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Systems approaches for tackling environmental issues

example about the system approach of solving environmental problems

Systems pervade the natural, societal and technological worlds – from bee swarms to traffic jams; natural to industrial ecosystems; and underpinning pervasive issues, from climate change to obesity.

Addressing environmental issues requires a move from linear and siloed thinking to ways of working that address the dynamic, interdependent complexity of real-world contexts. This means working at and across different scales e.g. from the Giardia parasite in a beaver to a whole forest ecosystem. We must recognise the nature of change as systems flip (potentially unpredictably) from one state to another and take into account the close coupling between natural and human systems as well as sub-systems, such as in forestry.

Systems thinking can help us to do this and, when done well, it encompasses the whole picture, highlights the broader context, considers interactions among multiple levels, recognises the dynamic shifts that occur over time, and encourages collaboration. Crucially, it can develop a deep appreciation of the structure and processes within a system that can help identify key points and levels where interventions might be made under a range of plausible scenarios.

The Supplementary Evidence Report to the 25 Year Environment Plan 1 said this:

“Systems models and futures analysis also provide the material to build the capacity and confidence of decision-makers and stakeholders to shape policies and interventions as 'hypotheses' that can be tested as part of adaptive management to better target investment where it will have most impact over the long-term.”

This was written with the firm belief that the value of taking a systems approach resides both in the process of constructing systems thinking and the existence and application of artefacts (such as systems maps) as a product . Here, systems thinking is best approached as a process of collective inquiry, helping to shape thinking, challenge assumptions and world-views and create insights. This involves deep and wide engagement and collaborative exploration of the issues, boundaries, drivers of change, structures and interactions within the system and sub-systems. Participants would identify questions to research and work on together to explore these questions; reflecting on and revising their understanding of the system as they progress. The various perspectives and insights generated must be synthesised and integrated into strategy and policy design; developing and testing ideas for interventions at appropriate levels and locations within the system that work for the system as a whole, breaking down and not reinforcing silos.

Taking the collective learning approach as the basis of systems thinking, there needs to be a strong focus on facilitating and enabling participants in the process of systems thinking and the creation of systems products to have traction and impact with stakeholders in government, business and civil society. This requires effective knowledge exchange and brokering to understand mutual needs, constraints and capabilities and to forge and maintain constructive dialogue.

As is often the case, no single approach or method can be sufficient and so it should be recognised that systems thinking methods, such as systems mapping, can make significant contributions alongside other approaches to tackling complexity and uncertainty, such as scenario planning – and often the two go hand-in-hand – for example in the UK National Ecosystem Assessment.

Having set out such broad purposes and parameters, it is important to adopt one or more organising frameworks, and in the context of systems thinking, there is a wide range of options available, many of which have track-records in tackling environmental issues. Key frameworks would include:

  • The European Environment Agency’s Drivers, Pressures, State, Impacts and Responses (DPSIR) model 2 and within that, the source-pathways-receptor framework. Moreover, the 25 YEP indicator framework was initially developed using a Pressures, Assets and Benefits framework (although this has since been revised).
  • Socio-ecological systems (SES) and socio-technical systems (STS) 3,4 with their foci on the flows of ecosystem goods and services across production and consumption systems such as energy, food and mobility. In particular, this focus is core to the development of the Integrated Assessment within the European Environment Agency’s 2020 State of Environment Report (SOER).
  • The seventeen UN Sustainable Development Goals (SDGs) 5 that help describe ‘ what good looks like ’ but also make clear the numerous intimate and ‘indivisible’ interlinkages between the goals; their synergies and trade-offs and cross-cutting issues.

Drilling down beneath the organising frameworks, there is a wealth of methods, techniques and procedures for developing systems thinking, including mathematical modelling such as agent-based models; diagrammatic methods such as causal-loop diagrams; and narrative techniques including scenario storylines. The fundamental and essential point here is that the objectives of the work must determine the methods – and not vice versa! As such, early work is needed to clarify the objectives and then to identify the most suitable methods required to meet those specific objectives.

Whichever approaches and methods are used, it is important to recognise certain features of systems thinking in relation to the environment:

  • that environmental systems are composed of nested and coupled sub-systems; exhibiting complex behaviour over multiple temporal and spatial scales;
  • that environmental and human systems are closely coupled; requiring a socio-ecological approach;
  • that there are multiple goals and drivers of change in play at any one time and scale;
  • that many natural capital assets can and do perform multiple functions;
  • that diverse expertise, perspectives and forms of knowledge from multiple disciplines and sectors are necessary; ‘getting the whole system in the room’; and
  • that knowledge and capability gaps will emerge requiring planning for research, experimentation, innovation and learning.

Overall, there are multiple benefits of systems thinking but these are not necessarily straightforward to achieve. Realising the benefits of systems thinking means building on innovation through collaboration, validating this through demonstration of relevance and benefit and embedding into practice through collective learning. The Institution of Environmental Sciences draws on the depth and breadth of the expertise of its members to champion the systems-based approaches required to tackle environment and sustainability challenges.

1. HM Government (2018)., A Green Future: Our 25 Year Plan to Improve the Environment, Annex 1: Supplementary evidence report., https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/673492/25-year-environment-plan-annex1.pdf

2. European Environment Agency (1997)., Air pollution in Europe 1997., https://www.eea.europa.eu/publications/92-9167-059-6-sum

3. Glaser, M. , Krause, G. , Ratter, B. M. and Welp, M. (2008): Human/Nature Interaction in the Anthropocene: Potential of Social-Ecological Systems Analysis , Gaia-Ecological Perspectives for Science and Society, 17 (1), pp. 77-80 .

4. Geels, F. W., 2002b,, 'Technological transitions as evolutionary reconfiguration processes: a multi-level perspective and a case-study',, Research Policy 31(8), pp. 1257-1274.

5. https://www.un.org/sustainabledevelopment/sustainable-development-goals/

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example about the system approach of solving environmental problems

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Systems Thinking and How It Can Help Build a Sustainable World: A Beginning Conversation

Megan Seibert | November 8, 2018 | Leave a Comment Download as PDF

example about the system approach of solving environmental problems

Perspective | style="text-align: left;"> Pine Watt | Unsplash

This article was originally published in the July 2018 Edition of The Solutions Journal

“For some, the development of systems thinking is crucial for the survival of humanity.” – John Sterman

“The light begins to twinkle from the rocks:   The long day wanes: the slow moon climbs: the deep Moans round with many voices. Come, my friends, ’Tis not too late to seek a newer world.”   – Alfred Lord Tennyson, Ulysses 

Humanity stands at a precipice. Overpopulation, resource scarcities, degraded ecosystem functioning from pollution and biodiversity loss, and anthropogenic climate change are damaging the life-supporting capacity of the planet. Diminishing returns on fossil fuel energy investments, combined with their dwindling availability and environmental harm, threaten industrial civilization. Many people recognize the need to transition to sustainable, resilient ways of living, but the prospect of such a transition is daunting, not only from a logistical perspective, but also because it requires new ways of thinking about and addressing complex problems. Widespread adoption of systems thinking represents one of society’s best bets for making real progress towards this daunting transition, but few actually understand what it is. This article is intended to introduce systems thinking into our common lexicon – to explain what it is at a basic level, how it can be used, and why it may very well be the key to humanity’s survival over the long run.

Let’s start at the very beginning. What is a system? 

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A system is a set of things interacting in a way that produces something greater than the sum of its parts. Systems can range in complexity. Compare, for instance, a car, which is relatively easy to understand and even diagnose when something goes wrong, to a tropical rainforest, which contains so many living and nonliving components that we’re only just beginning to understand how they work. All systems have a function or purpose that is brought about by the very nature of how the system is built. The universe can be viewed as a massive set of systems interacting in infinitely complex ways, with any given system containing various subsystems while simultaneously acting as a subsystem of a larger system.

This vast and almost cosmic beauty is part of the appeal of thinking in terms of systems. Yet before going further, it’s important to understand three concepts for framing discussions about systems:

  • Systems aren’t objective things that exist “out there.” They are subjective ways of thinking that humans have come up with to make sense of the complexities of the world.
  • Since “systems” are human constructions and can be thought of in infinitely many complex ways, we have to be clear about how we’re framing any particular system of interest. For example, what are its boundaries? What perspective are we taking when talking about it? How do its parts interact? And so forth.
  • It helps when framing a system to know why we’re even talking about it in the first place! Sure, we can wax poetic about abstract notions of “systemsness,” but ultimately, thinking about things as systems is useful because it helps us to understand the world and solve problems. When analyzing or discussing systems, try to ground them in the practical context of real-world  problems or phenomena, or the conversation will likely go nowhere fast.

Here are some examples of a system:

  • A human body is a set of DNA, cells, tissues, and organs that interrelate in complex ways to form a unique organism with higher intelligence and consciousness – a “greater whole” that simply cannot be explained by studying cells and neurons themselves.
  • A forest is a collection of plants, animals, soil, water, and countless other tiny creatures and materials interacting in vast food webs and biological processes that give rise to a unique landscape dominated by trees. (Another set of plants, animals, soil, and water interacting in different food webs and with different biological processes produces something entirely different – say a grassland or a desert.)
  • An economy is the set of rules, behaviors, and institutions that govern how people within a society exchange goods and services. Like the landscapes in the example above, all economies have the same general parts – people, goods, services, rules, and laws – but the unique ways in which they interact determine the unique form each one takes.

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We tend to think of structures or phenomena as systems when we know there are things interacting in complex (often elusive) ways that generate a result we want to understand better – and likely change or improve.

What, then, is systems thinking?

Systems thinking is concerned with expanding our awareness to see the relationships between parts and wholes rather than looking at just discrete, isolated parts. Holism, which is synonymous with systems thinking, derives its name from the Greek word holon , which refers to a universe made up of integrated wholes that cannot be understood by their parts alone. At its core, systems thinking means:

  • Looking at the big picture
  • Taking a wider perspective
  • Considering multiple perspectives
  • Peeling back the layers of the onion
  • Examining how things relate
  • Looking for root causes and improvements
  • Challenging and changing our paradigms

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Systems thinking is not new. Humans have been recognizing and contemplating the interaction between parts and wholes since the dawn of civilization. Vedic philosophy in ancient India is the oldest known example – almost 10,000 years ago – of humans thinking holistically. Indeed, holism is at the center of most Eastern cultural and spiritual traditions. In Western culture, the Pythagoreans of ancient Greece developed a school of thought based on cosmic wholes and harmony through numbers. It was around this time that the word “holon” originated. Indigenous peoples around the world are known for their ability to view people as part of a greater complex web of life that they respect and harmonize with. Some Amazonian tribes interact with and ingest plants in their environments that break down barriers in the mind, allowing for transcendent awareness and understanding. Yogic meditation can have a similar effect. Systems thinking is just a new name for a natural, innate way of relating to the world around us. Reductionism (looking at just the parts), dualism (viewing things as separate), and myopia (taking a narrow view) have increasingly supplanted this ancient way of relating, particularly in the last few hundred years of the industrial revolution. Systems thinking – the term given to the modern rebirth of holistic thinking in academic and professional fields – compels us to listen to our instincts, break down barriers, see the bigger picture, explore possibilities, and relearn much of what we’ve already known.

It’s worth emphasizing that it is these habits of the mind, not the terms we use to describe them, that are most important. We use the terms “systems thinking” or “holistic thinking” here because they’re common in our current lexicon, but remember that the Hindu realizing transcendent Oneness with the universe, the scientist using a formal systems thinking methodology to do research, and the American store clerk who understands that we no longer live in a democracy are all “thinking in systems,” whether they know it by that name or not.

Here are a few examples of shifting from reductionist, dualist, or myopic thinking to systems thinking:

Seibert_image4

  • Reductionist/dualist/myopic thinking . Humans are separate from nature and need to control it to make our lives better and realize the greatest possible material progress.
  •  Systems thinking . Humans evolved from nature and are inextricably linked to it. Nothing exists independent of the natural world. We depend upon it for our survival, even if the things it provides us – food, water, and natural resources for all the products we use – are coming from places we can’t readily see. If we use up and damage too much of nature, we’ll ruin the very habitat we and other creatures depend upon for survival.
  • Reductionist/dualist/myopic thinking . In order to reduce terrorism, we need to wage more war against the terrorists.
  • Systems thinking . Waging unprovoked war is what largely leads to terrorism in the first place. To stop terrorism, we need to stop the very actions that are causing it, not intensify those actions.
  • Reductionist/dualist/myopic thinking. People who are poor or struggling to get by just aren’t working hard enough.
  •  Systems thinking. Poverty and hardship are the inescapable result of our societal system. Modern industrial capitalism inherently creates “haves” and “have nots.” The unluckiest – those born into the wrong family or who are victims of the worst circumstances – suffer more than everyone else.

Systems thinking and power structures – there’s a reason most of us have never heard of it

Most people have never heard of systems thinking. Chances are the concept is relatively new to you if you’re reading this now. If so, or if you’re just naturally curious, you might ask, “Why should I think in systems and how is systems thinking more helpful than reductionist, dualist, or myopic thinking?” Hopefully the section above – and your own instincts – sufficiently answer this. Why would one choose to take a narrow, incomplete view of the world, seeing everything and everyone as separate, when we can take a wider perspective that recognizes the simple fact that things are interconnected in many different ways, leading to consequences because of those connections, not in spite of them?

Seibert_image5

Rather than asking why one should think in systems, perhaps the more piercing question is: why has holistic thinking been stamped out of us again and again over time, most vigorously so during the modern industrial age? The simple answer is that power and control are not compatible with a well-educated citizenry that sees the big picture. Modern industrial civilization is built upon the mechanization and commodification of society and nature, with those at the top benefiting from the enormous outputs generated by the “cogs in the wheel” toiling at the bottom. If we become aware of this vast, complex machine and start to understand how it works, we might want to break or change it! We might want to create a different system in which all parts of society and nature can flourish, not just those in power. Such is the struggle of humankind (at least within the last 10,000 or so years of civilization) and hence the struggle between power structures and holistic education. It’s no coincidence that our modern educational system is oriented around rote memorization of endless facts that most people find useless in their lives. If we learned what is really useful – how parts and wholes interact, tapping into the full capabilities of our minds – power structures would face a serious threat.

How to cultivate the habit of systems thinking

There’s no silver bullet to thinking in systems – no five simple steps, no condensed guidebook. But you don’t have to be an academic with a fancy degree. Systems thinking is a furnishing of the mind, a way of viewing the world that one simply develops more and more over time, like any other practice. Here are some basic habits to consider cultivating – whether you’re new to systems thinking or it’s been your modus operandi :

Seibert_image6

  • Be a critical thinker. Don’t just readily accept what anyone or any ideology says. Think for yourself. Consider the motivations behind what anyone says or claims and demand cogent reasoning and empirical or experiential evidence.
  • Be a badger. Badgers are known for their expert digging skills. Start unearthing things. Ask questions; be curious. Pull back the layers and continually dig deeper and wider.
  • See the connections. In the process of digging you’ll start to see all the complex ways in which things in our lives are intertwined. Pull on one string and find it’s connected to another – which is connected to yet another, and so on. Nothing exists in isolation. The parts dwell in the wholes and the wholes dwell in the parts. As the ancient Hindu text The Upanishads famously states, “Tat tvam asi” – “thou art that.”
  • Expand the time horizons of your thinking. The extent to which we think way back in time and way forward in time has a tremendous impact on how barriers are broken down – or erected – in our thinking and thus how we see the ways in which parts and wholes interact. Like the digging badger in a cosmic time machine, try going back before modern industrial times… then to the beginnings of the agricultural revolution 10,000 years ago… then to ancient hunters and gatherers… then to the first appearance of life on earth… then before there was an Earth… and likewise forward to your children’s time, and their children’s time… then 500 years and thousands and millions of years into the future, even when the Earth will eventually collide into the sun and the universe will expand to the point of heat death.
  • Expand the spatial horizons or your thinking. We can think about things on a very small spatial scale or a very large spatial scale, or anything in between – from atoms, to cells, to organisms, communities, ecosystems, the planet, the solar system, the galaxy, the universe, and even other universes.
  • Consider multiple perspectives. What is meant by “perspective” in systems thinking is not an opinion or position, but another way of framing a system, usually its boundaries and dynamics. For example, take someone who is fishing on a river. We can examine this scene from a number of perspectives. We can take an energetic/economic perspective, looking at the gas and money spent to drive to and from the river and how that measures against the return of energy gained through sustenance from any fish caught. We can look at the angler through a recreational/spiritual perspective, considering the joy, expansiveness, and oneness with nature one feels when being on a river trying to outsmart a fish, just like our ancient ancestors did millennia ago when trying to survive in the wild. We can look at the angler from the perspective of fish management by examining the role recreational fishing plays in managing a fish population. We can take a long-term evolutionary perspective and see that the knowledge, skills, and spiritual connections of  fishing are valuable – if not vital – to maintain and pass on to future generations. Surely ten other people could come up with ten other perspectives. Considering different perspectives is important because it generally expands our awareness and affects how we frame problems and intervene in systems.

Seibert_image7

How can systems thinking help to build a sustainable world?

Systems thinking is the ideal problem-solving framework for sustainability. The two go hand-in-hand. A sustainable community is one whose actions don’t diminish the social opportunities and ecosystem health for future generations while being resilient against social and ecological shocks or changes. This requires looking far into the future, thinking beyond ourselves about the greater collective (born and unborn, human and non-human), and looking deeper below the surface to understand how things really work. Systems thinking is precisely poised to help do just this.

Here are some starting places, corresponding to the habits outlined above, for how systems thinking can help move us towards sustainability.

Be a critical thinker

  • Think beyond what is espoused in the mainstream, whether it be a major political party, the conventional wisdom of a social group, or the media. That which becomes mainstream has a decent chance of being influenced (with or without good intentions) by the likes of power, group think, and myopic feel-good-ism.
  • Even if something isn’t mainstream, consider its motivations and whether it’s backed by cogent reasoning, empirical evidence, and/or evidence from your own experiences and observations. Look at original sources. Come to your own conclusions.
  • Most things in this world aren’t all good or all bad. Develop nuanced conceptions of people and ideas, realizing that both can be complex and multi-faceted.

Be a badger

  • Be curious about the world and understand how it really works. How do politics really work? Do we really live in a democracy? Where does all our “stuff” come from and where does our trash go? Are solar panels really “clean”? Is being vegan really the answer? Why are two billion people hungry every day? Why can’t I get a job despite my academic credentials and experience? Why has the US been at war for 15 years? Why are so many people miserable? Why were nearly all the global freedom leaders of the 20 th century assassinated and movements for freedom and justice squelched? Is the world I see around me the only reality that’s possible?

Expand the time horizons of your thinking

  • Look backwards to the deep history of humans, from ancient hunters and gatherers to stationary agrarians forming civilized societies. Doing so puts our current trajectory and way of life into perspective, highlighting: 1) that we are living through an extraordinarily short-lived Carbon Pulse marked by a way of life that is vastly different from what the vast majority of humans have ever experienced – or ever will experience, 2) potentialities for our future after the Carbon Pulse, and 3) a connection to our ancestors and the collective consciousness we carry from them.
  • Look forward to the future. If we want to understand how we can live equitably in healthy habitats in perpetuity, we must adopt a long-term outlook and think about the future consequences of our actions for humans – born and unborn – and non-humans alike. Think decades, a century, and several millennia out. Can our current trajectory and actions persist for that long? What legacy – that is, mix of opportunities and constraints – are we leaving for our posterity, other creatures, and the planet? Is the popular rhetoric about a sustainable future realistic?

Expand the spatial horizons or your thinking

  • An unfortunate advent of the Industrial Revolution is the widespread adoption of individual-oriented thinking rather than collective-oriented thinking. Our ability to shape a sustainable future is entirely dependent upon our ability to extend our sphere of concern beyond ourselves to our fellow brothers, sisters, creatures, and Mother Earth.
  • Think about the biophysical consequences of our individual and collective actions spread across the world, starting near you geographically and extending to far off places. What are the impacts of my actions – of our collective actions – on other people’s health and other ecosystems?

See the connections

Being a critically thinking badger with expanded spatial and temporal horizons will invariably reveal some key underlying connections – connections that represent the very heart of what we must address to move towards a sustainable world. Here are some:

  • A struggle between common people and those in power; between justice and corruption; between ignorance and truth seeking; between the forces of dark and light. We need to push past our fears to stoke the latent yet ever-present fire that burns in so many of us to fight against power and corruption for a better world we know in our hearts is possible.
  • Inputs and outputs. Nothing comes from nothing; there is no “away” to throw to. We must reconnect with these consummate ecological principles and open our eyes to see that which is not readily in front of us. Every physical product in modern industrial civilization requires, on the front end, energy and natural resources, and produces, on the back end, waste and pollution. We need to understand these dynamics and how they’ll have to be changed in a biophysically constrained world in order to maintain human and ecosystem health in perpetuity.
  • It takes energy to make energy. This can be succinctly described by the difference between energy and what is called exergy. Energy is, for example, the solar radiation that reaches the earth’s surface. But humans can’t directly use solar radiation – it has to be transformed into a useable form of energy that can do work for us, which is exergy. This transformation process itself requires energy. As we transition from the Carbon Pulse into a non-fossil fueled energy regime, it is of critical importance to understand how much net energy will be available, and in what forms – something sorely missing in most conversations about renewable energy.
  • It takes raw materials to make energy. The energy-to-exergy transformation process requires not only energy, but raw materials. In addition to assessing future net energy availability, we also have to analyze the metals, water, and other (often toxic) man-made substances that currently go into making renewable energy technologies and everything else in our lives, considering whether their supply can be sustained in perpetuity and what the impacts of their extraction and use will be.
  • Sustained life depends upon sustained healthy habitats. It’s easy to forget, living in our predominantly urban and suburban environments, that our lives depend inextricably upon the health and vitality of natural habitats. Even for those who appreciate this simple fact, having been disconnected from living directly off the land for several generations makes the practical, full implications of what this means challenging to grasp. Transitioning to a sustainable world will take more than focusing on energy and trying to preserve our current quality of life. It will require looking at the ecological implications of everything we do (from our population levels to manufacturing processes to the impacts of hydroelectric dams) and determining whether we find those impacts on the health of our habitats acceptable over the long-run. This process of evaluation is necessarily both objective (e.g. ecological integrity assessments) and subjective (e.g. value laden decisions by society about the conditions we chose to live in and how much “space” we chose to give to other creatures and the planet).
  • Politics matters. Journalist Jon Schwarz recently said, “Twenty years ago, U.S. elites had so successfully depoliticized America that simply caring about politics was like having a super-weird  hobby. It wasn’t even like being a Civil War re-enactor; it was like being a War of 1812 re-enactor. The social opprobrium meant that many of the people in grassroots politics were troubled kooks… If you can, make politics one of the centers of your life. Politics is absolutely a matter of life and death. Treat it like it is.” Let us not forget the definition of politics – the complex relations between people living in society. Getting these relations “right” – modifying existing laws, constitutions, and even forms of government, however minor or radical – is precisely what will shape the structural landscape that will either promote or hinder transitioning to a sustainable world. So is having the courage to talk about topics that matter – topics that all too often are placed under the “political” taboo simply because they make us uncomfortable or invoke ethics.

Consider multiple perspectives

  • There are many forms a sustainable world cannot take, but there are also many forms a sustainable world can take. A sustainable future will not be one size fits all. Consider, for example, that with a given amount of net energy and remaining natural resources, we could (hypothetically) cram nine billion on the planet in perpetuity, with little breathing room, or we could have two billion people living with more abundance. Also consider that one community may choose to live a subsistence lifestyle while another may choose to enjoy more luxuries.
  • Consider that the state of consciousness we know in the modern industrial world is not the only state of consciousness possible, nor necessarily the most helpful or desirable. As Timothy Leary famously said, “Turn on, tune in, and drop out….’Turn on’ [means] go within to activate your neural and genetic equipment. Become sensitive to the many and various levels of consciousness and the specific triggers that engage them…‘Tune in’ [means] to interact harmoniously with the world around you – externalize, materialize, express your new internal perspectives. ‘Drop out’ [suggests] an active, selective, graceful process of detachment from involuntary or unconscious commitments – a self-reliance, discovery of one’s singularity, a commitment to mobility, choice, and change.”  New scientific research coming out of the Imperial College of London’s Psychedelic Research Group suggests that mind manifesting psychedelics that have been used around the world for thousands of years may be an important key to bringing about systemic societal change through their ability to enhance cognitive connections and our sense of oneness with the world around us. Learn how to manifest the full holistic potential of your mind, whether through teacher plants, yoga, communing with nature, or other healthy forms of activation and development that resonate with you.
  • Challenge and evolve your paradigms. Use the knowledge you gain from growing a systems perspective to continually develop your paradigms about this world and the future, remaining flexible and nimble in your thinking, open to new information and considerations, always grounded in physical limits and ecological realities. Then use your particular talents and passions to help build a better future – because it can be better, but only if we act deliberately and quickly.

History has a knack for repeating itself. Waves of revolt, suppression, and enlightenment weave in and out of the fabric of the human experience, fading out in one generation to be replaced by something new, only to reappear in a new form decades or even millennia later. Such is the case with systems thinking. This age-old way of holistically viewing and relating to the world around us has taken new root in Western academic and professional fields under a new moniker. By understanding systems thinking and exploring its history and contemporary developments, we will find ourselves able to think more creatively (but with a solid grounding in physical realities) and be better prepared to tackle today’s environmental and social crises.

Megan is a systems thinker committed to steering humanity away from its failing trajectory.  She has an MS in Systems Science/Environmental Management and a dual engineering and international studies BS.  Her works spans the fields of sustainability, environmental action education, and government contracting.  She’s most concerned with the unprecedented transition civilization faces as the Fossil Fuel Age draws to a close and is working to spearhead a radical community-level action plan informed by holistic biophysical analysis. Megan lives near Corvallis, Oregon.

The  MAHB Blog  is a venture of the Millennium Alliance for Humanity and the Biosphere. Questions should be directed to   [email protected]

  • Open access
  • Published: 01 December 2015

A review on the systems approach for solving the complexity of the environmental problematique of cities in Africa

  • Innocent Chirisa 1 ,
  • Simbarashe O Dirwai 1 &
  • Abraham Matamanda 2  

Future Cities and Environment volume  1 , Article number:  11 ( 2015 ) Cite this article

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This article is woven to satisfy interlinked objectives of assessing the determinants for successful greening of the city and comparing and contrasting cities in their greening. A cross-sectional review of literature on the role of planning in city greening, the determinant factors for success and limits to this success has been done coupled by case analyses of five cities in the region. To better the situation and future of the African city space, it is imperative to examine the effect of this lack of interactive data and stakeholders’ input into urban greening efforts. Unless such a preliminary analysis is done and a model for addressing this, developed, Africa will miss what the industrial city of the eighteenth century missed which eventually led to misery and suffering as cities developed in an unsustainable manner.

Introduction

Unfortunately, when most cities in the Global North are embracing greening of cities as the way to go, Africa seems to be taking a piecemeal and generally a wait-and-see approach to the whole debate and practice (MO [ 11 , 16 ]). Some cities (like Kigali in Rwanda) have done very well while many seem lost in endeavours to serve as successful green cities by the obsession to deal with the brown agenda issues. Nevertheless, there are also limits to the endeavours. Absent in the assessment of the situation on the ground and possible scenarios for an African city systems analysis, at theoretical, policy and action research level. Most urban centres in Africa have disintegrated into nothing but places of misery and hopelessness and, with climate change impacts, the situation can get worse by each day. There are unaccounted prospects that are available in endeavours to green the African cities. This article identifies the complexity of environmental issues that cities of Africa face and are likely to face beyond 2050 [ 11 ]. It draws some lessons learnt from city greening initiatives implemented beyond Africa as well as those some of the cities are trying to implement. Thus, basing on evidence and experience from selected five cities in the region, this article provides a holistic view of the complex urban environmental systems and how they relate to the efforts by the cities to address environmental challenges. Conventional wisdom has it that the expected growth of the African cities in fifty years will lead to severe urban environmental problems as the challenges are reaching colossal magnitudes ever imagined – for example increasing slum conditions, pollution of water bodies, poor solid waste management practices, to mention but a few. Greening offers a panacea to some of these problems [ 14 , 20 ] and yet this is not as straightforward and simple as the word implies. Systems dynamics offers some platform on which one can comprehend the urban environmental challenges that urban systems can be encountering and, in this case, the African city. An analysis of various city greening efforts which follows in this article illustrates the complexity of the environmental system. The major thrust of the article is to provide a possible future of Africa beyond 50 years from now (cf. [ 11 ]).

Framing the concepts

Accurate projections for Africa in 50 years’ time are very difficult because of dynamic social and economic conditions [ 1 ]. The African Development Bank estimates suggest that Africa’s GDP could increase representing a major leap forward in standard of living and the resultant economic and population growth will be associated with rapid rural to urban migration and urbanisation. The rates of urbanisation in Africa will advance quickly with the proportion of urban dwellers likely to reach 65 % by 2060 [ 1 ] with most of these urban dwellers being youth below 25 years [ 11 ]. This implies larger cities characterised by a young population which is the most productive group hence most likely to strain the urban environments. Overall, cities are human artefacts, developed and modified over time according to perceived needs and values [ 18 , 19 ]. They play major role concentrating human activities to confined geographical spaces. Cities are not only providers of better employment, shelter and services but also as centres of culture, learning and technological development, and industrial centres for the processing of agricultural produce and manufacturing, and places to generate income [ 2 ]. In Africa, the rapid expansion and commercial development, along with population pressure in the region’s urban space, has ushered in deterioration to the urban environment as growth is unmatched with supply of the much needed services [ 12 ]. In most parts of the region, cities have become synonymous with decomposition of the urban fabric. Despite this negative set-in, the concept of greening seems to offer some of the much needed solutions to Africa.

Urban greening refers to an integrated method in the development and management of vegetation in cities, towns, urban and peri-urban areas. It has the components of urban forestry, urban agriculture or permaculture and agro-forestry [ 13 ]. Greening cities is not only about increasing the vegetation cover, rather it involves all those environmentally friendly initiatives that help to maintain the natural environment in a state of equilibrium while enhancing human well-being. The main objective is to reduce energy consumption, promote conservation and preservation of natural ecosystems in urban areas, reducing environmental degradation and harm while developing infrastructure that helps to sustain the environment. For this article greening cities will thus include efforts undertaken by various stakeholders in cities with the objective of conserving green spaces and establishing infrastructure that co-exists with the natural environment. In simple terms green cities are characterised by green buildings, green transportation, zero waste, clean air, green economy as well as access to nature. Copenhagen is one city that has made great strides in the greening pursuit. For the city 90 % of building waste is reused (zero litter), 36 % of commuters and 50 % of the citizens cycle to work or to school (green transport), 96 % of the residents live within 15 min walk from a recreational area (access to nature), 24 % was cut in carbon emission between 2005 and 2012 (clean air) [ 7 ]. Efforts to green Copenhagen city have not only been the responsibility of the local authorities, rather the successful story has been narrated because the city has managed to place public-private partnerships at the centre of its greening approach.

The green agenda in cities is about ensuring the natural ecosystems of land, water and air are part of the city and its management with the aim of improving the sustainability of ecosystems [ 14 ]. Yet, to achieve this, systems’ dynamics seems to be a promising tool. Traditionally a “silo approach” has been considered wherein urban development issues have been considered as independent components that are detached from other systems. For example, the transport issues are considered in isolation from the waste management and energy sector yet there is a relationship between all these. Systems dynamics becomes a panacea to solve the complex environmental problems in such a context because the approach views reality from a holistic point of view where the complex web of interrelated components that influence each other and also themselves are treated as causal closed chains or loops. Systems dynamic is a concept that considers the behaviour of a system together with the understanding of the system in terms of the underlying flows that relate to other systems. This way, system dynamics modelling can be a tool for understanding system behaviour over extended periods since no one system can be treated in isolation due to the way systems interlock just like the links of a chain. System dynamics, in short, highlights that, if one wants to fix an issue he is obliged to first understand the whole system. This understanding is made possible because the identification of integrating flows in all systems helps in understanding the interactions that will ultimately contribute to the formulation of policies that affect the whole urban setting. Although the literature on system dynamics modelling is very rich with applications in many fields [ 8 , 15 ], not many papers on developing system dynamics models were published so far. As urban environment models in system dynamics are extremely complex if an area is subdivided in many dynamic and interacting areas, managing complexity of the environmental network interactions is essential [ 16 ]. Environmental problems can be understood through system dynamics simulation. Systems approach to quantification of environmental problems allows better understanding of factors contributing to environmental degradation and more systematic assessment of various measures to solve the problem. No doubt, the urban environmental challenge facing African cities right now is the increasing stress on the environment and little time and resources to rectify the emerging problems [ 12 ]. With the heavily populated urban centres, natural bioremediation becomes increasingly fractious. Investment to limit the environmental footprint of the urban processes is required. Unfortunately most African governments are focussing on so many pressing and urgent challenges (health, unemployment and inequality). This is a daunting task in improving urban environments given the opportunity cost.

African cities and the green agenda: a review

As things stand, the expansion of African cities will be difficult to manage even with the best urban governance. Urban poverty is the most critical issue facing African cities at the moment. It is also often understated statistically and thus not likely to be addressed in its full dimensions [ 2 ]. One of the major consequences of the rapid urbanisation process has been the burgeoning supply of job seekers in both the modern (formal) and traditional (informal) sectors of the urban economy [ 17 ]. In most African countries, the supply of workers far outstrips the demand and the result is awfully high rates of unemployment and underemployment in African cities. Urban areas have become pools of squalid, sordid and morbid eyesores for majority of the people who live in absolute poverty. South Africa and Malawi for example use national-level poverty lines which indicate that poverty is lower within cities due to the overall level of amenities. National poverty lines are often defined as household consumption under $1 or $2 a day and yet these thresholds do not take into account a wide range of costs and hazards that urban residents face. For example, while rural residents can typically collect fuels, building materials, some foods, water, and a host of other basic needs from their surroundings, urban residents must pay to access all these.

In most African cities the poor are marginalised and are left with little option but to operate in the administrative or illegal margins in what has been referred to by UN Habitat as ‘self-help’ urbanisation. Too often African cities suffer from unplanned sprawl [ 6 ]. The region has the highest proportion of city dwellers in informal settlements and living in dire poverty in the world. The magnitude and severity of slum and informal settlements are significant indicators for the troubled African urban environment. The living standards in slum areas are agonisingly below the acceptable level. Overall, the water, soil and air qualities are greatly depreciated. Unlike other already developed and developing countries in the Asia region, in Africa, city growth has been through consistent underinvestment in agriculture leading to low productivity gains and low standards of living in rural areas forcing rural–urban migration hence city growth not credited to economic growth and prosperity. Most urban municipalities lack the financial autonomy and capacity to bankroll any initiatives they deem fit for implementation in their jurisdictions. This is also in light of the fact highlighted by [ 2 ] that unlike all other regions of the world, urbanisation in Africa has not contributed, through economies of scale and value-adding production chains, to overall growth in GDP. Finance and lack thereof has played a part in the limited success of the green agenda in African cities. Little research hence has been done and some of the mitigatory measures taken are those which are ‘readily available’. This is more pronounced because of inappropriate, unrealistic planning and building regulations, impractical planning theory and imported notions of urban aesthetics which do not reflect the needs of public authorities and the population, as well as current institutional capacities. Many local governments lack the capacity to fulfil their mandate in terms of human resources. This dire lack of skills and qualified personnel coupled with limited manpower can be attributed to Addis Ababa’s and Harare’s failed urban forestation in which the exotic trees which were planted without any professional advice are facing several problems detrimental to their survival (cf. [ 4 , 9 ]). The lack of municipality’s capacity means that even when better policies are designed, they are difficult to implement at the scale necessary.

Politics of the day is perhaps the most critical of the above issues in understanding the state of African cities especially looking at urban environmental greening which has not been prioritised by politicians. According to the Economist Intelligence Unit [ 6 ] one reason why environmental issues are often not prioritised by political elites is that, by definition, sustainability is a long-term issue, requiring investment now for a longer-term benefit in a resource constrained environment. With people struggling to meet basic needs of food, shelter, and water, those sorts of immediate priorities trump longer-term ones. In cases where politicians have been saved from this dilemma for example through external aid, the African governments have shown commitment to greening programmes for example Egypt’s Clean Technology Fund (CTF) investment plan. On the whole, the state of African cities is defined by its rapid urbanisation and corollary outcomes. A systems approach in city greening efforts helps in holistically handling a multiplicity of factors that can determine the success or failure of efforts like city greening. A number of greening initiatives have been done by some Africa cities.

Case of five cities in Africa

In this section we assess the greening efforts of the cities of Harare, Johannesburg, Nairobi, Addis Ababa and Cairo. These are megacities in Africa whose settings and contexts may proffer comparable evidence of contrasts and comparison.

Harare is the capital city of Zimbabwe with an estimated population of 2.8 million in its metropolitan area [ 9 ]. Water, waste and sanitation standards are generally dropping in Harare. The low consumption of electricity and water is probably due to their erratic supply. The city’s water source is located downstream of the populated area which makes it prone to water pollution. In recent years especially after the economic meltdown (from 1997 to 2008), there has been an increase in the problem of solid waste disposal where most open spaces in the urban area are now dump sites. The local authorities could not provide this facility which they did in the past and had to outsource three companies to carry out the waste removal [ 9 ]. Just like in Copenhagen, the communities have engaged in waste management in their respect areas due to awareness efforts made by the local authorities. The government through Environmental Management Agency (EMA) has also made a stance on plastic bags which were once for free and are now being sold to minimize their littering the environment. Environmental bodies like EMA have also come up with a ‘follow your waste’ strategy requiring local companies that produce goods and services which generate litter to follow up and make efforts to clean the waste generated by their products. The private sector (ABC bank, Goldtech electronics, First Pack among others) have also come in and provided bins to be placed in public places. Volunteer cleaners have also helped address this issue through a number of clean up campaigns in many of Zimbabwe’s urban centres.

Efforts to green the city through tree planting, maintaining open spaces and other natural ecosystems have been conspicuous with Harare with efforts being spearheaded by the local authorities and the private sector as well. The government of Zimbabwe has emphasised on the plantation of trees through a national tree planting day held the first week of every December and several organisations (NGOs, local authorities as well as individuals) participate in this annual event. Attempts have been made by Harare City Council since the 1970s to plant more trees in the central areas of the city and to boulevard the main highways out of town. A number of roads leading into the central business district of Harare are tree lined with Jacarandas and Australian bottlebrush trees with the north eastern part of the city centre having the densest concentration of trees. In 2010 Harare embarked on a programme to re-green the city. This was after a realisation on the part of the local authorities that the bulk of the city greenline was ageing and required urgent replacing. Trees were planted on parking bays along street pavements. The government both at central and local level have promoted the conservation of green spaces through statutory plans. An example is the Kopje Market Square Local Priority Plan No. 17 that emphasis the need to preserve the Harare Kopje in its natural state with all the trees and vegetation being undisturbed. Urban greening has not only been a responsibility of the local authorities in Harare but some initiatives have been taken by some property owners who have developed a culture of planting trees on their sites. Moreover, some NGOs such as COSMO have spearheaded the conservation of wetlands in te city of noteworthy Monavale vlei and Marlborough vlei, though there have been some conflicts with some locals who would rather have the wetlands converted into residential areas. The Sunday Mail notes that there is increasing greening culture by individuals in Harare which shows the increase in awareness towards green cities.

Greening efforts have also been observed in infrastructure development in Harare, which is mostly done by the private sector. Since most of the energy is consumed in built environment, efforts have been made to construct buildings that minimise environmental degradation. Eastgate Centre in Harare constructed by Old Mutual is a green building that mimics an anthill that makes use of natural ventilation and lighting thus reducing carbon emission and overall cutting costs of air conditioning and lighting. The building also has overhangs where vegetation is grown. The other building is one housing Ernst and Young offices (Angwa city) which incorporated rooftop gardening. Street and traffic lights are increasingly being replaced with solar powered lights in Harare which is an indication of a city that is transforming and adopting green initiatives through use of environmentally friendly solar energy.

Green tourism, despite all the statistics and research to support it, is still being carried out at lower levels in Zimbabwe and also there is still yet to be a documented framework on green hospitality by the Zimbabwe Tourism Authority (ZTA) [ 21 ]. In addition, because of human disturbances, rivers like Manyame which used to have perennial flow have changed since now most of the water is now sewage as evidenced by the proliferation of water hyacinth. There have been efforts by the African Development Bank (AfDB) and a recent US$144 million loan from China to improve water inflows into businesses and homes. There have also been efforts to refurbish the cities major sewage treatment plants, Prince Edward and Morton Jeffrey waterworks where US$78 million has been secured for the project.

A lot has also been done in the transport sector in efforts to green the city of Harare. In Harare, commuter omnibuses (popularly referred to as kombis) have been blamed for traffic congestion and the city in an attempt to address this problem created a holding bay a kilometre from the city which is to hold the kombis currently driving into the city centre. This will be complemented by a mass transit system which will bring forth shuttle buses ferrying people into the city centre. They have also changed roads which used to be two way (R Mugabe, Chinhoyi street, Innez terrace and Leopold Takawira) into one ways to increase traffic flow. Also in a move to increase smooth flow of traffic they have installed ‘intelligent’ robots (King George and Baines avenue) which adjust time with differing traffic flows to ensure smooth traffic movement. With the aid of Old mutual a private company, the city of Harare has also constructed Harare drive as a ring road to limit traffic passing through the already congested city centre. Shopping centre like Westgate, Gleneagles and Avondale and office parks (Arundel Business Park, Mt Pleasant Business Park) have also been put in place to limit the pressure on the central business district. From the colonial days, industrial clustering has been used to improve the impact of green manufacturing practices in industry by enabling reduced energy and water consumption levels, solid waste and wastewater minimisation strategies and enabling increased participation in corporate social responsibility activities. For instance, the plan for industrial clustering was implemented in Old Ardbennie Industrial area in Waterfalls.

Johannesburg

Johannesburg is South Africa’s most populous city, with around 3.9 million people. It is located in the Gauteng Province, which has a total population of over 10 million. Water, waste and sanitation standards are generally better than in many of the other cities in Africa. Though it has high consumption of electricity and water, it makes up on consumption with consistently strong environmental policies and good governance with strong local structures in place. In an attempt of greening the electricity sector, the city of Johannesburg is keen to promote solar power as a way to reduce carbon emissions and cut electricity costs. The authorities with funding from the Danish Development Agency have spent US$1.2 million installing solar water heaters in 700 low cost homes in Cosmo City, a housing development. To add on this, the Johannesburg Road Agency has been installing solar power signals at key city intersections since 2009. In addition to saving energy, the signals are not susceptible to power failures, which cause congestion and higher fuel consumption from queuing drivers. The City of Johannesburg is currently implementing a ‘Climate Proofing of Urban Communities’ Project in 700 Cosmo City low income houses. Climate proofing involves promoting development that reduces the risks of climate change. The project started in July 2008. It involves the installation of Solar Water Heater (SWH) units, as well as the installation of insulated Isoboard ceilings and distribution of Compact fluorescent lamps (CFL) to 700 low-income households in Cosmo City. The project is funded by the Danish International Development Agency (DANIDA) under the auspices of the embassy as part of a R15-million climate change mini-programme for Johannesburg.

The large population in Johannesburg culminates into large quantities of waste being produced in the city which calls for a consideration of the waste management in the city. Currently, plans are underway to convert landfill gas from several sites into electricity to power city homes. The conversion of landfill gas has the ability to minimise the negative effects of decomposing solid waste that often cause green house gas emission. Furthermore, the parks department has the mandate to maintain and refurbish green spaces and promote environmental projects, such as tree planting, bird watching, litter collection and river cleaning. A number of parks have been developed from wasteland alongside some townships with a view to create a greener environment for residents.

In an attempt to tackle greenhouse gases and increase the air quality, the city has implemented a high-speed train line, the Gautrain, which links downtown Johannesburg to Pretoria and the Rea Vaya bus rapid transit system connecting the densely populated township of Soweto with downtown Johannesburg. The long-term plan is for the Rea Vaya to cover more than 300 km and become a transport option for 80 % of the city’s residents. Officials say it is the single biggest initiative to tackle greenhouse gases in the city claiming that a switch 15 % of Johannesburg’s car users to Rea Vaya buses, which run on low-sulphur diesel, instead of using their private vehicles, the city would cut its CO2 emissions by 1.6 million tonnes by 2020 [ 6 ].

One other biggest urban environmental challenge for the city’s water department is the looming threat of acid mine drainage. The national Department of Water Affairs has plans to install a US$25 million pump to divert acid mine water from the city’s water sources. In its Growth and Development Strategy the city highlights plans and the need to harvest rain water. In 2008 the water department committed US$139 million in its capital budget to improving water infrastructure and sewer networks. Work is still ongoing and also the city promotes proper sanitation at water events such as the annual Water Festival held in April. In 2003 the city launched its Air Quality Management Plan having been updated forms part of the 2040 Growth Development Strategy launched in 2011. The proposal is for the additions of five air quality monitoring stations to the existing six. Officials are also in the process of establishing an air pollution control bylaw that will set acceptable industry and commercial emission levels.

Addis Ababa

Addis Ababa has an administrative area covering 500 km 2 with an estimated population of 2.7 million which is expected to grow by over 60 % [ 18 ] and is one of the densest cities in Africa. Addis Ababa was founded by the Ethiopian Emperor Menelik II in 1886, unlike most African cities, it has no colonial heritage. Though eucalyptus forests on the Entoto Mountains surrounding the city are protected and provide a watershed, there is limited public green space at 37 square metres per person in the city [ 4 ]. The city’s master plan calls for reforestation of bordering mountains, the revitalisation of existing city parks and the creation of new ones. The most significant new green space will be a pedestrian linear park winding some 5 km through the city centre [ 6 ].

Electricity consumption per capita is among the lowest in Africa at 1.8 gigajoules and CO2 emissions per capita from electricity consumption are 16 kg per capita which is also relatively low. Close to 90 % of Addis Ababa’s electricity is produced from renewable energy sources, the bulk of which is hydropower. There is however need to increase the use of solar energy which is abundant in the city.

According to official figures, an estimated 18 % of the city population lives in informal settlements but however, depending on definitions of informal settlements, other sources put the figure higher [ 6 ]. In Addis there is the integration of the poor unlike most African cities. The city’s most important initiatives are an attempt by the Ethiopian Institute of Architecture, Building Construction and City Development (EiABC) to develop green building codes. The city has received support from Swiss Federal Institute for Technology and the EiABC has been able to contract local private developers to design cheap and green building materials with particular emphasis on substituting Chinese-imported steel and glass with local stone, wood and adobe (a mix of sand, clay and straw) to cut building costs by up to a third, and raise environmental and aesthetic standards.

The city’s public transport system relies heavily on Anbessa, the state-owned bus companycomplemented by private minibuses. Addis benefits from a relatively cohesive culture, with income disparity lower than in many other African cities. This means that the city’s office workers are more likely to travel to work on public transport than in other African cities. For example, some 40 % of commuters use the Anbessa buses. By 2020, with population growth, Anbessa estimates it will be serving 6 million customers in and around Addis Ababa. In addition, Addis Ababa does have a relatively sophisticated traffic management system, but has yet to introduce any car-pooling lanes, no-car days or other congestion-reduction initiatives and has been failing to encourage citizens to take greener forms of transport. The Ethiopian government acknowledges the problem and has plans to gradually replace automobiles in the city with electric-powered cars, using tax incentives. Completion of the Chinese-backed Gotera Interchange on the city’s planned ring road has eased congestion and additional plans are ongoing to build a light rail line, estimated to transport 20,000 passengers a day [ 4 ].

The government has intentions to limit the use of plastic in local packaging and increase composting, in an effort to reduce the amount of waste destined for landfills which will be complimented by the city opening new landfill sites in the Doro, Dertu- Mojo, Bole and YekaAbado districts distributed across the city. Recycling remains limited but is likely to increase with the establishment of central recycling depots for metal and plastic. There is limited citywide waste collection. Instead city neighbourhoods (“kebeles”) are responsible for collecting rubbish in partnership with private companies, but collection costs remain high. As for policy Addis Ababa does not encourage proper waste management by citizens and fails to impose measures such as bans on littering and making waste dumping illegal [ 4 ]. On the other hand, Addis Ababa fares fairly well in the collection and disposal of special waste as it has facilities to deal with chemical and pharmaceutical waste.

Nairobi has an estimated population of 3.1 million people which is expected to double to 7 million by 2020 [ 6 ]. Most of the city’s energy is from renewable hydro power though insufficient generation and transmission infrastructure results in the frequent use of diesel-fuelled generators. Nairobi has seen increased utilisation of renewable energy resources with the construction of a wind power plant located in the Ngong Hills 22 km outside Nairobi which was completed in 2010 with six turbines producing up to 5 MW of power. A second phase of the project is expected to bring the total capacity to 11 MW. Green economy is another facet of greening cities and this has been at the fore of the greening initiatives in Nairobi where green jobs have been created. An example is the Climate Change Innovation Centre funded by the World Bank and the Danish government that aims to make Nairobi a centre of green technology while creating 4,600 jobs within five years. Moreover, Nairobi is the world headquarters of UN’s environmental and urban planning programmes. The UN campus in Gigiri was overhauled in 2011 with energy neutral offices for 1,200 employees. This includes 6,000 square metres of solar panels. The solar panels that cover the entire roof is a key feature of green buildings that help to limit energy use in buildings.

Growth in Nairobi is haphazard; the management of waste, sewage and water are urgent challenges. In an attempt to save forests, the Kenya Wildlife service in partnership with private companies is managing the Green Line Project which is an initiative to plant forest along 30 km of the perimeter of Nairobi National Park in the south of the city with the hope of creating a visible boundary between the park and surrounding new developments, and to discourage lobbying by land developers to cut slices off the park. The tree planting is also part of a wider initiative led by Nobel Prize winner Wangari Mathai to plant new trees throughout Nairobi to improve water catchment and biodiversity [ 6 ].

The three rivers that meet in Nairobi Ngong, Mathare and Nairobi Rivers are clogged with waste and during rains the rivers are thick with human excrement washed out of informal settlements. The Nairobi River Basin initiative, run by the Kenya Ministry of Environment, aims to recover the rivers, providing clear flow through the city, and increasing land and recreational value along the river bank. Planners hope to have reclaimed city centre sections of the river before 2020. Some progress has been made clearing the Nairobi River around the municipal dump at Dandora in the east of the city. Nairobi is incorporating new technology to help track environmental conditions with a new government online data portal that allows Kenyans to identify spending on water and energy, and to keep track of the state of the hydropower dams that are the source of most of the city’s energy.

In the transport sector, a ring road planned in the 1970’s is being constructed and an eight-lane superhighway linking Nairobi to the neighbouring town of Thika which also includes underpasses and footpaths. There is still more that has to be done in the transport sector especially when one considers the ill-developed non-motorised transport infrastructure in Nairobi that makes it very difficult for pedestrians and cyclists to move in the city.

Greater Cairo has a population estimated at 20 million people. It comprises the governorates of Cairo, Giza and Qalyubia. An estimated 7.1 million inhabitants occupy the 370 km 2 area within Cairo Governorate with an estimated 19,100 people per square kilometre. It is estimated that almost all households in Cairo have access to electricity, but the city fares less well in curbing electricity consumption. On average, Cairo consumes 8.0 gigajoules of electricity per capita but despite this high electricity consumption, CO2 emissions from electricity are an estimated 477 kg per capita [ 6 ]. The Supreme Council of Energy in 2007 ratified a plan to use renewable energy for 20 % of the nation’s needs by 2020. This plan relies mainly on the use of solar and wind energy, with 12 % coming from wind mills and wind farms and 8 % from hydro-power. Prior to this development, from 1991 to 2005, in an effort to encourage the use of wind energy, a joint project between the Egyptian New and Renewable Energy Authority (NREA) and Denmark’s Risø National Laboratory created a Wind Atlas for Egypt pinpointing the prime locations for wind farms and wind mills to be used for the establishment of these wind farms. Egypt announced a US$350 million Clean Technology Fund (CTF) investment plan in April 2011 that involves a combination of renewable energy production, clean transport and solid waste management projects [ 6 ]. In addition, the government has plans construct three pilot waste-to-energy plants in partnership with a private company [ 5 ]. The Al-Darrasa site, located outside the boundary of historic Old Cairo, was used as a place for dumping debris and rubble from the city for centuries but in 2005 inauguration of the Al-Azhar Park, a 30-ha development in Al-Darrasa changed this poor waste management and has contributed to improving the city’s air quality.

The government has made great strides in waste management as is evident from the government’s successful transferring of 15 million cubic metres of accumulated municipal waste from the residential areas of greater Cairo to controlled dumping sites, according to the Egyptian Environmental Affairs Agency. Furthermore, the private sector has contributed to the efforts made by the Egyptian government in managing waste in Cairo. For example, the initiatives by a German government agency, GIZ, which is carrying out a project to improve waste management in poor areas of Khanka and Khossos in Greater Cairo, have made a difference in improving the city’s landscape. Another waste management initiative with a grant of US$5.3 million from The Bill and Melinda Gates Foundation is a project for an analysis of the current system for waste collection, segregation and recycling, and the development of a new solid waste management strategy that stresses the role of the informal sector in waste management. Waste remains a challenge in Cairo where approximately 60 % of solid waste is managed by formal and informal waste collections, disposal or recycling with the rest being dumped on the streets or at illegal dumpsites.

A major programme is under way to improve traffic congestion and reduce polluting emissions from public transport vehicles. The work funded by the World Bank and the multi-donor Clean Technology Fund is being carried out through two schemes – the Egypt Urban Transport Infrastructure Development Project and the Carbon Finance Vehicle Scrapping and Recycling Programme. The government has also spent US$1.2 billion to improve air quality in Greater Cairo between 2006 and 2010 which shows the government’s commitment to ensure clean air in Cairo. The several projects involved in the programme, included moving polluting industries out of populated areas, increasing waste collections in informal areas (and thereby reducing waste burning in informal settlements), tree planting and improving Greater Cairo’s network of air monitoring stations [ 6 ]. The government has introduced fines for the burning of rice husks, which contributes to air pollution in the autumn after the harvest and have provided an alternative of several hundred special compressors to farmers for disposing of the husks. Although waste management has remained an issue in Cairo some efforts have been made by the informal sector as well to manage solid waste that is collected and reused with the aim of attaining a zero-litter city.

The urban transport project includes the provision of 1,100 new fuel-efficient buses to replace the old fleet, the construction of six bus rapid transit corridors and improvements to the traffic management system. To compliment this, the government intends to promote the use of the river for commercial transport as an alternative to using roads and thereby reducing traffic congestion. Plans include funding a management system to help coordinate river transport, and a committee has been established at the national level to improve safety for river travel. The government has also launched a pilot scheme in an area of Greater Cairo to encourage residents to ride bicycles, the installation of bicycle racks and the sale of bicycles at discounted prices. The promotion of non-motorised transport is a key component that characterise green cities in the developed world as is the case in Copenhagen where 36 % of commuters and 55 % of the city’s population cycle to work or school/college. There are efforts by the government to limit vehicle emissions by converting government cars from petrol to compressed natural gas complemented by the introduction of unleaded petrol and creating a national programme for vehicle testing and rehabilitating old taxis which have high fuel consumption rates.

The national government has taken action against water pollution mainly targeted on the Nile River by adopting 12 programmes for the protection of the river. The measures include preventing the flow of industrial effluents and sanitary drainage into the Nile, managing waste from Nile river vessels; treatment of agricultural waste; solid waste management; periodic monitoring of water cleanliness; and developing a water quality database [ 6 ]. Five plants have been established to receive waste from river cruisers and safely dispose of the waste in the sanitary drainage networks. Greening the city is also made possible when residents have access to nature hence the action by the government to combat the pollution of Nile River helps to ensure the river flows in its natural state thus increasing the natural ecosystems in the city.

Discussion and policy options

Cities in Africa are vulnerable to systemic shocks which are strong impacts affecting substantial parts or all of an urban system (nationally or internationally), rather than having just isolated (e.g., sector-based or merely local) impacts. These shocks have the potential to threaten the sustainability and survival of the urban system as a whole. The rate and extent of technological advancements and globalization have substantially increased their likelihood, geographic scope and potential magnitudes for example the global economic recession of 2008/09, while others have longer gestation periods, like climate change, the effects of which may be no less severe and will be much longer lasting. System dynamics can be used to solve these systematic shocks in African cities and avoid after effects which in the African context often leads to more challenges and problems.

A synthesis of the case studies provided in this discourse shows the greening efforts yet by the cities and there are many determinant factors to trend. To a great extent, South African cities have successful embraced city greening. This can be attributed to the aspect of good governance after building on from the political resources accumulated after the end the apartheid government in 1994. These political resources lead to strong environmental policies which include strategies, codes and plans to monitor and improve the urban environment which have also been a factor in the success of greening in South Africa’s cities. One of the cities with the lowest green ratio, Addis Ababa has no colonial heritage and still maturing governance. Funding is an important factor determining the success or failure of city greening efforts. In a number of cities some well-known successful projects, have effectively received funding from some from external aid by donor agencies and a few from government funding. Financing has been important for the successful greening of cities. For example, Johannesburg received US$5.3 million from The Bill and Melinda Gates Foundation and US$1.2 million from Danish Development Agency; Cairo received US$350 million Clean Technology Fund (CTF) and US$5.3 million from The Bill and Melinda Gates Foundation [ 6 ].

For an initiative to be successful in Africa like elsewhere, there is first need to understand the society in which it is to be implemented and incorporate it. Good practices can be taken from Egypt where informal sector is working on facilitating the formalising process [ 5 ]. This has made it easier for policies to be implemented especially the brown agenda policies. Addis Ababa has also incorporated the poor along with the rich in dealing with greening issues and this can be credited for the success the greening agenda in the housing sector. The poor make up the majority of populations of the cities, and the quality of the urban economy and environment are shaped by people with middle to low incomes. Given the numbers and needs of the lower income population, it is imperative that they play a central role in planning and implementing urban greening as shown by the initiatives of Addis Ababa Ethiopian and Institute of Architecture, Building Construction and City Development (EiABC) of developing green building codes. Attitudes towards the environment and socio-demographic factors such as age, education determine the success of city greening. These determinants are mainly through the interaction of the households and the environments (i.e. household behaviour and water use, waste generation and recycling and willingness to pay for “environmental” goods). Strengthening people and open space interaction has been a practice that has made the greening a success story in cities of South Africa. To achieve this there are capacity building and empowerment programmes and environmental theme days (Water Week, Wetlands Day, Arbor Week, and Environment Week) [ 3 ]. It is critical that a part of any urban greening investment goes toward building local institutional capacity.

Government participation and support is important for the success of city greening initiatives. In Africa, government is largely the financing organ to major projects and programmes. Although national governments are not usually involved in the specifics of urban greening programs, they can greatly facilitate country-wide goal attainment by setting policy objectives and standards, and by providing institutional and network support to regional and local governments that do not have the funds to develop such resources on their own [ 10 ]. By also integrating urban greening into national environmental plans and performing evaluations, the central government can monitor progress made in urban greening throughout the country. Required often, is a robust legal framework. Normally, laws and regulations have to be enacted at various levels of government, and designed that they complement each other rather than have overlaps and gaps. Regarding laws and regulations, the more developed cities in South Africa have tend to be more active, while in the generality of most sub-Saharan Africa this has not be the case. South African cities have city departments, often under the direction of a city council, to directly oversee and implement policies at the urban level [ 6 ].

Missing in existing literature on African cities is a critical review of the systems approach in solvingthe issues of these all-important locales. Offered in this short paper, is a review of aspects of complexity andsystems approach in the definition of environmental parameters of development in African cities.

Urban environmental challenges in Africa are increasingly becoming the centre of focus of African governments and philanthropist. Conventional greening approaches, by themselves, will prove ineffective in addressing these challenges and will, in fact, just treat the symptoms. If cities are to be sustainable in the next fifty years, there is need for a change in the greening approach. Given the much localised characteristics of countries, cities and settlements, it is worth stressing that in the design of any intervention, a strongly situational approach is essential. Urban environments in African cities should be prepared for the expected substantial growth. For urban authorities to fully deal with the urban environmental challenges, environmental impact assessment procedures should be followed and improved cross-sectoral linkages and joint enforcement of environmental laws and standards should occur. Though this sounds complex, the use of system dynamics in trying to understand all the factors that come into play and how they affect each other will go a long way in ensuring policies fully address issues raised during assessments. Because of the dynamism of African cities, systems dynamics can help solve the African urban environmental problems through system stability. The urban challenges in Africa can be attributed to ignoring (long run) feedback which has led to unintended consequences. System dynamics will help solve this by conceptualising a more stable system whose long run effects can be assessed.

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Chirisa, I., Dirwai, S.O. & Matamanda, A. A review on the systems approach for solving the complexity of the environmental problematique of cities in Africa. Fut Cit & Env 1 , 11 (2015). https://doi.org/10.1186/s40984-015-0010-y

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Systems Science Approaches for Global Environmental Health Research: Enhancing Intervention Design and Implementation for Household Air Pollution (HAP) and Water, Sanitation, and Hygiene (WASH) Programs

Joshua rosenthal.

1 Division of Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Department of Health and Human Services, Washington, DC, USA

Raphael E. Arku

2 Department of Environmental Health Sciences, Department of Environmental Health Sciences, University of Massachusetts, Amherst, Amherst, Massachusetts, USA

Jill Baumgartner

3 Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada

4 Department of Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA

Thomas Clasen

5 Department of Environmental Health, Emory University, Atlanta, Georgia, USA

Joseph N.S. Eisenberg

6 Department of Epidemiology, University of Michigan, Ann Arbor, Michigan, USA

Peter Hovmand

7 Center for Community Health Integration, School of Medicine, Case Western Reserve University, Cleveland, Ohio, USA

Pamela Jagger

8 School for Environment and Sustainability, University of Michigan, Ann Arbor, Michigan, USA

Douglas A. Luke

9 Center for Public Health Systems Science, Brown School, Washington University in St. Louis, St. Louis, Missouri, USA

Ashlinn Quinn

Gautam n. yadama.

10 School of Social Work, Boston College, Boston, Massachusetts, USA

The authors declare they have no actual or potential competing financial interests.

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Background:

Two of the most important causes of global disease fall in the realm of environmental health: household air pollution (HAP) and poor water, sanitation, and hygiene (WASH) conditions. Interventions, such as clean cookstoves, household water treatment, and improved sanitation facilities, have great potential to yield reductions in disease burden. However, in recent trials and implementation efforts, interventions to improve HAP and WASH conditions have shown few of the desired health gains, raising fundamental questions about current approaches.

Objectives:

We describe how the failure to consider the complex systems that characterize diverse real-world conditions may doom promising new approaches prematurely. We provide examples of the application of systems approaches, including system dynamics, network analysis, and agent-based modeling, to the global environmental health priorities of HAP and WASH research and programs. Finally, we offer suggestions on how to approach systems science.

Systems science applied to environmental health can address major challenges by a ) enhancing understanding of existing system structures and behaviors that accelerate or impede aims; b ) developing understanding and agreement on a problem among stakeholders; and c ) guiding intervention and policy formulation. When employed in participatory processes that engage study populations, policy makers, and implementers, systems science helps ensure that research is responsive to local priorities and reflect real-world conditions. Systems approaches also help interpret unexpected outcomes by revealing emergent properties of the system due to interactions among variables, yielding complex behaviors and sometimes counterintuitive results.

Discussion:

Systems science offers powerful and underused tools to accelerate our ability to identify barriers and facilitators to success in environmental health interventions. This approach is especially useful in the context of implementation research because it explicitly accounts for the interaction of processes occurring at multiple scales, across social and environmental dimensions, with a particular emphasis on linkages and feedback among these processes. https://doi.org/10.1289/EHP7010

Introduction

Two environmental risk factors underlie the substantial global disease burden affecting young children and vulnerable populations around the world: household air pollution (HAP) exposure and deficiencies in water, sanitation, and hygiene (WASH). Both HAP and WASH face challenges to underlying assumptions regarding the viability of known interventions to achieve intended and sustained reductions in disease burden. Observational studies of HAP have consistently shown strong associations with adverse health conditions throughout the life course, including pneumonia, hypertension, and adverse birth outcomes ( Thakur et al. 2018 ), as well as all-cause mortality ( Hystad et al. 2019 ). Poor WASH is also associated with a range of adverse health outcomes, including enteric viral, bacterial, and protozoan pathogen infections and diarrheal disease, schistosomiasis, and soil-transmitted helminth infections ( Clasen et al. 2015 ; GBD 2016 Risk Factors Collaborators 2017 ). Technological solutions designed, investigated, and evaluated in laboratory settings, including clean cookstoves and water filters, offer promising solutions, but seldom deliver expected results when implemented in real-world settings ( Clasen and Smith 2019 ; Sesan et al. 2018 ). Recently completed randomized controlled trials (RCTs) have returned disappointing results for interventions that are already in widespread use, raising questions regarding basic assumptions of efficacy, effectiveness, potential for scale-up, and technology and policy options to address these priority environmental health concerns ( Luby et al. 2018 ; Mortimer et al. 2017 ; Null et al. 2018 ).

In the present paper we consider HAP and WASH together, despite their different exposures and etiologies, because of four important commonalities. First, the greatest disease burden for both is concentrated in the poorest populations of low- and middle-income countries (LMICs), especially in rural and peri-urban areas. Second, although both HAP and WASH are subject to important policy-based social service and infrastructure challenges, they are commonly addressed with interventions at the household and community levels (e.g., cleaner cookstoves, latrines, and water filtration devices). Third, these technology-focused interventions have both struggled to achieve the high levels of reach, adoption, and adherence necessary to reduce exposure and improve health. Finally, the emissions/effluents from one home have wider ecological impacts or spillover effects, suggesting that high levels of adoption are required in order to achieve favorable health outcomes at individual, household, and community scales ( Sesan et al. 2018 ).

Here we argue that a systems science approach offers powerful, underused tools to develop guidance for intervention design and implementation in HAP and WASH. Systems science is the application of scientific methods to the understanding of complex systems. It allows for consideration of problems and solutions across multiple dimensions, at various scales, and is dynamic in scope. A focus on systems challenges reductionist explanations that seek to explain behavior in terms of properties of constituent parts ( Bunge 1997 ; Capra and Luisi 2014 ) and considers emergent properties that cannot be understood by reducing behavior to constituent terms. Challenges to implementing and sustaining evidence-based interventions, especially in complex socio-environmental arenas, are often systemic in nature; and thus, systems science methods may be especially appropriate ( Luke et al. 2018 ). Systems science is an important complement to the randomized controlled trial approach that has dominated both HAP and WASH research in recent years. Unexpected and often disappointing trial results suggest that a different approach to studying implementation processes and outcomes is needed.

In public health, systems science methods have been applied to a wide range of issues including tobacco policy and regulation, cancer prevention, infectious disease, obesity and diabetes prevention, intentional injury, health systems, drug abuse/addiction, and others ( Luke and Stamatakis 2012 ). To date, their application to environmental health challenges is limited ( Currie et al. 2018 ).

Where HAP Programs Have Underdelivered

Since the 1950s, many efforts at scales ranging from the local to the national have sought to improve household access to clean cookstoves ( Manibog 1984 ). The vast majority of efforts prior to 2000 focused on decreasing consumption of solid fuels to reduce perceived pressure on forest resources, and subsequently, global warming contributions from the same fuels accelerated interest in the field ( Rosenthal 2015 ; K Smith et al. 2000 ). With recognition of the enormous health burden of HAP over the past decade, the household energy sector has turned its focus to promoting measures with the greatest potential for mitigating this adverse health burden. Focusing on health goals has raised expectations for clean cooking technologies and their implementation considerably, but supposedly clean cookstove interventions have not always delivered desired improvements in health. Despite air pollution reductions of 50% or more in the most successful programs, post-intervention levels of fine particulate matter ( PM ≤ 2.5 μ m in aerodynamic diameter) generally remained 2–40 times higher than the World Health Organization interim air quality guideline of 35 μ g / m 3 average annual concentration in homes with the new stoves ( Quansah et al. 2017 ).

Randomized trials with cleaner stoves or fuels have also returned mixed results ( Alexander et al. 2017 ; Mortimer et al. 2017 ; Smith et al. 2011 ; Thompson et al. 2011 ; Tielsch et al. 2016 ). Some of the shortfalls are related to inherent limitations of the technologies. A well-understood limitation of previous household energy programs was that air pollution emissions from improved cookstoves in community settings greatly exceeded what was expected based on laboratory testing ( Coffey et al. 2017 ; Eilenberg et al. 2018 ). Clean fuel interventions with liquefied petroleum gas (LPG), electric induction, ethanol, biogas, and pellet-fueled gasifier stoves have consistently outperformed traditional and improved solid-fuel stoves in both laboratory and field studies ( Champion and Grieshop 2019 ; Jagger et al. 2017 ; Wathore et al. 2017 ) and are the most likely to achieve the desired air quality and health benefits.

Beyond these technology lessons, several implementation issues have emerged, leading HAP interventions to fall short of expectations. These include low initial adoption rates for clean fuels and stoves ( Lewis and Pattanayak 2012 ; Mobarak et al. 2012 ; Troncoso and Soares da Silva 2017 ), concomitant use of polluting stoves (stove stacking) among adopters ( Masera et al. 2000 ; Puzzolo et al. 2016 ), and issues with supply chains and cost for both stoves and fuels ( Jagger and Das 2018 ; Puzzolo et al. 2019 ).

Finally, because most relevant LMIC homes are relatively open to outdoor air, ambient air quality sets the floor for household exposure levels. Evidence is growing that household cooking can be a major contributor to poor local outdoor air quality ( Butt et al. 2016 ; Chowdhury et al. 2019a ; Snider et al. 2018 ) and its associated health effects ( Conibear et al. 2018 ). Still poorly understood are the degree and conditions under which greater density and coverage of clean cooking interventions at scale can reliably reduce background ambient pollution (although see Chowdhury et al. 2019b for estimates for India).

The accumulating body of evidence from experiments, observational studies, and models over the past two decades has led to a consensus in the health science community that achieving major reductions in levels of HAP will require at least three important shifts in the way HAP programs and policies are conceived. First, the technology, including fuels, must emit little to no particulate matter ( Wathore et al. 2017 ; World Health Organization 2014 ). Second, individual households must rely on clean fuels and stoves for the overwhelming majority of their energy needs, with only very occasional combustion of polluting fuels for any purpose ( Johnson and Chiang 2015 ). Third, local ambient conditions need also be relatively clean, probably requiring high, effective coverage of clean cooking at the community and larger levels (see, e.g., Weaver et al. 2019 ).

Where WASH Programs Have Underdelivered

Through the first half of the last century, higher-income settings implemented large-scale infrastructural solutions such as sewerage, centralized water and wastewater treatment, and piped water to homes, achieving significant improvements in health ( Cutler and Miller 2005 ). In contrast, in rural LMIC settings, such investments were (and continue to be) insufficient, especially in sewerage. As such, improvements in water and sanitation were traditionally focused on expanding access to improved, but low-cost, technologies (protected wells and springs, pit latrines) that were suitable for scaling up in rural settings. Other household-scale approaches, including behavior change programs to promote handwashing and promotion of water filtration devices to provide clean water at the point of use, were steadily advanced by development agencies and local nongovernmental organizations ( Dangour et al. 2013 ; Darvesh et al. 2017 ). These approaches were believed to be cost-effective solutions because they did not require substantial expenditures on infrastructure. With limited resources, lower-income countries continued to implement these low-cost approaches to WASH, with some affirming evidence of efficacy in randomized and nonrandomized trials and systematic reviews through the early 2000s ( Esrey et al. 1985 , 1991 ).

In more recent years, a number of rigorous randomized trials of low-cost interventions in WASH yielded findings of little or no effect for most outcomes of interest, including diarrhea, linear growth, stunting, and helminth infections ( Clasen et al. 2015 ). Examples include large sanitation trials in India ( Clasen et al. 2014 ; Patil et al. 2014 ) and trials of multiple WASH and nutrition interventions in Bangladesh ( Luby et al. 2018 ), Kenya ( Null et al. 2018 ), and Zimbabwe ( Humphrey et al. 2019 ). Except for the Bangladesh trial, which reported protective effects from the sanitation and handwashing interventions, none of these trials showed that WASH interventions were protective against diarrhea or stunting. Some of these studies were effectiveness trials of programmatically delivered interventions in which investigators explored the effects of improved water supplies, household water treatment, improved sanitation, and handwashing with soap ( Clasen et al. 2014 ; Dangour et al. 2013 ; Patil et al. 2014 ; Sinharoy et al. 2017 ). In those cases, the investigators generally found poor intervention quality, coverage, uptake, or use—failures that could explain the lack of protective health effects because of incomplete interruption of exposure to human enteric pathogens.

Correct, consistent, and sustained adoption of healthy WASH practices, including handwashing with soap, was also shown to present important behavior change barriers ( Freeman et al. 2014 ; Martin et al. 2018 ). Increasingly, poor compliance was shown to be fundamentally limiting for most WASH interventions ( Brown and Clasen 2012 ). However, even in efficacy trials in which the interventions achieved high levels of compliance ( Humphrey et al. 2019 ; Luby et al. 2018 ; Null et al. 2018 ), results were disappointing.

These recent studies highlight both our limited understanding of which modes of diarrheal disease transmission are dominant and how these might vary by environmental context. Some pathways, such as food and animals, are not effectively addressed with basic WASH infrastructure. Furthermore, we are learning that many water supply systems periodically deliver unsafe water, often due to intermittent operation ( Bivins et al. 2017 ). Studies of low-cost approaches to drinking water revealed that, even if safe at the point of collection, water was often contaminated with fecal pathogens during storage in the home ( Levy et al. 2008 ; Wright et al. 2004 ). In some cases, exposures may also arise from zoonotic agents not addressed by conventional sanitation interventions ( Berendes et al. 2018 ; Daniels et al. 2016 ; Penakalapati et al. 2017 ).

WASH is considerably more mature than the HAP field and offers experience with more types of interventions. The HAP community can learn a great deal from WASH experiences ( Clasen and Smith 2019 ; Sesan et al. 2018 ). However, results from these recent WASH trials also suggest that major challenges underlie the successful implementation of environmental health interventions in low-income settings. Despite a much deeper evidence base to draw upon, WASH continues to be a major public health concern for most low-income countries.

Diagnosing the Problem

When outcome measures fall short of expectations, commentators frequently focus on the inadequacies of the technology. As described in the preceding two sections, there are numerous examples of projects that have underdelivered because of shortcomings of the technology. Many improved biomass stoves do not deliver the air pollution reductions required to greatly minimize adverse health impacts, although they do reduce fuel use and may provide some reduction in air pollution ( Jagger et al. 2017 ). Improved water supplies do not necessarily provide water that is safe at the point of collection ( Bain et al. 2014 ), in part because most household water treatment options fail to cover the full array of waterborne pathogens or fail to keep water safe after it is treated ( Shaheed et al. 2014 ). Even if water is effectively treated, the benefits of household-level water quality interventions are realized only if they are used exclusively ( Enger et al. 2013 ).

Furthermore, focusing primarily on the household level may not always be the appropriate scale of implementation and evaluation. Household–to–ambient air pollution interactions ( Huang et al. 2015 ), and herd immunity to diarrheal disease demonstrate substantial indirect effects at the community level ( Fuller and Eisenberg 2016 ). Associated nonlinear exposure–response relationships ( Burnett et al. 2014 ; Jung et al. 2017 ) mean that density and coverage of the intervention may be critical. In some cases, this becomes evident during efficacy-stage testing under relatively controlled conditions. However, it is more commonly apparent when the interventions are deployed in communities at scale.

Importantly, even effective, scaled interventions may fall short when attempted in a new environment. LPG cooking interventions may be very effective where air pollution exposure is dominated by indoor sources, but in highly polluted urban environments, they may not reduce fine particulate and other air pollutants sufficiently to reach the threshold levels thought necessary to achieve health gains ( Liu et al. 2019 ), but see Chowdhury et al. ( 2019b ) for an estimate of ambient effects of a large-scale clean fuel intervention in India. A WASH intervention that is effective in one setting may be wholly ineffective in another where there is a different dominant pathogen or transmission pathway ( Eisenberg et al. 2007 ). Relevant social variables include individual, community, or institutional behaviors. This may also reflect larger-scale systems problems related to supply chains, availability of substitutes (e.g., freely available biomass fuels), price instability, and regulatory challenges that these technologies confront in real-world settings, especially in LMICs ( Thomas 2016 ).

Finally, sustained effectiveness of most public health interventions is a major challenge. In low resource settings, program leaders often find themselves trapped in cycles of iterative attempts to maintain or improve outcomes in the original communities. Decline in adherence to proven use protocols is common without continual reinforcement of behavior change messages over time ( Brown and Clasen 2012 ) or adequate supply or servicing of new technologies required to achieve exposure reductions ( Jagger and Das 2018 ). For example, wide-scale efforts to encourage community-led total sanitation in rural low-income settings have suffered from backsliding (slippage) to open defecation ( Venkataramanan et al. 2018 ). Importantly, even if an intervention is effective initially, exogenous conditions may change over time. For example, economic factors such as price increases or supply chain failures make clean fuels ( Puzzolo et al. 2019 ) and water treatment devices ( Schmidt and Cairncross 2009 ) and their maintenance less accessible. Migration to and from communities may fundamentally affect social or environmental patterns necessary to maintain the health benefits of the intervention ( Eisenberg et al. 2006 ). Climate change brings unforeseen flooding, potentially distributing contaminants across the household and the village ( Mertens et al. 2019 ). For each of these likely developments, the intervention no longer works in the way it did under prior conditions.

RCTs have enormous power to provide internal validity (i.e., high causal inference/attribution) and insights into adoption and adherence in tightly controlled experimental settings. RCTs also have a critical role in defining the scalable unit of an intervention (e.g., a combined technology and behavior change package). However, external validity of trial results can be often limited by contextual factors that underlie success or shortfalls in programs, including environmental and social differences of the sort described in the preceding paragraphs. Given the recent history of failures of larger effectiveness and translational trials for these environmental health interventions, some may conclude that they just are not effective, or alternatively, that they were inadequately deployed.

However, the importance of both endogenous and contextual factors as critical determinants of sustainability and scalability suggests that different analytical approaches are needed to support the development of effectiveness and translational studies whether these are conducted through RCTs or other approaches.

Cycles in Environmental Health Intervention Policies

At the policy level, we often see a pattern of initial success, excitement, and hype around a potentially effective intervention, followed by real-world failures or shortfalls and subsequent loss of political and financial support to the next big thing. Even potentially important interventions are often rolled out before there is sufficient evidence to guide them, and a wave of premature enthusiasm and associated financing effectively set them up for failure ( Little et al. 2012 ). Clean cooking and water filtration interventions illustrate this pattern elegantly. Gartner’s Hype Cycle ( Fenn and Raskino 2008 ) is a widely employed conceptual tool from the technology business community to describe and predict the path of development to establishment of a new technology in industry. In Figure 1 we map the history of HAP and WASH interventions onto Gartner’s Hype Cycle to illustrate how a similar pattern has unfolded in the environmental health intervention science and policy communities.

Figure 1A is a line graph titled Gartner s Hype Cycle Household Air Pollution Clean Cooking History, plotting expectations (y-axis) across time, including technology trigger, peak of inflated expectations, trough of disillusionment, slope of enlightenment, and plateau of productivity (x-axis). The Clean Cooking History includes the following points: Stove research and development, epidemiologists raise profile of Household Air Pollution health burden, Companies and nongovernmental organizations develop improved cookstoves (I C S), Cookstove Alliance forms, Mass media hype begins, Cookstove programs proliferate, Evidence that improved cookstoves aren t clean enough, Discover that most people don t use improved cookstoves exclusively, Better understand role of ambient conditions, Critical reviews and press proliferate, Less than 5 percent of the potential audience has adopted fully, World Health Organization issues Indoor Air Quality Guidelines, Exploring cleaner technologies and more sophisticated approaches, Methodologies and best practices developing, Third-generation products and interventions, and High-growth adoption phase begins: 20 to 30 percent of the potential audience has adopted the innovation. Figure 1B is a line graph titled Gartner s Hype Cycle Water, Sanitation, and Hygiene Water Filtration History, plotting expectations (y-axis) across time, including technology trigger, peak of inflated expectations, trough of disillusionment, slope of enlightenment, and plateau of productivity (x-axis). The Water Filtration History includes the following points: Evidence of water quality improvements in reducing diarrheal disease, Successful use of Household water treatment and safe storage in controlling cholera outbreaks, Early trials and systematic reviews suggesting dramatic reductions of diarrheal disease, Rapid development of new technologies and approaches by public and private sectors, World Health Organization establishes performance guidelines for Household water treatment and safe storage, Large-scale rollout of Household water treatment and safe storage at scale in multiple countries, Evidence of poor adherence to water quality interventions, Criticism of unblinded trials with subjective outcome measures, Studies show large declines in use over time, Reduced donor investments in Household water treatment and safe storage strategy, Refinement of Household water treatment and safe storage roles in specific settings, including in disaster response, Release of World Health Organization testing data, Focus on user behaviors and the technology/user interface, Continued iteration on technology design and delivery, Re-framing use from  silver bullet  to one strategy in Water, Sanitation, and Hygiene tool box, and Research on sustainability and multi-household approaches that reduce need for behavior change.

(A) Household air pollution (HAP)—clean cookstove history mapped onto Gartner’s Hype Cycle (adapted from Fenn and Raskino 2008 ). (B) Water, sanitation, and hygiene (WASH)—point-of-use water treatment history mapped onto Gartner’s Hype Cycle (adapted from Fenn and Raskino 2008 ). Note: HWTS, household water treatment and safe storage; NGO, nongovernmental organization; R&D, research and development; WHO, World Health Organization.

The clean cooking movement received new impetus and rapid increases in investment beginning in 2010 when the HAP disease burden was recognized, and improved cookstoves were perceived by the policy community to offer win-win-win solutions for health, climate, and women’s empowerment ( Bhattacharyya and Light 2010 ). As the scientific and development communities gradually uncovered significant shortcomings in cookstove programs enthusiasm began to wane and international funding for this work declined precipitously ( Figure 1A ). Today, some believe that we may have lost important momentum because of these incautious efforts ( Ezzati and Baumgartner 2017 ). Ironically, this has occurred as the field is maturing scientifically and greater understanding of what benefits are achievable as well as a greatly improved understanding of the socioeconomic and environmental conditions for successful and sustainable interventions are accruing rapidly.

Parallels in WASH include household water filtration technology ( Figure 1B ) and community-led total sanitation (CLTS), both potentially transformative interventions initially supported by substantial research and programmatic efforts, only to have rigorous trial results reveal limitations to the approaches ( Brown et al. 2019 ). Enthusiasm for household-level chlorination has been tempered by challenges in supply chains, low adoption, and new evidence on the prevalence of chlorine-resistant diarrheagenic agents such as Cryptosporidium ( Kotloff et al. 2019 ). The fact that interventions may not live up to the initial hype should not mask the real benefits such solutions can deliver: for example, household water treatment technologies have shortcomings, but systematic reviews of the evidence base reveal the approach’s potential as an interim solution that can improve water quality and reduce diarrheal disease in vulnerable populations ( Clasen et al. 2015 ).

Whether intervention shortfalls are due to efficacy, effectiveness, implementation challenges, or changes to underlying enabling conditions, failures may result in enormous social and financial costs. Before the Clean Cooking Alliance focused their efforts toward clean fuels, approximately 40 million homes received improved stoves (ISO tiers 1–2) ( Global Alliance for Clean Cookstoves 2017 ) for which there is little to no evidence of health benefits (although some reductions in greenhouse gas emissions, quantity of fuel used, and time spent collecting fuels are possible). Worse are projects that have led to large negative unintended consequences, usually due to incomplete knowledge about the context. A tragic example is the arsenic poisoning epidemic in Bangladesh that resulted from a decades-long program to reduce diarrheal disease by installing tube wells into naturally contaminated soil strata ( AH Smith et al. 2000 ). A consequence is that today populations in Bangladesh are still grappling with diarrheal disease from exposure to contaminated surface water, simultaneous with poisoning from exposure to arsenic from tube wells ( Yunus et al. 2016 ) and social conflict regarding access to safe water ( Sultana 2011 ).

What Implementation Science Offers

Implementation science (IS) is a relatively new field that provides a set of theoretical, analytical, and experimental methods to understand the processes that make interventions successful and sustainable in service delivery programs at scale and over time ( Colditz and Emmons 2019 ; Glasgow et al. 2004 ; Madon et al. 2007 ; Rosenthal et al. 2017 ; Yamey 2011 ). The field draws on conceptual frameworks that focus analysis on those types of organizational and community processes that are required for broad and sustained uptake of new evidence-based programs and policies ( Damschroder et al. 2009 ; Tabak et al. 2012 ). Increasingly, implementation science frameworks are linked to RCTs that aim to evaluate hypotheses regarding the importance of specific approaches to, for example, individual or household behavior change, or adoption of an intervention by institutions in a given setting ( Curran et al. 2012 ).

The ability to understand the aspects of programs that enable successful transfer from one setting to another is a key component of successful implementation at scale. Because multisite RCTs are expensive, programs are sometimes launched based on efficacy studies or limited range effectiveness trials ( Madon et al. 2007 ) without examining implementation variables such as context-specific adoption or maintenance needs that may be critical to the success of the intervention. The implicit assumption in these cases is that future projects can invest in adapting effective interventions to new conditions elsewhere in the world. Implementation research uncovers factors that may be influential to success by offering experimental and analytical frameworks to systematically consider and evaluate individual and institutional behaviors and environmental variables that are often obscured or assumed to be constant ( Glasgow et al. 1999 ; Rosenthal et al. 2017 ).

Systems Science Expands Implementation Science Approaches

Systems science offers powerful, underused tools to develop lessons for intervention design and implementation in both HAP and WASH. Broadly speaking, systems science is the application of scientific methods to the understanding of complex systems ( Galea et al. 2010 ). Systems science has been embraced by some in the implementation science community because of its utility to describe, analyze, and simulate complex systems that defy traditional methods ( Burke et al. 2015 ; Hammond and Dubé 2012 ). Importantly, systems science does not make an a priori commitment that all phenomena are best understood in terms of systems. Rather, it provides tools for consideration of phenomena that may not be amenable to analyses focusing on decomposed system elements and their properties. Challenges to implementing and sustaining evidence-based interventions, are often systemic in nature and, thus, systems science methods may offer much promise ( Luke et al. 2018 ).

System science methods include a variety of tools that generally fall under the following categories: network analysis (NA), system dynamics (SD), agent-based modeling (ABM), and in some cases Geographic Information Systems (GIS). Some of these methods are more descriptive and oriented toward statistical analysis and visualization of data (e.g., network analysis, geographic information systems), whereas others are more focused on theory development and computational modeling (e.g., system dynamics, agent-based modeling) ( Table 1 ). The use of GIS is already well developed in the environmental health community (see, e.g., Nuckols et al. 2004 ; Peng et al. 2018 ). Here, we focus primarily on opportunities for advancing HAP and WASH with three modeling approaches, network analysis, system dynamics, and agent-based modeling. In Table 1 and the following paragraphs we provide a brief description of each of these methods and references for more detailed information.

Three systems science tools.

Network analysis focuses on the relationships among sets of actors. The actors can be any type of entity that can have a relationship or tie with others: point sources, persons, animals, organizations, countries, websites, documents, and even genes. These networks provide information on social structure that can play an important role in either promoting or mitigating disease processes. Almost all NA makes use of one or more of three different analytic modes: network visualization, network description, and statistical modeling of networks.

System dynamics (SD) is based on the idea that system behavior (e.g., the frequency of exposures over continuous time) results from the interplay of a set of feedback mechanisms or loops relating accumulations and their corresponding rates of change or flows. Models of the feedback system can be described informally using a series of causal diagrams or more formally as a system of nonlinear ordinary differential equations that can be simulated on a computer. System behavior is then explained in terms of an explicit set of balancing and reinforcing feedback loops, whether that is at an aggregate, individual, or multilevel system ( Richardson 2020 ; Sterman 2018 ). The visual conventions of SD (e.g., casual loop diagrams, stock and flow diagrams) have evolved into a set of participatory methods for involving communities and other stakeholders in the process of conceptualizing, formulating, and analyzing the results of models called group model building (e.g., Hovmand 2014 ; Richardson and Andersen 1995 ; Vennix 1996 ).

Agent-based modeling (ABM) uses computer simulation to study complex systems from the ground up, by analyzing how individual elements of a system (agents) behave as a function of individual properties, their environment, and their interactions with each other. Through these behaviors, emergent properties of the overall system are revealed. Compared with SD, this results in a form of decentralized modeling where there is no formalized definition of global system behavior; that is, feedback mechanisms are implicit (vs. explicit in SD) and emerge through the interaction of agents within their environment.

Transparency and replicability remain cornerstones of science, and systems science tools are no exception ( Barton et al. 2020 ). In principle, computational models used in systems science (e.g., system dynamics and agent-based modeling) offer an added level of transparency relative to more traditional statistical approaches by making assumptions fully explicit (e.g., as a set of differential equations or programming code) that can be independently explored and modified to test the implications of assumptions, measurement errors, research designs, and so on through sensitivity analysis. This has the advantage over traditional statistical approaches in that one can explore the robustness of a policy to system states beyond what has been historically observed or collected as part of an experiment—something that is highly relevant when we consider structural changes in environmental health triggered by global trends such as climate change, pandemics, and forced displacement of populations due to conflict and environmental disasters. However, this potential to be more transparent is often lost when computational models become overly complicated, lack adequate documentation, or require computational resources with limited access ( Meadows and Robinson 1985 ; Pilkey and Pilkey-Jarvis 2007 ). A number of standards have, therefore, emerged for reporting guidelines (e.g., Caro et al. 2012 ; Rahmandad and Sterman 2012 ).

Although applications of systems approaches are growing in other health implementation arenas, to date, systems applications in environmental health have been limited. Eisenberg et al. ( 2012 ) reviewed the history of diarrheal disease research and identified how systems approaches would be helpful in understanding interdependencies across multiple enteric disease transmission pathways. Zelner et al. ( 2012 ) used network analysis to understand connectedness between and within communities in relation to reported diarrheal events and found that high connectedness between communities enhances risk of disease, as expected. However, high connectedness within a community reduces risk of diarrheal disease. More recently, scientists applied systems approaches to understand user behavior in relation to interventions. For example, Kumar et al. ( 2017 ) have examined behavioral dynamics at the community scale to understand the process of adopting of LPG cooking, and Chalise et al. ( 2018 ) analyzed abandonment of biogas digesters and their stoves.

Systems science methods applied to environmental health can be useful for:

  • Enhancing understanding of existing system structures and behaviors—and the types of feedback mechanisms causing those behaviors—that can accelerate or impede environmental health aims, including potential unintended consequences.
  • Developing understanding and agreement on a problem among stakeholders, which is particularly important for WASH and HAP because of their dependence on numerous stakeholders at the household, community, and policy maker levels. Group model building, for example, can help bring the diversity of choices and associated tradeoffs among relevant stakeholders to the surface, and can facilitate decision-making.
  • Guiding intervention and policy formulation, where simulations can be particularly useful to estimate the effects of interventions before attempting them at scale in populations, potentially sidestepping some of the significant economic and social costs of testing the interventions in situ . For example, Mellor et al. ( 2014 ) simulated a water filter intervention to predict sustainability of effectiveness in reducing diarrheal incidence.

If systems approaches are developed in an iterative participatory process that engages study populations, policy makers, and implementers, they help ensure that research is responsive to local priorities and reflects real-world conditions, making research a more co-creative and less an extractive process ( Dilling and Lemos 2011 ; Eisenberg et al. 2012 ; Israel et al. 2005 ; Mauser et al. 2013 ).

When a model fails to generate the expected outcomes, this can be due to faulty data or model structure. Faulty data are due to measurement error, either systematic or random. Faulty model structure is due to building a model incorrectly (discovered through verification testing) or building a model of a theory correctly, but based on a theory that is faulty (discovered through validation testing). Weak measurement models, verification testing, or validation testing can contribute to a false confidence in a model of a system. A strong program of validating measurements and verifying that a model has been built correctly can, however, falsify a theory. In doing so, a model can typically be advanced that can offer a stronger alternative explanation for emergent behavior of a system. In either case (whether discovering and correcting measurement errors and specification errors or advancing a stronger alternative explanation), the process is consistent with a progressive program of scientific research ( Lakatos 1970 ).

Some Suggested Applications of Systems Science to HAP and WASH Problems

An important goal for the application of systems science in the fields of HAP and WASH is to accelerate learning and flatten the Hype Cycle ( Figure 2 ) to achieve steadier gains in health outcomes. This might be characterized as accelerating the pace of innovation, partly through failing fast or quickly identifying strategies that may not result in changes in exposure. Using systems approaches in the early design and planning stages of a WASH or HAP interventional program can help scientists and policy-makers think more clearly about the larger socioeconomic and environmental context in which disease occurs and about how innovations may work at the development, intervention, program, and sustainment stages over time. In this way, we would hope to head off many pitfalls, reduce the temporal and financial costs of failed trials and interventions, and achieve public health impacts more reliably.

Figure 2 is a line graph titled Aspirational Cycle Research and Development to scaled effective interventions, plotting expectations (y-axis) across time, including technology trigger, peak of inflated expectations, trough of disillusionment, slope of enlightenment, and plateau of productivity (x-axis). Line is a smoother curve than that of Fig. 1.

Aspirational (flattened hype) cycle that accelerates research, development, and scale-up successes of effective environmental health interventions employing systems science methods in the context of implementation science (hypothetical curve). Note: R&D, research and development.

Below are examples of topics in which systems methods might be applied productively to complex questions in WASH and HAP.

  • Behavioral adherence requirements : Behavioral data—such as from stove use and water filter use monitors—is increasingly integrated into intervention deployment, behavior monitoring, and exposure assessment efforts. These rich, time-series data could be combined with network analysis and agent-based models ( Ginexi et al. 2014 ) to help predict the effects of a program roll out on exposure for different levels of adherence to a HAP or WASH intervention. In this way, investigators could examine their assumptions and identify potential challenges prior to large-scale and expensive exposure and health assessments. Several investigators have done work in this area using quantitative microbial risk assessment ( Hayashi et al. 2019 ).
  • Community engagement and design : Community engagement is widely understood to be important for acceptability and sustainability of an intervention ( Israel et al. 2005 ). It is especially important when complex individual behaviors have either social or exposure spillover effects on other households. Incorporating community-based system dynamics ( Hovmand 2014 ) or another form of group model building in designing a community-based WASH intervention can be extremely useful to identify critical behaviors and develop a consensus understanding of how the sanitation and hygiene practices of one group affects others. An intervention that emerges from this process is much more likely to be sustained and enforced by the community members individually not only because of acceptability borne of community participation but also because it is more likely to have correctly identified local facilitators and barriers to implementation ( Powell et al. 2017 ).
  • Predicting effectiveness : Sanitation interventions aim to interrupt transmission of specific enteric infections, but threshold effects reflecting herd immunity can be important mediators of benefits ( Fuller and Eisenberg 2016 ). An analog in HAP interventions would be the question of thresholds for coverage in community-scale programs that reduce ambient pollution sufficiently to yield health benefits for the population. In the sanitation–infection domain, these thresholds have been explored with transmission models in a variety of contexts including drinking water. Population-scale models can be developed using system dynamics tools that force investigators to specify their mechanistic assumptions and to investigate how changing these assumptions around exposures, intervention coverage, and adherence might impact disease incidence. These can be also be linked to networks to evaluate how different features of community structure affect outcomes.
  • Dissemination/diffusion of interventions : Behaviors, including the adoption of new HAP and WASH technologies, often diffuse through communities through social connections and word of mouth ( Rogers 2003 ; Valente et al. 2015 ). Social network analysis helps elucidate the underlying structure of social ties and could be useful in determining the patterns of information transfer in a community that will influence the adoption and use of an environmental health technology. Understanding the underlying social network structure, moreover, could shed light on potential interventions that could act through social networks to influence adoption and use. For example, social ties could be used to leverage group incentives for sustained use of LPG cooking (where each member of a group receives the incentive only if all members demonstrate the desired behavior) or to identify well-connected influencers who might be good targets for marketing and outreach efforts.
  • Emergent patterns from individual variation : Fuel and stove stacking is ubiquitous in clean cooking programs, and yet we have a limited understanding of the choices people will make when multiple clean and traditional cooking options are available. Moreover, population-scale exposure results from hundreds of thousands of individual choices. Individual (agent-based) models based on survey data of the diverse choices people have made and their socioeconomic and environmental covariates may offer opportunities to explore what will happen at scale in relation to diverse influences, as well as the relative strength of these influences on outcomes.
  • Scale-up, bottlenecks, and delays : Advanced gasifier stoves fueled by compressed wood pellets show promise as a clean cooking technology for both health- and climate-related objectives ( Champion and Grieshop 2019 ). These stove/fuel combinations are being explored in development programs and commercial projects, but the technology faces significant challenges in scale-up and maintenance over time because of feedstock supply, pellet production, distribution, and costs to end users ( Jagger and Das 2018 ). If enough demand is generated, stress to multiple points in the supply chain is likely to emerge and this could in turn dampen demand or create a new problem with unlawful harvesting of trees for feedstock. System dynamics models are particularly well suited to this type of problem because they allow us to model complex conditions and time delays across a wide variety of factors, including policy, economic, social, and physical variables and then to see how these play out over time in a given setting through simulation.
  • Sustaining interventions : System science approaches are essential to understand and solve implementation problems that result from feedback between technologies, social and cultural norms, and livelihood options that are nonlinear and dynamic. Chalise et al. ( 2018 ) identified such feedback mechanisms in sustained use of improved biogas stoves in two communities; one community that sustained high levels of exclusive biogas use and an adjacent community that largely abandoned the technology. Using qualitative group model building and subsequent system dynamics simulation they traced the community interactions that led to solutions for technical and maintenance problems with fuel digesters and thus increased use of the cleaner technology in the one community, compared with frustration and abandonment by the other. The group model building and simulation highlight multiple household, technical, and social factors that are interlinked in a feedback structure and cannot be fully understood in isolation.
  • Adaptability to environmental change : Behaviors and associated technologies that may function well in avoiding or mitigating environmental health challenges today may change quickly or lag in their ability to adapt to future changes. Climate change raises a host of challenges for WASH programs, in particular, and many of these are influenced by social context. Cherng et al. ( 2019 ) illustrated the use of social networks in Ecuadorian communities to measure social cohesion across safe water sourcing practices, and agent-based models to understand how these structured communities will be able to adapt under changing flooding and drought conditions.

How to Approach Systems Modeling Productively in Environmental Health

Environmental health scientists are increasingly turning from assessing risks to designing and testing interventions. Research outcomes can catalyze programs and policies that operate at large spatial scales, and as in HAP and WASH, with great dependence on individual and community behaviors, it behooves us to approach interventions with an appreciation of the complexity of these systems. Environmental health research provides rigorous methodologies and a wide variety of large data sets from both interventional and observational studies on population-based exposures, behaviors, and correlated health outcomes. Combining rich data sources with systems modeling may be especially productive to explore the sensitivity of hoped-for outcomes to assumptions regarding implementation variables such as household, community, or institutional adoption and adherence. This may also help us consider potential effects of adapting effective interventions to locally important contextual variables such as other exposure sources, cost constraints, policy, and infrastructure influences.

As in any interdisciplinary team, depth in both analytical methods and the environmental health challenge is necessary for success. System scientists come from diverse backgrounds, including sociology, ecology, physics, operations research, management, engineering, public health, and computer science, to name a few. Spending time at the outset of a collaboration between environmental health, systems scientists, and where relevant, policy experts, is critical to familiarizing one another with basic concepts and terminology in their respective fields and to agreeing on the aims, value, and limitations of various approaches.

For the systems science community, it is important to recognize that most environmental health scientists are trained to undertake population-based risk assessments using inferential statistics. Systems approaches often include expert opinion, key informant inputs, or other soft evidence in addition to experimental results to parameterize a working model. Furthermore, systems modeling is often conducted with significant data gaps [indeed, this can be a primary reason for undertaking such modeling, see Wallace et al. ( 2015 )]. Significant discussions at the outset may be necessary to ensure that environmental health scientists understand and are comfortable with the approach.

For environmental health scientists, it may be useful to approach systems modeling as a means of exploring and testing their own assumptions. We recommend, where possible, an iterative process including participatory or co-creative activities to frame questions, conceptualizing an initial model, collecting data to parameterize it, evaluating model output in relation to observations, improving the model, and then testing interventions in silico before beginning larger population-based studies. Such approaches can allow for evaluation of the plausibility of health effects via changes in exposure at levels that may be health-relevant—before undertaking large trials using distal outcomes. Engagement with social, behavioral, and political scientists in the conceptualization, parameterization, and analysis of systems models can be important to avoid the omission of critical sociodemographic, economic, institutional, and macro-level processes that shape implementation. Participatory methods, such as group model building, may be particularly useful, especially with community-based research activities.

There are a variety of ways to build an interdisciplinary team that include systems scientists within the constraints of project budgets. At the most basic level, integrating systems modeling into environmental health research can be done by bringing a single experienced systems modeler into a project at the design stage and continuing through analyses and interpretation of results. At this level, the additional expertise may not be a large cost burden for a project, and it could conceivably fit within, for example, a National Institutes of Health R21 exploratory grant designed to assess basic feasibility of an intervention. Of course, modelers may raise questions that require new data, including, for example, relationships between community members, expectations of policy-makers, or economic influences. Modelers may also suggest participatory processes, surveys, and focus group exercises that the environmental health researchers did not anticipate. These in turn may require engagement of more disciplinary expertise or significant computational coding time and, thus, further expansion of the team to the R01 or larger level. However, one of the strengths of modeling generally is the ability to work within the constraints posed by the available data by drawing on a variety of existing or easily acquired sources to parameterize factors that may be especially challenging to estimate from what can be collected within a given project. The key here is understanding which factors require greater certainty for a given question and end-use plan, and which factors may allow for greater uncertainty.

Summary and Conclusion

Systems science approaches offer important and underexploited tools for environmental health, especially in complex environments that change over time. We work in a time of rapid innovation and with a public health policy community that increasingly seeks evidence to support decision-making. Both observational and experimental studies in environmental health generate large data sets that can be used in both a priori and post hoc systems modeling. These data have often been gathered using extended, multiyear efforts, often with high collection costs. Increased use of systems modeling in implementation research for environmental health may offer cost-effective approaches with both heuristic value and practical output to improve design and deployment of interventions to improve public health. HAP and WASH science are particularly suitable to systems modeling given the complexity of the socioeconomic and environmental contexts that often regulate their effectiveness, implementability, and scale-up potential. Many other environmental health interventions may also benefit from their use. Increased collaboration among systems and environmental health scientists has the potential to accelerate lessons and flatten the Hype Cycle that slows and sometimes dooms promising interventions for public health before we understand their potential.

Acknowledgments

Financial support from the NIH Common Fund’s Global Health program for the Clean Cooking Implementation Science Network and from the Boston College School of Social Work enabled the 2018 workshop where these ideas were initially developed. The findings and conclusions in this report are those of the authors and do not necessarily represent the official views of the U.S. National Institutes of Health or Department of Health and Human Services.

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  • State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, China

Introduction

The world is facing multiple environmental challenges simultaneously, such as environmental pollution, climate change, energy security, resource availability, ecosystem degradation, and biodiversity loss. Tackling these challenges has become increasingly complex due to the growing cross-temporal, cross-spatial, and cross-system human-environment interactions ( Liu et al., 2015 ; Wang et al., 2019 ). Environmental Systems Engineering (ESE) applies systematic thinking and methods to simulate and manage the complex human-environment interactions, aiming to seek effective solutions for sustainable development. ESE research has made significant progress in the past few decades, including: 1) construction of comprehensive analysis framework; 2) identification of key drivers and boundaries of environmental systems change; 3) investigation of influencing mechanisms of human-nature interactions; 4) development of mathematical simulation and modeling methods; 5) integrated utilization of data from different sources; and 6) expansion of environmental systems modeling across spatial, temporal and organizational scales.

Despite the remarkable achievements, the ESE field still needs to be overcome several roadblocks. This brief paper focuses on three of them: 1) The integration of multiple temporal and spatial scales; 2) the cross-disciplinary interactions and influence; and 3) the deep uncertainty in the non-linear dynamics of society-economy-environment systems. For each challenge, the paper examines the grand challenges and obstacles and presents priority areas for future research. The purpose of the paper is to call for more interest and endeavors in ESE, and facilitate significant progress and new breakthroughs towards sustainable development.

Addressing Multiple Scales Over Time and Space

Complexities in human-nature interactions exist across multiple spatial and temporal scales. From the spatial perspective, “telecoupling” has been recognized as one of the research hotpots in ESE ( Lenschow et al., 2016 ). The term refers to the fact that environmental challenges are not limited to local or regional boundaries, but are global issues. On the one hand, environmental pollution can disseminate far away from its sources, driven by natural environmental vectors and the global flow of people, goods, information, energy, and materials. For instance, it is found that plastic trash has spread to remote or even inaccessible ocean islands, with the transport of ocean currents ( Cózar et al., 2017 ). Another typical case is that tropical rainforest deforestation not only harms health and employment of local residents, but also exacerbates global climate change and related risks ( Vargas Zeppetello et al., 2020 ). On the other hand, the globalization of human activities has led to situations where the production, consumption or the policies made in one place may enhance or harm the sustainability of other places. To reveal such spatial externalization effects of production and consumption, the environmental footprint family was developed ( Wiedmann and Lenzen, 2018 ; Zheng et al., 2018 ). Such telecoupling drives ESE research to expand from regional scale considerations to those also at the global scale.

Human-nature interactions are complex over temporal scales. Due to the limitations of human cognition, many environmental problems with lagging effects are hard to detect in the short term ( Milfont and Demarque, 2015 ). Decisions made today may have long-term, irreversible consequences in the future. A typical example is the carbon lock-in effect caused by existing and planned energy infrastructure, which committed carbon emissions in the next few decades ( Seto et al., 2016 ). Identifying and informing the long-term and unintended consequences of such decisions is essential to avoid locking the system on an unsustainable path.

Incorporating the spatial externalization and temporal lagging effect of environmental issues into the analysis framework with higher resolution and across larger spatial-temporal scales, is thus an inevitable goal in the future of ESE. Two fundamental improvements are needed to achieve this. The first is to develop integrated observation systems (such as the Global Earth Observation System) and data sets combining both nature and human elements, which provide data for analysis at various spatial-temporal scales. The second is to develop multi-scale analysis framework and modeling methods; for instance, an integrated framework that couples macro policy analysis with micro behavior simulation ( Elsawah et al., 2020 ). On the way to constructing such a framework, an advanced methodology, including up-scaling, and down-scaling methods to resolve mismatching problems among different sub-systems, must also be further developed.

Bridging Frameworks and Methods Across Disciplines

The multi-disciplinary nature of environmental issues is a fundamental grand challenge in ESE research. On the one hand, problem solving in environmental systems relies on the investigation of driving factors and policy interventions in human systems, requiring an integration of natural science and social science. Examples are: The factors that drive human demands for goods and services being at the center of greenhouse gas emissions and pollution control ( Baiocchi et al., 2010 ); and cultural barriers and consumer awareness that can explain the gaps between the stated objective of policies and their realistic implementation effectiveness ( Kirchherr et al., 2018 ). On the other hand, the multi-disciplinary connections mean that dealing with one environmental issue may cause associate effects on other systems. For example, the large-scale use of biofuels may cause cross-system impacts on land use, water scarcity, food supply and human health ( Weng et al., 2019 ; Li et al., 2020 ). To address complex interactions across systems, a comprehensive assessment with multi-dimensional mindset, as well as an in-depth cooperation among discrete subjects (like earth system science, life science, ecology, and sociology) is in need ( Wang et al., 2020 ).

Though inspiring progress on facilitating cross-disciplinary communication has been made, strictly speaking, researchers are still trapped in their own discipline, often lacking trust and open minds for how other disciplines may impact their own. Further integration of knowledge, methods and ideas from different subjects are necessary at three levels. The first is the integration of professional knowledge, theories and methods that straddle disciplines. This level of integration is relatively easy to achieve, thus it becomes the main contribution area of interdisciplinary research in ESE. For example, the framework of ecosystem services has been used to define ecological values from the economic perspective as a way to emphasize society’s dependence on natural ecosystems ( Braat and de Groot, 2012 ). The term “resilience” is used in various contexts, with similar meaning closely related with the capability to return to a stable state from disruption ( Bhamra et al., 2011 ). In addition, quantitative models from different disciplines are soft-linked to solve more comprehensive problems ( Krook-Riekkola et al., 2017 ). Despite the progress, we need to enhance model transparency and promote data sharing across and between disciplines. The second level of integration is to incorporate the interaction and feedback dynamics of different systems into the analysis framework, such as considering human adaptive behavior when assessing the impact of environmental pollution and natural disasters ( Di Baldassarre et al., 2015 ), and considering cultural contexts and social norms when designing policy tools to tackle with sustainability problems ( Zheng et al., 2021 ). This level of integration is more challenging due to the complexity and uncertainty of human activities and multiple interacting influencing factors. The third level is the recognition and appreciation of diverse ideologies in different subjects. Researchers in different fields often lack trust in the rationality of research ideas and methods from other disciplines, which hinders the bridge between various academic islands ( Mooney et al., 2013 ). For example, natural science research focuses on quantitative analysis and usually overlooks the value of qualitative assessment that is often used in social science research. Acknowledging the diversity of knowledge itself is both rather challenging yet critical to solving major environmental problems.

Managing Deep Uncertainties From Systematic Changes

Interactive environmental and socioeconomic systems are each undergoing interrelated dynamical changes. According to theory in system dynamics, quantitative models can be used to capture key processes in environmental system dynamics and their interactions with socioeconomic systems, aiming to predict future trends based on the changing patterns in the past. However, most changes in reality are non-linear, especially when the system undergoes a sudden, substantial and irreversible transition. This non-linear change causes large uncertainty in extrapolation and projection. There are numerous examples of systematic nonlinear changes in the environmental field, such as ecological collapse caused by lake eutrophication, desertification, and ocean acidification ( Moore, 2018 ). To learn more about such systematic changes, existing research has focused on identifying risk thresholds, for example, in the quantification of planetary boundaries ( Rockström et al., 2009 ). Additionally, socio-economic systems also experience systemic changes brought by unexpected incidents such as regime changes, institutional changes, the introduction of disruptive technologies, and financial crises etc. Compared with natural systems, systematic changes in social economics are often more frequent and more difficult to predict because of unknown events and “black swan” events like the COVID-19 epidemic.

Traditional uncertainty analysis in ESE modeling focuses on three aspects: Data quality, appropriate model structure and confidence in model parameters. Such analysis fails to address the endogenous problems caused by the nonlinear dynamics, however ( Lempert, 2002 ). In order to enable analysis on deep uncertainty and better support policy decisions, the following potential research directions are recommended in future ESE research:

• acquiring larger-scale and higher-resolution socioeconomic and natural system observation data (e.g., relying on new data types and sources from social media, mobile phones, satellite remote sensing, and sensors, etc.);

• investigating the driving factors and influencing mechanisms of nonlinear changes in the system;

• developing exploratory modeling and analysis ( Kwakkel and Pruyt, 2013 ) based on richer future scenario settings and incorporating the simulation of adaptive feedback dynamics;

• and enhancing the understanding of modelers and stakeholders on various uncertainty sources ( Mielke et al., 2017 ), and develop participatory modeling ( Jones et al., 2009 ) to extend uncertainty management from model simulation to management practice.

The complement of such work requires wider international cooperation on mutual use of information systems and data, more financial support for knowledge exchange, and active policy involvement in scientific research. Although challenging, the work is meaningful as it would deliver enhanced modeling ability and deeper research-policy integration in ESE.

Concluding Remarks

This short paper addresses three major challenges and the corresponding priority research areas in ESE: The complexity of human-nature system across various spatial-temporal scales; the integration of multiple disciplines; and the deep uncertainties resulting from nonlinear dynamics. It is worth mentioning that these areas of future research are not isolated from each other. In most cases, they need to be addressed simultaneously. For example, interdisciplinary research across multiple spatial-temporal scales often has the problem of scaling mismatch among subsystems, and the spatial/temporal scale selection will significantly affect the uncertainty of the model. A growing community needs to be better connected and governed if it is to deliver more progress and breakthroughs in the ESE field by addressing these challenges.

Author Contributions

The author confirms being the sole contributor of this work and has approved it for publication.

Conflict of Interest

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: global environmental issues, system engineering, uncertainty, multi-disciplinary, multi-disciplinary, cross-disciplinary

Citation: Wang C (2021) Grand Challenges in Environmental Systems Engineering. Front. Environ. Sci. 9:809627. doi: 10.3389/fenvs.2021.809627

Received: 05 November 2021; Accepted: 18 November 2021; Published: 06 December 2021.

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*Correspondence: Can Wang, [email protected]

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Home > Books > Sustainable Development - Authoritative and Leading Edge Content for Environmental Management

GIS for Environmental Problem Solving

Submitted: 14 December 2011 Published: 01 August 2012

DOI: 10.5772/50098

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Koushen douglas loh.

  • Department of Ecosystem Science and Management, Texas A&M University, USA

Sasathorn Tapaneeyakul

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1. Introduction

The authors are affiliated with the Laboratory of Systems Technology Applications in Renewable Resources (The STARR LAB) at Texas A&M University. The purpose of this chapter is to provide a synopsis of the cumulative research and teaching work for the past twenty years from the STARR LAB. The aim of chapter is to demonstrate holistic understandings of what key environmental issues and problems people are facing and how their concerns may be addressed with the help of geographic information systems (GIS).

We are the environments, and the environments are us. There are many environmental issues and problems the society is facing. Some major categories include environmental disasters, ecological services, and perceptions of environments by people, just to name a few. In terms of environmental disasters, hurricanes, earthquake and wildfires are some examples that exert enormous direct impacts on people’s lives. Their increasing recurrences have elevated public awareness on the vulnerability and risks of the environments we live in. An awareness of environmental issues leads to an increase in people’s perceptions regarding the surrounding environments. There are many factors contributing to such perceptions. Combined considerations of pertinent factors result in an overall perception. One plausible combined index is called quality of life (QOL). QOL is a practical measurement of the state of an environment. Environmental awareness also raises people’s concerns on the sustainability of the ecological services. Ecological services refer to public goods, tangible or intangible, rendered to us by environments and ecosystems. Air, water, food, fiber, and fuel we consume are good examples. Sustaining these services is of great importance to all environmental stakeholders.

There are many ways to help stakeholders gain insights to environmental issues and problems. One handy approach is the use of GIS. GIS are systems of hardware, software, data, people, organizations and institutional arrangements for collecting, storing, analyzing and disseminating information about areas of the Earth [ 1 ]. Such technologies enable analyses of spatial-temporal patterns for a geographic span of interest and generations of easy-to-comprehend reports such as maps and images. GIS are maturing and proliferating rapidly in parallel to the quantum leap of personal computer (PC) platforms. It greatly enhances people’s ability to know about their environments. Given the advantages, GIS have emerged as a popular subject matter among interested learners on college campuses as well as in environmental fields. A good indicator of this assertion is the sustaining popularity of Environmental GIS courses the authors teach at Texas A&M University. Other institutions are reporting a similar phenomenon.

All things considered, it is timely to provide a rundown of GIS for Environmental Problem-Solving as a chapter of this book. Main thrusts of our presentation consist of four parts. They are: 1) Introduction (this section); 2) Research method; 3) Illustrations of GIS for environmental problem solving applications; and 4) Concluding remarks.

2. Research method

Systems approach is a key research method to incorporate GIS into problem-solving process in addressing environmental issues and problems. The essence of this approach is to envision and to enact relevant endeavors into a cohesive sequence of steps. The whole process is called developing and implementing a GIS project. A typical sequence of steps in a GIS project includes framing the problem, defining a project area, identifying and acquiring data, extracting and preparing data, editing spatial data, geospatial analysis, and generating maps and reports.

2.1. Framing the problem

The first step in solving any problem is to frame the problem. The purpose of this step is to help narrow down the scope and identify the problem to make it easier to solve. This helps address the questions you want to answer. Specifically, what do you want to accomplish from looking at this problem? What are the goal and objectives you are planning to address from the problem?

Then, the next question is what is the potential information associated with the problem? Pertinent information includes:

Scope: To lay out tasks, data, and time frame to solve a problem, a scope needs to be defined so that you know how much information you are dealing with. The scope varies depending upon the nature and objectives of the problem. Questions on whether the problem is looking at a specific region, a particular group of population, or a particular phenomenon are worth investigating. Also, is the problem asking for information, maps, or more in-depth analysis of the problem?

Scale: Is the problem focusing on an institutional scale (individual, family, municipal, state, national, or international) and/or ecological scale (plant, plot, ecosystem, landscape, biome, or global)? As addressed in [ 2 ], stakeholders at different spatial scales can (and should) assign different values to environment and ecosystem under interest.

Type of information: two distinctive types of information are quantitative and qualitative. You need to specify if the problem is looking for quantitative and/or qualitative information. Quantitative information focuses on some sort of value or measurable information. Number of population affected by a hurricane or the amount of oil spilled into an ocean are quantifiable. Qualitative information, on the other hand, represents some sort of status that needs to be stated. Wildlife species affected by a hurricane or types of chemical released into a river are some of the examples.

It is also helpful to construct an outline or diagram of the problem so that it is easy for you and/or stakeholders to determine necessary steps, to better organize the tasks, and to be able to comprehend the problem at hand.

Consider the following real world examples using the above criteria:

Example 1: The 2005 Hurricane Katrina

The scope is the Hurricane Katrina in New Orleans, Louisiana. This pertains to the Greater New Orleans Region. Information of interest includes population affected, infrastructural damage, hazardous materials, and situations that might arise afterward. Given this information, one possible answer is the number of population affected as the quantitative information. Quantitative information includes, but not limited to, current stage of hazardous waste, groups of population, animal species, and housing.

Example 2: Bastrop County Complex Fire

The scope is a major wildfire in Bastrop County, Texas in 2011. Information of interest is effects on both human and animal, economics losses, effects on land and environments, and infrastructural damage. With the defined information, possible answers include the number of affected people and animals, income losses from the incident, and the loss of species’ habitats, which are accounted as quantitative information. Households and habitats affected by the fire, problem of land degradation and fragmentation, time frame for recovering, and preventive plans are some of the qualitative information that seeks answers.

Example 3: West Nile Virus in Brazos County

Brazos County, Texas and the surrounding areas is the scope of interest. Became widespread in the recent years (with the highest number of 7 severe cases in human being accounted for in 2006 [ 3 ]), West Nile virus has been under surveillance for residents in the County. Critical information that needs to be asked include: What causes the West Nile virus?; How can you track the spread of the West Nile virus?; and Where has West Nile virus been found in this location? Quantitative answers are the current number of infected individuals and the past records. Possible locations and trends that may be associated with the spread of the West Nile virus serve as the qualitative answer to the problem.

2.2. Defining a project area

With an identified problem, you can proceed to define a project area. This step delineates a confined boundary of an area of interest. The information from Step 2.1 helps specify the proper location where the problem occurred and address the possible questions and answers under interest. The process pinpoints the focus of the problem while eliminate unnecessary areas or secondary scope of interest from the picture. Not only that this can help save time, but it also allows you to pay closer attention to the essence of the project. At this stage, the conceptual project area should be carefully thought out before attempting to acquire data, i.e., map layers, in the next step.

  • ArcGIS is a registered trade mark of Environmental System Research Institute (ESRI), Inc.

For example, one may select an administrative boundary of a local jurisdiction from a base map layer as the project area as in the cases of Bastrop County Complex Fire and West Nile Virus in Brazos County. One may also “union” multiple local jurisdictions into a broader geographic span for addressing issues that are of cross-boundary nature. The project boundary resulted from one way or another serves as the “cookie cutter” for clipping data from relevant layers and tables in the ensuing steps to expedite problem-solving. As in the case of Hurricane Katrina, at least five parishes (Louisiana’s equivalent of counties in other states) should be included as the project area of the Hurricane analysis.

2.3. Identifying and acquiring data

Once the project area is defined, the next step is to locate and acquire needed data. Before looking for data, the methodology needs to be analyzed to establish what data is needed. The most important question that needs to be answered is: Why do I need this data? If the data is truly needed, then this question is easily answered. If not, then the data is most likely not necessary to solve the problem.

To be able to work with data in GIS, you need to understand the nature and procedural steps of working with data in GIS as follows:

2.3.1. GIS datasets formats

Typical formats of datasets, which allow you to conveniently work with multiple information or map layers, include spatial and attribute data.

Spatial data comes in the forms of raster and vector and is generally organized into so-called layers or thematic maps.

Raster data is digital image composed by rectangular grids or cells that contain numeric information from a defined range to characterize geographic features. Digital Elevation Model or DEM is a form of raster data important in depicting a terrain. It provides crucial information on the topologies of a geographic span.

Vector or shapefile data is constructed as points, lines, and polygons to represent geographical features.

Attribute data is information used to describe characteristics of a locale. The data is organized in a table containing information linked to a spatial feature by a common identifier. This gives you details or certain types of information associated with each specific feature.

2.3.2. GIS data sources

  • See http://geo.data.gov/geoportal/ for more information.
  • See http://www.tnris.org/get-data/ for more information.

2.3.3. Map projections and coordinate systems

Each map layer contains a coordinate system, which allows one to identify the location of the map and to be able to display, manipulate, and integrate the map layer with other layers for further applications and analysis. It is therefore imperative to understand the fundamentals of map projections and coordinate systems.

A coordinate system is a grid that may be used to define where a particular location is. Two common types of coordinate system are:

Geographic Coordinate System: This uses 3D spherical surface to define locations. Often incorrectly referred to as datum, geographic coordinate system includes not only datum, but also angular unit of measure and prime meridian. Points on Earth’s surface are referenced by latitude and longitude, while angles are measured by degree.

Projected Coordinate System: Commonly referred to as map projections, projected coordinate system is defined on flat, 2D surface with constant lengths, angles, and area. X, Y coordinates are presented on grid. It is based on geographic coordinate system.

See [ 4 ] for further explanations on coordinate systems

2.4. Extracting and manipulating data

The fourth step is data extraction and manipulation. In this step, one is to extract data from a conceivably larger original source file. Reduction of the size of datasets and their consolidation expedite the ensuing data management and processing. The project area defined at the onset (the cookie cutter) dictates the extent and size of data to be extracted and prepared.

Typically, data acquired may exist in various forms and shapes, e.g. different coordinate systems and file formats. It is a MUST to prepare and consolidate all datasets into a commonly operable format. GIS have a database management system component to support the proper management of both spatial and attribute data. It also enables convenient linking and relating of various data records by their locations on a common coordinate system. Some common tasks you will encounter during the data extraction and manipulation steps are as follows:

Re-projecting data: This is a basic essential step in any analysis using GIS. The purpose is to convert a particular piece of data from one coordinate system to another. Working with GIS employs more than one map layer, therefore acquired datasets may contain different projections. Different data projections lead to distortion of data and inaccuracy in the analysis.

For example, in Figure 1 , a residential area polygon (in blue) is projected to Geographic Coordinate System: GCS_North_American_1983. The same residential area polygon (in yellow) is in Projected Coordinate System: NAD_1927_UTM_Zone_16N. As shown in Figure, there are some discrepancies in the map layers with different coordinate systems. If this re-projection step is not taken, any analysis preformed will be inaccurate leading to much larger problem in subsequent analysis with multiple layers.

Conversion of raster to vector: Not only data comes in different coordinate systems, the file formats can also be varied; most commonly in the forms of raster or vector (shapefile). Especially with the growing use of GIS, datasets in shapefile have become more available. Shapefile data usually comes embedded with attribute data, which allows user to easily select and manipulate the information of interest. Therefore, converting a raster file to vector enables user to intersect other data with the available vector data. Suppose you have acquired and managed shapefile layers of affected area by Hurricane Katrina and population layer in the Greater New Orleans Region, by intersecting these two layers, you can extract the areas in which population were affected by the Hurricane.

Reclassification: To extract specific data from a raster, i.e., specific elevation data, reclassification is performed. Given the Hurricane problem, flooding can be assessed as one major result of the incident. In order to extract only the flooded area resulted from the Hurricane, reclassification is utilized to distinguish a specific range of elevation in which flooding occurred from others. This will allow you to analyze the effects pertaining to the flooded area.

Selecting by attributes: The purpose is to extract desired attribute data for analysis. This can be done through conditional statement imposed in attribute data table to select only specific information of interest. Considering an attribute table of chemical sites located within the Hurricane flooding zone, one can select only specific sites containing particular chemicals of interest for further analysis and map report.

Exporting data: To make a temporary layer permanent in a current map, data resulted from steps such as that of above need to be exported and saved in a current working folder. Otherwise, the file may be lost or difficult to locate when you want to revisit and work on it.

example about the system approach of solving environmental problems

Same layer file with different coordinate systems resulting in 20-meter difference on the map.

2.5. Editing spatial data

Oftentimes, acquired data might not be in the most suitable shape or boundary for problem under consideration. Options to edit spatial data in GIS allow one to manage the data in such a way that is more manageable and ready to be analyzed.

Typical editing tools consist of creating new features, cutting polygons, modifying features, and extending the basic skills to other tasks such as clipping a feature to a desired shape and area.

Creating new features: When creating a new feature, a blank data set is being defined by the editor. A blank data set is like an empty pie shell, while creating a new feature is like filling the pie shell. This task is only used if a new feature is desired or a single part feature is to be converted into a multi-part feature when the second part of the feature does not already exist.

Cutting polygon features: This process is a shortcut to creating a multi-part feature from a single part feature. Simply put, this process is used like a set of scissors to cut an existing feature into multiple parts.

Modifying features: This task is used when an existing feature does not cover the area that is desired. The attribute data will remain the same, while the feature will be modified to suit one’s need.

Clipping features: Clipping is a process that is like using a “cookie cutter” to remove a portion of a feature permanently. The attribute data will also be changed due to a permanent removal of the feature.

2.6. Geospatial analysis

Upon data readiness, a project may move on to the sixth step of spatial-temporal analyses. There are many useful procedures for these endeavors. Especially with the versatilities of GIS software, one can utilize extended range of applications available. Some common tools that one should be familiar with and were used specifically for the ensuing applications in this chapter include:

Distance analysis: A suite of tools to produce distance maps are commonly available in GIS. In ArcGIS, distance tools are available under Spatial Analyst option. Euclidean distance tool measures straight-line distance from the center of cell to the nearest object of interest, i.e., your source. Another alternative is the cost distance tool, which incorporates travel cost from different paths into the analysis. The products from these tools are distance maps in raster representing proximity maps with a range of distance values from the source. For instance, one can find proximities from pollution sources at defined interval to any locales within a defined area map.

Map algebra: Another useful application, which you will encounter at certain point of analysis, is map algebra. This can be used for computations of raster data to create spatial patterns that depict locales of a particular concern or interest. Raster calculator, a Spatial Analyst application, allows for this useful procedure by inputting specified mathematical functions and expressions in the calculator. The result will be raster values and layer corresponding to the specified function.

The use of analytic procedures mentioned above and other tools in a proper order results in useful information for a problem under study.

2.7. Generating maps and reports

The final major step is to generate maps and reports. One picture is better than a thousand words. To this end, GIS come handy in presenting information in maps, images, 3D graphs, tables, and other forms. It also expedites the import and export of these presentations between GIS and other software environments, e.g. a word or a graphic processor. With the acceleration of PC powers, the sky is the limit to GIS’ capability of generating maps and reports. It is worth noting that you should understand what the readers are looking for when creating the maps and write ups, i.e., what is the focus or message that you want to communicate to others? This should align with the proposed information of interest.

example about the system approach of solving environmental problems

Diagram of problem-solving steps

3. Illustration of GIS for environmental problem solving applications

To illustrate how GIS are used to help address environmental issues and problems, two cases are described herewith in this section. The first one is on flood assessment, and the second is a QOL analysis. The applications help prepare for the building framework of spatial appraisal and valuation of environment and ecosystems (SAVEE), which will be discussed in the following section, tremendously.

3.1. Flood assessment

Considered one of the costliest [ 5 ] and most destructive natural disasters in the history of the United States, Hurricane Katrina provides a number of opportunity to understand the risk of nature, and how one could expect to understand and learn from such disastrous effects. The aforementioned problem-solving steps allow us to contemplate the steps as follows:

Step 1: Framing the problem

An analysis of the scenario indicated that Hurricane Katrina occurred in the Greater New Orleans Area. Field measurements and distributions on the majority of victims indicated that roughly those under 1 meter in elevation were initially affected by the flooding [ 6 ]. Given this information, the scenario was that every location below 1 meter in elevation was affected and any location that is above this level was unaffected by the flood water. This particular area of impact needed to be delineated. The information of interest included area and population affected by the Hurricane. Additional scenarios of water-rise were then set for 5, 10, and 15 meters to emulate different levels of flooding.

Step 2: Defining the project area

In this case, the City of New Orleans and its five neighboring parishes suffered by the storm were identified as the study area.

Step 3: Identifying and acquiring data

The best type of data for delineating the affected area is the elevation data (DEM). DEMs, Satellite Imagery, and Census datasets were collected from Atlas, the GIS data central from the State of Louisiana [ 7 ]. This included DEM, jurisdiction boundaries, street maps of the study area, and Census data.

Step 4: Extracting and preparing data

Initially, the DEM and Census data came projected as GCS_North_American_1983. By assigning a projected coordinate system to the data, further analysis could be proceeded. Given the information, we projected the data to NAD_1927_UTM_Zone_16N. Hillshades of the DEMs were also generated to visually inspect different elevations in the data. Sink holes pervaded in the DEMs were also been filled to prevent erroneous and prepare for proper flow direction process.

Then, the second crucial step was to extract the flooded area from the total area. This employed the reclassification process in which the elevation value was changed to 1 meter to separate the flooded area from the non-flooded area (elevation above 1 meter). In short, the reclassification divided the elevation data into the flooded area and the non-flooded area. The rest of the water-rise scenarios then followed using the same reclassification step as well as the ensuing steps.

Next, this flooded elevation data was converted into vector to prepare for further analysis. The converted flooded layer was the result of the conversion process as well as exporting the data into a new permanent flooded area layer.

In terms of the Census data, the parishes were merged into one layer so that it was more convenient to work with in the subsequent steps for analyzing the total effect on population.

Step 5: Editing spatial data

The acquired data contained certain parts that were irrelevant to the analysis. Lake Ponchartran, for instance, should not be counted toward the flooded area. Therefore, by editing the data, some unnecessary information of interest were taken out. Pertinent steps of editing the lake included: 1) Creating a new blank shapefile; 2) Using the blank shapefile as the base for editing tools to create a new feature around the lake area; and 3) Using the newly created feature as a cookie cutter to clip off the lake area from the flooded area layer. The result was the flooded area without the lake that was ready to be incorporated into other analysis.

Step 6: Geospatial analysis

At this stage, socio-economic analyses were conducted to assess the damage and impact on the livelihoods of residents of the affected areas. Census data developed was used directly for this purpose. Combining census data with the emulated flooded areas, patterns of suffering by which racial stakeholders and by what economic classes were clearly displayed.

Based on the flooded area layer in Step 5 , we proceeded to calculate the area under the layer’s attribute table. Visual Basic Code to calculate the area (available from [ 8 ]) or a Calculate Geometry option, an automated tool in ArcGIS, derived the numbers of area affected by the Hurricane. Mathematical formula imposed helped convert the numbers into desirable units such as acres.

Benefited from the above numbers, the population affected was conveniently calculated. Census data contains racial information that represents groups of population in different parishes. By intersecting the flooded area layer with the merged parishes layer, representing population profile in the areas, affected population was extracted. The overall statistics in the attribute table identified the total population affected by the flooding. Figure 3 demonstrates map layers resulting from the above problem-solving steps.

Step 7: Generating maps and report

Upon generating desired information and analysis, each pertinent map was composed as a map report containing a map title, legend (showing values of the map layer), north arrow, and scale bar. Then, the map reports were exported as image files to be included in a report. The report addressed the finding results of effects from the Hurricane as illustrated by the maps and relevant discussions of further applications and analysis that can later be applied based on this project.

example about the system approach of solving environmental problems

Illustrations of selected problem-solving steps for flood assessment in reference to the 2005 Hurricane Katrina flooding in the Greater New Orleans Region.

3.2. Quality of life assessment

QOL is emerging as a major indicator to monitor citizen’s livelihood and wellbeing at the grassroots level. By virtue of its focuses, QOL helps inform local people and organizations of their living environment and optimize the allocations of resources to improve the community development. Canada is perhaps more aggressive in setting up a national framework for QOL [ 9 ]. In the U.S., states such as Utah [ 10 ]; cities such as San Francisco, California [ 11 ]; and organizations, including nonprofit organizations such as the Quality of Life Foundation [ 12 ] have been vigorously promoting such term as one of their agendas.

Categories of data to support the development of QOL indicators range from education, environment, economics, social, and justice to transportation/mobility. However, the use of GIS to track QOL progress is still at its infancy stage. City of College Station, Texas, with its advanced GIS installation and rich collection of data, stands to gain a lead role in this area and to provide even superior services to its residents when it embarks on this path.

There are three issues and opportunities in the development of QOL indicators. They are:

Combining subjective values with objective measurements to create consensus and develop common ground to accommodate multiple perspectives of stakeholders.

Combining the use of both spatial and attribute information to develop base layer and indices in environment, crimes, recreation, etc. For example:

Overlay of census blocks with subdivisions or other neighborhood entities (e.g. apartment complex) to establish the baseline reference (population, its composition, income level, education level, and number of household of an entity)

Overlay of crime type, frequency, and location data with entities on the base layer

Developing a composite score (ranking) of QOL for each neighborhood entity on the base layer

As you set forth to do your research, as in the case of QOL assessment, you are most likely facing with three puzzling situations:

1. Pertinent data/information comes in a variety of forms

It is plausible that the data/information you are facing and plan to collect exists in at least two forms. They are categorical and numeric. Examples of categorical information include “Yes” or “No” on whether a city (or any local jurisdiction) has a neighborhood improvement in place or not program; “Very Good,” “Good,” “Fair,” and “Bad” on how such a program is being regarded by the communities; and “Highly favorable,” “Favorable,” and “Least Favorable” on how service rendered by the program is perceived by the beneficiaries.

Quite often, information of categorical nature is derived from one’s “gut feeling.” It may also be convenient to summarize some judgments based on historical data, on some kinds of trends, or on some opinion surveys/polls.

There are two types of numeric information: discrete and continuous. Population of an ethnic group residing in a particular Census unit is an example of discrete type. Example of continuous type is the percentage of an ethnic group versus the total population in such a unit.

2.How to “add” “oranges” and “apples”

When one has data and information of various types in hand, he/she will ask this question:

“How do I add them together?” Indeed, you cannot add oranges and apples together at their original forms. The trick is to convert and normalize all of them into the same numerical scale, say between -1 and +1.

So, what is normalization? Normalization is the act of taking many sets of data that have no clear correlation and placing them under the same quantitative scale. Essentially, normalization allows us to compare apples and oranges. Some decisions must be made prior to normalizing any type of data. The questions include:

What are the important factors?

Which factors are positive and which factors are negative?

How much should each of these factors count in relation to the overall project?

For categorical type of information, what you do is to fix the “best” and the “worst” at +1 and -1 respectively. This is plausible as +1 can represent the best case and -1 the worst. When both ends are fixed, one may logically deduce that a numeric value of “0” represents “Inconclusive.” Furthermore, one may come up with a scheme saying that “+0.25” is “somewhat better”, “+0.5” is “better” and “+0.75” is “much better.” One can also say that “-0.25” is “somewhat worse”, “-0.5” is “worse”, and “-0.75” is “much worse.” As a result, you are converting and normalizing categorical or qualitative data into numeric or quantitative information as illustrated in Figure 4 .

example about the system approach of solving environmental problems

Illustration of the qualitative – quantitative information conversion scheme.

For numeric information, the conversion and normalization is less complicated. Say you deem the ratio of white population in a Census unit at 50% is the best mix (most favorable), in terms of quality of life; 100% or 0% is least favorable. For the best mix, you believe it should be given a score of +1 and for the least favorable a 0. Given this range, you may apply the following equations to convert and normalize the percentage into values in the range (0,+1)

where E is the expected (best value of x)

Another method is to convert distance to an object from such measurements as miles to the uniform score between (-1 and +1). For example, one may decide that the presence of oil well is bad for quality of life. Evidently, the household right at the oil well would have absolutely unfavorable score of -1. The negative effect most likely would tap off as the distance reaches certain threshold, e.g. 1 mile or 5,280 feet. The tapering effect can then be described by a negative exponential equation as:

where x is the distance to oil well(s)

The normalization equation for strictly negative attribute based on the negative exponential equation above becomes:

The translation of the equation is “If Condition < X, True, False.” This means if an input value (distance value) falls under the condition (less than X), then the output is negative value. Otherwise, the output is zero. It might be helpful to put this in the oil well scenario above:

The above equation is set so that if the distance to an oil well is less than 1 mile, then the output is negative. As the locales get closer to the actual oil wells path, the more negative they will become (with the minimum at -1). At a distance of 1 mile or greater all the output values are set to zero as shown in Figure 5 .

example about the system approach of solving environmental problems

Negative decay graph showing the more negative values as the locales get closer to oil wells.

At any rate, once you have all factors converted and normalized into the scheme of (-1, +1), then the values can be “operated” on to add up their contributions to the overall quality of life assessment of a city. This is done by applying the following formula:

The method is derived from an expert system algorithm called Emycin [ 13 ]. The operations utilize map algebra calculation to integrate two values at a time, i.e., pair-wise calculation, while avoiding the problem of double-counting. The calculations are performed iteratively until all normalized layers are exhausted. As illustrated in Figure 6 , the operation calculates the values of two attributes at a time to derive the final score, which is the integration of the values in all attributes. Through fuzzy logic operations, two QOLs (different factor contributing to QOL) can be integrated at a time until all QOLs are exhausted. Iteration 1 integrates QOL 1 and QOL 2 so that only the overlapping portion of both factors values remains. By taking this portion to integrate with another QOL, QOL 3 , the final result is the overlapping portion among three factors; QOL 1 , QOL 2 , and QOL 3 .The results can be color-coded as a gradient map of integrated and locale-specific QOL in the range of (-1, +1).

The nicety of the Emycin formula is that:

Regardless of the number of factors being used, you always “operate” on two of them in each iteration. This is called pair-wise calculation.

Depending on the score values of the two factors, there will be only one of the equations that is applicable.

Unlike many “ordinary” algorithms, this formula allows both positive and negative contributions from factors under considerations, which is more realistic.

Regardless of the number of factors being considered and operated on, the resulted score will always be bounded between -1 and +1.

Regardless of the sequence each factor is put into pair-wise calculation, the result is always the same.

Once all factors are exhausted in the calculation, one can always convert the result back to the qualitative scheme to make it more comprehensive to lay persons or people one intend to interpret the results to.

A word of caution: Both -1 and +1 are “singular” points. In other words, if you come up with a score on the contribution of a factor to be either -1 or +1, then other factors’ contributions will not matter anymore. This is not a surprise or unreasonable. Because -1 means absolutely “bad” and +1 means absolutely “good.” When you have a factor that determines the quality of life to be absolutely bad, then indeed why bother to waste time to assess other factors?

To this end, one may want to adjust or shift the score from a factor that is somewhat different from the absolute values of -1 or +1 so that the pair-wise calculation may proceed logically. Again, this is not unreasonable as there is hardly anything that one can claim that is absolutely good or bad.

example about the system approach of solving environmental problems

Illustration of pair-wise calculations.

3. Incomplete information

An additional nicety to the above approach is that one can proceed to conduct studies under incomplete information. The condition of incomplete information actually happens quite often in real life. With the kind of flexibility boasted by Emycin, you “add” the contributions from whatever data you are able to get your hand on for a city in determining its quality of life. In the case of comparing multiple cities, you may get this and that for one jurisdiction while not the same categories for all of them. By nature of the conversion, normalization, and pair-wise calculation, you would be able to derive scores on the same scheme and will be able to make comparisons.

With better understanding on the assessment framework, it is time to put such theory into real application.

The information of interest for this case is the factors contributing to the QOL of a city/community. Relevant questions include:

What defines a high quality of life?: This depends on who the target audience is: elderly community, students, or married couples.

What factors can contribute to the QOL?:

Distance to: hospitals, schools, university, parks, landfill, oil wells, etc.

Census Data Analysis: racial mix, relative income of a population, and number of children per household

In the case of the QOL assessment, the project area was the City of College Station, Texas.

Step 3: Identify and acquiring data

Acquiring data from the City’s GIS Department is crucial. From the rich collection of datasets rendered by the City [ 14 ], a number of data layers were selected for the ensuing analysis endeavor. They included census data, roads and streets, railroads, parks and green spaces, residence subdivisions, landfills, oil wells, schools, hospitals, flood plains, crime statistics, and many more.

DEMs for College Station were acquired and converted into raster. This represented the base map of College Station for the following steps. Selecting only the areas pertaining to College Station attribute was also another important preparation step since we were looking at the QOL in College Station and nothing else.

Basemap that contains areas beyond College Station were clipped off, and only the College Station boundary was left for the analysis.

Based on the identified QOL factors, proximities to parks, green spaces, schools, hospitals, and some other geographic features were regarded as positive contributing factors. On the other hand, closeness to such factors as landfills, oil wells, railroads, crime occurrences, and flood plains were considered to have negative impacts. The contributions of these factors, positive or negative, were mathematically formulated as distance functions from objects on corresponding data layers. In the ensuing steps, proximity maps encoded with distance functions were generated. The results from each factor layer were then combined with fuzzy logic calculation to form an integrated index between (-1, 1). Any number greater than 0 indicated a good QOL with anything below 0 representing bad index. The index was coded in a color scheme with a gradient from red to green. The color-coded QOL maps displayed clearly the patterns of QOL of the City at every specific neighborhood and locale.

To better illustrate this, four QOL factors, QOL 1 , QOL 2 , QOL 3 , and QOL 4 , were used as an example for the calculation (see Figure 7) . The first fuzzy operation employed two QOLs, QOL 1 and QOL 2 , to derive QOL 12 . The locales within defined proximity to QOL 1 were color-coded in green representing high QOL with the values approaching 1. On the other hand, those in red represented low QOL with the values approaching -1. Next, QOL 12 was integrated with QOL 3 resulting in QOL 123 . The last operation was QOL 123 and QOL 4 as shown in the final integrated map of QOL 1234 . The map results in the color gradient reflecting more green in the portion where high QOLs overlap (in the middle of the map) while the outer portion becomes more yellow to orange as a result of integrated low QOLs.

At this stage, twenty sample residential addresses were selected and tabulated in a table. By linking this table to the Address Locator tool in ArcGIS, the residential addresses were shown as a point shapefile on the map. To pinpoint the QOL of each selected address, Identify Tool was used to indicate the QOL index associated with such address.

Map reports of this project were individually created to reflect normalized layer of each factor contributing to QOL. The normalized values (within -1 to 1 range) were shown in the legend to reflect the results from the analysis. The combined layers resulted from Emycin algorithms were exported into a group of combined layer of strictly positive factors, combined layer of strictly negative factors, and combined layer of the combination of positive and negative factors. The report concludes how QOL assessment was made possible with useful applications of GIS. Future development of applications from the QOL assessment such as the linkage to SAVEE framework was also discussed.

The QOL index illustrated in the above example sheds light on the shape of things to come with SAVEE. First of all, one may acquire the land price and/or real estate information from local authority of a jurisdiction. Using the SAVEE methodology, such information can be converted into the (0, 1) range for services provided by specific environments and ecosystems in an area. Similarly, the QOL index above may also be computed in the same range. Spatial statistical analyses can then be conducted to determine correlation between land prices and QOL. Useful information may be thus generated to pave way for “spatial acres of an environment or ecosystem”.

4. Concluding remarks

The chapter depicts the natures and categories of environmental issues people are facing and how GIS can be deployed to help address them. In the context of environmental problem-solving, the systems approach for applying GIS is presented; and a few practical cases are illustrated. This organization casts a holistic view for readers to gain better comprehension of the subject matter.

example about the system approach of solving environmental problems

Iteration of map algebra to incorporate fuzzy logic to compute contributions of relevant factors to locale-specific QOL in College Station, Texas.

Problem-solving starts with shaping a mental model on to formulate a solution to the issue at hand. Steps of the solution process are then implemented through the use of appropriate data and tools enabled by GIS. Skills and knowledge to facilitate these endeavors can be best advanced by hands-on practices. For this purpose, interested readers may access the full set of documentation of learning modules at http://starr.tamu.edu/gis2012a/. The materials are from a senior course the authors teach at Texas A&M University. It bears the same title as this chapter, “GIS for Environmental Problem-Solving.” First conceived in the 1990s, the course has gained and maintained its popularity among the student bodies. The learning modules include well-organized step-by-step instructions of applications in ArcGIS presented in this chapter. Being offered online since 2006, this course has proven to be easy yet comprehensive for self-learning, even among students with no prior GIS background.

It is worthwhile mentioning that the approach mentioned above is for a typical GIS project for environment. There is, however, usually one step short. That is asserting monetary values associated with the environment. This issue is emerging as a priority matter in the environmental research community. For example, the monetary losses from the BP Oil Spill in the Gulf of Mexico are yet to be more plausibly determined. Taking on this issue, the authors here at the STARR LAB are developing a new research methodology called Spatial Appraisal and Valuation of Environment and Ecosystems (SAVEE). The aim of this effort is to define “spatial acre” that attaches monetary values to a geographic span of interest.

One main thrust of SAVEE is to cross-reference economic development and ecological sustainability in the framework of Sustainable Development declared in the 1992 Earth Summit and being enhanced continuously ever since. Economic development is tangible and comes with a price tag. It is plausible to assume that the intensity of development of a locale of interest can be reflected in its real estate value, which is generally available. Sampling some locales of their real estate values leads to a price list of real estate values. This price list is then converted into a uniform range between 0 and 1, a well-behaved index representing the intensity of development of locales. On the other hand, ecological sustainability of an area of interest normally does not come with a price tag. However, one may systematically incorporate pertinent ecological services it renders into consideration and develop an index that has the same range of (0, 1). The numbers approaching 1 represent higher sustainability, and the opposites represent lower indices. On the basis of equitability between development and environment, the two index systems may then be mapped. The mapping leads to assigning monetary values associated with development sites to ecological locales with comparable index numbers.

Learning is a life-long process; so are the advances of knowledge and technologies. On the environmental GIS front, asserting monetary values to a system under study has become an imperative. The authors are hopeful that the general framework stipulated in SAVEE shall be advanced to explore this new territory. Only labeling it with dollar signs would make stakeholders appreciate more of our environment of its values. After all, without such dollar values, it is difficult for stakeholders and authorities to understand the magnitude of the environmental problems at hand. It is contended that SAVEE and other similar effort will make a significant contribution to environmental sectors in general and the advancement of GIS.

  • 3. West Nile Virus Activity in Brazos County [Internet]. College Station (TX): AgriLIFE Extension; Agricultural and Environmental Safety; 2012 cited 2012 April 21]. Available from: http://www-aes.tamu.edu/public-health-vector-and-mosquito-control/brazos-county-mosquito-borne-disease-surveillance/west-nile-virus-activity-in-brazos-county/
  • 4. Maling D.H 1991 Coordinate Systems and Map Projections for GIS. In: Maguire D.J, Goodchild M.F, Rhind D.W, editors. Geographical Information Systems: Principles and Applications. London: Longman Group UK. 1 135 146
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  • 11. City and County of San Francisco as Successor to the Redevelopment Agency [Internet]. San Francisco: San Francisco Redevelopment Agency; 2012 cited 2012 April 27]. Available from: http://www.sfredevelopment.org/
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  • See [4] for further explanations on coordinate systems

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Critical transitions in the Amazon forest system

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  • Climate and Earth system modelling
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The possibility that the Amazon forest system could soon reach a tipping point, inducing large-scale collapse, has raised global concern 1 , 2 , 3 . For 65 million years, Amazonian forests remained relatively resilient to climatic variability. Now, the region is increasingly exposed to unprecedented stress from warming temperatures, extreme droughts, deforestation and fires, even in central and remote parts of the system 1 . Long existing feedbacks between the forest and environmental conditions are being replaced by novel feedbacks that modify ecosystem resilience, increasing the risk of critical transition. Here we analyse existing evidence for five major drivers of water stress on Amazonian forests, as well as potential critical thresholds of those drivers that, if crossed, could trigger local, regional or even biome-wide forest collapse. By combining spatial information on various disturbances, we estimate that by 2050, 10% to 47% of Amazonian forests will be exposed to compounding disturbances that may trigger unexpected ecosystem transitions and potentially exacerbate regional climate change. Using examples of disturbed forests across the Amazon, we identify the three most plausible ecosystem trajectories, involving different feedbacks and environmental conditions. We discuss how the inherent complexity of the Amazon adds uncertainty about future dynamics, but also reveals opportunities for action. Keeping the Amazon forest resilient in the Anthropocene will depend on a combination of local efforts to end deforestation and degradation and to expand restoration, with global efforts to stop greenhouse gas emissions.

The Amazon forest is a complex system of interconnected species, ecosystems and human cultures that contributes to the well-being of people globally 1 . The Amazon forest holds more than 10% of Earth’s terrestrial biodiversity, stores an amount of carbon equivalent to 15–20 years of global CO 2 emissions (150–200 Pg C), and has a net cooling effect (from evapotranspiration) that helps to stabilize the Earth’s climate 1 , 2 , 3 . The forest contributes up to 50% of rainfall in the region and is crucial for moisture supply across South America 4 , allowing other biomes and economic activities to thrive in regions that would otherwise be more arid, such as the Pantanal wetlands and the La Plata river basin 1 . Large parts of the Amazon forest, however, are projected to experience mass mortality events due to climatic and land use-related disturbances in the coming decades 5 , 6 , potentially accelerating climate change through carbon emissions and feedbacks with the climate system 2 , 3 . These impacts would also involve irreversible loss of biodiversity, socioeconomic and cultural values 1 , 7 , 8 , 9 . The Amazon is home to more than 40 million people, including 2.2 million Indigenous peoples of more than 300 ethnicities, as well as afrodescendent and local traditional communities 1 . Indigenous peoples and local communities (IPLCs) would be harmed by forest loss in terms of their livelihoods, lifeways and knowledge systems that inspire societies globally 1 , 7 , 9 .

Understanding the risk of such catastrophic behaviour requires addressing complex factors that shape ecosystem resilience 10 . A major question is whether a large-scale collapse of the Amazon forest system could actually happen within the twenty-first century, and if this would be associated with a particular tipping point. Here we synthesize evidence from paleorecords, observational data and modelling studies of critical drivers of stress on the system. We assess potential thresholds of those drivers and the main feedbacks that could push the Amazon forest towards a tipping point. From examples of disturbed forests across the Amazon, we analyse the most plausible ecosystem trajectories that may lead to alternative stable states 10 . Moreover, inspired by the framework of ‘planetary boundaries’ 11 , we identify climatic and land use boundaries that reveal a safe operating space for the Amazon forest system in the Anthropocene epoch 12 .

Theory and concepts

Over time, environmental conditions fluctuate and may cause stress on ecosystems (for example, lack of water for plants). When stressing conditions intensify, some ecosystems may change their equilibrium state gradually, whereas others may shift abruptly between alternative stable states 10 . A ‘tipping point’ is the critical threshold value of an environmental stressing condition at which a small disturbance may cause an abrupt shift in the ecosystem state 2 , 3 , 13 , 14 , accelerated by positive feedbacks 15 (see Extended Data Table 1 ). This type of behaviour in which the system gets into a phase of self-reinforcing (runaway) change is often referred to as ‘critical transition’ 16 . As ecosystems approach a tipping point, they often lose resilience while still remaining close to equilibrium 17 . Thus, monitoring changes in ecosystem resilience and in key environmental conditions may enable societies to manage and avoid critical transitions. We adopt the concept of ‘ecological resilience’ 18 (hereafter ‘resilience’), which refers to the ability of an ecosystem to persist with similar structure, functioning and interactions, despite disturbances that push it to an alternative stable state. The possibility that alternative stable states (or bistability) may exist in a system has important implications, because the crossing of tipping points may be irreversible for the time scales that matter to societies 10 . Tropical terrestrial ecosystems are a well-known case in which critical transitions between alternative stable states may occur (Extended Data Fig. 1 ).

Past dynamics

The Amazon system has been mostly covered by forest throughout the Cenozoic era 19 (for 65 million years). Seven million years ago, the Amazon river began to drain the massive wetlands that covered most of the western Amazon, allowing forests to expand over grasslands in that region. More recently, during the drier and cooler conditions of the Last Glacial Maximum 20 (LGM) (around 21,000 years ago) and of the mid-Holocene epoch 21 (around 6,000 years ago), forests persisted even when humans were already present in the landscape 22 . Nonetheless, savannas expanded in peripheral parts of the southern Amazon basin during the LGM and mid-Holocene 23 , as well as in the northeastern Amazon during the early Holocene (around 11,000 years ago), probably influenced by drier climatic conditions and fires ignited by humans 24 , 25 . Throughout the core of the Amazon forest biome, patches of white-sand savanna also expanded in the past 20,000–7,000 years, driven by sediment deposition along ancient rivers 26 , and more recently (around 800 years ago) owing to Indigenous fires 27 . However, during the past 3,000 years, forests have been mostly expanding over savanna in the southern Amazon driven by increasingly wet conditions 28 .

Although palaeorecords suggest that a large-scale Amazon forest collapse did not occur within the past 65 million years 19 , they indicate that savannas expanded locally, particularly in the more seasonal peripheral regions when fires ignited by humans were frequent 23 , 24 . Patches of white-sand savanna also expanded within the Amazon forest owing to geomorphological dynamics and fires 26 , 27 . Past drought periods were usually associated with much lower atmospheric CO 2 concentrations, which may have reduced water-use efficiency of trees 29 (that is, trees assimilated less carbon during transpiration). However, these periods also coincided with cooler temperatures 20 , 21 , which probably reduced water demand by trees 30 . Past drier climatic conditions were therefore very different from the current climatic conditions, in which observed warming trends may exacerbate drought impacts on the forest by exposing trees to unprecedented levels of water stress 31 , 32 .

Global change impacts on forest resilience

Satellite observations from across the Amazon suggest that forest resilience has been decreasing since the early 2000s 33 , possibly as a result of global changes. In this section, we synthesize three global change impacts that vary spatially and temporally across the Amazon system, affecting forest resilience and the risk of critical transitions.

Regional climatic conditions

Within the twenty-first century, global warming may cause long-term changes in Amazonian climatic conditions 2 . Human greenhouse gas emissions continue to intensify global warming, but the warming rate also depends on feedbacks in the climate system that remain uncertain 2 , 3 . Recent climate models of the 6th phase of the Coupled Model Intercomparison Project (CMIP6) agree that in the coming decades, rainfall conditions will become more seasonal in the eastern and southern Amazonian regions, and temperatures will become higher across the entire Amazon 1 , 2 . By 2050, models project that a significant increase in the number of consecutive dry days by 10−30 days and in annual maximum temperatures by 2–4 °C, depending on the greenhouse gas emission scenario 2 . These climatic conditions could expose the forest to unprecedented levels of vapour pressure deficit 31 and consequently water stress 30 .

Satellite observations of climatic variability 31 confirm model projections 2 , showing that since the early 1980s, the Amazonian region has been warming significantly at an average rate of 0.27 °C per decade during the dry season, with the highest rates of up to 0.6 °C per decade in the centre and southeast of the biome (Fig. 1a ). Only a few small areas in the west of the biome are significantly cooling by around 0.1 °C per decade (Fig. 1a ). Dry season mean temperature is now more than 2 °C higher than it was 40 years ago in large parts of the central and southeastern Amazon. If trends continue, these areas could potentially warm by over 4 °C by 2050. Maximum temperatures during the dry season follow a similar trend, rising across most of the biome (Extended Data Fig. 2 ), exposing the forest 34 and local peoples 35 to potentially unbearable heat. Rising temperatures will increase thermal stress, potentially reducing forest productivity and carbon storage capacity 36 and causing widespread leaf damage 34 .

figure 1

a , Changes in the dry season (July–October) mean temperature reveal widespread warming, estimated using simple regressions between time and temperature observed between 1981 and 2020 (with P  < 0.1). b , Potential ecosystem stability classes estimated for year 2050, adapted from current stability classes (Extended Data Fig. 1b ) by considering only areas with significant regression slopes between time and annual rainfall observed from 1981 through 2020 (with P  < 0.1) (see Extended Data Fig. 3 for areas with significant changes). c , Repeated extreme drought events between 2001–2018 (adapted from ref. 39 ). d , Road network from where illegal deforestation and degradation may spread. e , Protected areas and Indigenous territories reduce deforestation and fire disturbances. f , Ecosystem transition potential (the possibility of forest shifting into an alternative structural or compositional state) across the Amazon biome by year 2050 inferred from compounding disturbances ( a – d ) and high-governance areas ( e ). We excluded accumulated deforestation until 2020 and savannas. Transition potential rises with compounding disturbances and varies as follows: less than 0 (in blue) as low; between 1 and 2 as moderate (in yellow); more than 2 as high (orange–red). Transition potential represents the sum of: (1) slopes of dry season mean temperature (as in a , multiplied by 10); (2) ecosystem stability classes estimated for year 2050 (as in b ), with 0 for stable forest, 1 for bistable and 2 for stable savanna; (3) accumulated impacts from extreme drought events, with 0.2 for each event; (4) road proximity as proxy for degrading activities, with 1 for pixels within 10 km from a road; (5) areas with higher governance within protected areas and Indigenous territories, with −1 for pixels inside these areas. For more details, see  Methods .

Since the early 1980s, rainfall conditions have also changed 31 . Peripheral and central parts of the Amazon forest are drying significantly, such as in the southern Bolivian Amazon, where annual rainfall reduced by up to 20 mm yr −1 (Extended Data Fig. 3a ). By contrast, parts of the western and eastern Amazon forest are becoming wetter, with annual rainfall increasing by up to 20 mm yr −1 . If these trends continue, ecosystem stability (as in Extended Data Fig. 1 ) will probably change in parts of the Amazon by 2050, reshaping forest resilience to disturbances (Fig. 1b and Extended Data Fig. 3b ). For example, 6% of the biome may change from stable forest to a bistable regime in parts of the southern and central Amazon. Another 3% of the biome may pass the critical threshold in annual rainfall into stable savanna in the southern Bolivian Amazon. Bistable areas covering 8% of the biome may turn into stable forest in the western Amazon (Peru and Bolivia), thus becoming more resilient to disturbances. For comparison with satellite observations, we used projections of ecosystem stability by 2050 based on CMIP6 model ensembles for a low (SSP2–4.5) and a high (SSP5–8.5) greenhouse gas emission scenario (Extended Data Fig. 4 and Supplementary Table 1 ). An ensemble with the 5 coupled models that include a dynamic vegetation module indicates that 18–27% of the biome may transition from stable forest to bistable and that 2–6% may transition to stable savanna (depending on the scenario), mostly in the northeastern Amazon. However, an ensemble with all 33 models suggests that 35–41% of the biome could become bistable, including large areas of the southern Amazon. The difference between both ensembles is possibly related to the forest–rainfall feedback included in the five coupled models, which increases total annual rainfall and therefore the stable forest area along the southern Amazon, but only when deforestation is not included in the simulations 4 , 37 . Nonetheless, both model ensembles agree that bistable regions will expand deeper into the Amazon, increasing the risk of critical transitions due to disturbances (as implied by the existence of alternative stable states; Extended Data Fig. 1 ).

Disturbance regimes

Within the remaining Amazon forest area, 17% has been degraded by human disturbances 38 , such as logging, edge effects and understory fires, but if we consider also the impacts from repeated extreme drought events in the past decades, 38% of the Amazon could be degraded 39 . Increasing rainfall variability is causing extreme drought events to become more widespread and frequent across the Amazon (Fig. 1c ), together with extreme wet events and convective storms that result in more windthrow disturbances 40 . Drought regimes are intensifying across the region 41 , possibly due to deforestation 42 that continues to expand within the system (Extended Data Fig. 5 ). As a result, new fire regimes are burning larger forest areas 43 , emitting more carbon to the atmosphere 44 and forcing IPLCs to readapt 45 . Road networks (Fig. 1d ) facilitate illegal activities, promoting more deforestation, logging and fire spread throughout the core of the Amazon forest 38 , 39 . The impacts of these pervasive disturbances on biodiversity and on IPLCs will probably affect ecosystem adaptability (Box 1 ), and consequently forest resilience to global changes.

Currently, 86% of the Amazon biome may be in a stable forest state (Extended Data Fig. 1b ), but some of these stable forests are showing signs of fragility 33 . For instance, field evidence from long-term monitoring sites across the Amazon shows that tree mortality rates are increasing in most sites, reducing carbon storage 46 , while favouring the replacement by drought-affiliated species 47 . Aircraft measurements of vertical carbon flux between the forest and atmosphere reveal how southeastern forests are already emitting more carbon than they absorb, probably because of deforestation and fire 48 .

As bistable forests expand deeper into the system (Fig. 1b and Extended Data Fig. 4 ), the distribution of compounding disturbances may indicate where ecosystem transitions are more likely to occur in the coming decades (Fig. 1f ). For this, we combined spatial information on warming and drying trends, repeated extreme drought events, together with road networks, as proxy for future deforestation and degradation 38 , 39 . We also included protected areas and Indigenous territories as areas with high forest governance, where deforestation and fire regimes are among the lowest within the Amazon 49 (Fig. 1e ). This simple additive approach does not consider synergies between compounding disturbances that could trigger unexpected ecosystem transitions. However, by exploring only these factors affecting forest resilience and simplifying the enormous Amazonian complexity, we aimed to produce a simple and comprehensive map that can be useful for guiding future governance. We found that 10% of the Amazon forest biome has a relatively high transition potential (more than 2 disturbance types; Fig. 1f ), including bistable forests that could transition into a low tree cover state near savannas of Guyana, Venezuela, Colombia and Peru, as well as stable forests that could transition into alternative compositional states within the central Amazon, such as along the BR319 and Trans-Amazonian highways. Smaller areas with high transition potential were found scattered within deforestation frontiers, where most forests have been carved by roads 50 , 51 . Moreover, 47% of the biome has a moderate transition potential (more than 1 disturbance type; Fig. 1f ), including relatively remote parts of the central Amazon where warming trends and repeated extreme drought events overlap (Fig. 1a,c ). By contrast, large remote areas covering 53% of the biome have low transition potential, mostly reflecting the distribution of protected areas and Indigenous territories (Fig. 1e ). If these estimates, however, considered projections from CMIP6 models and their relatively broader areas of bistability (Extended Data Fig. 4 ), the proportion of the Amazon forest that could transition into a low tree cover state would be much larger.

Box 1 Ecosystem adaptability

We define ‘ecosystem adaptability’ as the capacity of an ecosystem to reorganize and persist in the face of environmental changes. In the past, many internal mechanisms have probably contributed to ecosystem adaptability, allowing Amazonian forests to persist during times of climate change. In this section we synthesize two of these internal mechanisms, which are now being undermined by global change.

Biodiversity

Amazonian forests are home to more than 15,000 tree species, of which 1% are dominant and the other 99% are mostly rare 107 . A single forest hectare in the central and northwestern Amazon can contain more than 300 tree species (Extended Data Fig. 7a ). Such tremendous tree species diversity can increase forest resilience by different mechanisms. Tree species complementarity increases carbon storage, accelerating forest recovery after disturbances 108 . Tree functional diversity increases forest adaptability to climate chance by offering various possibilities of functioning 99 . Rare species provide ‘ecological redundancy’, increasing opportunities for replacement of lost functions when dominant species disappear 109 . Diverse forests are also more likely to resist severe disturbances owing to ‘response diversity’ 110 —that is, some species may die, while others persist. For instance, in the rainy western Amazon, drought-resistant species are rare but present within tree communities 111 , implying that they could replace the dominant drought-sensitive species in a drier future. Diversity of other organisms, such as frugivores and pollinators, also increases forest resilience by stabilizing ecological networks 15 , 112 . Considering that half of Amazonian tree species are estimated to become threatened (IUCN Red list) by 2050 owing to climate change, deforestation and degradation 8 , biodiversity losses could contribute to further reducing forest resilience.

Indigenous peoples and local communities

Globally, Indigenous peoples and local communities (IPLCs) have a key role in maintaining ecosystems resilient to global change 113 . Humans have been present in the Amazon for at least 12,000 years 114 and extensively managing landscapes for 6,000 years 22 . Through diverse ecosystem management practices, humans built thousands of earthworks and ‘Amazon Dark Earth’ sites, and domesticated plants and landscapes across the Amazon forest 115 , 116 . By creating new cultural niches, humans partly modified the Amazonian flora 117 , 118 , increasing their food security even during times of past climate change 119 , 120 without the need for large-scale deforestation 117 . Today, IPLCs have diverse ecological knowledge about Amazonian plants, animals and landscapes, which allows them to quickly identify and respond to environmental changes with mitigation and adaptation practices 68 , 69 . IPLCs defend their territories against illegal deforestation and land use disturbances 49 , 113 , and they also promote forest restoration by expanding diverse agroforestry systems 121 , 122 . Amazonian regions with the highest linguistic diversity (a proxy for ecological knowledge diversity 123 ) are found in peripheral parts of the system, particularly in the north-west (Extended Data Fig. 7b ). However, consistent loss of Amazonian languages is causing an irreversible disruption of ecological knowledge systems, mostly driven by road construction 7 . Continued loss of ecological knowledge will undermine the capacity of IPLCs to manage and protect Amazonian forests, further reducing their resilience to global changes 9 .

CO 2 fertilization

Rising atmospheric CO 2 concentrations are expected to increase the photosynthetic rates of trees, accelerating forest growth and biomass accumulation on a global scale 52 . In addition, CO 2 may reduce water stress by increasing tree water-use efficiency 29 . As result, a ‘CO 2 fertilization effect’ could increase forest resilience to climatic variability 53 , 54 . However, observations from across the Amazon 46 suggest that CO 2 -driven accelerations of tree growth may have contributed to increasing tree mortality rates (trees grow faster but also die earlier), which could eventually neutralize the forest carbon sink in the coming decades 55 . Moreover, increases in tree water-use efficiency may reduce forest transpiration and consequently atmospheric moisture flow across the Amazon 53 , 56 , potentially reducing forest resilience in the southwest of the biome 4 , 37 . Experimental evidence suggests that CO 2 fertilization also depends on soil nutrient availability, particularly nitrogen and phosphorus 57 , 58 . Thus, it is possible that in the fertile soils of the western Amazon and Várzea floodplains, forests may gain resilience from increasing atmospheric CO 2 (depending on how it affects tree mortality rates), whereas on the weathered (nutrient-poor) soils across most of the Amazon basin 59 , forests might not respond to atmospheric CO 2 increase, particularly on eroded soils within deforestation frontiers 60 . In sum, owing to multiple interacting factors, potential responses of Amazonian forests to CO 2 fertilization are still poorly understood. Forest responses depend on scale, with resilience possibly increasing at the local scale on relatively more fertile soils, but decreasing at the regional scale due to reduced atmospheric moisture flow.

Local versus systemic transition

Environmental heterogeneity.

Environmental heterogeneity can reduce the risk of systemic transition (large-scale forest collapse) because when stressing conditions intensify (for example, rainfall declines), heterogeneous forests may transition gradually (first the less resilient forest patches, followed by the more resilient ones), compared to homogeneous forests that may transition more abruptly 17 (all forests transition in synchrony). Amazonian forests are heterogeneous in their resilience to disturbances, which may have contributed to buffering large-scale transitions in the past 37 , 61 , 62 . At the regional scale, a fundamental heterogeneity factor is rainfall and how it translates into water stress. Northwestern forests rarely experience water stress, which makes them relatively more resilient than southeastern forests that may experience water stress in the dry season, and therefore are more likely to shift into a low tree cover state. As a result of low exposure to water deficit, most northwestern forests have trees with low drought resistance and could suffer massive mortality if suddenly exposed to severe water stress 32 . However, this scenario seems unlikely to occur in the near future (Fig. 1 ). By contrast, most seasonal forest trees have various strategies to cope with water deficit owing to evolutionary and adaptive responses to historical drought events 32 , 63 . These strategies may allow seasonal forests to resist current levels of rainfall fluctuations 32 , but seasonal forests are also closer to the critical rainfall thresholds (Extended Data Fig. 1 ) and may experience unprecedented water stress in the coming decades (Fig. 1 ).

Other key heterogeneity factors (Extended Data Fig. 6 ) include topography, which determines plant access to groundwater 64 , and seasonal flooding, which increases forest vulnerability to wildfires 65 . Future changes in rainfall regimes will probably affect hydrological regimes 66 , exposing plateau (hilltop) forests to unprecedented water stress, and floodplain forests to extended floods, droughts and wildfires. Soil fertility is another heterogeneity factor that may affect forest resilience 59 , and which may be undermined by disturbances that cause topsoil erosion 60 . Moreover, as human disturbances intensify throughout the Amazon (Fig. 1 ), the spread of invasive grasses and fires can make the system increasingly homogeneous. Effects of heterogeneity on Amazon forest resilience have been poorly investigated so far (but see refs. 37 , 61 , 62 ) and many questions remain open, such as how much heterogeneity exists in the system and whether it can mitigate a systemic transition.

Sources of connectivity

Connectivity across Amazonian landscapes and regions can contribute to synchronize forest dynamics, causing different forests to behave more similarly 17 . Depending on the processes involved, connectivity can either increase or decrease the risk of systemic transition 17 . For instance, connectivity may facilitate forest recovery after disturbances through seed dispersal, but also it may spread disturbances, such as fire. In the Amazon, an important source of connectivity enhancing forest resilience is atmospheric moisture flow westward (Fig. 2 ), partly maintained by forest evapotranspiration 4 , 37 , 67 . Another example of connectivity that may increase social-ecological resilience is knowledge exchange among IPLCs about how to adapt to global change 68 , 69 (see Box 1 ). However, complex systems such as the Amazon can be particularly vulnerable to sources of connectivity that spread disturbances and increase the risk of systemic transition 70 . For instance, roads carving through the forest are well-known sources of illegal activities, such as logging and burning, which increase forest flammability 38 , 39 .

figure 2

Brazil holds 60% of the Amazon forest biome and has a major responsibility towards its neighbouring countries in the west. Brazil is the largest supplier of rainfall to western Amazonian countries. Up to one-third of the total annual rainfall in Amazonian territories of Bolivia, Peru, Colombia and Ecuador depends on water originating from Brazil’s portion of the Amazon forest. This international connectivity illustrates how policies related to deforestation, especially in the Brazilian Amazon, will affect the climate in other countries. Arrow widths are proportional to the percentage of the annual rainfall received by each country within their Amazonian areas. We only show flows with percentages higher than 10% (see  Methods for details).

Five critical drivers of water stress

Global warming.

Most CMIP6 models agree that a large-scale dieback of the Amazon is unlikely in response to global warming above pre-industrial levels 2 , but this ecosystem response is based on certain assumptions, such as a large CO 2 -fertilization effect 53 . Forests across the Amazon are already responding with increasing tree mortality rates that are not simulated by these models 46 , possibly because of compounding disturbance regimes (Fig. 1 ). Nonetheless, a few global climate models 3 , 14 , 71 , 72 , 73 , 74 indicate a broad range for a potential critical threshold in global warming between 2 and 6 °C (Fig. 3a ). These contrasting results can be explained by general differences between numerical models and their representation of the complex Amazonian system. While some models with dynamic vegetation indicate local-scale tipping events in peripheral parts of the Amazon 5 , 6 , other models suggest an increase in biomass and forest cover (for example, in refs. 53 , 54 ). For instance, a study found that when considering only climatic variability, a large-scale Amazon forest dieback is unlikely, even under a high greenhouse gas emission scenario 75 . However, most updated CMIP6 models agree that droughts in the Amazon region will increase in length and intensity, and that exceptionally hot droughts will become more common 2 , creating conditions that will probably boost other types of disturbances, such as large and destructive forest fires 76 , 77 . To avoid broad-scale ecosystem transitions due to synergies between climatic and land use disturbances (Fig. 3b ), we suggest a safe boundary for the Amazon forest at 1.5 °C for global warming above pre-industrial levels, in concert with the Paris Agreement goals.

figure 3

a , Five critical drivers of water stress on Amazonian forests affect (directly or indirectly) the underlying tipping point of the system. For each driver, we indicate potential critical thresholds and safe boundaries that define a safe operating space for keeping the Amazon forest resilient 11 , 12 . We followed the precautionary principle and considered the most conservative thresholds within the ranges, when confidence was low. b , Conceptual model showing how the five drivers may interact (arrows indicate positive effects) and how these interactions may strengthen a positive feedback between water stress and forest loss. These emerging positive feedback loops could accelerate a systemic transition of the Amazon forest 15 . At global scales, driver 1 (global warming) intensifies with greenhouse gas emissions, including emissions from deforestation. At local scales, driver 5 (accumulated deforestation) intensifies with land use changes. Drivers 2 to 4 (regional rainfall conditions) intensify in response to drivers 1 and 5. The intensification of these drivers may cause widespread tree mortality for instance because of extreme droughts and fires 76 . Water stress affects vegetation resilience globally 79 , 104 , but other stressors, such as heat stress 34 , 36 , may also have a role. In the coming decades, these five drivers could change at different rates, with some approaching a critical threshold faster than others. Therefore, monitoring them separately can provide vital information to guide mitigation and adaptation strategies.

Annual rainfall

Satellite observations of tree cover distributions across tropical South America suggest a critical threshold between 1,000 and 1,250 mm of annual rainfall 78 , 79 . On the basis of our reanalysis using tree cover data from the Amazon basin (Extended Data Fig. 1a ), we confirm a potential threshold at 1,000 mm of annual rainfall (Fig. 3a ), below which forests become rare and unstable. Between 1,000 and 1,800 mm of annual rainfall, high and low tree cover ecosystems exist in the Amazon as two alternative stable states (see Extended Data Table 2 for uncertainty ranges). Within the bistability range in annual rainfall conditions, forests are relatively more likely to collapse when severely disturbed, when compared to forests in areas with annual rainfall above 1,800 mm (Extended Data Fig. 1a ). For floodplain ecosystems covering 14% of the forest biome, a different critical threshold has been estimated at 1,500 mm of annual rainfall 65 , implying that floodplain forests may be the first to collapse in a drier future. To avoid local-scale ecosystem transitions due to compounding disturbances, we suggest a safe boundary in annual rainfall conditions at 1,800 mm.

Rainfall seasonality intensity

Satellite observations of tree cover distributions across tropical South America suggest a critical threshold in rainfall seasonality intensity at −400 mm of the maximum cumulative water deficit 37 , 80 (MCWD). Our reanalysis of the Amazon basin (Extended Data Fig. 1c ) confirms the critical threshold at approximately −450 mm in the MCWD (Fig. 3a ), and suggests a bistability range between approximately −350 and −450 mm (see Extended Data Table 2 for uncertainty ranges), in which forests are more likely to collapse when severely disturbed than forests in areas with MCWD below −350 mm. To avoid local-scale ecosystem transitions due to compounding disturbances, we suggest a safe boundary of MCWD at −350 mm.

Dry season length

Satellite observations of tree cover distributions across tropical South America suggest a critical threshold at 7 months of dry season length 79 (DSL). Our reanalysis of the Amazon basin (Extended Data Fig. 1d ) suggests a critical threshold at eight months of DSL (Fig. 3a ), with a bistability range between approximately five and eight months (see Extended Data Table 2 for uncertainty ranges), in which forests are more likely to collapse when severely disturbed than forests in areas with DSL below five months. To avoid local-scale ecosystem transitions due to compounding disturbances, we suggest a safe boundary of DSL at five months.

Accumulated deforestation

A potential vegetation model 81 found a critical threshold at 20% of accumulated deforestation (Fig. 3a ) by simulating Amazon forest responses to different scenarios of accumulated deforestation (with associated fire events) and of greenhouse gas emissions, and by considering a CO 2 fertilization effect of 25% of the maximum photosynthetic assimilation rate. Beyond 20% deforestation, forest mortality accelerated, causing large reductions in regional rainfall and consequently an ecosystem transition of 50−60% of the Amazon, depending on the emissions scenario. Another study using a climate-vegetation model found that with accumulated deforestation of 30−50%, rainfall in non-deforested areas downwind would decline 67 by 40% (ref.  67 ), potentially causing more forest loss 4 , 37 . Other more recent models incorporating fire disturbances support a potential broad-scale transition of the Amazon forest, simulating a biomass loss of 30–40% under a high-emission scenario 5 , 82 (SSP5–8.5 at 4 °C). The Amazon biome has already lost 13% of its original forest area due to deforestation 83 (or 15% of the biome if we consider also young secondary forests 83 that provide limited contribution to moisture flow 84 ). Among the remaining old-growth forests, at least 38% have been degraded by land use disturbances and repeated extreme droughts 39 , with impacts on moisture recycling that are still uncertain. Therefore, to avoid broad-scale ecosystem transitions due to runaway forest loss (Fig. 3b ), we suggest a safe boundary of accumulated deforestation of 10% of the original forest biome cover, which requires ending large-scale deforestation and restoring at least 5% of the biome.

Three alternative ecosystem trajectories

Degraded forest.

In stable forest regions of the Amazon with annual rainfall above 1,800 mm (Extended Data Fig. 1b ), forest cover usually recovers within a few years or decades after disturbances, yet forest composition and functioning may remain degraded for decades or centuries 84 , 85 , 86 , 87 . Estimates from across the Amazon indicate that approximately 30% of areas previously deforested are in a secondary forest state 83 (covering 4% of the biome). An additional 38% of the forest biome has been damaged by extreme droughts, fires, logging and edge effects 38 , 39 . These forests may naturally regrow through forest succession, yet because of feedbacks 15 , succession can become arrested, keeping forests persistently degraded (Fig. 4 ). Different types of degraded forests have been identified in the Amazon, each one associated with a particular group of dominant opportunistic plants. For instance, Vismia forests are common in old abandoned pastures managed with fire 85 , and are relatively stable, because Vismia trees favour recruitment of Vismia seedlings in detriment of other tree species 88 , 89 . Liana forests can also be relatively stable, because lianas self-perpetuate by causing physical damage to trees, allowing lianas to remain at high density 90 , 91 . Liana forests are expected to expand with increasing aridity, disturbance regimes and CO 2 fertilization 90 . Guadua bamboo forests are common in the southwestern Amazon 92 , 93 . Similar to lianas, bamboos self-perpetuate by causing physical damage to trees and have been expanding over burnt forests in the region 92 . Degraded forests are usually dominated by native opportunistic species, and their increasing expansion over disturbed forests could affect Amazonian functioning and resilience in the future.

figure 4

From examples of disturbed forests across the Amazon, we identify the three most plausible ecosystem trajectories related to the types of disturbances, feedbacks and local environmental conditions. These alternative trajectories may be irreversible or transient depending on the strength of the novel interactions 15 . Particular combinations of interactions (arrows show positive effects described in the literature) may form feedback loops 15 that propel the ecosystem through these trajectories. In the ‘degraded forest’ trajectory, feedbacks often involve competition between trees and other opportunistic plants 85 , 90 , 92 , as well as interactions between deforestation, fire and seed limitation 84 , 87 , 105 . At the landscape scale, secondary forests are more likely to be cleared than mature forests, thus keeping forests persistently young and landscapes fragmented 83 . In the ‘degraded open-canopy ecosystem’ trajectory, feedbacks involve interactions among low tree cover and fire 97 , soil erosion 60 , seed limitation 105 , invasive grasses and opportunistic plants 96 . At the regional scale, a self-reinforcing feedback between forest loss and reduced atmospheric moisture flow may increase the resilience of these open-canopy degraded ecosystems 42 . In the ‘white-sand savanna’ trajectory, the main feedbacks result from interactions among low tree cover and fire, soil erosion, and seed limitation 106 . Bottom left, floodplain forest transition to white-sand savanna after repeated fires (photo credit: Bernardo Flores); bottom centre, forest transition to degraded open-canopy ecosystem after repeated fires (photo credit: Paulo Brando); bottom right, forest transition to Vismia degraded forest after slash-and-burn agriculture (photo credit: Catarina Jakovac).

White-sand savanna

White-sand savannas are ancient ecosystems that occur in patches within the Amazon forest biome, particularly in seasonally waterlogged or flooded areas 94 . Their origin has been attributed to geomorphological dynamics and past Indigenous fires 26 , 27 , 94 . In a remote landscape far from large agricultural frontiers, within a stable forest region of the Amazon (Extended Data Fig. 1b ), satellite and field evidence revealed that white-sand savannas are expanding where floodplain forests were repeatedly disturbed by fires 95 . After fire, the topsoil of burnt forests changes from clayey to sandy, favouring the establishment of savanna trees and native herbaceous plants 95 . Shifts from forest to white-sand savanna (Fig. 4 ) are probably stable (that is, the ecosystem is unlikely to recover back to forest within centuries), based on the relatively long persistence of these savannas in the landscape 94 . Although these ecosystem transitions have been confirmed only in the Negro river basin (central Amazon), floodplain forests in other parts of the Amazon were shown to be particularly vulnerable to collapse 45 , 64 , 65 .

Degraded open-canopy ecosystem

In bistable regions of the Amazon forest with annual rainfall below 1,800 mm (Extended Data Fig. 1b ), shifts to degraded open-canopy ecosystems are relatively common after repeated disturbances by fire 45 , 96 . The ecosystem often becomes dominated by fire-tolerant tree and palm species, together with alien invasive grasses and opportunistic herbaceous plants 96 , 97 , such as vines and ferns. Estimates from the southern Amazon indicate that 5−6% of the landscape has already shifted into degraded open-canopy ecosystems due to deforestation and fires 45 , 96 . It is still unclear, however, whether degraded open-canopy ecosystems are stable or transient (Fig. 4 ). Palaeorecords from the northern Amazon 98 show that burnt forests may spend centuries in a degraded open-canopy state before they eventually shift into a savanna. Today, invasion by alien flammable grasses is a novel stabilizing mechanism 96 , 97 , but the long-term persistence of these grasses in the ecosystem is also uncertain.

Prospects for modelling Amazon forest dynamics

Several aspects of the Amazon forest system may help improve earth system models (ESMs) to more accurately simulate ecosystem dynamics and feedbacks with the climate system. Simulating individual trees can improve the representation of growth and mortality dynamics, which ultimately affect forest dynamics (for example, refs. 61 , 62 , 99 ). Significant effects on simulation results may emerge from increasing plant functional diversity, representation of key physiological trade-offs and other features that determine water stress on plants, and also allowing for community adjustment to environmental heterogeneity and global change 32 , 55 , 62 , 99 . For now, most ESMs do not simulate a dynamic vegetation cover (Supplementary Table 1 ) and biomes are represented based on few plant functional types, basically simulating monocultures on the biome level. In reality, tree community adaptation to a heterogenous and dynamic environment feeds into the whole-system dynamics, and not covering such aspects makes a true Amazon tipping assessment more challenging.

Our findings also indicate that Amazon forest resilience is affected by compounding disturbances (Fig. 1 ). ESMs need to include different disturbance scenarios and potential synergies for creating more realistic patterns of disturbance regimes. For instance, logging and edge effects can make a forest patch more flammable 39 , but these disturbances are often not captured by ESMs. Improvements in the ability of ESMs to predict future climatic conditions are also required. One way is to identify emergent constraints 100 , lowering ESMs variations in their projections of the Amazonian climate. Also, fully coupled ESMs simulations are needed to allow estimates of land-atmosphere feedbacks, which may adjust climatic and ecosystem responses. Another way to improve our understanding of the critical thresholds for Amazonian resilience and how these link to climatic conditions and to greenhouse gas concentrations is through factorial simulations with ESMs. In sum, although our study may not deliver a set of reliable and comprehensive equations to parameterize processes impacting Amazon forest dynamics, required for implementation in ESMs, we highlight many of the missing modelled processes.

Implications for governance

Forest resilience is changing across the Amazon as disturbance regimes intensify (Fig. 1 ). Although most recent models agree that a large-scale collapse of the Amazon forest is unlikely within the twenty-first century 2 , our findings suggest that interactions and synergies among different disturbances (for example, frequent extreme hot droughts and forest fires) could trigger unexpected ecosystem transitions even in remote and central parts of the system 101 . In 2012, Davidson et al. 102 demonstrated how the Amazon basin was experiencing a transition to a ‘disturbance-dominated regime’ related to climatic and land use changes, even though at the time, annual deforestation rates were declining owing to new forms of governance 103 . Recent policy and approaches to Amazon development, however, accelerated deforestation that reached 13,000 km 2 in the Brazilian Amazon in 2021 ( http://terrabrasilis.dpi.inpe.br ). The southeastern region has already turned into a source of greenhouse gases to the atmosphere 48 . The consequences of losing the Amazon forest, or even parts of it, imply that we must follow a precautionary approach—that is, we must take actions that contribute to maintain the Amazon forest within safe boundaries 12 . Keeping the Amazon forest resilient depends firstly on humanity’s ability to stop greenhouse gas emissions, mitigating the impacts of global warming on regional climatic conditions 2 . At the local scale, two practical and effective actions need to be addressed to reinforce forest–rainfall feedbacks that are crucial for the resilience of the Amazon forest 4 , 37 : (1) ending deforestation and forest degradation; and (2) promoting forest restoration in degraded areas. Expanding protected areas and Indigenous territories can largely contribute to these actions. Our findings suggest a list of thresholds, disturbances and feedbacks that, if well managed, can help maintain the Amazon forest within a safe operating space for future generations.

Our study site was the area of the Amazon basin, considering large areas of tropical savanna biome along the northern portion of the Brazilian Cerrado, the Gran Savana in Venezuela and the Llanos de Moxos in Bolivia, as well as the Orinoco basin to the north, and eastern parts of the Andes to the west. The area includes also high Andean landscapes with puna and paramo ecosystems. We chose this contour to allow better communication with the MapBiomas Amazonian Project (2022; https://amazonia.mapbiomas.org ). For specific interpretation of our results, we considered the contour of the current extension of the Amazon forest biome, which excludes surrounding tropical savanna biomes.

We used the Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Continuous Fields (VCF) data (MOD44B version 6; https://lpdaac.usgs.gov/products/mod44bv006/ ) for the year 2001 at 250-m resolution 124 to reanalyse tree cover distributions within the Amazon basin, refining estimates of bistability ranges and critical thresholds in rainfall conditions from previous studies. Although MODIS VCF can contain errors within lower tree cover ranges and should not be used to test for bistability between grasslands and savannas 125 , the dataset is relatively robust for assessing bistability within the tree cover range of forests and savannas 126 , as also shown by low uncertainty (standard deviation of tree cover estimates) across the Amazon (Extended Data Fig. 8 ).

We used the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS; https://www.chc.ucsb.edu/data/chirps ) 127 to estimate mean annual rainfall and rainfall seasonality for the present across the Amazon basin, based on monthly means from 1981 to 2020, at a 0.05° spatial resolution.

We used the Climatic Research Unit (CRU; https://www.uea.ac.uk/groups-and-centres/climatic-research-unit ) 128 to estimate mean annual temperature for the present across the Amazon basin, based on monthly means from 1981 to 2020, at a 0.5° spatial resolution.

To mask deforested areas until 2020, we used information from the MapBiomas Amazonia Project (2022), collection 3, of Amazonian Annual Land Cover and Land Use Map Series ( https://amazonia.mapbiomas.org ).

To assess forest fire distribution across the Amazon forest biome and in relation to road networks, we used burnt area fire data obtained from the AQUA sensor onboard the MODIS satellite. Only active fires with a confidence level of 80% or higher were selected. The data are derived from MODIS MCD14ML (collection 6) 129 , available in Fire Information for Resource Management System (FIRMS). The data were adjusted to a spatial resolution of 1 km.

Potential analysis

Using potential analysis 130 , an empirical stability landscape was constructed based on spatial distributions of tree cover (excluding areas deforested until 2020; https://amazonia.mapbiomas.org ) against mean annual precipitation, MCWD and DSL. Here we followed the methodology of Hirota et al. 104 . For bins of each of the variables, the probability density of tree cover was determined using the MATLAB function ksdensity. Local maxima of the resulting probability density function are considered to be stable equilibria, in which local maxima below a threshold value of 0.005 were ignored. Based on sensitivity tests (see below), we chose the intermediate values of the sensitivity parameter for each analysis, which resulted in the critical thresholds most similar to the ones previously published in the literature.

Sensitivity tests of the potential analysis

We smoothed the densities of tree cover with the MATLAB kernel smoothing function ksdensity. Following Hirota et al. 104 , we used a flexible bandwidth ( h ) according to Silverman’s rule of thumb 131 : h  = 1.06 σn 1/5 , where σ is the standard deviation of the tree cover distribution and n is the number of points. To ignore small bumps in the frequency distributions, we used a dimensionless sensitivity parameter. This parameter filters out weak modes in the distributions such that a higher value implies a stricter criterion to detect a significant mode. In the manuscript, we used a value of 0.005. For different values of this sensitivity parameter, we here test the estimated critical thresholds and bistability ranges (Extended Data Table 2 ). We inferred stable and unstable states of tree cover (minima and maxima in the potentials) for moving windows of the climatic variables. For mean annual precipitation, we used increments of 10 mm yr −1 between 0 and 3500 mm yr −1 . For dry season length, we used increments of 0.1 months between 0 and 12 months. For MCWD, we used increments of 10 mm between −800 mm and 0 mm.

Transition potential

We quantified a relative ecosystem transition potential across the Amazon forest biome (excluding accumulated deforestation; https://amazonia.mapbiomas.org ) to produce a simple spatial measure that can be useful for governance. For this, we combined information per pixel, at 5 km resolution, about different disturbances related to climatic and human disturbances, as well as high-governance areas within protected areas and Indigenous territories. We used values of significant slopes of the dry season (July–October) mean temperature between 1981 and 2020 ( P  < 0.1), estimated using simple linear regressions (at 0.5° resolution from CRU) (Fig. 1a ). Ecosystem stability classes (stable forest, bistable and stable savanna as in Extended Data Fig. 1 ) were estimated using simple linear regression slopes of annual rainfall between 1981 and 2020 ( P  < 0.1) (at 0.05° resolution from CHIRPS), which we extrapolated to 2050 (Fig. 1b and Extended Data Fig. 3 ). Distribution of areas affected by repeated extreme drought events (Fig. 1c ) were defined when the time series (2001–2018) of the MCWD reached two standard deviation anomalies from historical mean. Extreme droughts were obtained from Lapola et al. 39 , based on Climatic Research Unit gridded Time Series (CRU TS 4.0) datasets for precipitation and evapotranspiration. The network of roads (paved and unpaved) across the Amazon forest biome (Fig. 1d ) was obtained from the Amazon Network of Georeferenced Socio-Environmental Information (RAISG; https://geo2.socioambiental.org/raisg ). Protected areas (PAs) and Indigenous territories (Fig. 1e ) were also obtained from RAISG, and include both sustainable-use and restricted-use protected areas managed by national or sub-national governments, together with officially recognized and proposed Indigenous territories. We combined these different disturbance layers by adding a value for each layer in the following way: (1) slopes of dry season temperature change (as in Fig. 1a , multiplied by 10, thus between −0.1 and +0.6); (2) ecosystem stability classes estimated for year 2050 (as in Fig. 1b ), with 0 for stable forest, +1 for bistable and +2 for stable savanna; (3) accumulated impacts from repeated extreme drought events (from 0 to 5 events), with +0.2 for each event; (4) road-related human impacts, with +1 for pixels within 10 km from a road; and (5) protected areas and Indigenous territories as areas with lower exposure to human (land use) disturbances, such as deforestation and forest fires, with −1 for pixels inside these areas. The sum of these layers revealed relative spatial variation in ecosystem transition potential by 2050 across the Amazon (Fig. 1f ), ranging from −1 (low potential) to 4 (very high potential).

Atmospheric moisture tracking

To determine the atmospheric moisture flows between the Amazonian countries, we use the Lagrangian atmospheric moisture tracking model UTrack 132 . The model tracks the atmospheric trajectories of parcels of moisture, updates their coordinates at each time step of 0.1 h and allocates moisture to a target location in case of precipitation. For each millimetre of evapotranspiration, 100 parcels are released into the atmosphere. Their trajectories are forced with evaporation, precipitation, and wind speed estimates from the ERA5 reanalysis product at 0.25° horizontal resolution for 25 atmospheric layers 133 . Here we use the runs from Tuinenburg et al. 134 , who published monthly climatological mean (2008–2017) moisture flows between each pair of 0.5° grid cells on Earth. We aggregated these monthly flows, resulting in mean annual moisture flows between all Amazonian countries during 2008–2017. For more details of the model runs, we refer to Tuinenburg and Staal 132 and Tuinenburg et al. 134 .

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

All data supporting the findings of this study are openly available and their sources are presented in the Methods.

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Acknowledgements

This work was inspired by the Science Panel for the Amazon (SPA) initiative ( https://www.theamazonwewant.org/ ) that produced the first Amazon Assessment Report (2021). The authors thank C. Smith for providing deforestation rates data used in Extended Data Fig. 5b . B.M.F. and M.H. were supported by Instituto Serrapilheira (Serra-1709-18983) and C.J. (R-2111-40341). A.S. acknowledges funding from the Dutch Research Council (NWO) under the Talent Program Grant VI.Veni.202.170. R.A.B. and D.M.L. were supported by the AmazonFACE programme funded by the UK Foreign, Commonwealth and Development Office (FCDO) and Brazilian Ministry of Science, Technology and Innovation (MCTI). R.A.B. was additionally supported by the Met Office Climate Science for Service Partnership (CSSP) Brazil project funded by the UK Department for Science, Innovation and Technology (DSIT), and D.M.L. was additionally supported by FAPESP (grant no. 2020/08940-6) and CNPq (grant no. 309074/2021-5). C.L. thanks CNPq (proc. 159440/2018-1 and 400369/2021-4) and Brazil LAB (Princeton University) for postdoctoral fellowships. A.E.-M. is supported by the UKRI TreeScapes MEMBRA (NE/V021346/1), the Royal Society (RGS\R1\221115), the ERC TreeMort project (758873) and the CESAB Syntreesys project. R.S.O. received a CNPq productivity scholarship and funding from NERC-FAPESP 2019/07773-1. S.B.H. is supported by the Geneva Graduate Institute research funds, and UCLA’s committee on research. J.A.M. is supported by the National Institute of Science and Technology for Climate Change Phase 2 under CNPq grant 465501/2014-1; FAPESP grants 2014/50848-9, the National Coordination for Higher Education and Training (CAPES) grant 88887.136402-00INCT. L.S.B. received FAPESP grant 2013/50531-0. D.N. and N.B. acknowledge funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 820970. N.B. has received further funding from the Volkswagen foundation, the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement no. 956170, as well as from the German Federal Ministry of Education and Research under grant no. 01LS2001A.

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Graduate Program in Ecology, Federal University of Santa Catarina, Florianopolis, Brazil

Bernardo M. Flores, Carolina Levis & Marina Hirota

Geosciences Barcelona, Spanish National Research Council, Barcelona, Spain

Encarni Montoya

Potsdam Institute for Climate Impact Research, Member of the Leibniz Association, Potsdam, Germany

Boris Sakschewski, Da Nian & Niklas Boers

Institute of Advanced Studies, University of São Paulo, São Paulo, Brazil

Nathália Nascimento & Carlos A. Nobre

Copernicus Institute of Sustainable Development, Utrecht University, Utrecht, The Netherlands

Met Office Hadley Centre, Exeter, UK

Richard A. Betts

Global Systems Institute, University of Exeter, Exeter, UK

Center for Meteorological and Climatic Research Applied to Agriculture, University of Campinas, Campinas, Brazil

David M. Lapola

School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, UK

Adriane Esquível-Muelbert

Birmingham Institute of Forest Research, University of Birmingham, Birmingham, UK

Department of Plant Sciences, Federal University of Santa Catarina, Florianopolis, Brazil

Catarina Jakovac

Department of Plant Biology, University of Campinas, Campinas, Brazil

Rafael S. Oliveira & Marina Hirota

Division of Impacts, Adaptation and Vulnerabilities (DIIAV), National Institute for Space Research, São José dos Campos, Brazil

Laura S. Borma & Luciana V. Gatti

Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany

Niklas Boers

Luskin School for Public Affairs and Institute of the Environment, University of California, Los Angeles, CA, USA

Susanna B. Hecht

Naturalis Biodiversity Center, Leiden, The Netherlands

Hans ter Steege

Quantitative Biodiversity Dynamics, Utrecht University, Utrecht, The Netherlands

Science Panel for the Amazon (SPA), São José dos Campos, Brazil

Julia Arieira

Sustainable Development Solutions Network, New York, NY, USA

Isabella L. Lucas

Environmental Change Institute, University of Oxford, Oxford, UK

Erika Berenguer

Centro Nacional de Monitoramento e Alerta de Desastres Naturais, São José dos Campos, Brazil

José A. Marengo

Graduate Program in Natural Disasters, UNESP/CEMADEN, São José dos Campos, Brazil

Graduate School of International Studies, Korea University, Seoul, Korea

Program in Atmospheric and Oceanic Sciences, Princeton University, Princeton, NJ, USA

Caio R. C. Mattos

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Contributions

B.M.F. and M.H. conceived the study. B.M.F. reviewed the literature, with inputs from all authors. B.M.F., M.H., N.N., A.S., C.L., D.N, H.t.S. and C.R.C.M. assembled datasets. M.H. analysed temperature and rainfall trends. B.M.F. and N.N. produced the maps in main figures and calculated transition potential. A.S. performed potential analysis and atmospheric moisture tracking. B.M.F. produced the figures and wrote the manuscript, with substantial inputs from all authors. B.S. wrote the first version of the ‘Prospects for modelling Amazon forest dynamics’ section, with inputs from B.M.F and M.H.

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Correspondence to Bernardo M. Flores or Marina Hirota .

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Extended data figures and tables

Extended data fig. 1 alternative stable states in amazonian tree cover relative to rainfall conditions..

Potential analysis of tree cover distributions across rainfall gradients in the Amazon basin suggest the existence of critical thresholds and alternative stable states in the system. For this, we excluded accumulated deforestation until 2020 and included large areas of tropical savanna biome in the periphery of the Amazon basin (see  Methods ). Solid black lines indicate two stable equilibria. Small grey arrows indicate the direction towards equilibrium. (a) The overlap between ~ 1,000 and 1,800 mm of annual rainfall suggests that two alternative stable states may exist (bistability): a high tree cover state ~ 80 % (forests), and a low tree cover state ~ 20% (savannas). Tree cover around 50 % is rare, indicating an unstable state. Below 1,000 mm of annual rainfall, forests are rare, indicating a potential critical threshold for abrupt forest transition into a low tree cover state 79 , 104 (arrow 1). Between 1,000 and 1,800 mm of annual rainfall, the existence of alternative stable states implies that forests can shift to a low tree cover stable state in response to disturbances (arrow 2). Above 1,800 mm of annual rainfall, low tree cover becomes rare, indicating a potential critical threshold for an abrupt transition into a high tree cover state. In this stable forest state, forests are expected to always recover after disturbances (arrow 3), although composition may change 47 , 85 . (b) Currently, the stable savanna state covers 1 % of the Amazon forest biome, bistable areas cover 13 % of the biome (less than previous analysis using broader geographical ranges 78 ) and the stable forest state covers 86 % of the biome. Similar analyses using the maximum cumulative water deficit (c) and the dry season length (d) also suggest the existence of critical thresholds and alternative stable states. When combined, these critical thresholds in rainfall conditions could result in a tipping point of the Amazon forest in terms of water stress, but other factors may play a role, such as groundwater availability 64 . MODIS VCF may contain some level of uncertainty for low tree cover values, as shown by the standard deviation of tree cover estimates across the Amazon (Extended Data Fig. 8 ). However, the dataset is relatively robust for assessing bistability within the tree cover range between forest and savanna 126 .

Extended Data Fig. 2 Changes in dry-season temperatures across the Amazon basin.

(a) Dry season temperature averaged from mean annual data observed between 1981 and 2010. (b) Changes in dry season mean temperature based on the difference between the projected future (2021−2050) and observed historical (1981−2010) climatologies. Future climatology was obtained from the estimated slopes using historical CRU data 128 (shown in Fig. 1a ). (c, d) Changes in the distributions of dry season mean and maximum temperatures for the Amazon basin. (e) Correlation between dry-season mean and maximum temperatures observed (1981–2010) across the Amazon basin ( r  = 0.95).

Extended Data Fig. 3 Changes in annual precipitation and ecosystem stability across the Amazon forest biome.

(a) Slopes of annual rainfall change between 1981 and 2020 estimated using simple regressions (only areas with significant slopes, p  < 0.1). (b) Changes in ecosystem stability classes projected for year 2050, based on significant slopes in (a) and critical thresholds in annual rainfall conditions estimated in Extended Data Fig. 1 . Data obtained from Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), at 0.05° spatial resolution 127 .

Extended Data Fig. 4 Changes in ecosystem stability by 2050 across the Amazon based on annual rainfall projected by CMIP6 models.

(a) Changes in stability classes estimated using an ensemble with the five CMIP6 models that include vegetation modules (coupled for climate-vegetation feedbacks) for two emission scenarios (Shared Socio-economic Pathways - SSPs). (b) Changes in stability classes estimated using an ensemble with all 33 CMIP6 models for the same emission scenarios. Stability changes may occur between stable forest (F), stable savanna (S) and bistable (B) classes, based on the bistability range of 1,000 – 1,800 mm in annual rainfall, estimated from current rainfall conditions (see Extended Data Fig. 1 ). Projections are based on climate models from the 6 th Phase of the Coupled Model Intercomparison Project (CMIP6). SSP2-4.5 is a low-emission scenario of future global warming and SSP5-8.5 is a high-emission scenario. The five coupled models analysed separately in (a) were: EC-Earth3-Veg, GFDL-ESM4, MPI-ESM1-2-LR, TaiESM1 and UKESM1-0-LL (Supplementary Information Table 1 ).

Extended Data Fig. 5 Deforestation continues to expand within the Amazon forest system.

(a) Map highlighting deforestation and fire activity between 2012 and 2021, a period when environmental governance began to weaken again, as indicated by increasing rates of annual deforestation in (b). In (b), annual deforestation rates for the entire Amazon biome were adapted with permission from Smith et al. 83 .

Extended Data Fig. 6 Environmental heterogeneity in the Amazon forest system.

Heterogeneity involves myriad factors, but two in particular, related to water availability, were shown to contribute to landscape-scale heterogeneity in forest resilience; topography shapes fine-scale variations of forest drought-tolerance 135 , 136 , and floodplains may reduce forest resilience by increasing vulnerability to wildfires 65 . Datasets: topography is shown by the Shuttle Radar Topography Mission (SRTM; https://earthexplorer.usgs.gov/ ) 137 at 90 m resolution; floodplains and uplands are separated with the Amazon wetlands mask 138 at 90 m resolution.

Extended Data Fig. 7 The Amazon is biologically and culturally diverse.

(a) Tree species richness and (b) language richness illustrate how biological and cultural diversity varies across the Amazon. Diverse tree communities and human cultures contribute to increasing forest resilience in various ways that are being undermined by land-use and climatic changes. Datasets: (a) Amazon Tree Diversity Network (ATDN, https://atdn.myspecies.info ). (b) World Language Mapping System (WLMS) obtained under license from Ethnologue 139 .

Extended Data Fig. 8 Uncertainty of the MODIS VCF dataset across the Amazon basin.

Map shows standard deviation (SD) of tree cover estimates from MODIS VCF 124 . We masked deforested areas until 2020 using the MapBiomas Amazonia Project (2022; https://amazonia.mapbiomas.org ).

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Flores, B.M., Montoya, E., Sakschewski, B. et al. Critical transitions in the Amazon forest system. Nature 626 , 555–564 (2024). https://doi.org/10.1038/s41586-023-06970-0

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DOI : https://doi.org/10.1038/s41586-023-06970-0

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