmental issues of sustainability [ 15 ]. While the environmental goals of sustainability remain the key driver of contemporary eco-city projects, they are also mobilized in the pursuit of politico–economic ends. New eco-city projects are shaped in loci by policy agendas tailored around specific economic and political goals to be achieved through the strategies of urban sustainability adopted by eco-city development actors as reflections of broader policy priorities [ 7 ]. For example, an eco-city initiative could be the product of local government agendas seeking economic growth to preserve its political institutions. The eco-city model should go beyond the environmental and economic dimensions of sustainability to include the social aspects related to local communities. Attractiveness does not depend on the environmental performance and economic prosperity of districts, but rather on a broader agenda entailing a balanced form of the environmental, economic, and social concerns of sustainability. To fully achieve the goals of sustainable urban districts, the social and economic aspects of sustainability need to be supported by concrete planning practices and development strategies. Sustainable urban development is a matter of balancing short-term (e.g., equity, job creation, income equality, etc.) with long-term (e.g., resource management, pollution mitigation, waste minimization, etc.) aims so as to understand what kinds of investments in the physical and digital infrastructures and partnerships provide the best societal and environmental benefits. Randeree and Ahmed [ 22 ] examine the social sustainability effectiveness of eco-smart cities by using a case study approach to investigate the social, environmental, and economic performance of eco-city development. The authors found that eco-cities substantively contribute to environmental and economic innovation as part of sustainable urban development, and also have the potential to fuse achievements in innovation, technology, and economic enterprise with the social imperative of functional urban habitats. This is predicated on the assumption that successful sustainable urban development requires greater consideration for the social imperative.

When it comes to eco-urbanism, the least focus is on the social dimension of sustainability. Based on case study analysis of how ecological and social welfare concerns are being addressed and integrated into urban planning in three Swedish eco-cities, Khan et al. (2020 [ 21 ]) show that eco-social policy integration in practice is only established to a limited degree in terms of planning and development, and the issues of ecological justice and equity and the relationship between socio-economic factors and consumption-related environmental impacts are hardly addressed. A framework for social sustainability (Fig. 2 ) has been proposed by the Young Foundation in 2011 based on international experiences [ 19 ]. It consists of four essential dimensions, namely amenities and social infrastructure, social and cultural life, voice and influence, and space to grow. This framework can be considered in the ongoing eco-city district projects across the globe in terms of guiding eco-urbanism as a way of taking into account the needs of residents and the significance of their participation and engagement in decision-making processes pertaining to the development and management of the eco-city districts that are actually built for them. These four dimensions play a key role in designing eco-city districts into socially sustainable communities, which incorporate physical and technological interventions as well as social services and practices.

figure 2

A framework for social sustainability

Emphasizing one of the dimensions of sustainability remains a shortcoming (failure to meet certain standards in plans) and deficiency (lacking some necessary elements) in the urban context. Indeed, urban sustainability is a holistic approach to thinking, meaning that all the dimensions of sustainability are equally important. In fact, the debates about sustainable urban forms are rarely understood outside their expert communities. However, it is widely acknowledged that balancing the goals of sustainability is conflicting, as different aspects of sustainability rely on different criteria for success. Despite the enticing and holistic character of sustainability, the existing conflicts between its goals “cannot be shaken off so easily” as they “go to the historic core of planning and are a leitmotif in the contemporary battles in our cities,” rather than being “merely conceptual, among the abstract notions of ecological, economic, and political logic” ([ 85 ], p. 296). Therefore, urban planners will in the upcoming years “confront deep-seated conflicts among economic, social, and environmental interests that cannot be wished away through admittedly appealing images of a community in harmony with nature. Nevertheless, one can diffuse the conflict and find ways to avert its more destructive fall-out” ([ 85 ], p. 9).

Emerging planning practices and development strategies

The recent wave of research on integrated models for sustainable urban development tends to capture important aspects of the urban planning and design, architecture, social, environmental, economic, and governance systems performances of SIDs systematically through interdisciplinary and cross-disciplinary work. These models explore the development of SIDs as a model for high-density, high-liveability future cities through case study research allowing for an evaluation, comparison, and learning on how SIDs (e.g., green cities and dense cities) are best planned and realized in various human settlement systems. As part of the case study analysis conducted by Bibri [ 57 ], the Development Strategy Gothenburg 2035 states that the city will be able to attain both a green and compact city while taking into account potential conflicts such as access to green areas and risk issues as something of importance when building additional structures [ 86 ]. This research area also focuses on improving the performance of the eco-city by partly or completely integrating its green design principles and environmental technology solutions with the design strategies of the compact city. Indeed, it has become of high relevance and importance to integrate the eco-city and compact city models so as to consolidate and harness their strategies and solutions to deliver the best outcomes of sustainability. According to a recent literature review carried out by Bibri [ 2 ], this integration can be justified by the fact that:

The eco-city needs to improve its social performance, which is better in the compact city;

The compact city needs to enhance its environmental performance, which is of high focus in the eco-city; and

Both models contribute differently to economic sustainability, with the former focusing on green-tech innovation strategy and the latter on mixed-land use strategy.

Another argument supporting the integration of these prevailing models of sustainable urbanism is that they have already many overlaps among them in their ideas, concepts, and visions, as well as their practices and policies. This means that these models are compatible and not mutually exclusive, yet with some distinctive concepts and key differences. Some of the attempts undertaken so far to integrate these models tend to provide ideal forms of human settlements, combine some ideas from each one of these models to form new partly integrated models, or strengthen one model through adding principles from the other, all with the objective to integrate and balance the dimensions of sustainability in order to harness its synergistic effects and thus boost its benefits. However, as this work is more often than not based on design with respect to the discipline of planning and architecture, it tends to focus more on creativity, common sense, ideal target pursuit, and future scenarios, rather than fact-based evidence explanation, empirically grounded research, or scientific finding-oriented exploration. This is in contrast to the eco-city and the smart city, which represent innovative models of urbanism based on a scientific approach to urban development.

Farr [ 16 ] discusses the combination of the different elements of eco-urbanism, sustainable urban infrastructure, and new urbanism, and extends this integrated approach to close the loop on resource use and bring everything into the city. The aim of the integrated approach to closure the cycles of natural resources, energy, and waste within cities involve minimizing the consumption of natural resources, essentially non-renewable and slowly renewable ones; minimizing the production of waste by reusing and recycling; lowering air and water pollution; and promoting natural and green areas and biodiversity. Integrating eco-urbanism and new urbanism also entails enhancing the quality of life by affording greater accessibility to activities, services, and facilities within a short distance. This is about proximity, i.e., how close jobs, amenities, and services are to where people live, which is generally calculated based on the travel time and distance to their homes. Register [ 87 ] is credited for coining the phrase “access by proximity,” which suggests the closeness to important functions and activities, such as housing, work spaces, shops, educational and cultural facilities, places for socializing, as necessary to create ecologically healthy cities characterized by walkable centers, transit villages, discontinuous boulevards, and agricultural land close by. The International Economics and Finance Society (IEFS) [ 88 ] illustrates 15 interdependent dimensions to describe cities as interconnected urban ecosystems (Fig. 3 ).

figure 3

Cities as interconnected urban ecosystems. Source: IEFS [ 88 ]

Important to note is that access proximity is typically associated with the compact city under its mixed land-use strategy. This is adopted to achieve various benefits of environmental, economic, and social sustainability (e.g., [ 57 , 74 , 89 ]). Proximity enables the city to be self-sustaining by having everything that people need within the community, including stores, employers, service providers, energy generation, waste disposal and processing, and small-scale agricultural production (community gardens and/or vertical gardening). The latter is typically associated with the eco-city (e.g., [ 18 , 79 ]).

Based on a case study analysis performed by Bibri and Krogstie [ 15 ], the mixed land use and social mix approaches are key to the planning and development strategies of the eco-districts of SRS and Western Harbor, Sweden (Table 5 ). These approaches are aimed at a lively and long-run sustainable city with a balance between environmental, economic, and social factors. Here, the diversity of functions and architectures gives a good base for services, retail trade, and public transport, and also induces people to live and work in the area. Mixed-land use has not only economic benefits, but also environmental and social benefits through sustainable travel behavior and equal access to services and facilities, respectively. Strong support for the sustainable development advantages of a diverse and vibrant built environment—a mixed-use city—is well reflected in a series of debates with politicians and experts and open community meetings with respect to Stockholm and Malmö’s future and key urban development initiatives.

In addition, the aim of Western Harbor is to become an international leading example of an environmentally sound, densely populated district, integrating all three dimensions of sustainability. This also applies to SRS as the sustainability and environmental program for SRS states that this district “shall be developed into a world class environmental city district” ([ 90 ], p. 11), While the program is designed to be generic and with a continuous focus, it still includes some long-term specified goals given that SRS has been selected as one of 18 projects around the world to be part of the Clinton Climate Initiative (CCI), which aims to develop climate-positive urban districts [ 90 ]. Furthermore, the program states long-term goals within ecological, economic, and social sustainability [ 90 ], of which some examples are presented in Table 6 .

Concerning environmentally efficient transport, for instance, a recent comparative study of the urban transport eco-urbanism characteristics of Malmö, Stockholm, and other Swedish cities compared to cities from the USA, Australia, Canada, and Asia, Kenworthy [ 65 ] found that while density is critical in determining many features of eco-urbanism, especially mobility patterns and particularly how much public transport, walking, and cycling are used, Swedish cities maintain healthy levels of all these more sustainable modes. Engwicht (1992) advocates eco-cities where people can move via walking, cycling, and mass transit without fear of traffic and toxins. Dongtan Eco-City, a 500,000 resident project, is planned to achieve densities of 84–112 people per acre, which will support efficient mass transit and land and social mixes, and also to have parks, lakes, and other public open spaces scattered around the densely designed neighborhoods [ 91 , 92 ) underscores the risks involved in heavily marketing an eco-city project which fails to come to fruition, and assumes that it lost its momentum, and due to the delay of its construction, it has been given a mixed reception [ 92 ].

In addition, Bibri and Krogstie [ 93 ] suggest an integrated model of sustainable urban development comprising the design and technology solutions of the eco-city and the design strategies of the compact city. This suggestion is based on several reasons distilled from an extensive literature review, namely:

Being one of the most significant intellectual and practical challenges for more than four decades, the development of a desirable model of sustainable urbanism continues to motivate and inspire collaboration between researchers, academics, and practitioners to create more effective design strategies and advanced environmental technology solutions based on a more integrated and holistic perspective.

Different scholars and planners may develop different combinations of design concepts to meet the requirements of sustainability. They might come up with different forms, where each form emphasizes different concepts and contributes differently to sustainability.

While there is nothing wrong with sustainable urban forms being different, it can be beneficial extremely and of strategic value to find innovative ways of combining their distinctive concepts and blurring their key differences toward an integrated model of urban development emphasizing the synergistic effects of the different dimensions of sustainability. Especially, sustainable urban forms are compatible and not mutually exclusive.

Neither real-world cities nor academics have yet developed convincing models of sustainable urban form, and the components of such form are still not yet fully specified.

More in-depth knowledge on planning practices is needed to capture the vision of sustainable urban development, so too is a deeper understanding of the multi-faceted processes of change to achieve sustainable urban forms. This entails conceptualizing multiple pathways toward attaining this vision and developing a deeper understanding of the interplay between social and technical solutions for sustainable urbanism.

The compact city has a form and thus is governed by static planning and design tools, whereas the eco-city has no clearly defined form, thereby the relevance for the integration of these models of urbanism into one model that can accelerate sustainable development toward achieving the optimal level of sustainability.

With respect to the latter, for example, the eco-city has long been conceived as amorphous (formless), and the form has been of less focus in eco-urbanism. The eco-city has tended to focus on the way the urban landscape is organized and steered rather than the spatial pattern of the characteristic physical objects in built-up areas. What counts most is how the eco-city is managed and governed as a social organization. As argued by Talen and Ellis ([ 94 ], p. 37), social, economic, and cultural factors are far more important in determining the quality of the city than any choice of spatial arrangements. However, one consistent result emerging from the analysis of six eco-city projects performed by Rapoport and Vernay [ 24 ] is that design is much more frequently mentioned than management as a driver of sustainability. Cheshmehzangi et al. (120) introduce the new concept of “eco-fusion” through an exploratory case study project, and suggest the importance of multi-scalar practice in the broader field of eco-urbanism. The authors point out the associations between eco-fusion and sustainable urban development in terms of integrating the natural and built environment as the best practice of eco-development in urbanism. In terms of findings, the authors highlight integrated methods in eco-urbanism and provide new avenues for eco-planning/eco-design strategies. Bibri [ 95 ] provides a detailed account of the synergistic effects and combined benefits of integrating eco-urbanism and compact urbanism in an attempt to improve and advance the environmental, economic, and social goals of sustainability. However, there is a need to develop new integrated planning paradigms, research methodologies, and implementation processes to support higher population densities, higher standards of environmental sustainability, and enhanced liveability.

Evaluation of sustainable urban districts

The importance of the assessment of the results and progress of integrated models for sustainable urban development is justified by the resources involved in the associated endeavors. Generally, it makes it impossible, without any assessment, to ensure that investments are directed in the best possible manner and resources are allocated appropriately, to confirm that the practical endeavors are heading in the desired direction, and to avoid future mistakes and lock-ins [ 14 , 96 , 97 ]. Accounting for sustainable urban development will require incorporation of considerable additional aspects and parameters (Brandon and Lombardi 2005). Nevertheless, many of the components constituting the built part of modern cities are relatively well studied and understood as single units [ 14 ]. Conversely, unlike the systems and the interconnections between their individual constituents when assembled remain less explored and empirically underdeveloped. However, given their scale compared to cities, sustainable urban districts/SIDs can function as small-scale models with respect to developing evaluation processes and methods for assessing urban sustainability. The district scale is a good arena for deepening the knowledge base on urban sustainability, and recent evaluation programs in this regard are considered as a stepping stone toward a more systematic approach for evaluating sustainable urban districts [ 98 ]. Other evaluation improvements are evident in the growing number of district developments with the stated ambition of being sustainable (e.g., [ 55 ]). In addition, while many of these new developments attract attention and are marketed more strongly, creating pressure to demonstrate progress [ 99 , 100 ] made in the area of evaluating and monitoring the built environment in relation to sustainability, many more challenges still remain and need to be addressed and overcome. These challenges pertain mainly to the concept of sustainable development being a value-based subject with multiple interpretations, which parameters to select in order to determine what urban sustainability entails, and the collection of evaluation information and its use and presentation, but to name a few. All in all, more studies are needed to investigate evaluation methods and strategies for determining progress toward sustainable urban district development. This research should capture important aspects related to design, architecture, social, environmental, economic, and governance systems performances systematically through more effective frameworks. Currently, it is difficult to evaluate how and to what extent sustainable urban districts contribute to sustainability. As a result, city governments, urban planners, and landscape architects are grappling with the dimensions of sustainable urban districts and forms by means of a variety of planning, design, and policy approaches [ 101 , 102 ].

Eco-cities as techno-enviro-economic experiments for transition

The idea of eco-city experiment has become increasingly prevalent and popular as a guiding concept and trope used by scholars, policymakers, and corporate actors alike. Eco-cities have been conceptualized as materializations of trends toward developing and implementing urban socio-technical and enviro-economic experiments ([ 5 ]; Bulkeley et al. 2013). In turn, these experiments can be seen as part of transitions-focused theories and management approaches, which aim at economic and societal transitions toward low-carbon economies and cities [ 103 ]. Much of the recent focus on eco-cities has largely drawn on the role of specific techno-environmental solutions to notions of urban, climate, and energy crisis, which  is part of the broader strategic policy and environmental discourse of ecological modernization. This concerns the policy and planning spheres pertaining to new-build eco-city projects where the modernizing strategies at a variety of scales focus on the eco-city as a techno-social response to environmental and economic concerns in modernization trajectories (see Simon and Molella 2013 [ 8 ]). Based on case study research, Cugurullo [ 7 ] investigates how new eco-city projects interpret and practice urban sustainability by focusing on the policy context that underpins their development. The author states that eco-city projects interpret sustainability as ecological modernization and practice urban environmentalism almost exclusively in economic terms. Generally, ecological modernization argues that the economy benefits from moves toward environmentalism. One of the basic assumptions of ecological modernization relates to environmental readaptation of economic development. While eco-city projects can be seen as manifestations of ecological modernization, they also need to be considered as attempts at fashioning urban techno-economic fixes to the problems of environmental despoliation, energy insecurities, and concerns over adaptation to climate change [ 68 ]. In this context, the ecological modernization approach “involve both structural change at the macro-economic level, through broad sectoral shifts in the economy, and at the micro-economic level: for example, through the use of new and clean technologies by individual firms” ([ 104 ], p. 66). Eco-cities depict new urban visions that jointly involve entrepreneurial states and capital in the engineering and envisioning of urban environments. Entrepreneurial governments from Asia to the United Arab Emirates (UAE) are often initiators of mega urban projects [ 105 ]. Seen from this perspective, the eco-city as an entrepreneurial city is dependent on the active remaking of urban environments and ecologies [ 106 ] and based on the integration of states and markets in the financing of new urban and infrastructure projects [ 107 ]. There is an intersection between the agendas of green urban entrepreneurship and ecological modernization and the creation of urban sustainability entrepreneurship. This draws on the long-standing concept of creative destruction in entrepreneurship research so that it becomes the driving force for the establishment of a sustainable economic–environmental–urban system.

As a form of green entrepreneurial urban enterprise, the eco-city constitutes a major force in the overall transition toward a more sustainable urban development paradigm, acting as exemplary solutions for social transformation. Transition denotes changes from one socio-technical landscape or regime to another. In recent years, there has been increased interest in academic and policy circles in the idea that long-term environmental problems entail fundamental transitions in socio-technical regimes. This builds on efforts to apply knowledge from empirical and theoretical analysis of the past socio-technical shifts of governing transitions in techno-urban systems. Recommendations for radical shifts to sustainable socio-technological regimes—transforming technological systems for ecological sustainability—entail, as stated by Smith ([ 108 ], p. 131), “concomitantly radical changes to the socio-technical landscape of politics, institutions, the economy and social values” ([ 108 ], p. 131). Socio-technological regimes [ 109 ]—are to be brought about by the actions and networks of existing actors within institutions in the ambit of emerging smart eco-cities. Established socio-technological regimes can induce and support the transformation of socio–technical constellations (e.g., industry associations, research communities, policy networks, and advocacy/special-interest groups) toward improving and advancing ecological sustainability at the macro level. Accordingly, the socio-technical landscape forms an exogenous environment beyond the direct influence of regime actors (macro-economics, deep cultural patterns, macro-political developments). Changes at the landscape level usually take place slowly (decades) [ 110 ]. This is due to the nature, scale, complexity, and intricacy of the landscape. That is, the overall socio-technical context which comprises societal beliefs and values and world views as intangible dimensions, as well as the material structures and mechanisms pertaining to various institutions and the functions of the economy and related marketplace dynamics as tangible aspects. The potential confines are predicated on the assumption that innovative technological niches are vital sources of innovation that may hold potential for providing solutions for tensions in the extant socio-technical regime. The adaptation process is confined by structures within existing, mainstream regime (Smith [ 108 ], p. 453). Indeed, it may be that existing socio-technical contexts close down spaces for alternative approaches, except at times of tension when new trajectories are actively being sought, as with the current concerns over climate change and the need to reduce carbon emissions. The latter applies to smart eco-cities as experiments for transition, as demonstrated by many studies across the globe.

Less clear has been how the transition to the eco-city may materialize. The extent to which ecologically sustainable urban transformation could be achieved can be usefully explored by drawing on research work on transition governance and innovative technological strategic niche development. Research within social studies of technology has dealt with the transformation of socio-technological regimes and highlighted the role and contribution of innovative technological strategic niches in transition governance [ 108 , 111 ]. Transition governance is often discussed with reference to sustainable development as an alternative model of environmental governance and its possible use as an approach to change. It aims to direct the gradual, continuous process of the transformation of socio-technical practices and socio-political landscapes from one equilibrium to another [ 112 , 113 ]. It indirectly influences and redirects the choices and decisions of strategic actors toward various forms of sustainability, instead of seeking to control the uncertainties of change [ 114 ]. In this respect, it seeks the outcome of change to mitigate inherent uncertainties, generate desirable or anticipated socio-political outcomes, and augment resilience capabilities during the transformation of socio-technical regimes [ 112 , 113 ]. These denote  “interconnected systems of artefacts, institutions, rules, and norms” ([ 109 ], p. 3). Socio-technical regimes stabilize existing trajectories in various ways, including regulations and standards, sunk capital investments in technological infrastructures and competencies, adaptation of lifestyles to technical systems, and cognitive routines [ 110 ]. They shape technological innovation systems [ 115 ] and may host a range of innovative technological strategic niches, which generate innovations to challenge the status-quo. The concept of the niche is taken from socio-technical transitions studies, which analyze the processes through which innovations come about and are taken up in society more widely (e.g., [ 116 , 117 ]). Transition governance emphasizes the role and contribution of these niches in the process of transitioning to ecological modernization. Innovative technological niches (e.g., [ 108 , 111 , 115 ]) constitute areas at which the space is provided for radical innovative experiments. Raven ([ 118 ], p. 48) defines a technological niche as “a loosely defined set of formal and informal rules for new technological practice, explored in societal experiments and protected by a relative small network of industries, users, researchers, policy, makers, and other involved actors.” One strand of research within social studies of new technology centers on innovative experiments in alternative, sustainable technological niches, and draws lessons from the challenges these niches face in the context of a dominant, unsustainable socio-technological regime ([ 108 ], p. 128). A technological niche forms the micro-level where drastic novelties emerge and are initially unstable socio-technical configurations; niche-innovations are developed by small networks of dedicated actors, thereby their low performance [ 110 ].

Within smart eco-cities as experiments for transition, innovative techno-enviro-economic niches (related to green-tech innovation, green entrepreneurship, sustainable innovation, technological innovation, sectoral innovation, and so on)  are increasingly seen as “nurturing socio-technical configurations, which grow and displace incumbent regime activities” ([ 109 ], p. 9), as well as providing lessons and insights to policymakers to manage ecological urban sustainability transitions. However, it remains to be seen if these transformative changes will be realized and sustained, and techno-enviro-economic niche activities will go mainstream. This depends on the extent to which the emerging models of smart eco-cities as a set of techno-enviro-economic innovations will solve the challenges of sustainability and provide concrete added value for sustainability. Many city government and municipal agencies within the ecologically advanced nations (e.g., Sweden, Denmark, Germany, the Netherlands, the UK, etc.) are increasing supporting new development projects as central demonstration sites for environmentally and economically sustainable urban development while capitalizing on advanced technologies. The hybridization of concerns over environmental and economic sustainability in the eco-city is a reflection of the economic-environmental nature of the problems and challenges that eco-cities are dealing with and designed to solve. Whiile adaptation to climate change may be one of the key driving logics behind the engineering of new eco-cities, economic transition policies and reforms enacted within eco-city projects are clearly concerned with economic sustainability. This is manifest in the confluence of urban entrepreneurialism and eco-city development, as they are both seen as pathways toward urban sustainability [ 119 ]. This is clearly seen in new-build smart eco-cities globally. This involves connecting the concept of urban entrepreneurialism with that of experimentation for transition.

Often marketing themselves as models for sustainable urban development or smart eco-cities, the recently built eco-cities or launched eco-city projects in Sweden (e.g., [ 13 , 15 , 20 , 35 ]), Germany (e.g., [ 43 ]), China [ 44 ], the UK and the Netherlands [ 37 ], and France (Jolivet and Bond 2018) represent only sites of innovation, or are still under the banner of experimentation. In critically engaging with the notion of “urban experiment” and its articulation through the associated concepts of “living labs,” “future labs,” “urban labs,” and the like, Caprotti and Cowley [ 120 ] introduce seven specific areas that need critical attention when considering urban experiments, namely normativity, crisis discourses, experimental subjects definition, boundaries and boundedness, historical precedents, “dark” experiments, and non-human experimental agency. However, eco-city initiatives tend to focus largely on integrating the environmental and economic goals of sustainability while embracing and leveraging what smart cities have to offer in terms of advanced technological solutions for sustainability. The idea of experimentation is associated particularly with the tendency for new technologies and ways of working to be trialed at a limited scale, often through cross-sectoral partnership approaches for learning purposes (e.g., [ 103 , 121 , 122 ]). Smart eco-cities can be viewed both as potential niches for introducing and testing new technologies, and where sustainable economic and environmental reforms can be rolled out in areas which are both spatially proximate and internationally oriented. The former concerns the surrounding region and the latter pertains to networks of knowledge, technology, and policy transfer and learning.

With reference to an example of the currently leading practical initiatives of smart eco-cities, one of the strengths of the smart eco-city district of SRS in terms of the environment, which gives it an advantage over other new-build eco-city projects, lies in its green-tech innovations. SRS is expected to contribute substantially to the economic growth of Stockholm’s potential as an innovation hub. The two main economic growth sectors in SRS are the innovation sector and the service sector. With respect to the former, in particular the innovation center for sustainable technology, sustainability initiatives will become the focal point for the district to showcase sustainable development lifestyles. The innovation center is planned to feature the latest developments in clean technologies and show how the associated solutions are tested and applied. SRS will serve as platform for presenting the area to the public and interested parties and an important showcase to the outside world. It will also serve as an international meeting place where the city, the business community, and research institutions work together to profile and demonstrate Swedish know-how in urban sustainability. The formal organization in the SRS project works in parallel with the SRS Innovation Arena, which involves industry experts, businesses, and citizens, to build up practical knowledge [ 123 ]. The SRS project will provide opportunities to many development and construction companies and benefits to green-tech companies. Furthermore, SRS aims to take the lead in realizing the latest innovations within environmental technology and sustainable development, and affords particularly great opportunities for climate-adapted and future-oriented development, from pioneering energy-efficient technical solutions in building and infrastructure to the development of smart electricity networks that enable local production and distribution of electricity. However, drawing on a 10-year-long study of the digital footprint of SRS, Khan, Hildingsson and Garting (2020) show that ecological sustainability remains local in that it is situated in the specific spatial, temporal, and political context of the eco-city project. In other words, eco-city district projects are associated with particular places, initiatives, histories, technologies, values, and perspectives. Consequently, the authors suggest that the transition toward ecological sustainability always involves the transition of ecological sustainability in order for a transition to become ecologically sustainable. In particular, as revealed by  Joss and Cowley [ 124 ] based on a comparative case study analysis, national policy is found to exercise a strong shaping role in what sustainable development for future cities is understood to be, which helps explain the considerable differences in priorities and approaches across countries.

However, the current model of the smart city is being promoted with significant investment of resources by numerous industrial actors [ 125 ], not least in relation to the model of the eco-city. The outcome is a very competitive market where the risk of the prevalence of stand-alone profit-making agendas becomes evident in smart cities [ 126 ] as well as smart eco-cities. The huge market of the smart city may well undermine economic development through the isolated ICT branding exercises of industrial actors [ 127 ]. This is most likely to have a bearing on the outcomes of environmental and socio-economic reforms taking place in emerging eco-cities. This risk becomes imminent when looking at the market growth of the smart city and the smart eco-city. It becomes evident why the ICT industry and the private sector view the idea of the smart city as an opportunity to promote digital transformation [ 128 ], including in relation to the smart eco-city. For example, China has made eco-city development its legislative priority and national strategy, investing a US$ 618 billion in a 5-year period from 2011 to 2015 that could reach as high as US$ 1, 124 billion in the period of 2016–2020 (Wu et al. 249). The smart city is increasingly advocated by governments as the primary means to deliver urban sustainability. However, while the smart city movement has created numerous initiatives globally, almost all of them have failed or lack adequate potential to generate sustainable urban futures [ 129 ]. The rationale behind this inadequacy is that the current practices of smart city portray technologically determined, reductionist, and techno-centric approaches to urban development.

It can be argued that the ability of smart eco-cities to achieve their utopian ambitions is limited by the realities of complex practices and the profit-driven, neoliberal approach to planning and development. The plans and publicity materials of eco-city projects, notably those promoted in Asia and the Middle East as ambitious, technologically driven projects led mainly by the state and private sector actors contain bold claims, attractive designs, ambitious targets, and innovative technologies to advertise their “eco-ness” [ 10 ]. Cugurullo [ 7 ] reveals that the eco-city developers capitalize on sustainability by building an urban platform to develop and commercialize clean-tech products, and concludes that the city is an example of a high-tech urban development informed by market analysis rather than ecological studies. The performance of eco-cities has long been heatedly debated and also criticized in terms of their profit-seeking and image-building projects simply capped with impressive names, although most of the eco-city projects are still under experimentation [ 63 ].

Smart solutions for improving the social performance of the eco-city

Conventional paradigms of eco-urbanism require new responses under the current circumstances of urbanization and the complex challenges of sustainability in light of the rise of digitalization. The scales and rates of urbanization fundamentally disrupt the challenges that eco-urbanism research and practice must contend with. The eco-city is a quintessential example of a paradigm that is advocated just as much as it is criticized in the context of sustainability and technology. The critic pertains to, among others, the social performance of the eco-city in relation to technological solutions. Social proposals have long tended to be couched in speculative language in terms of investments and initiatives These issues reflect the challenge of incorporating the social dimension of sustainability into a design-oriented and technology-led approach characterizing the eco-city. In the existing literature on urban sustainability, the social factors are shadowed by the ecological aspects [ 130 ], as well as ignored in assessment methods [ 131 ]. Nevertheless, a set of new measures have recently been developed and implemented in emerging eco-city districts, which are expected to strengthen the influence of the social goals of sustainability over eco-city development practices. Among the strategies being adopted in this regard are:

Citizen involvement and consultation

Inclusiveness in terms of the level of participation in decision-making

Security and safety of individuals and their living environment

Efficiency in public services delivery

Equity in terms of affordable housing for different income groups.

These strategies have recently become at the core of smart cities in response to the challenge of developing technologies that support equity and fairness and enhance the quality of life, as well as ensuring informed participation and creating shared knowledge for democratic city planning and governance. Concerning citizen involvement and consultation in regard to eco-city development and management, for example, it is deemed of crucial importance to improve social cohesion, among others. This is predicated on the assumption that sustainable urban districts can only be created by the cooperation between such stakeholders as residents, landowners, developers, energy companies, academics, and city administrators through dialogue in order to shape and manage eco-city development. Indeed, the planning of eco-city development involves complex socio–technical constellations of a variety of actors interacting with and influencing each other on multiple scales [ 95 ]. At the core of this dynamic interplay is the engagement of many stakeholders in continuous dialogue to determine the programs associated with the development and implementation of eco-cities. However, there seems to be a lack of structures for collaboration between the different stakeholders of eco-cities (see, e.g., [ 20 , 123 ]), which is at the core of urban governance. The most serious obstacle for the effective transformation of cities into becoming ecological and/or smart is the lack of appropriate governance arrangements for the majority of cities. Researchers and practitioners have argued that many of the challenges for cities to become smart exceed the scope and capabilities of their current organizations, institutional arrangements, and governance structures (e.g., [ 132 , 133 ]).

Furthermore, citizen participation in smart cities relate to planning, governance, innovation, and democratization (e.g., [ 3 , 28 , 134 , 135 , 136 , 137 , 138 , 139 ]). Seçkiner Bingöl (120) concludes that citizen participation in smart sustainable cities is only considered as a set of mechanisms aimed at supporting good governance, and recommends using these mechanisms to highlight other aspects of sustainability, such as securing comprehensiveness, promoting gender equality, and to focus on other aspects of citizen participation, such as real participation and democratic effectiveness. Moreover, Gebresselassie and Sanchez (2018) reveal that transport apps have the potential to address the equity and inclusion challenges of social sustainability by employing universal design in general-use apps, including cost-conscious features, providing language options, and specifically developing smartphone apps for persons with disabilities. In addition, Bibri and Krogstie [ 3 ] distill a number of solutions offered by smart cities to improve citizen participation in the context of sustainable cities and eco-cities, including, but are not limited to the following:

Online platform which makes it easier for citizens to find out about planning issues and land use (i.e., local development plans, zoning regulations, building permits, planning applications, heritage sites, etc.), combining such data as street maps, aerial photography, historical maps, architectural heritage, areas of special protection, nature reserves, population census, and education services.

Crowdsourcing platform which addresses important city issues related to different areas. The citizenry makes a difference as new forms of data and advice will be implemented using crowd-sourcing.

Interactive platform which allows citizens to feedback, rate, and shape the type of experiences they want to have.

Open government portal which improves the transparency of the city management, where a number of initiatives can be realized with respect to engaging citizens in various solutions.

Online platform which engages citizens in dialogue so as to gather input on their needs and demands, evaluate all their suggestions, and identify and solve important urban issues.

Platform where the citizens’ complaints related to infrastructure, transport, healthcare, environment, and other issues are communicated to relevant agencies. Citizens can track the status of their application and control the execution of their filed complaints.

Special portals which enables citizens to report the economic problems in the city.

Geoinformation portals to involve citizens in the provision of urban amenities, to advise existing problems to executive bodies, and to manage urban systems more effectively.

Classrooms where citizens can learn about the principles and applications of digital technologies, and gain access to tools that allow them to innovate and participate in urban projects.

Space designed to attract startups and skilled innovators to develop new technologies leveraging the data produced by the extensive IoT infrastructure of the city.

Co-innovation center which enables close collaboration among local technology customers, governmental agencies, startups, academics, and developers to create new business models, innovative ideas, and technological solutions.

Participatory platform which connects companies, local authorities, universities, start-ups, citizens, associations, and so on to support decision-making processes, allowing the collection, processing, monitoring, and analysis of large amounts of data to generate deep insights pertaining to different uses and applications.

Participatory democracy platform which allows citizens to see and discuss proposals put forward by the city government, and submit their own. Such platform is used to create the city’s government agenda, with proposals coming directly from participating citizens.

A city council which allows the provision of services by public agencies remotely and mobile kiosks, where one can receive various certificates, publish a complaint, get necessary information, and so on. This is to improve the convenience of public services received by citizens.

Digital literacy programs and digital inclusion of minorities and vulnerable groups.

The social sustainability aspects in smart cities focus on the quality of life and the efficient use of human and social capital (e.g., Ahvenniemi et al. 2017 [ 101 , 140 , 141 , 142 , 143 , 144 , 145 , 146 ];). Smart cities put data and digital technologies to work to make better decisions and enhance the quality of life based on deep insights generated through advanced data analytics. More comprehensive, integrated, real-time data give city agencies the ability to monitor various events as they unfold, understand how demand patterns are changing, and respond with faster and lower-cost solutions. So, the quality of life is much better as smart cities offer better safety and security, inclusiveness, ease of seeking and obtaining public services, cost efficient health care, quality education, and opportunities for participation in governance. Therefore, smart cities provide many new opportunities to enhance the social performance of eco-cities as a model of sustainable urbanism. The socially oriented aspects of sustainability are observed in smart cities [ 48 ] thanks to the symbiotic relationship between advanced ICT and urbanization. The human nature of urbanization and the social issues engendered by urban growth, such as social vulnerability, socio-spatial segregation, socio-economic disparity, social inequality, and public health decrease have underlined the importance of the social aspects of sustainability in emerging smart eco-cities. Smart cities provide great potential to enable eco-cities to move beyond their narrow environmental-economic goals to tackle the pressing social issues engendered by urbanization. The basic idea is to retain the best of what we already have that have been successfully enacted in real-world eco-cities, making use of the things that have been demonstrably better in the past in terms of environmental and economic sustainability, while being selective in adopting the best of what is emerging and promising in regard to advanced technological solutions, making use of the things that will add a whole new dimension to sustainability in terms of not only enhancing its social aspects and thus balancing its dimensions, but also harnessing its synergistic effects and thus boosting its benefits.

The second generation of smart cities is framed as a decentralized, people-centric approach where smart technologies are employed as tools to tackle social problems, foster collaborative participation, and address the needs of citizens [ 147 ]. Still the smart technology solutions catering to governmental agencies and civic society are currently unclear as to whether they improve the quality of life of all citizens, or whether they benefit a specific “elitist” part of society or prioritize a certain societal group over others that is digitally skilled or can financially afford these solutions [ 48 ]. Hatuka and Zur [ 148 ] argue that the initiatives that focus on social needs and address inequality in smart cities are still at the margins, and by way of conclusion, they call for shifting the focus from the city to society in developing digital initiatives and cultivating smart social urbanism. Trencher [ 147 ] argues that while scholars critique the first-generation, corporate-led model of smart cities for failing to tackle people-oriented agendas and to authentically respond to the needs of residents, many point to the potential to move beyond narrow environmental and economic objectives to address and overcome social issues. The author claims that the techno-economic and centralized approach rather pertains to the first generation model of smart cities, whose primary focus is on the diffusion of smart technologies for corporate interests. This however raises the question as to what trade-offs the so-called socially oriented smart cities are willing to make in order to contribute to the social aspect and quality of life over the economic benefits, including what the cost of these trade-offs will be.

Eco-city planning and development: key problems, issues, and challenges

Since the late1980s, eco-cities have been one of the leading global paradigms of sustainable urbanism and the most preferred response to the challenges of environmental sustainability. As a result, significant advances have been achieved in knowledge of green design and environmental technology, and a multitude of exemplary practical initiatives have been realized across the globe, thereby raising the profile of eco-cities and ecological urban sustainability. The change is still inspiring and the academic and practical endeavor continues to induce and motivate scholars, practitioners, and policymakers alike to enhance the existing models of the eco-city, or to propose integrated models of eco-city development in response to new major global trends or shifts. In particular, the rate and scale of urbanization will escalate over the coming years, and consequently, eco-cities will face new challenges, including creating cost-efficient environments, improving life quality for citizens, maintaining economic growth, and being able to handle dynamic and complex concepts that evolve over time. In the current climate of the unprecedented urbanization of the world, it has become even more challenging for eco-cities to reconfigure themselves more environmentally, economically, and socially sustainable without the use of advanced ICT. Therefore, policymakers, planners, and mangers within the ecologically advanced nations (e.g., Sweden, Denmark, the Netherlands, Germany, the UK, and France) need to promote, develop, and implement innovative solutions for operational management, development planning, and governance to address and overcome the negative effects of urbanization and the complex challenges of sustainability. In a nutshell, new circumstances require new responses.

Yet knowing if we are actually making any progress toward eco-cities as an approach to sustainable cities is problematic. There is a very contradictory, conflicting, and fragmented picture that arises of change on the ground. Given these complex conditions, it is sometimes hard to see where the common challenges of eco-cities may be identified. In addition, producing robust models of the eco-city has been one of the most significant intellectual and practical challenges since the late 1980s (e.g., [ 59 , 102 , 149 ]; Rapoport and Verney [ 24 , 150 ]). Neither academics nor real-world cities have yet developed convincing models of the eco-city and have not yet gotten specific enough in terms of the design and technology components of the eco-city, despite the advances achieved in eco-urbanism. In other words, it has been very difficult to translate ecological sustainability into the built environment. Indeed, cities are generally characterized by “wicked problems” [ 151 ]. This implies that the physical, environmental, economic, and social problems of eco-cities are difficult to define, unfold in unpredictable ways, and defy the standard principles of science and rational decision-making. Wicked problems are difficult to grasp and impossible to solve—normally because of their complex and interconnected nature. They lack clarity in both their aims and solutions, and are subject to real-world constraints which hinder risk-free attempts to find and apply a solution. As a consequence, when tackling wicked problems, they become worse due to the unforeseen consequences which were overlooked because of treating the system under study in too immediate and simplistic terms, or failing to approach that system from a holistic perspective. Rittel and Webber [ 151 ] argue that the essential character of wicked problems is that they cannot be solved in practice by a central planner. Therefore, it is impossible to plan eco-cities as urban complexities due to the lack of a complete form of knowledge of the consequences of design and technology proposals or interventions, which is evidently impossible. In addition, realizing the status of the eco-city requires making countless decisions and complex negotiations about urban form, ecological design, urban design, environmental technologies, policy measures, and governance arrangements. Accordingly, the conflicts and contradictions associated with sustainable urban development thinking and practice will continue without conceptual anchor [ 150 ].

Research within eco-cities has, over the last two decades, produced contradictory, uncertain, weak, non-conclusive, and questionable results (e.g., [ 6 , 19 , 20 , 129 , 152 ]). The overall outcome of this research relates mostly to the actual effects or presumed benefits claimed to be delivered by the green design and environmental technology solutions adopted as part of the planning of eco-cities. This relates to the broader tradition within urban scholarship of critical analysis of utopian and “top-down” urban planning [ 153 ]. In short, the issue of eco-cities has, both in discourse and practice, been problematic. Besides, much of what we know about eco-cities to date has been gleaned from studies that are characterized by data scarcity and employ traditional data collection and analysis methods, especially case studies. These are associated with inherent limitations, biases, and constraints, often as a result of relying on selective samples. Also, case studies are by definition low in external validity as to how well the outcomes are applicable to real-world settings. Too often, the results obtained from research remain context-dependent and thus cannot be generalized to other districts or cities. In other words, a common limit of case studies is that they do not lend themselves to generalizability due to the issue of the representativeness of the subjects investigated—accessible population—with respect to the target population.

Critically interrogating eco-city projects without taking into account the question of scale renders the analysis of the operationalization of sustainable development quite difficult. A large body of work has addressed various scales and treated questions, such as urban design and planning, environmental governance, and sustainable technologies, from a single street to macro-scale, metropolitan city. There is a variety of notable studies on the implementation of eco-city ideas at various scales (see, e.g., [ 15 , 154 , 155 ]; Simon and Molella 2013 [ 8 , 13 ]; Pow and Neo [ 69 , 156 ]). Work on eco-cities should be explicitly aware of the scalar aspects of eco-city projects and of how the geographies of scale are in themselves interlinked with other scales, and with processes operating across scales. With respect to the latter, Bibri [ 83 ] concludes that conceiving scales as outcomes of processes and planning accordingly hold in fact great potential for attaining the goals of sustainability beyond a single scale. Sustainability outcomes are multi-scalar in nature, which justifies the need to integrate scales that have clear synergies in their management and planning and need to be coupled. This synergic integration produces combined effects that are greater than the sum of the separate effects of different scales with regard to sustainability benefits.

Scale relates to urban geography and architecture discourses [ 157 , 158 , 159 , 160 ]. In geography, scale classifies, with large approximation, the size of a land area. It is the extent of an area at which a phenomenon or a process occurs. In architecture, scale denotes “different level of complexity of the components internally arranged to construct a whole” ([ 161 ], p. 245). Moreover, some propose that scale could be defined by its functions [ 162 ] or by administration boundaries [ 163 ]. According to Bibri [ 83 ], scale is the geographical or physical structure that both shapes process and emerges from process. In this respect, the scales considered in eco-city projects need to be addressed as relationships between spaces of different dimensions, where the constructed whole of one scale can be a mere component among components at another. However, the scale of city district can enable a better understanding of the built environment as a complex entity and its sub-systems interconnections. It shows promise as an entity to start transforming the built environment in line with the dimensions of sustainability toward a more holistic view in terms of incorporating the full scope of sustainability. Still, it is important to analyze the different scales at which eco-city projects can be considered in order to be able to adequately examine their functioning and planning, and also place them into a more relevant context. Especially, the contradictions associated with sustainability go deeper, as the same effort might increase one aspect of sustainability on one scale (e.g., the city) while decreasing it on another (e.g., the neighborhood). Also, as concluded by Farreny et al. ([ 155 ], p. 1131), “the design of neighborhoods in different locations will lead to different results: there is no unique path to achieving urban sustainability or a uniform solution.” Several studies have addressed these aspects at regional and city levels from different perspectives (e.g., [ 6 , 152 ]).

Furthermore, as large-scale system transformation of an entire city at once can be associated with numerous challenges, finding ways for step-wise transitions could be more desirable. To date, much of these transitions have been addressed by aiming to improve individual structures within cities [ 164 ]. The scale of city district represents a promising entity as it is usually large enough to include required parameters and systems while retaining a holistic view [ 14 , 165 ]. In addition, city district developments have become a common expansion route for growing cities and a common way to address and implement sustainability in the built environment (Joss 2015 [ 11 , 14 ]). They have also become a form of urban innovation lab for testing new ideas and technologies, and could act to lower the threshold to engage essential stakeholders and drive development [ 15 , 166 ]. However, it is vital to point out that some important challenges remain to be managed when working with the district scale, including the variation in how to define the borders of an urban district. Also, data and information collection can pose a great challenge because information is rarely collected with the correct resolution [ 13 , 41 , 42 ]. Therefore, there is a need to find more effective ways to address and implement the spatial scaling of eco-cities in an attempt to increase the outcomes of sustainability. This relates to the emerging model of sustainable cities, which is increasingly being enabled by urban computing and intelligence in terms of planning and design. Bibri [ 167 ] analyzes and discusses the emerging conceptions of and approaches to spatial scales that should be considered in the planning and design of data-driven smart eco-cities. The author highlights the innovative potential of urban computing and intelligence for enhancing and transforming the spatial scaling of eco-cities, and argues that data-driven technologies allow eco-cities to monitor, understand, and analyze the different aspects of their spatial scaling for generating the kind of designs that improve sustainability.

Visions of cities of the future highlight issues central to the analysis of the wider trend toward developing new eco-city projects as a form of experimentation. These concerns are mirrored in the literature on the confluence of interest in enacting sustainable urbanism [ 168 ]. However, other questions seem to be overlooked in recent research on eco-urbanism. This involves the need for eco-cities to be the focus of sustained critical engagement which focuses more clearly on the questions of scale and internal social resilience. With respect to the former, eco-city projects need to be considered in light of wider economic, political, and ideological contexts to make sense of new-build co-city projects over and beyond a limited focus on eco-cities as premium ecological enclaves [ 6 , 68 ]. Regarding the latter, eco-cities should not be considered “only as empty containers into which a new, ecologically sensitive urban society can be inserted, but as potentially problematic spaces in that the social and political are often elided from, or glossed over in techno-rational plans for these new cities” ([ 6 ], p. 12). Eco-cities as social systems and communities should have the ability to withstand shocks and return to a state close to normal. Social resilience denotes the ability of a social system, not only to bounce back from events causing a shock through robust behavior, but also to adapt and learn from the past behaviors to surpass the previous state by extending its capacity. Moreover, Cugurullo [ 129 ] questions the sustainability of the eco-city by investigating the extent to which it is developed in a controlled and systematic manner. With reference to eco-city projects in China, Cai and Tang (120) point out that the eco-city construction has always been led by the central government and enacted by a top-down approach. Further, Cugurullo [ 129 ] counterclaims the mainstream view of eco-urbanism, arguing that what is promoted as cohesive settlements shaped by a homogeneous vision of the sustainable city are actually fragmented cities made of disconnected and often incongruous pieces of urban fabric. Eco-cities also lack social cohesion in terms of the willingness of residents to engage and cooperate with each other in order to prosper, despite the value of involving local communities in decision-making processes in regard to enabling residents to have a say in the development of eco-city projects. As noted by Caprotti [ 6 ], in new exemplars of eco-urbanism, few ties bind neighbors to one another, and there is little common or shared interest between residents as members of the urban polity. The author highlights the need to focus on the macro-scale aspects of social resilience at the systemic level, as well as on the micro-mechanisms through which communities and their resilience are built in terms of the appropriation of empty spaces as social spaces and the formation of identities in the eco-city, among others.

In addition, the focus in eco-urbanism has long been on long-term approaches to urban planning, the inability of simulation models to address the current conceptions of the eco-city as a complex system in terms of its optimal design, and the inefficient mechanisms used in city operational management, and traditional governance processes and institutional systems. It follows that most of the inadequacies, shortcomings, struggles, and bottlenecks related to eco-cities are due to how these forms of human settlements have been studied, understood, planned, designed, and managed for several decades. The problems and challenges facing eco-cities are increasingly more complex due to the flows and channels of information, the divergence of agents, the heterogeneity of actors, the dispersion of power, and the difficulty of decision-making (e.g., [ 2 , 17 , 20 , 41 , 42 , 62 , 123 ]; Verney and Rapoport 2011 [ 24 ]). Nevertheless, this is drastically changing thanks to the innovative potential role of big data technologies in this regard. The abundance of urban data, coupled with their analytical power, opens up for new opportunities for innovation in eco-cities, particularly in relation to linking their infrastructures to their operational functioning and planning through control, optimization, management, and improvements, and thus tightly interlinking and integrating their systems and domains (see [ 35 ] for practical initiatives). Indeed, it has been argued that eco-cities need to embrace and leverage what smart cities have to offer in terms of advanced technological solutions so as to achieve the desired outcomes of sustainability under what is labeled “data-driven smart eco-cities.” Bibri (2021e [ 169 ]) proposes an applied theoretical framework for strategic sustainable urban development planning based on a case study analysis. The novelty of this framework lies in integrating notonly eco-city design strategies and technology solutions; data-driven smart city technologies, competences, and solutions for sustainability; and environmentally data-driven smart sustainable city solutions and strategies, but also compact urban design strategies. These combined hold great potential to improve and advance the contribution of sustainable cities to the goals of sustainability through harnessing its synergistic effects and balancing its dimensions. This is what the eco-city has long sought to achieve as a model for sustainable urban development.

The evolving model of the data-driven smart eco-city

Integrating the prevailing paradigm of sustainable urbanism and the emerging paradigm of smart urbanism.

Data-driven smart eco-cities is an emerging global paradigm of urbanism that combines and integrates data-driven cities and eco-cities in terms of their dimensions, strategies, and solutions. This paradigm is increasingly justified by the need to monitor, understand, analyze, plan, design, and manage emerging eco-cities in more innovative ways to achieve the desired outcomes of sustainability, especially when engineering and constructing new-built eco-cities. This need is increased by the ongoing quest and growing motivation for achieving the SDG 11, as well as responding to the challenges of urbanization and its unintended negative consequences.

The phenomenon of the data-driven city has materialized as a result of the emergence of big data science and analytics and the wider adoption of the underlying technologies, the vast deployment of the IoT, the explosive growth of urban data, and the transformation of urban landscape in the light of urbanization [ 2 ]. These developments can be used in a range of proposals for a conceptual framework for the data-driven city. This emerging paradigm of urbanism is too often associated with “smarterness” under what is labeled “data-driven smart cities” (e.g., [ 31 ]; Dornhöfer et al. [ 169 , 170 ]; Sutherland and Cook 2017 [ 171 , 172 ]). This is due to the fact that big data technologies are seen as an advanced form of ICT that can bring more innovative solutions to a number of complex problems and challenges pertaining to sustainability and urbanization. There is no definite definition or a single conceptual unit of a data-driven city, nor is there an agreed industry or academic description thereof. In a broader sense, a data-driven city is a city that implements datafication for enhancing and optimizing its operations, functions, services, strategies, designs, and policies to some purpose. We currently experience intensive datafication of society, and we see the dawn of the datafied society in everyday life, manifested in the various forms of big data technologies permeating the very fabric of the contemporary city. Datafication (e.g., [ 173 , 174 , 175 ]) represents an urban trend which defines the key to the core functioning of the city through a reliance on big data analytics and its core enabling and driving technologies in terms of their use for enhancing decision-making processes pertaining to a wide range of practical uses and applications within various urban domains. In a sense, the data-driven city concept emphasizes big data technologies to bring about transformations or changes to city life, which are often claimed to be for the better. This entails embracing and bridging the gap between the basic elements used in the management of the city, including unobtrusive and ubiquitous sensing, intelligent computing, cooperative communication, and massive data management and analytics, as well as governmental and services agencies. Nikitin et al. [ 136 ] describe the data-driven city as a city that is characterized by the ability of city management agencies to use technologies for data generation, processing, and analysis aimed at the adoption of solutions for improving the living standards of citizens thanks to the development of social, economic and ecological areas of the urban environment. Bibri [ 2 ] conceives of the data-driven city as digitally instrumented, datafied, and networked for enabling large-scale computation to enhance decision making processes across various urban domains for the purpose of improving and optimizing operational management mechanisms and planning development approaches in line with the environmental, economic, and social goals of sustainability.

Drawing on examples from various cities, Caprotti [ 23 ] traces the convergence between eco-urbanism and smart urbanism in the past two decades by tracing the eco-city and smart city’s conceptual trajectories and how these have become enmeshed into what has been termed “the smart eco-city” from the mid-2010s onwards. The author places the smart eco-city within a broader concern with harnessing the IoT, big data, digital lifestyles, and infrastructures to connect the urban to green economy visions, strategies, and pathways. In the context of this study, the data-driven smart eco-city as an integrated and holistic model of urbanism is approached from the perspective of combining and integrating the strengths of eco-cities and data-driven smart cities and harnessing the synergies of their strategies and solutions in ways that enable eco-cities to improve and advance their contribution to the goals of sustainability through green design and planning, renewable technologies, and environmental governance on the basis of the innovative data-driven applied technology solutions being offered by smart cities. Unlike the smart eco-city in regard to its focus, the data-driven smart eco-city is seen as an experimental city for testing and introducing environmental, economic, and social reforms and bringing about transformations to the built, sustainable, smart, social, and technological infrastructures of the urban landscape [ 3 ]. As such, it can be defined as a city that has the ability to use the IoT and big data technologies to generate, process, analyze, and harness urban data for the purpose of extracting deeper insights that can be used and leveraged to make well-informed decisions to address the existing and new problems, issues, and challenges related to sustainability and urbanization. These data-driven solutions can be adopted by city management agencies and city planning and policy offices to improve sustainability, efficiency, resilience, equity, and life quality. An integrated model for strategic sustainable urban development developed by Bibri (2021f [ 176 ) based on empirical research combines and integrates—eco-cities, data–driven smart cities, and environmentally data-driven smart sustainable cities—in terms of their strategies and solutions under a novel approach to urbanism: data-driven smart eco-cities. The main contribution of this work lies in providing innovative ways of building future models for sustainable urban development as well as practical insights into developing strategic planning processes of transformative change towards sustainability based on integrated approaches in the era of big data.

Data-based urban management

Urban management tackles the demands of cities which are expanding and redeveloping with policies for land use, structures, and service networks. As such, it should control urban growth and sprawl, change the development focus to the optimization of the urban structure and the renewal of inefficient land use in built-up areas, and encourage the polycentric urban fabric under the condition of sustainable (ecological and compact) development. Theory defines management as the process of designing and maintaining an environment in which individuals, working together in groups, efficiently accomplish selected aims [ 177 ]. In the urban sector, people form groups to accomplish aims that they could not achieve as individuals, so individuals have to work together in departments or on projects, while urban organizations must also integrate their activities.

Eco-cities across the globe are increasingly using data and technology to extract useful knowledge to perform critical urban processes and practices to achieve key environmental, economic, and social goals. They are gradually becoming dependent upon their data to operate properly—and even to function at all, and taking any possible quantifiable metric and squeezing value out of it to enhance decision-making pertaining to a wide variety of practical applications and uses in response to the complex challenges of sustainability and the negative effects of urbanization. In particular, urbanization creates enormous environmental, social, economic, and spatial changes, which provide great opportunities for sustainability, with the potential to apply advanced technologies to use resources more efficiently and control them more safely, to promote more sustainable land use, and to preserve the biodiversity of natural ecosystems and reduce pressure on the related services for the purpose of improving economic and societal outcomes. This is indeed at the core of emerging data-driven smart eco-cities, especially new build eco-city projects. In the near future, the performance of data-driven smart eco-cities will be measured, monitored, evaluated, and improved based on the ability of having control over the storage, management, processing, and analysis of the available data, as well as on the quality of the knowledge derived from these data in the form of applied intelligence within many spheres of city administration, such as transport, traffic, street lighting, energy, environment, mobility, waste, water, building, public safety, healthcare, education, and so forth.

The emerging city management mechanisms and approaches related to the administration, organization, and planning of eco-cities enable and support data-driven, model-driven, and evidence-based decisions. Generally, data-based city management involves a number of city management agencies and policy and planning offices that use technologies for generating, processing, and analyzing data to adopt solutions and strategies for improving and advancing sustainability. It is a basic driver for the transformation of urban operations, functions, services, designs, and policies. Therefore, it is expected to dramatically change the principles of eco-city development, to bring cohesion and congruence to urban strategies, and to unify the expectations of different urban actors in ways that render plans feasible and adequate to the reality of urban places and facilitate a shared vision of sustainable urban development.

In eco-urbanism, big data technologies and their novel applications are increasingly seen as the key driver for sustainable development, especially in relation to integrating the three dimensions of sustainability. This involves harnessing the synergies between green design, renewable energy, smart energy, environmental governance, citizen-centered service innovation, intelligent city infrastructure, digitalized water production and distribution, citizen behavior change, and climate change mitigation and adaptation. Therefore, there has been a conscious push for eco-cities across the globe to be smarter and thus more sustainable by developing and implementing data-driven technology solutions so as to optimize operational efficiency, enhance functions, strengthen infrastructure resilience, and improve social equity and life quality. This trend is evinced by many topical studies conducted recently on eco-cities or low-carbon cities (e.g., [ 23 , 35 , 37 , 39 , 40 , 41 , 42 , 43 , 44 , 62 , 63 , 178 , 179 , 180 , 181 ]). This is owing to the core enabling and driving technologies of the IoT and big data analytics offered by smart cities in relation to sustainability (e.g., [ 2 , 30 , 31 , 45 , 47 , 136 , 147 , 182 , 183 , 184 , 185 , 186 , 187 , 188 , 189 ]). With recent developments in big data computing, smart cities offer the opportunity for establishing different urban centers and platforms as hubs of innovation (e.g., [ 31 , 136 , 137 , 190 ]) for adopting data strategies for a common good-oriented urban development. Underlying the functioning of data-driven smart eco-cities is a set of platforms and centers associated with data-oriented competences and practices, including (see [ 95 ] for a descriptive account):

Horizontal information systems

Operations centers and dashboards

Research and innovation centers

Educational centers and training programs

Strategic planning and policy centers

These competences relate to the degree of the readiness of the eco-city to introduce data-driven technology as well as to the degree of the implementation of applied technology solutions with respect to city management. The degree of readiness is characterized by the availability and development level of the technological infrastructure and competencies needed to generate, transmit, analyze, and visualize data. The degree of implementation demonstrates the extensive use of the applied technology solutions in city operational management and development planning in regard to the different areas of sustainability.

The processes and practices of eco-urbanism are becoming highly responsive to a form of data-driven urbanism. One of the consequences of data-driven urbanism is that the systems and domains of eco-cities are becoming much more tightly interlinked and coordinated respectively. And also, vast troves of data are being generated, analyzed, and exploited to understand the multiple complexities and wicked problems inherently embodied in eco-cities so as to make them cleaner, safer, more efficient, liveable, more equitable, more resilient, and, above all, more organized. Indeed, most of the problems, issues, and challenges related to eco-cities largely relate to how these human settlements should be monitored, understood, analyzed, planned, and managed in order to improve their sustainability performance. It is argued  that more innovative solutions and sophisticated methods are needed to address and overcome these concerns. Especially, it has become increasingly feasible to attain important improvements of environmental and economic sustainability by integrating eco-cities and smart cities (see, e.g., [ 37 , 43 , 44 ]) as prevalent models of urbanism thanks to the proven role and untapped potential of data-driven technologies as an advanced form of ICT. Bibri and Krgostie (120) develop a novel model for data-driven smart sustainable cities of the future based on the outcomes of four case studies, performed on six of the ecologically and technologically leading cities in Europe. This empirically grounded model combines and integrates the leading global paradigms of urbanism—smart eco-cities, compact cities, data-driven smart cities, environmentally data-driven smart sustainable cities—in terms of their dimensions, strategies, and solutions. This research work revolves around the enabling role and innovative potential of advanced ICT, especially the IoT and big data technologies, in meeting the United Nations’ Sustainable Development Goal (SDG) 11. It is also motivated by a number of intertwined factors, which have had a clear bearing on the performance of sustainable cities (specifically, eco-cities, and compact cities) with respect to their contribution to the three goals of sustainability. These factors include, but are not limited to, the following:

There is an increased need to tackle the problematicity surrounding sustainable cities in terms of their development planning approaches, operational management mechanisms, and fragmentary design strategies and environmental technology solutions pertaining to compact cities and eco-cities, respectively.

Sustainable cities are quintessential complex systems in the sense that they are:

Dynamically changing urban environments,

Self-organizing social network embedded in space and enabled by various infrastructures and activities, and

Developed through a multitude of individual and collective decisions from the bottom up to the top down.

The escalating trend of urbanization and its unintended negative consequences.

Sustainable cities are full of contestations, conflicts, and contingencies that are not easily captured, steered, and predicted respectively. In a nutshell, they are characterized by wicked problems.

Sustainable cities and smart cities are weakly connected as approaches and extremely fragmented as landscapes, both at the technical and policy levels.

The real challenge for the future lies in moving genuinely past the assumption that there are only two contrasting, mutually exclusive realities.

An “either/or” approach will hamper progress toward urban sustainability.

In consideration of the above, the authors construct a vision of a sustainable future based on the theoretical and practical knowledge gained from conducting case study research:

A form of human settlements that secures and upholds environmentally sound, economically viable, and socially beneficial development through the synergistic integration of the more established strategies of sustainable cities and the more innovative solutions of data-driven smart cities toward achieving the long-term goals of sustainability ([ 191 ], p. 13).

This vision is a future state where most of the aforementioned problems, issues, and challenges related to sustainable cities have been solved by means of the data-driven technologies and solutions offered by smart cities of the future.

Applied data-driven approaches and solutions for city management: the case of Stockholm City

Therefore, recent research has started to give more attention to what has been called data-driven smart eco-cities, revolving around integrating eco-cities and smart cities in a variety of ways in the hopes of reaching the optimal level of environmental and economic sustainability. As a corollary of this, there is a host of unexplored opportunities toward new approaches to data-driven smart eco-cities. Within the framework of smart cities, many topical studies have addressed a number of areas of environmental sustainability in relation to operational management and development planning using the IoT and big data technologies (e.g., [ 83 , 192 , 193 , 194 ]; Rathore et al. 2016 [ 184 , 195 ]). The data-driven technology solutions offered in this regard are increasingly being utilized and implemented in emerging data-driven smart eco-cities with respect to their processes and practices. One of the cases often used as the best practice example in the literature on ecological urban sustainability is Stockholm City. And the success of the city makes it a good sample to highlight in regard to environmental solutions in the context of the emerging model of the data-driven smart eco-city.

Shahrokni et al. [ 40 ] identify the inefficiency of waste management and transportation using big data analytics and GIS, and suggest potential improvements. As an outcome of an extensive data curation process, the authors develop a series of new waste generation maps based on a large data set consisting of half a million entries of waste fractions, weights, and locations. These maps serve to describe what waste fraction comes from where and the way it is collected. Moreover, the authors analyze the route efficiency and construct the maps of selected vehicle routes in detail, as well as assess the efficiencies of the routes using the efficiency index (kg waste/km). As a conclusion, substantial inefficiencies are revealed and a shared waste collection vehicle fleet is suggested among other intervention measures to increase the efficiency of waste management.

Pasichnyi et al. [ 179 ] present a novel data-driven smart approach to strategic planning of building energy retrofitting, using data about actual building heat energy consumption, energy performance certificates (EPCs), and reference databases. This approach allows a holistic city-level analysis of retrofitting strategies thanks to the aggregated projections of the energy performance of each building, such as energy saving, emissions reduction, and required social investment. The case investigated demonstrates the potential of rich urban energy datasets and data science techniques for better decision making and strategic planning. The proposed approach allows change in total energy demand from large-scale retrofitting to be assessed, and explores its impact on the supply side, thereby enabling more precisely targeted and better coordinated energy efficiency programs. In addition, Pasichnyi et al. [ 179 ] review the existing applications of the data of EPCs and propose a new method for assessing their quality using data analytics. The authors identify 13 application domains from a systematic mapping of the analyzed material, revealing increases in the number and complexity of studies as well as advances in applied data analytics techniques. Prior to these two related studies, Shahrokni et al. [ 39 , 40 ] evaluated the energy efficiency potential of different building vintages, and found that the retrofitting potential of the building stock to current building codes can reduce heating energy use by 1/3.

Bibri and Krogstie [ 35 ] investigate the innovative potential and enabling role of data-driven smart solutions in improving and advancing environmental sustainability in the context of smart cites and eco-cities under what can be labeled “environmentally data-driven smart sustainable cities.” The results show that smart grids, smart meters, smart buildings, smart environmental monitoring, and smart urban metabolism are the main data-driven smart solutions applied in eco-cities and smart cities in regard to environmental strategies and practices. There is a clear synergy between these solutions in terms of their interaction to produce combined effects greater than the sum of their separate effects—with respect to the environment. This involves energy efficiency improvement, environmental pollution reduction, renewable energy adoption, and real-time feedback on energy flows, with high temporal and spatial resolutions. The authors conclude that data-driven decisions are unique to each city, so are environmental challenges, and that big data are the answer, but each city sets its own questions based on what characterize it in terms of visions, policies, strategies, pathways, and priorities.

Shahrokni et al. [ 41 ] present the first implementation of smart urban metabolism (SUM) in a Smart Eco-City R&D project, and further analyze some challenges and barriers to this implementation and discuss the potential long-term implications of the findings. Four key performance indicators (KPIs) are generated in real time based on the integration of heterogeneous, real-time data sources, namely kilowatt-hours per square meter, carbon dioxide equivalents per capita, kilowatt-hours of primary energy per capita, and share of renewables percentage. These KPIs are fed back on three levels (household, building, and district) on four interfaces developed for different audiences. Speaking of performance indicators at the building and household levels, advanced ICT has made it easier to collect performance parameters from the built environment so to be able to carry out a detailed evaluation of energy consumption. Holmstedt et al. [ 196 ] examine the potential of using dynamic and high resolution meter data for the evaluation of energy consumption in buildings and households. The novelty identified with this approach is that it can increase the level of detail in the evaluation results and ease the detection of deviations in the structures performance. The authors found that the commonly used indicator energy use per heated floor area remains an inadequate tool for communication when taking a holistic approach to building energy evaluation. Further, as with all ICT-based solutions, there are several challenges, barriers, and issues that need to be addressed and overcome, just as there are opportunities that need to be embraced and explored. One of the challenging barriers identified by Shahrokni et al. [ 41 ] lies in accessing and integrating siloed data from the different data owners. Moreover, there are some instances when some residents choose simply not to be involved in, or opt out of, providing data due to privacy concerns. Adding to this is the technical issues related to emission factors, system boundaries, data structure, ontology, heterogeneous data, and multiple sensors tracking the same flow. Also, Holmstedt et al. [ 196 ] identify several limitations associated with using dynamic and high resolution meter data for the evaluation of energy consumption in buildings and households, namely data collection and management, preservation of personal integrity, and incentives to react to the given evaluation information. Nevertheless, the SUM framework involves a number of long-term opportunities, which include:

Allowing citizens, city officials, and other stakeholders to receive real-time feedback on the consequences of their choices in a systematic way

Enabling a new understanding of the causalities that govern urbanism

Understanding the GHG emissions resulting from the consumption of electricity, heat, and the generation of waste

Allowing the follow-up and evaluation of the evolution of urban metabolism, and facilitating the identification of the cause-and-effect relationships of metabolic flows

Providing rich datasets on energy and material flows at the city level in terms of both production- and consumption-based approaches

In particular, integrating and analyzing data from different city systems and domains to provide real-time feedback to different stakeholders can support intelligent decision making, generate new insights, and make these stakeholders aware of the effects of their actions. This is important to meet the vision of the real-time feedback as outlined in the city’s sustainability program for SRS, which represents the joint collaboration effort of utilities, developers, citizens, as well as the departments of the city administration [ 123 ].

To sum up, like Stockholm City, many eco-cities across the globe are making substantial efforts for improving their environmental sustainability performance and meeting their sustainability commitments by utilizing the IoT and big data technologies in urban development. They are increasingly relying on the applied technology solutions offered by smart cities in their endeavor to become data-driven smart eco-cities. However, they still tend to focus largely on the environmental dimension of sustainability. Therefore, emerging eco-cities need to balance the three dimensions of sustainability as they evolve by leveraging what urban computing and intelligence have to offer in terms of planning and design. Bibri [ 83 ] analyzes and discusses the enabling role and innovative potential of urban computing and intelligence in the long-term, short-term, and joined-up planning of data-driven smart eco-cities, as well as devises a novel framework for urban intelligence and planning functions as an advanced form of decision support. The author argues that the fast-flowing torrent of urban data, coupled with its analytical power, is of crucial importance to the effective planning and efficient design of this integrated model of urbanism. This is enabled by the kind of data-driven and model-driven decision support systems associated with urban computing and intelligence. The novelty of the proposed framework lies in its essential technological and scientific components and the way in which these are coordinated and integrated given their clear synergies to enable urban intelligence and planning functions. These utilize, integrate, and harness complexity science, urban complexity theories, sustainability science, urban sustainability theories, urban science, data science, and data-intensive science in order to fashion powerful new forms of simulation models and optimization methods. These in turn generate optimal designs and solutions that improve sustainability, efficiency, resilience, equity, and life quality.

The relationship between the planning and governance of data-driven smart eco-cities

Ecological planning is, as stated by Ndubisi [ 197 ], “a way of mediating the dialogue between human actions and natural processes based on the knowledge of the reciprocal relationship between people and the land” [ 198 ]. This knowledge is derived from ecology as opportunities and constraints for decision-making in the management of the natural environment and ecosystems based on strategies and methods for creating green, safe, vibrant, and healthy urban environments. In general, planning involves the application of scientific and technical processes to build consensus among a group of choices for decision-making purposes. Smart ecological planning relies on urban computing and intelligence in terms of enabling enhanced decision-making processes pertaining to the built environment and land use with respect to both their development, design, and regulation as well as the infrastructure connecting urban areas at multiple levels, including transportation system, communication system, and distribution network. Urban computing refers to the process of generating, integrating, processing, analyzing, and synthesizing colossal amount of data from heterogeneous sources for some purpose, ways of improving sustainability, efficiency, resilience, equity, and life quality. Urban intelligence involves the use of big data analytics and the underlying core enabling technologies to devise more effective solutions in the form of designs and responses using advanced simulation models, optimization methods, and intelligent decision support systems. As an integrated approach, urban computing and intelligence (e.g., [ 199 , 200 , 201 ]) represents a holistic approach to exploiting the vast troves of data generated in cities to improve urban forms, urban infrastructure, urban environment, and urban services, as well as urban operational management and development planning systems [ 83 ]. As such, urban computing and intelligence helps make well-informed decisions, and can also create feedback loops between human activities and the urban environment.

One can think of many examples in the context of the eco-city. As a first example related to a bus network, buses should carry passengers who wait a few minutes to be transported over a few kilometers. Measuring the time in between buses at each stop, possibly together with the number of passengers waiting, gives the planner the basis for a feedback control solution. This entails communicate with buses to enforce desired standards of service, quickly place more or fewer units in service where these parameters start to deviate from the ideal metrics. Consequently, the quality of service as measured by per person waiting time will improve. This type of strategy can be operated automatically by an algorithm with access to the necessary measurements and actions. As a second example, by analyzing data on urban domains (e.g., traffic, environment, and healthcare) public authorities can quickly identify underperforming domains, evaluating improvement and cost-saving potentials, and prioritizing actions for performance efficiency interventions. As a third example, by analyzing the energy consumption of the previous years, public authorities can predict the demand for the next year and take the necessary actions to fulfill that demand, and can also make energy plans for various periods of the year. Overall, the efforts “dedicated to connecting unobtrusive and ubiquitous sensing technologies, advanced data management and analytics models, and novel visualization methods to structure intelligent urban computing systems for smart cities” ([ 199 ], p. 675) are increasingly being utilized to develop innovative solutions and sophisticated methods for eco-city development planning and operational management.

Advanced data analytics techniques have become of crucial importance to policy and governance processes due to their role in enabling and enhancing evidence-based decisions, cooperative communication channels, learning and sharing mechanisms, and collective intelligence tools. These are seen as the key driver of accelerating eco-city transitions. The outcome of big data analytics can be used as the evidence base for formulating policies and tracking their effectiveness and impact. Using a data-driven approach to investigate all available evidence from research can generate well-informed decisions based on accurate and meaningful information. The outcome can also enhance stakeholders’ collaboration capabilities, increase the capacity to handle challenges, and improve technologies’ usefulness, all aimed at achieving the objectives of smart eco-city development in terms of sustainability. This depends on the potential of the interaction and synergy of smart governance components within a specific context. Collective intelligence emerges from the collaboration and collective efforts of many actors and appears in consensus decision making.

The term “governance” has been widely used to describe the relationships between civil society, the state, and private sector with various interpretations. UNDP [ 202 ] defines governance as “the system of values, policies and institutions by which a society manages its economic, political, and social affairs through interactions within and among the state, civil society, and private sector.” Urban governance refers to how government and stakeholders decide to plan, manage, and finance urban areas. That is, how different actors and institutions are engaged in the planning and steering of the city. These actors and institutions influence urban planning outcomes, including agencies of government, non-governmental organizations (NGOs), community-based organizations (CBOs), trade unions, political parties, businesses, and industries. Urban governance involves a continuous process of negotiation and contestation over the allocation of social and material resources and political power. It is, therefore, profoundly political, influenced by the creation and operation of political institutions, i.e., government capacity to make and implement decisions and the extent to which these decisions recognize and respond to the interests and needs of citizens. This relates to the concept of “good governance,” which is widely promoted and considered essential to the effective planning of eco-city development. There are a number of principles for a good governance performance, including:

Sustainability: balancing social, economic, and environmental needs for present and future generations

Subsidiarity: taking decisions at the lowest appropriate level of government

Equity or inclusiveness: level of participation in decision-making and access to basic services

Transparency and accountability: of decisions

Civic engagement: of citizens

Security: of individuals and their living environment

Efficiency: in services delivery and locally sustainable economic development promotion.

Regarding efficiency, for instance, urban governance should be efficient in such a way as to not use more of societal resources than is necessary to achieve the desired outcomes, as well as fair in that it should not in a structural way prioritize certain social groups over others. However, as regards eco-city development, a good governance performance by local authorities entails providing citizens with a quick and flexible response, allowing them to know and understand the various challenges facing the city and to meet their daily needs. For example, based on the data generated and analyzed on residents’ expectations and use of urban space, new streets, parking lots, service facilities, public spaces, public transport routes, and waste sorting stations can be planned in response to new demands. Another aspect of good governance is to have a continuous and creative interaction with civil society. This can stimulate a cultural cooperation between local authorities and citizens, which helps reduce the impact of negative externalities, promoting a greater inclusiveness and efficiency thanks to smart city governance [ 135 ]. Gil-Garcia ([ 203 ], pp. 274) states that smart city governance is expected to “use sophisticated information technologies to interconnect and integrate information, processes, institutions, and physical infrastructure to better serve citizens and communities.” As argued by Sarker et al. (120), smart city governance is a combined function of public, private, and community stakeholders that seek to adapt data-driven technologies in their practices for ensuring urban sustainability.

Smart city governance is a new paradigm of urban policy, planning, and management that is able to solve emerging challenges of urban areas while ensuring urban sustainability. Data-driven technologies provide an opportunity to transform the conventional practices into smart practices at all levels of urban actors in terms of city management. Smart city governance has emerged as a result of the innovative potential and growing role of advanced ICT in the functioning of smart cities and smart eco-cities (e.g., [ 123 , 127 , 204 , 205 , 206 , 207 , 208 ]). This has made local, regional, and national governmental agencies rethink their functions in regard to the use of new technologies in upgrading administrative systems as part of e-government by streamlining city operations, enhancing decision making processes, and improving the different aspects of sustainability. Smart city governance denotes the capacity of employing technology and innovation as a set of intelligent and adaptive acts and activities for facilitating and supporting enhanced decision making and planning through better collaboration among different stakeholders. From a socio-technical perspective, and based on an extensive literature review, smart city governance means [ 135 ]:

Making the right policy choices or decisions;

Developing innovative governance structures via ICT; and

Governing with a focus on the outcome, i.e., dealing with substantive urban challenges.

A systematic literature review conducted by Ruhlandt [ 209 ] indicates that various smart city governance definitions exist, and also reveals substantial variances in contextual factors, measurement techniques, and outcomes among the concepts of smart city governance. However, the planning of smart eco-city development is linked to governance in that it is largely government-led and its effectiveness depends on the capacity of local administrations and the acceptance by the majority of urban actors, a function of different stakeholder groups, based on regulatory capacities and framework, and a core mechanism for delivering wider social and environmental goals. Similarly, the governance of smart eco-city development relates to planning in that it plays a critical role in shaping the physical and social character of urban areas; determines the sharing of costs and distribution of resources among different stakeholder groups; affects residents’ ability to engage in decision-making, influencing local government accountability and responsiveness to citizen demands; and influences the quantity and quality of local services and enhances their delivery.

In light of the above, the most evident relationship between planning and governance in the context of the smart eco-city lies in that the latter manages a range of actors and institutions and their relationships. This determines and influences how eco-city development can be planned through forging partnerships with and among key stakeholders. Therefore, building smart eco-cities involves complex socio-technical constellations of a variety of actors interacting with and influencing each other on multiple scales. Tu put it differently, smart eco-cities represents a socio-technical assemblage of technologies, services, business models, organizations, regulations, users, norms, visions, and challenges. In this context, smart city governance is seen as a way of supporting the decision-making process and the implementation of policies toward a common good-oriented urban development. It requires the engagement of the administration authorities and governmental agencies at all levels, the socio-economic agents, and the civil society. Here, advanced ICT, particularly big data technologies, plays an important role by contributing to facilitating the exchange of information and the sharing of knowledge, streamlining the operations of urban systems; improving the management of information flows and communication channels; enhancing the mechanisms of learning and coordination; and supporting the development of networks while promoting the cohesion of social, territorial, and spatial components of the city. The socio-technical system of the eco-city is where ICT is embedded and interacts with a social context, comprising formal institutions (e.g., laws, regulations, and standards) and informal institutions (e.g., norms, practices, and values), as well as the built environment and the physical infrastructure and the associated processes. The importance of ICT in governing smart eco-cities lies in that it supports innovative models of urban planning and management, such as appropriate tools, technological resources, procedures, and forms of action, to create conditions conducive to assisting the decision-making agents in dealing with different challenges. Indeed, maintaining the process of eco-city development is an enormous challenge in terms of planning and management, and thus requires an effective collective approach to coordinating actions and decision-making processes, thereby the necessity of advanced forms of smart city governance. These are particularly intended to facilitate more effective city government capacity with respect to the ability of institutions to deliver public services and design and implement development strategies, the adaptability to changing needs or shifting priorities, and the stability achieved through institutionalizing and disseminating good practices.

The smart eco-city depicts a great picture where big data technology can be applied to all aspects of urban development and governance. Based on case study research, Kramers et al. [ 123 ] provide insights on how the city administration integrates ICT solutions for urban sustainability into processes of planning in terms of governing the smart eco-city. The authors track how ICT becomes an integral part of the environmental program for the SRS district, how this type of technology is conceived in terms of relation to the planning and implementation of other urban technologies, as well as what expected effects are highlighted. The main conclusion drawn by the authors concerns how ICT and sustainability can be merged in the planning phase of new eco-city developments, and ultimately, how a city administration can govern a city toward a smart eco-city. Smart governance “is about crafting use of new forms of human collaboration through the use of ICT to obtain better outcomes and more open governance processes” [ 132 ]. By leveraging the power of big data through advanced analytics, technically derived knowledge can be gained to understand the urban issue and enhance decision-making [ 204 ]. Jiang et al. [ 135 ] propose a framework for smart urban governance on the basis of three intertwined key components, namely spatial, institutional, and technological components. The authors found that smart urban governance varies remarkably depending on the context in which the environmental, economic, and social challenges facing the city are embedded, which in turn affects and influences the governance modes and ICT functionalities applied in that context. They also indicate that a focus on substantive urban challenges helps to define appropriate modes of governance and develop dedicated technologies that can contribute to solving specific smart urban challenges. It can be concluded that smart urban governance should promote a context-based, socio-technical approach to governing smart eco-cities by understanding their problems, issues, and challenges within their own socio-cultural context. It is important to acknowledge the decisive role of context in analyzing alternative approaches to smart urban governance (e.g., [ 210 , 211 ]) in terms of its development, application, and effects with respect to sustainability (e.g., [ 212 ]).

The negative implications and potential risks of smart urbanism and smart governance

Urbanism is concerned with the study of urban phenomena in terms of the urbanization and organization of cities, as well as the practice of urban planning and development. As an emerging academic field, smart urbanism analyzes and reflects on the varieties and outcomes of smart city development. This involves a large body of mainly applied studies that highlight the opportunities, potentials, benefits, and risks of the solutions of the smart city. Smart urbanism provides a flexible and responsive means of addressing the challenges of urban growth and renewal, tackling environmental problems and building a more socially inclusive society. According to Marvin et al. [ 34 ], smart urbanism, “the rebuilding of cities through the integration of digital technologies with buildings, neighborhoods, networked infrastructures and people” is being socially constructed as a unique emerging “solution” to the majority of problems facing contemporary cities, or as “the response to almost every facet of the contemporary urban question.” Big data science and analytics as an instance of science and technology embodies an unprecedentedly transformative power—manifested not only in the form of transforming the knowledge of sustainable urbanism, but also in enhancing its practices and fostering its progress under what can be labeled “data-driven smart sustainable urbanism” [ 213 , 214 ]. Some downsides remain unavoidable though.

The discourse of smart urbanism is deeply originated in normative visions of the future where the salient driving factor for transformation is technology and its constant advancement. However, not only is our current understanding of the opportunities and risks of smart urbanism limited, but we should also expect some pitfalls that are yet to be seen, as new advancements in big data analytics and artificial intelligence will emerge together with unanticipated changing directions of their use for other purposes than what people wish. This is predicated on the assumption that all technological developments come with their dark side. Indeed, future models for smart cities have been extensively criticized in the literature for reflecting techno-utopian, neoliberal approaches to urban development. There is a lack of the theoretical basis and empirical evidence required to holistically evaluate the potential effects and hidden agenda of the transformative processes within smart urbanism in connection with the practices, operations, and institutions of modern society [ 2 ]. Yigitcanlar (120) highlights some of the fundamental shortfalls around smart city conceptualization and practice.

While smart urbanism offers seemingly seductive visions of the future in relation to eco-cities, it raises a number of concerns. In recent years, numerous studies have addressed the negative implications of smart urbanism and the ramifications of the infiltration of the associated socially disruptive technologies (e.g., the IoT) into urban life (e.g., [ 34 , 46 , 125 , 211 , 215 , 216 , 217 ]), including technocratic reductionism, technocentricity, city governance corporatization, dataveillance and geo-surveillance, privacy encroachment, and mind control and manipulation. In addition, smart urbanism has been criticized due to the fact that it:

Ignores social, political, cultural, economic, and historical contexts shaping urban life;

Curtails the opportunities for wider perspectives beyond technical systems and scientific processes;

Breaks city systems into pieces and reduces urban life to logic, calculative, and algorithmic rules and procedures to make the city measurable, tractable, and controllable;

Lacks the acknowledgement that the urban is not confined to the administrative boundaries of the city;

Misses on the importance of local social-economic, cultural-political, and environmental contingencies in analyzing the development, implementation, and effects of urban policies;

Marginalizes certain groups and create multiple divides between those who have access to smart applications and those who do not, especially in relation to public or social services;

Reinforces neoliberal economic growth, focuses on more affluent populations, and disempowers citizens;

Distorts the individuality of neighborhoods and strips off the particularities of urban fabrics and localities, such as the history, feelings, concerns, knowledge, and trajectories of urban communities; and

Risks to psychologically disconnect inhabitants from the environment and disrupts their relationship with nature as a result of their overexposure to technology.

While there is a pervasive belief that new technologies will prevent social, economic, and environmental collapse, data-driven technological fixes show that the negative unintended consequences of science and technology are inherently unpredictable; sustainability improvements do not offer lasting solutions; and advanced technologies, considering the current paradigm of economic growth, do not promote sustainability but instead hasten collapse. Some authors dispute the net contribution of smart urbanism to sustainable urbanism [ 218 , 219 ]. The failure and inadequacy of smart city initiatives to generate sustainable urban futures is justified by the fact that the current practice of smart urbanism portrays technologically determined, reductionist, and techno-centric approaches to the eco-city, to reiterate. These approaches overlook urban, human, and social complexities, and create conditions for new forms of social control, increased social inequality, and marginalization [ 220 ], to reiterate.

Similarly, smart city governance has been criticized due to the fact that it is strongly driven by government policies and the interests and agenda of high-tech companies and corporations (e.g., [ 221 , 222 ]). Many studies have focused on the potential risks and negative implications of the technocratic, corporate-led approach to smart city governance (e.g., [ 135 , 190 , 220 , 221 , 222 , 223 , 224 , 225 , 226 , 227 , 228 , 229 , 230 ]), including:

Concealing those urban issues, conflicts, and controversies that cannot be represented by digital models and embedded in data analytics techniques;

Emphasizing either the government as the prime initiator of innovative solutions, or the private sector as the provider of ICT-based solutions;

Treating city governance merely as a management problem that can be dealt with by making use of the power of big data and related analytics techniques;

Perceiving important urban problems as being solvable primarily through the application of technologically derived knowledge, or reframing urban problems into technological problems and solutions;

Neglecting the role of contextualization and place-based knowledge in shaping the process of governance;

Ignoring citizen’s involvement in policymaking and public service design, especially in relation to marginalized people who lack digital capabilities and power to engage in the policy making process

Focusing too much on the technical, engineering, and economic dimensions of urban governance while missing on the role of social processes in configuring its meaning in practice;

Leaving the smart to the powerful (government and corporate elites) rather than foregrounding it in the lifeworld of different stakeholders, especially citizens and civil society;

Developing policies that are largely featured with the corporatization and neo-liberalization of urban governance; and

Leading to highly unequal urban societies, characterized by unequal power relations, social exclusion, and unbalanced distributions of costs and benefits.

The technocratic governance challenges may prevent data-driven smart eco-cities from achieving the desired outcomes of sustainability. What smart urbanism entails and the way it functions raises several critical questions, including whether the governance of data-driven smart eco-cities will become too technocratic as well. Addressing this contemporary concern is of equal importance in achieving the desired outcomes of sustainability by means of advanced technologies. The ideals of eco-urbanism in seeking to take advantage from the emerging data-driven applied solutions being offered by smart urbanism require a “reinvention of governance” Barns ([ 204 ], p. 6). To put it differently, there is a need for transformative approaches to smart governance (e.g., [ 135 , 138 , 139 , 231 , 232 ]). Meijer and Bolívar (2016 [ 233 ) argue for new forms of human collaboration in smart governance to attain the desired outcomes, as well as open and transparent processes. Jiang (120) argue for the urgency and need for a transformative perspective on ICT-enabled urban governance as a context-based, socio-technical way of governing cities in response to the problems associated with the technocratic, corporate-led approach to smart governance. Regardless, smart eco-cities need to be framed as complex, dynamic, poly-centric, open, contingent, and relational systems that are full of contingencies, conflicts, contestations, cultural specificities, political tensions, competing interests, and wicked problems, instead of being cast as bounded, measurable, knowable, controllable, and manageable systems that can be steered mechanical ways and predicted based on computational models. This is predicated on the assumption that computational and scientific approaches to cities have been perceived as inadequate to solve urban problems. These are often best solved through social-political solutions, citizen participation, and deliberative democracy, instead of technocratic and top-down modes of governance [ 234 ]. All in all, policy frameworks, community approaches, and technology intelligences and their implementation and management to govern smart eco-cities should be interconnected and balanced to drive and secure sustainable urban futures for all.

Knowledge gaps in the area of data-driven smart eco-cities

The area of data-driven smart eco-cities is still in the early stages of its development. Therefore, there are many problems and questions that need to be addressed, which in turn offers a large number and wide range of research opportunities. Accordingly, the numerous knowledge gaps that need to be filled in this regard can be approached from a variety of perspectives, including theoretical, empirical, evaluative, discursive, and futuristic. These involve technical, scientific, socio-political, socio-economic, socio-technical, environmental, and institutional aspects. The knowledge gaps are identified to be critically important for the functioning, dissemination, success, and expansion of the evolving model of the data-driven smart eco-city. They relate to various topics, of which the most relevant to this study are listed in Table 7 . Addressing these gaps means supporting and advancing the integration of eco-urbanism and smart urbanism on the basis of the IoT and big data technologies in a bid to integrate and balance the three goals of sustainability.

Conclusions

The underlying idea of SIDs is to combine the strategies of urban sustainability, or to follow the principles of urban ecology, to achieve more sustainable cities in terms of increasing and balancing their environmental, economic, and social benefits and dimensions, respectively. The basic idea of data-driven smart eco-cities is to explicitly bring together eco-cities and smart cities as practical urban endeavors to address and overcome the key problems, issues, and challenges facing eco-cities in ways that continuously optimize, evaluate, and enhance their performance with respect to sustainability as to its tripartite composition. As a strategic sustainable urban development approach, data-driven smart eco-cities are being represented as a flexible and responsive means of rising up to the challenges of sustainability in the face of the escalating trend of urbanization. They are also socially constructed as a unique emerging solution to the majority of environmental, economic, and social problems faced by eco-cities today. Modern eco-cities holding unparalleled potential to address and overcome these challenges and problems largely depends on how and the extent to which they can be planned, designed, and governed in response to emerging societal trends, scientific discoveries, and technological advances. Appropriately engineering and building or redesigning and restructuring urban places as eco-cities/districts and adopting innovative solutions to make urban living more ecologically sustainable is a continuous endeavor toward achieving the three goals of sustainability.

This paper provided a comprehensive state-of-the-art literature review on SIDs and data-driven smart eco-cities. Specifically, it endeavored to deliver a detailed analysis, critical evaluation, compelling synthesis, and well-worked discussion of the available research covering the topic of eco-cities, sustainable urban districts, and smart cities in terms of their integration—with new insights and perspectives as a result of combining the narrative and best-evidence synthesis approaches to the literature review. In doing so, it identified, described, and discussed the conceptual, theoretical, discursive, and practical underpinnings of eco-urbanism. This is meant to facilitate the integration and fusion of the different disciplinary fields underlying the field of eco-urbanism for the sheer purpose of generating the kind of interactional and unified knowledge needed to gain a broader understanding of and readily explore the topic on focus. Afterwards, this paper shed light on the ideals, benefits, and guiding principles of eco-cities with respect to urban sustainability and urban ecology. Then, it delved into sustainable integrated cities in terms of shortcomings and deficiencies, emerging planning practices and development strategies, and evaluation of sustainable urban districts, with a focus on the integration of eco-urbanism and compact urbanism. Next, it addressed eco-cities as techno-enviro-economic experiments for transition, discussing several issues from various perspectives, including ecological modernization, transition studies, innovation studies, and social studies of technology. This is followed by the role of the solutions being offered by the smart city in improving the social performance of the eco-city, with an emphasis on citizen participation and the quality of life, together with some critical perspectives. Subsequently, this paper discussed eco-city planning and development with regard to current deficiencies, shortcomings, difficulties, and uncertainties, focusing on the key problems, issues, and challenges pertaining to eco-cities from a variety of perspectives. Following that, it detailed and documented the evolving model of the data-driven smart eco-city in terms of integrating the emerging paradigm of smart urbanism and the prevailing paradigm of sustainable urbanism, data-based urban management and the potential role of the applied data-driven technology solutions for operational management and development planning in boosting the performance of eco-cities, and applied data-driven approaches and solutions for city management using the case of Stockholm City. This is followed by elucidating the relationship between the planning and governance of data-driven smart eco-cities, supported by a detailed conceptual and analytical discussion. Lastly, this paper attempted to develop a critical understanding of smart urbanism and smart governance, focusing on their potential risks and negative implications from different perspectives and how and to what extent the emerging data-driven smart eco-cities can be consequently affected by these drawbacks.

This study revealed that eco-city district developments are increasingly embracing compact city strategies and becoming a common expansion route for growing cities to achieve urban ecology or urban sustainability. It also showed that new eco-city projects are increasingly capitalizing on data-driven smart technologies to implement environmental, economic, and social reforms. This is being accomplished by combining the strengths of eco-cities and smart cities and harnessing the synergies of their strategies and solutions in ways that enable eco-cities to improve their performance with respect to the three dimensions of sustainability. This in turn means that big data technologies will change eco-urbanism in fundamental and irreversible ways in terms of how eco-cities will be understood, analyzed, planned, designed, and governed. However, smart urbanism poses significant risks and drawbacks that need to be addressed and overcome in order to achieve the desired outcomes of ecological sustainability in its broader sense. One of the key critical questions raised in this regard pertains to the potentiality of the technocratic governance of emerging data-driven smart eco-cities and the associated negative implications and hidden pitfalls.

The contribution of this review lies in providing a valuable reference for scholars, practitioners, and policymakers, and the necessary material to inform them of the latest developments in the area of SIDs and data-driven smart eco-cities. This review enables scholars to focus their work on the identified real–world opportunities and challenges pertaining to these integrated models for urban development. Practitioners and policymakers can make use of the outcome of this review to identify the weaknesses of eco-cities and to find more effective ways to address these weaknesses based on the emerging applied data-driven technology solutions offered by smart cities. However, while advanced technologies can bring numerous advantages to eco-urbanism, it is important to acknowledge the fact that they can be problematic, and therefore, policy-makers and planners should be careful when employing them.

It is hoped that this study will provide the grounding for further in-depth research particularly in the emerging area of data-driven smart eco-cities. Especially, a large part of the problems in this area is still not addressed, with many diverse critical aspects being fleshed out as part of the research endeavors being undertaken within different disciplines or fields. There are also many problems that have not been addressed well or appropriately by any of the existing research in the areas of both SIDs as well as data-driven smart eco-cities. This pertains particularly to how to integrate and balance the dimensions of urban sustainability using advanced technologies, as well as to the multiple forms of integrating eco-cities or districts and smart cities at the technical and policy levels so as to make actual progress toward urban sustainability. There is a host of unexplored opportunities toward new approaches to data-driven smart eco-urbanism. This is key to mitigating the extreme fragmentation and the weak connection pertaining to eco-cities and smart cities through developing multiple visions of sustainable futures. Data-driven smart eco-cities are a fertile area of interdisciplinary, transdisciplinary, and cross-disciplinary research involving numerous intriguing and multifaceted questions awaiting scholars and practitioners from across many city-related academic or scientific disciplines.

Availability of data and materials

Not applicable.

Abbreviations

Clinton Climate Initiative

International Economics and Finance Society

Greenhouse gases

Information and communication technology

Internet of things

Key performance indicators

Non-governmental organizations

Sustainable development goal

  • Sustainable integrated districts

Stockholm Royal Seaport

Smart urban metabolism

Community-based organizations

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A systematic approach to searching: an efficient and complete method to develop literature searches

Associated data.

Creating search strategies for systematic reviews, finding the best balance between sensitivity and specificity, and translating search strategies between databases is challenging. Several methods describe standards for systematic search strategies, but a consistent approach for creating an exhaustive search strategy has not yet been fully described in enough detail to be fully replicable. The authors have established a method that describes step by step the process of developing a systematic search strategy as needed in the systematic review. This method describes how single-line search strategies can be prepared in a text document by typing search syntax (such as field codes, parentheses, and Boolean operators) before copying and pasting search terms (keywords and free-text synonyms) that are found in the thesaurus. To help ensure term completeness, we developed a novel optimization technique that is mainly based on comparing the results retrieved by thesaurus terms with those retrieved by the free-text search words to identify potentially relevant candidate search terms. Macros in Microsoft Word have been developed to convert syntaxes between databases and interfaces almost automatically. This method helps information specialists in developing librarian-mediated searches for systematic reviews as well as medical and health care practitioners who are searching for evidence to answer clinical questions. The described method can be used to create complex and comprehensive search strategies for different databases and interfaces, such as those that are needed when searching for relevant references for systematic reviews, and will assist both information specialists and practitioners when they are searching the biomedical literature.

INTRODUCTION

Librarians and information specialists are often involved in the process of preparing and completing systematic reviews (SRs), where one of their main tasks is to identify relevant references to include in the review [ 1 ]. Although several recommendations for the process of searching have been published [ 2 – 6 ], none describe the development of a systematic search strategy from start to finish.

Traditional methods of SR search strategy development and execution are highly time consuming, reportedly requiring up to 100 hours or more [ 7 , 8 ]. The authors wanted to develop systematic and exhaustive search strategies more efficiently, while preserving the high sensitivity that SR search strategies necessitate. In this article, we describe the method developed at Erasmus University Medical Center (MC) and demonstrate its use through an example search. The efficiency of the search method and outcome of 73 searches that have resulted in published reviews are described in a separate article [ 9 ].

As we aimed to describe the creation of systematic searches in full detail, the method starts at a basic level with the analysis of the research question and the creation of search terms. Readers who are new to SR searching are advised to follow all steps described. More experienced searchers can consider the basic steps to be existing knowledge that will already be part of their normal workflow, although step 4 probably differs from general practice. Experienced searchers will gain the most from reading about the novelties in the method as described in steps 10–13 and comparing the examples given in the supplementary appendix to their own practice.

CREATING A SYSTEMATIC SEARCH STRATEGY

Our methodology for planning and creating a multi-database search strategy consists of the following steps:

  • Determine a clear and focused question
  • Describe the articles that can answer the question
  • Decide which key concepts address the different elements of the question
  • Decide which elements should be used for the best results
  • Choose an appropriate database and interface to start with
  • Document the search process in a text document
  • Identify appropriate index terms in the thesaurus of the first database
  • Identify synonyms in the thesaurus
  • Add variations in search terms
  • Use database-appropriate syntax, with parentheses, Boolean operators, and field codes
  • Optimize the search
  • Evaluate the initial results
  • Check for errors
  • Translate to other databases
  • Test and reiterate

Each step in the process is reflected by an example search described in the supplementary appendix .

1. Determine a clear and focused question

A systematic search can best be applied to a well-defined and precise research or clinical question. Questions that are too broad or too vague cannot be answered easily in a systematic way and will generally result in an overwhelming number of search results. On the other hand, a question that is too specific will result into too few or even zero search results. Various papers describe this process in more detail [ 10 – 12 ].

2. Describe the articles that can answer the question

Although not all clinical or research questions can be answered in the literature, the next step is to presume that the answer can indeed be found in published studies. A good starting point for a search is hypothesizing what the research that can answer the question would look like. These hypothetical (when possible, combined with known) articles can be used as guidance for constructing the search strategy.

3. Decide which key concepts address the different elements of the question

Key concepts are the topics or components that the desired articles should address, such as diseases or conditions, actions, substances, settings, domains (e.g., therapy, diagnosis, etiology), or study types. Key concepts from the research question can be grouped to create elements in the search strategy.

Elements in a search strategy do not necessarily follow the patient, intervention, comparison, outcome (PICO) structure or any other related structure. Using the PICO or another similar framework as guidance can be helpful to consider, especially in the inclusion and exclusion review stage of the SR, but this is not necessary for good search strategy development [ 13 – 15 ]. Sometimes concepts from different parts of the PICO structure can be grouped together into one search element, such as when the desired outcome is frequently described in a certain study type.

4. Decide which elements should be used for the best results

Not all elements of a research question should necessarily be used in the search strategy. Some elements are less important than others or may unnecessarily complicate or restrict a search strategy. Adding an element to a search strategy increases the chance of missing relevant references. Therefore, the number of elements in a search strategy should remain as low as possible to optimize recall.

Using the schema in Figure 1 , elements can be ordered by their specificity and importance to determine the best search approach. Whether an element is more specific or more general can be measured objectively by the number of hits retrieved in a database when searching for a key term representing that element. Depending on the research question, certain elements are more important than others. If articles (hypothetically or known) exist that can answer the question but lack a certain element in their titles, abstracts, or keywords, that element is unimportant to the question. An element can also be unimportant because of expected bias or an overlap with another element.

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Schema for determining the optimal order of elements

Bias in elements

The choice of elements in a search strategy can introduce bias through use of overly specific terminology or terms often associated with positive outcomes. For the question “does prolonged breastfeeding improve intelligence outcomes in children?,” searching specifically for the element of duration will introduce bias, as articles that find a positive effect of prolonged breastfeeding will be much more likely to mention time factors in their titles or abstracts.

Overlapping elements

Elements in a question sometimes overlap in their meaning. Sometimes certain therapies are interventions for one specific disease. The Lichtenstein technique, for example, is a repair method for inguinal hernias. There is no need to include an element of “inguinal hernias” to a search for the effectiveness of the Lichtenstein therapy. Likewise, sometimes certain diseases are only found in certain populations. Adding such an overlapping element could lead to missing relevant references.

The elements to use in a search strategy can be found in the plot of elements in Figure 1 , by following the top row from left to right. For this method, we recommend starting with the most important and specific elements. Then, continue with more general and important elements until the number of results is acceptable for screening. Determining how many results are acceptable for screening is often a matter of negotiation with the SR team.

5. Choose an appropriate database and interface to start with

Important factors for choosing databases to use are the coverage and the presence of a thesaurus. For medically oriented searches, the coverage and recall of Embase, which includes the MEDLINE database, are superior to those of MEDLINE [ 16 ]. Each of these two databases has its own thesaurus with its own unique definitions and structure. Because of the complexity of the Embase thesaurus, Emtree, which contains much more specific thesaurus terms than the MEDLINE Medical Subject Headings (MeSH) thesaurus, translation from Emtree to MeSH is easier than the other way around. Therefore, we recommend starting in Embase.

MEDLINE and Embase are available through many different vendors and interfaces. The choice of an interface and primary database is often determined by the searcher’s accessibility. For our method, an interface that allows searching with proximity operators is desirable, and full functionality of the thesaurus, including explosion of narrower terms, is crucial. We recommend developing a personal workflow that always starts with one specific database and interface.

6. Document the search process in a text document

We advise designing and creating the complete search strategies in a log document, instead of directly in the database itself, to register the steps taken and to make searches accountable and reproducible. The developed search strategies can be copied and pasted into the desired databases from the log document. This way, the searcher is in control of the whole process. Any change to the search strategy should be done in the log document, assuring that the search strategy in the log is always the most recent.

7. Identify appropriate index terms in the thesaurus of the first database

Searches should start by identifying appropriate thesaurus terms for the desired elements. The thesaurus of the database is searched for matching index terms for each key concept. We advise restricting the initial terms to the most important and most relevant terms. Later in the process, more general terms can be added in the optimization process, in which the effect on the number of hits, and thus the desirability of adding these terms, can be evaluated more easily.

Several factors can complicate the identification of thesaurus terms. Sometimes, one thesaurus term is found that exactly describes a specific element. In contrast, especially in more general elements, multiple thesaurus terms can be found to describe one element. If no relevant thesaurus terms have been found for an element, free-text terms can be used, and possible thesaurus terms found in the resulting references can be added later (step 11).

Sometimes, no distinct thesaurus term is available for a specific key concept that describes the concept in enough detail. In Emtree, one thesaurus term often combines two or more elements. The easiest solution for combining these terms for a sensitive search is to use such a thesaurus term in all elements where it is relevant. Examples are given in the supplementary appendix .

8. Identify synonyms in the thesaurus

Most thesauri offer a list of synonyms on their term details page (named Synonyms in Emtree and Entry Terms in MeSH). To create a sensitive search strategy for SRs, these terms need to be searched as free-text keywords in the title and abstract fields, in addition to searching their associated thesaurus terms.

The Emtree thesaurus contains more synonyms (300,000) than MeSH does (220,000) [ 17 ]. The difference in number of terms is even higher considering that many synonyms in MeSH are permuted terms (i.e., inversions of phrases using commas).

Thesaurus terms are ordered in a tree structure. When searching for a more general thesaurus term, the more specific (narrower) terms in the branches below that term will also be searched (this is frequently referred to as “exploding” a thesaurus term). However, to perform a sensitive search, all relevant variations of the narrower terms must be searched as free-text keywords in the title or abstract, in addition to relying on the exploded thesaurus term. Thus, all articles that describe a certain narrower topic in their titles and abstracts will already be retrieved before MeSH terms are added.

9. Add variations in search terms (e.g., truncation, spelling differences, abbreviations, opposites)

Truncation allows a searcher to search for words beginning with the same word stem. A search for therap* will, thus, retrieve therapy, therapies, therapeutic, and all other words starting with “therap.” Do not truncate a word stem that is too short. Also, limitations of interfaces should be taken into account, especially in PubMed, where the number of search term variations that can be found by truncation is limited to 600.

Databases contain references to articles using both standard British and American English spellings. Both need to be searched as free-text terms in the title and abstract. Alternatively, many interfaces offer a certain code to replace zero or one characters, allowing a search for “pediatric” or “paediatric” as “p?ediatric.” Table 1 provides a detailed description of the syntax for different interfaces.

Field codes in five most used interfaces for biomedical literature searching

Searching for abbreviations can identify extra, relevant references and retrieve more irrelevant ones. The search can be more focused by combining the abbreviation with an important word that is relevant to its meaning or by using the Boolean “NOT” to exclude frequently observed, clearly irrelevant results. We advise that searchers do not exclude all possible irrelevant meanings, as it is very time consuming to identify all the variations, it will result in unnecessarily complicated search strategies, and it may lead to erroneously narrowing the search and, thereby, reduce recall.

Searching partial abbreviations can be useful for retrieving relevant references. For example, it is very likely that an article would mention osteoarthritis (OA) early in the abstract, replacing all further occurrences of osteoarthritis with OA . Therefore, it may not contain the phrase “hip osteoarthritis” but only “hip oa.”

It is also important to search for the opposites of search terms to avoid bias. When searching for “disease recurrence,” articles about “disease free” may be relevant as well. When the desired outcome is survival , articles about mortality may be relevant.

10. Use database-appropriate syntax, with parentheses, Boolean operators, and field codes

Different interfaces require different syntaxes, the special set of rules and symbols unique to each database that define how a correctly constructed search operates. Common syntax components include the use of parentheses and Boolean operators such as “AND,” “OR,” and “NOT,” which are available in all major interfaces. An overview of different syntaxes for four major interfaces for bibliographic medical databases (PubMed, Ovid, EBSCOhost, Embase.com, and ProQuest) is shown in Table 1 .

Creating the appropriate syntax for each database, in combination with the selected terms as described in steps 7–9, can be challenging. Following the method outlined below simplifies the process:

  • Create single-line queries in a text document (not combining multiple record sets), which allows immediate checking of the relevance of retrieved references and efficient optimization.
  • Type the syntax (Boolean operators, parentheses, and field codes) before adding terms, which reduces the chance that errors are made in the syntax, especially in the number of parentheses.
  • Use predefined proximity structures including parentheses, such as (() ADJ3 ()) in Ovid, that can be reused in the query when necessary.
  • Use thesaurus terms separately from free-text terms of each element. Start an element with all thesaurus terms (using “OR”) and follow with the free-text terms. This allows the unique optimization methods as described in step 11.
  • When adding terms to an existing search strategy, pay close attention to the position of the cursor. Make sure to place it appropriately either in the thesaurus terms section, in the title/abstract section, or as an addition (broadening) to an existing proximity search.

The supplementary appendix explains the method of building a query in more detail, step by step for different interfaces: PubMed, Ovid, EBSCOhost, Embase.com, and ProQuest. This method results in a basic search strategy designed to retrieve some relevant references upon which a more thorough search strategy can be built with optimization such as described in step 11.

11. Optimize the search

The most important question when performing a systematic search is whether all (or most) potentially relevant articles have been retrieved by the search strategy. This is also the most difficult question to answer, since it is unknown which and how many articles are relevant. It is, therefore, wise first to broaden the initial search strategy, making the search more sensitive, and then check if new relevant articles are found by comparing the set results (i.e., search for Strategy #2 NOT Strategy #1 to see the unique results).

A search strategy should be tested for completeness. Therefore, it is necessary to identify extra, possibly relevant search terms and add them to the test search in an OR relationship with the already used search terms. A good place to start, and a well-known strategy, is scanning the top retrieved articles when sorted by relevance, looking for additional relevant synonyms that could be added to the search strategy.

We have developed a unique optimization method that has not been described before in the literature. This method often adds valuable extra terms to our search strategy and, therefore, extra, relevant references to our search results. Extra synonyms can be found in articles that have been assigned a certain set of thesaurus terms but that lack synonyms in the title and/or abstract that are already present in the current search strategy. Searching for thesaurus terms NOT free-text terms will help identify missed free-text terms in the title or abstract. Searching for free-text terms NOT thesaurus terms will help identify missed thesaurus terms. If this is done repeatedly for each element, leaving the rest of the query unchanged, this method will help add numerous relevant terms to the query. These steps are explained in detail for five different search platforms in the supplementary appendix .

12. Evaluate the initial results

The results should now contain relevant references. If the interface allows relevance ranking, use that in the evaluation. If you know some relevant references that should be included in the research, search for those references specifically; for example, combine a specific (first) author name with a page number and the publication year. Check whether those references are retrieved by the search. If the known relevant references are not retrieved by the search, adapt the search so that they are. If it is unclear which element should be adapted to retrieve a certain article, combine that article with each element separately.

Different outcomes are desired for different types of research questions. For instance, in the case of clinical question answering, the researcher will not be satisfied with many references that contain a lot of irrelevant references. A clinical search should be rather specific and is allowed to miss a relevant reference. In the case of an SR, the researchers do not want to miss any relevant reference and are willing to handle many irrelevant references to do so. The search for references to include in an SR should be very sensitive: no included reference should be missed. A search that is too specific or too sensitive for the intended goal can be adapted to become more sensitive or specific. Steps to increase sensitivity or specificity of a search strategy can be found in the supplementary appendix .

13. Check for errors

Errors might not be easily detected. Sometimes clues can be found in the number of results, either when the number of results is much higher or lower than expected or when many retrieved references are not relevant. However, the number expected is often unknown, and very sensitive search strategies will always retrieve many irrelevant articles. Each query should, therefore, be checked for errors.

One of the most frequently occurring errors is missing the Boolean operator “OR.” When no “OR” is added between two search terms, many interfaces automatically add an “AND,” which unintentionally reduces the number of results and likely misses relevant references. One good strategy to identify missing “OR”s is to go to the web page containing the full search strategy, as translated by the database, and using Ctrl-F search for “AND.” Check whether the occurrences of the “AND” operator are deliberate.

Ideally, search strategies should be checked by other information specialists [ 18 ]. The Peer Review of Electronic Search Strategies (PRESS) checklist offers good guidance for this process [ 4 ]. Apart from the syntax (especially Boolean operators and field codes) of the search strategy, it is wise to have the search terms checked by the clinician or researcher familiar with the topic. At Erasmus MC, researchers and clinicians are involved during the complete process of structuring and optimizing the search strategy. Each word is added after the combined decision of the searcher and the researcher, with the possibility of directly comparing results with and without the new term.

14. Translate to other databases

To retrieve as many relevant references as possible, one has to search multiple databases. Translation of complex and exhaustive queries between different databases can be very time consuming and cumbersome. The single-line search strategy approach detailed above allows quick translations using the find and replace method in Microsoft Word (<Ctrl-H>).

At Erasmus MC, macros based on the find-and-replace method in Microsoft Word have been developed for easy and fast translation between the most used databases for biomedical and health sciences questions. The schema that is followed for the translation between databases is shown in Figure 2 . Most databases simply follow the structure set by the Embase.com search strategy. The translation from Emtree terms to MeSH terms for MEDLINE in Ovid often identifies new terms that need to be added to the Embase.com search strategy before the translation to other databases.

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Schematic representation of translation between databases used at Erasmus University Medical Center

Dotted lines represent databases that are used in less than 80% of the searches.

Using five different macros, a thoroughly optimized query in Embase.com can be relatively quickly translated into eight major databases. Basic search strategies will be created to use in many, mostly smaller, databases, because such niche databases often do not have extensive thesauri or advanced syntax options. Also, there is not much need to use extensive syntax because the number of hits and, therefore, the amount of noise in these databases is generally low. In MEDLINE (Ovid), PsycINFO (Ovid), and CINAHL (EBSCOhost), the thesaurus terms must be adapted manually, as each database has its own custom thesaurus. These macros and instructions for their installation, use, and adaptation are available at bit.ly/databasemacros.

15. Test and reiterate

Ideally, exhaustive search strategies should retrieve all references that are covered in a specific database. For SR search strategies, checking searches for their recall is advised. This can be done after included references have been determined by the authors of the systematic review. If additional papers have been identified through other non-database methods (i.e., checking references in included studies), results that were not identified by the database searches should be examined. If these results were available in the databases but not located by the search strategy, the search strategy should be adapted to try to retrieve these results, as they may contain terms that were omitted in the original search strategies. This may enable the identification of additional relevant results.

A methodology for creating exhaustive search strategies has been created that describes all steps of the search process, starting with a question and resulting in thorough search strategies in multiple databases. Many of the steps described are not new, but together, they form a strong method creating high-quality, robust searches in a relatively short time frame.

Our methodology is intended to create thoroughness for literature searches. The optimization method, as described in step 11, will identify missed synonyms or thesaurus terms, unlike any other method that largely depends on predetermined keywords and synonyms. Using this method results in a much quicker search process, compared to traditional methods, especially because of the easier translation between databases and interfaces (step 13). The method is not a guarantee for speed, since speed depends on many factors, including experience. However, by following the steps and using the tools as described above, searchers can gain confidence first and increase speed through practice.

What is new?

This method encourages searchers to start their search development process using empty syntax first and later adding the thesaurus terms and free-text synonyms. We feel this helps the searcher to focus on the search terms, instead of on the structure of the search query. The optimization method in which new terms are found in the already retrieved articles is used in some other institutes as well but has to our knowledge not been described in the literature. The macros to translate search strategies between interfaces are unique in this method.

What is different compared to common practice?

Traditionally, librarians and information specialists have focused on creating complex, multi-line (also called line-by-line) search strategies, consisting of multiple record sets, and this method is frequently advised in the literature and handbooks [ 2 , 19 – 21 ]. Our method, instead, uses single-line searches, which is critical to its success. Single-line search strategies can be easily adapted by adding or dropping a term without having to recode numbers of record sets, which would be necessary in multi-line searches. They can easily be saved in a text document and repeated by copying and pasting for search updates. Single-line search strategies also allow easy translation to other syntaxes using find-and-replace technology to update field codes and other syntax elements or using macros (step 13).

When constructing a search strategy, the searcher might experience that certain parentheses in the syntax are unnecessary, such as parentheses around all search terms in the title/abstract portion, if there is only one such term, there are double parentheses in the proximity statement, or one of the word groups exists for only one word. One might be tempted to omit those parentheses for ease of reading and management. However, during the optimization process, the searcher is likely to find extra synonyms that might consist of one word. To add those terms to the first query (with reduced parentheses) requires adding extra parentheses (meticulously placing and counting them), whereas, in the latter search, it only requires proper placement of those terms.

Many search methods highly depend on the PICO framework. Research states that often PICO or PICOS is not suitable for every question [ 22 , 23 ]. There are other acronyms than PICO—such as sample, phenomenon of interest, design, evaluation, research type (SPIDER) [ 24 ]—but each is just a variant. In our method, the most important and specific elements of a question are being analyzed for building the best search strategy.

Though it is generally recommended that searchers search both MEDLINE and Embase, most use MEDLINE as the starting point. It is considered the gold standard for biomedical searching, partially due to historical reasons, since it was the first of its kind, and more so now that it is freely available via the PubMed interface. Our method can be used with any database as a starting point, but we use Embase instead of MEDLINE or another database for a number of reasons. First, Embase provides both unique content and the complete content of MEDLINE. Therefore, searching Embase will be, by definition, more complete than searching MEDLINE only. Second, the number of terms in Emtree (the Embase thesaurus) is three times as high as that of MeSH (the MEDLINE thesaurus). It is easier to find MeSH terms after all relevant Emtree terms have been identified than to start with MeSH and translate to Emtree.

At Erasmus MC, the researchers sit next to the information specialist during most of the search strategy design process. This way, the researchers can deliver immediate feedback on the relevance of proposed search terms and retrieved references. The search team then combines knowledge about databases with knowledge about the research topic, which is an important condition to create the highest quality searches.

Limitations of the method

One disadvantage of single-line searches compared to multi-line search strategies is that errors are harder to recognize. However, with the methods for optimization as described (step 11), errors are recognized easily because missed synonyms and spelling errors will be identified during the process. Also problematic is that more parentheses are needed, making it more difficult for the searcher and others to assess the logic of the search strategy. However, as parentheses and field codes are typed before the search terms are added (step 10), errors in parentheses can be prevented.

Our methodology works best if used in an interface that allows proximity searching. It is recommended that searchers with access to an interface with proximity searching capabilities select one of those as the initial database to develop and optimize the search strategy. Because the PubMed interface does not allow proximity searches, phrases or Boolean “AND” combinations are required. Phrase searching complicates the process and is more specific, with the higher risk of missing relevant articles, and using Boolean “AND” combinations increases sensitivity but at an often high loss of specificity. Due to some searchers’ lack of access to expensive databases or interfaces, the freely available PubMed interface may be necessary to use, though it should never be the sole database used for an SR [ 2 , 16 , 25 ]. A limitation of our method is that it works best with subscription-based and licensed resources.

Another limitation is the customization of the macros to a specific institution’s resources. The macros for the translation between different database interfaces only work between the interfaces as described. To mitigate this, we recommend using the find-and-replace functionality of text editors like Microsoft Word to ease the translation of syntaxes between other databases. Depending on one’s institutional resources, custom macros can be developed using similar methods.

Results of the method

Whether this method results in exhaustive searches where no important article is missed is difficult to determine, because the number of relevant articles is unknown for any topic. A comparison of several parameters of 73 published reviews that were based on a search developed with this method to 258 reviews that acknowledged information specialists from other Dutch academic hospitals shows that the performance of the searches following our method is comparable to those performed in other institutes but that the time needed to develop the search strategies was much shorter than the time reported for the other reviews [ 9 ].

CONCLUSIONS

With the described method, searchers can gain confidence in their search strategies by finding many relevant words and creating exhaustive search strategies quickly. The approach can be used when performing SR searches or for other purposes such as answering clinical questions, with different expectations of the search’s precision and recall. This method, with practice, provides a stepwise approach that facilitates the search strategy development process from question clarification to final iteration and beyond.

SUPPLEMENTAL FILE

Acknowledgments.

We highly appreciate the work that was done by our former colleague Louis Volkers, who in his twenty years as an information specialist in Erasmus MC laid the basis for our method. We thank Professor Oscar Franco for reviewing earlier drafts of this article.

Biomarkers of meat and seafood intake: an extensive literature review

Affiliations.

  • 1 1Department of Nutrition, Exercise and Sports, University of Copenhagen, Rolighedsvej 30, 1958 Frederiksberg C, Denmark.
  • 2 2Department of Veterinary Public Health & Food Safety, Ghent University, Salisburylaan 133, 9820 Merelbeke, Belgium.
  • 3 3School of Agriculture and Food Science, Institute of Food & Health, University College Dublin, Belfield 4, Dublin, Ireland.
  • 4 4Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Yliopistonranta 1, 70210 Kuopio, Finland.
  • PMID: 31908682
  • PMCID: PMC6937850
  • DOI: 10.1186/s12263-019-0656-4

Meat, including fish and shellfish, represents a valuable constituent of most balanced diets. Consumption of different types of meat and fish has been associated with both beneficial and adverse health effects. While white meats and fish are generally associated with positive health outcomes, red and especially processed meats have been associated with colorectal cancer and other diseases . The contribution of these foods to the development or prevention of chronic diseases is still not fully elucidated. One of the main problems is the difficulty in properly evaluating meat intake, as the existing self-reporting tools for dietary assessment may be imprecise and therefore affected by systematic and random errors. Dietary biomarkers measured in biological fluids have been proposed as possible objective measurements of the actual intake of specific foods and as a support for classical assessment methods. Good biomarkers for meat intake should reflect total dietary intake of meat, independent of source or processing and should be able to differentiate meat consumption from that of other protein-rich foods; alternatively, meat intake biomarkers should be specific to each of the different meat sources (e.g., red vs. white; fish, bird, or mammal) and/or cooking methods. In this paper, we present a systematic investigation of the scientific literature while providing a comprehensive overview of the possible biomarker(s) for the intake of different types of meat, including fish and shellfish, and processed and heated meats according to published guidelines for biomarker reviews (BFIrev). The most promising biomarkers are further validated for their usefulness for dietary assessment by published validation criteria.

Keywords: Biomarkers of food intake; Fish; Poultry; Processed meat; Protein sources; Red meat; Seafood.

© The Author(s) 2019.

Publication types

Literature Reviews

  • Getting Started
  • Choosing a Type of Review
  • Developing a Research Question
  • Searching the Literature
  • Searching Tips
  • ChatGPT [beta]
  • Documenting your Search
  • Using Citation Managers
  • Concept Mapping
  • Concept Map Definition

MindMeister

  • Writing the Review
  • Further Resources

Additional Tools

Google slides.

GSlides can create concept maps using their Diagram feature. Insert > Diagram > Hierarchy will give you some editable templates to use.

Tutorial on diagrams in GSlides .

MICROSOFT WORD

MS Word can create concept maps using Insert > SmartArt Graphic. Select Process, Cycle, Hierarchy, or Relationship to see templates.

NVivo  is software for qualitative analysis that has a concept map feature. Zotero libraries can be uploaded using ris files. NVivo Concept Map information.

A concept map or mind map is a visual representation of knowledge that illustrates relationships between concepts or ideas. It is a tool for organizing and representing information in a hierarchical and interconnected manner. At its core, a concept map consists of nodes, which represent individual concepts or ideas, and links, which depict the relationships between these concepts .

Below is a non-exhaustive list of tools that can facilitate the creation of concept maps.

extensive literature review and

www.canva.com

Canva is a user-friendly graphic design platform that enables individuals to create visual content quickly and easily. It offers a diverse array of customizable templates, design elements, and tools, making it accessible to users with varying levels of design experience. 

Pros: comes with many pre-made concept map templates to get you started

Cons : not all features are available in the free version

Explore Canva concept map templates here .

Note: Although Canva advertises an "education" option, this is for K-12 only and does not apply to university users.

extensive literature review and

www.lucidchart.com

Lucid has two tools that can create mind maps (what they're called inside Lucid): Lucidchart is the place to build, document, and diagram, and Lucidspark is the place to ideate, connect, and plan.

Lucidchart is a collaborative online diagramming and visualization tool that allows users to create a wide range of diagrams, including flowcharts, org charts, wireframes, and mind maps. Its mind-mapping feature provides a structured framework for brainstorming ideas, organizing thoughts, and visualizing relationships between concepts. 

Lucidspark , works as a virtual whiteboard. Here, you can add sticky notes, develop ideas through freehand drawing, and collaborate with your teammates. Has only one template for mind mapping.

Explore Lucid mind map creation here .

How to create mind maps using LucidSpark: 

Note: U-M students have access to Lucid through ITS. [ info here ] Choose the "Login w Google" option, use your @umich.edu account, and access should happen automatically.

extensive literature review and

www.figma.com

Figma is a cloud-based design tool that enables collaborative interface design and prototyping. It's widely used by UI/UX designers to create, prototype, and iterate on digital designs. Figma is the main design tool, and FigJam is their virtual whiteboard:

Figma  is a comprehensive design tool that enables designers to create and prototype high-fidelity designs

FigJam focuses on collaboration and brainstorming, providing a virtual whiteboard-like experience, best for concept maps

Explore FigJam concept maps here .

extensive literature review and

Note: There is a " Figma for Education " version for students that will provide access. Choose the "Login w Google" option, use your @umich.edu account, and access should happen automatically.

extensive literature review and

www.mindmeister.com

MindMeister  is an online mind mapping tool that allows users to visually organize their thoughts, ideas, and information in a structured and hierarchical format. It provides a digital canvas where users can create and manipulate nodes representing concepts or topics, and connect them with lines to show relationships and associations.

Features : collaborative, permits multiple co-authors, and multiple export formats. The free version allows up to 3 mind maps.

Explore  MindMeister templates here .

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Computer Science > Artificial Intelligence

Title: artificial intelligence for literature reviews: opportunities and challenges.

Abstract: This manuscript presents a comprehensive review of the use of Artificial Intelligence (AI) in Systematic Literature Reviews (SLRs). A SLR is a rigorous and organised methodology that assesses and integrates previous research on a given topic. Numerous tools have been developed to assist and partially automate the SLR process. The increasing role of AI in this field shows great potential in providing more effective support for researchers, moving towards the semi-automatic creation of literature reviews. Our study focuses on how AI techniques are applied in the semi-automation of SLRs, specifically in the screening and extraction phases. We examine 21 leading SLR tools using a framework that combines 23 traditional features with 11 AI features. We also analyse 11 recent tools that leverage large language models for searching the literature and assisting academic writing. Finally, the paper discusses current trends in the field, outlines key research challenges, and suggests directions for future research.

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