Sustainability Science

Key Issues

Ariane König, Jerome Ravetz, Ariane König, Jerome Ravetz

• Publisher: Routledge
• Year: 2017
• Print ISBN: 9781138659278
• eBook ISBN: 9781317216629
• Topic: Economics
• Subtopic: Sustainable Development
• Index: Economics

Sustainability Science: Key Issues is a comprehensive textbook for undergraduates, postgraduates, and participants in executive trainings from any disciplinary background studying the theory and practice of sustainability science. Each chapter takes a critical and reflective stance on a key issue or method of sustainability science. Contributing authors offer perspectives from diverse disciplines, including physics, philosophy of science, agronomy, geography, and the learning sciences.

This book equips readers with a better understanding of how one might actively design, engage in, and guide collaborative processes for transforming human-environment-technology interactions, whilst embracing complexity, contingency, uncertainties, and contradictions emerging from diverse values and world views. Each reader of this book will thus have guidance on how to create and/or engage in similar initiatives or courses in their own context.

Sustainability Science: Key Issues is the ideal book for students and researchers engaged in problem and project based learning in sustainability science.

Part I

Embracing complexity and alternative futures

Conceptual tools and methods

3 Systems approaches for transforming social practice

Design requirements

Ariane König

Our whole education tends to only draw simple logical conclusions and defining obvious cause and effect relationships [which] have no existence in reality. In reality all is indirect effects, networks, connections and time delays. Our civilisation will succeed […] only if it acquires a far greater knowledge of systemic connections in complex systems.

Frederic Vester (2007)

Challenges to designing systems approaches for societal transformation

Accelerating and interconnected change in the spheres of technology, economy, environment, and society raises existential questions about future reliable access to land, water, food, and energy in a growing number of geographic areas. In light of these considerations, perhaps one of the greatest challenges society faces lies in the way of how we conceive of and organize knowledge and learning in research, education, business, and governance. For example, water, food, and energy systems, and in particular the human, technological, and ecological elements of these systems, are usually considered separately by experts dedicated to specific knowledge fields. Disciplinary silos direct attention at a specific system, its spatial and time scales, and build on assumptions that usually reduce complexity, uncertainties, and aggregating contradictory perspectives. Science relies on peer review and career reward systems that often suppress specific kinds of questions, divergence, and contradictions, and to make matters worse, science education usually conveys specific sets of beliefs and worldviews as set frames for perceiving reality (Kuhn, 1962). Researchers in the natural and social science not only rely on different theories and methods, but also on disparate language and sets of values and worldviews that shape how they order and represent knowledge; they often don’t understand each other, even when they genuinely try to engage in joint research. Developing some mutual understanding and shared language and representations can take years. Moreover, disciplines mostly drive towards abstract and generalisable knowledge rather than considering situated knowledge and locally specific interdependencies, experiences, and preferences. These may be essential for finding shared approaches to act on potential threats, for example, of resource depletion and pollution.

This chapter compares diverse analytic frameworks developed by researchers to understand ourselves and the environments we live in as complex and dynamic systems, in which our conceptions, actions, and environments change interdependently. The frameworks we discuss in this chapter are designed to help us conceive of the environments we relate to as different interacting spheres (or sub-systems) of humans and society, technology, economy, and environment (or eco-systems). The frameworks help us to understand how the overall behaviour of each of these sub-systems and the entire set may depend on how each sub-system is connected to and depends on changes in the other sub-systems. The different frameworks we present on how humans relate to and depend on their environments have emerged from different fields of knowledge, including environmental science, economics and management, and human ecology.

There are several challenges to the design of such concepts and tools to guide collaborative sustainability science on dynamic complex systems, which we address in this chapter. First, insights from cognitive psychology suggest that cognitive pitfalls in trying to embrace such complexity in science, policy, and practice are many-fold (see Chapter 4 by Sonnleitner); our minds start to struggle when confronted with analysing relationships between just six or seven semi-interdependent variables. In practice and policy making, we are all caught up in the usual linear thinking we all have been trained for at school. We also usually lack the ability to embrace uncertainty and ignorance. These circumstances usually lead to policies and action that are ineffective or have side effects unintended from solutions (Forrester, 1969). Such pitfalls include incorrect descriptions of goals due to a focus on solving individual problems, such as energy security in the face of variable oil prices. The resulting one-dimensional analysis then does not consider feedbacks between systems and instead irreversibly foregrounds one possible solution for further analysis rather than trying to grasp a big systemic picture and first suggesting solutions that take account of interactions (Dörner, 1975). For example, in building the Aswan Dam for hydro-energy in Egypt, the huge and real impacts on food yields from a reduction in the annual flooding in the Nile valley, which had sustained Egyptian civilisation for millennia, was apparently entirely neglected. Further cognitive pitfalls also evident in this example include authoritarian behaviour from leaders and experts in technocracies in the face of uncertainty and ignorance. This leads to a reduced field of vision from consideration only of perspectives of those in power. Our current education systems, with a focus on identifying linear cause–effect relationships with disciplined knowledge fields and analytic practices, are particularly problematic. Society must now unlearn these assumptions and relearn different approaches to the understanding of complex problems by engaging in different processes for sense-making, knowing, learning, and creating (Boyden, 1981; Senge, 1990; Schön, 1993; Vester, 2002).

Radically new concepts of knowledge and learning, and associated social processes and spaces, are required (Wegerif, 2007, building on the sociologist Castells; see also the introduction of this book). Moreover, these new practices must emerge from and be fit for the networked society of the future, rather than an industrial society of the past. A networked society holds many untapped opportunities for scientific knowledge co-creation processes. Knowledge gaps and contradictions may become more easily apparent and less troubling to embrace, and these can present spaces for creativity (Wegerif, 2007). A serious challenge here is the requirement for new types of quality criteria for legitimating the emerging new shared knowledge.

In order to explore the emergence of different frameworks to address these challenges, this chapter first provides a short overview on the foundations of systems thinking that have been specifically developed to better understand human–environment interactions in the 1970s and 1980s, which are considered of significant influence to the selection of more recent frameworks presented in this chapter. Subsequently we develop a set of criteria to assess whether conceptual frameworks designed to describe human–environment interactions are suitable for structuring transformative sustainability sciences as a collaborative research process with diverse stakeholders (as described in Chapter 1 of this book). The subsequent section then singles out different systems approaches that are complementary in that they direct attention to different dynamics in human–environment interactions as they have emerged from different fields of knowledge, including environmental science, economics and management, and human ecology, and briefly discusses their merits and limitations. The frameworks presented in this chapter are selected on the basis that they have proven influential in science, policy, and practice. The conclusion highlights their significance and the urgent need to start thinking in terms of systemic interactions. It also stresses the need to use such frameworks as a basis for new approaches to combining research, learning, policy making, and social practice toward more sustainable governance.

Foundations of systems thinking: 1970s and 1980s

The exploding scientific literature with attempts to conceptualise and develop solutions to improve human–environment interactions was already of concern in the 1970s (Ehrlich & Holdren, 1971, 1972). Ecologist Paul Ehrlich and nuclear physicist John Holdren cautioned that theoretical solutions to problems are often not operational and that sometimes they are not solutions at all. A simple framework for realistic analysis as a basis for developing practical solutions was deemed necessary. The resulting ‘IPAT equation’ (Figure 3.1) conceives of environmental Impacts as a function of the size of the human Population, multiplied by the Affluence that affects material flows from our societal metabolism in the form of consumption and production, the impacts of which are in turn modulated (both positively and negatively) by the Technologies that are produced and in use.

This simple framework to structure discussions must be used with care, as there is no explicit warning that the variables on the right side of the equation are not independent: institutions, values, and beliefs can influence all of the variables and the nature of their interactions among them, and their interactions vary in space and over time. Further detailing of the main variables in italics and brackets is derived from Holdren et al. (1995).

This equation was developed to draw attention to what the authors deem are the major factors that affect the human–environment relationship. The framework clearly points out that at a sustainability limit there will be trade-offs between population, resource through-put, and thus economic activity per person, strongly suggesting that simply associating societal progress with economic growth no longer makes sense. Technology was considered an important factor for independent consideration in the mediation of impacts of a growing human population on environments in which it is embedded.

Figure 3.1 The IPAT equation

Basic principles: stocks, flows, constraints, feedbacks, and experience

Stocks and flows: At the core of the problem for closer analysis – that of survival of a growing human population on a bounded planet – from an economist’s point of view stand finite stocks of essential resources on earth that may be depleted or degraded by human activities more quickly by outflows than they can be renewed or replaced by inflows (Daly, 1973, 1977, 1991; Daly et al., 1989). Representations of stock–flow models as in Figure 3.2 were developed in early systematic analysis of the relation of economy and a planet with finite resources. Accordingly, flows replenishing and depleting key stocks should be in equilibrium over time. Stocks can decouple inflows from outflows, allowing for these to be out of balance for a limited amount of time, depending on their flow rates.

Constraints. These examples of stocks and flows of non-renewable sources and renewable sources also clearly demonstrate the principle of ‘constraints on system behaviour’. Systemic constraints are determined by the size of the stock of a non-renewable resource and/or the rate of the flow for renewable resources upon which the functioning of the system depends.

Figure 3.2 Stock–flow models of resource use on a finite planet (Daly, 1991)

This stock–flow model is based on Daly’s work (1973, 1977, 1991; Daly et al., 1989) highlighting requisites for resource use on a finite planet

1 Rates of use of renewable resources do not exceed regeneration rates

2 Rates of use of non-renewables resources do not exceed rates of development of renewables substitutes

3 Rates of pollution emission do not exceed assimilative capacities of the environmentFigure also adapted from Dyball and Newell (2015, p. 95)

Feedbacks. Any self-organised system, including any life form on earth, depends on mechanisms to change and adapt flows in order to regulate the maintenance of its stocks. The vital mechanisms that regulate the inflow and outflow of these stocks in response to internal or external changes are called feedbacks. For example, any living system at any scale (unicellular, multi-cellular organisms, ecosystem) depends on the ability to regulate its activities in response to changes in its environment, including the availability of nutrients, water, temperature, or other elements it may depend upon or that may threaten its survival (Figure 3.3). A feedback loop can be defined as “a closed chain of causal connections from a stock, through a set of pre-programmed responses that are dependent of the level of the stock, to have a direct impact on a flow in order to change the stock” (Meadows, 2008). Multi-cellular organisms can thus be seen as a set of stocks and flows (in animals, for example, stocks of blood, muscles, bones, brainpower, experience, and cunning) that are interconnected and regulated to function in a coordinated manner by the presence of feedback loops, based on signalling pathways between its cells, including protein or peptide based (e.g. hormonal) and electrical impulses.

Ambient air temperature rises in summer can trigger adapting balancing responses in humans to counteract undesirable levels of bodily heat gain. Responses include automatic sweating or a conscious decision to jump into a cold pool – both responses enhance heat loss through skin. Similarly, if it gets colder in winter our body’s metabolic rate may increase automatically, burning more fat and sugars to generate heat to keep warm, or we may intentionally dress warmer or turn our indoor heating up.

Figure 3.3 depicts feedback loops that are balancing or stabilizing in their overall effect on the stock level. Balancing loops are sources of resistance to change in a system. Reinforcing feedback loops, on the other hand, can jeopardise the stable maintenance of stock levels by reinforcing a particular dynamic trend relating to a stock level. For example, considering the stock of fertile top soil, soil e...

Table of contents

• Cover
• Title
• Copyright
• Dedication
• Contents
• List of figures
• List of tables
• List of boxes
• List of contributors
• About this book
• Acknowledgements
• Introduction
• Part I Embracing complexity and alternative futures: conceptual tools and methods
• Part II What might transformations look like? Sectoral challenges and interdependence
• Part III Tracking, steering and judging transformation
• Index