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Sustainability Policy Analysis: What Is It? What Can It Do for Us?

A paper to be presented at the 2nd International Conference on Government Performance Management and Leadership, Portland State University, Oct. 1-2, 2011

Karen J. Baehler and Daniel J. Fiorino, American University

Few concepts are as influential or pervasive in contemporary political discourse as that of sustainability.

The concept gained currency with the report of the World Commission on Environment and Development

in 1987, Our Common Future. Its political relevance was established at the Rio Earth Summit in 1992. It

became the basis for national plans, local initiatives, corporate planning and goals, and a UN commission.

It has become a central political, social, and ecological discourse of global society.

Despite its currency, especially in global settings, the concept has been criticized widely. Among

the criticisms are (1) that it is too vague to provide a practical guide for policy makers and (2) that it

presumes positive relationships among economic and environmental factors that may not exist. The view

taken here is that these are fair criticisms, especially for the commonly-accepted definitions that start with

the idea of sustainable development and present the concept more in terms of an aspiration than an

operational concept. We focus on sustainability as a concept and propose a more operational approach.

The intent of this paper is to set out an exploratory framework for a field and practice of policy

analysis based on sustainability. Part One sets out a working definition of sustainability based on a

systems framework. The systems include the three standard elements of most definitions (the ecological,

economic, and social), but it adds a fourth, the governance system. Sustainability is described as the task

of sustaining each of these four systems over time and maintaining a balance among them. Part Two

probes further into the basis for and implications of a complex systems approach to sustainability. It

argues that taking a complex systems approach leads to different priorities, strategies, and methods

compared to conventional analytical tools that are used in environmental policy analysis. The discussion

is organized on the basis of three characteristics of complex systems: the tension between stability and

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instability, resilience in response to stress, and capacities for self-organization. Part Two includes a brief

critique of analytical tools that are used routinely in the environmental field. The final section considers

the issue of why it matters to develop a field of sustainability policy analysis.

Part One: Sustainability as an Analytical Framework

There is no shortage of definitions of sustainability. Most commonly-used is that of the World

Commission on Environment and Development (1987), of sustainable development as “development

which meets the needs of the current generation without compromising the ability of future generations to

meet their own needs.” Although this captures the two core ideas of the concept—of not foreclosing

options for future generations and of reconciling economic and environmental goals—it is hardly the

basis for an analytical framework. Sustainable development is defined in the National Environmental

Policy Act (NEPA) of 1969 as “to create and maintain conditions, under which humans and nature can

exist in productive harmony, that fulfill the requirements of present and future generations.” Other

definitions stress a balancing the economy and environment goals. Nasrin Khalili (2011), for example,

defines it as “maintaining a sustainable economy that can prevent liquidation of natural capital.” More

specific is Robert Paehlke’s (2006) definition as “the capacity to continuously produce the necessities of a

quality human existence within the bounds of a natural world of undiminished quality.”(57)

A useful, systems approach to sustainability comes from a 1997 essay by John Robinson and Jon

Tinker, who view the economy, environment, and human society as “three interacting, interconnected,

and overlapping ‘prime systems.’”(74) Like most systems, these share the characteristics of stability,

resilience, and self-organization, which are discussed extensively below. These systems are stable to the

extent they can manage change over time. Resiliency is the ability to absorb and adapt to external stress.

Systems are self-organizing by being able to search for and maintain equilibrium. Systems survive in their

ability to manage and adapt to change and maintain equilibrium. Our premise is that each system is

essential for collective survival; no one system should be allowed to it threatens another.

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There are empirical and normative aspects to these systems, both of which should be incorporated

into a field of sustainability policy analysis. The empirical aspect has two parts: (1) all four systems are

essential for human well-being and collective survival and (2) they are interconnected and interdependent.

Traditional policy analysis focuses on understanding and explaining issues and relationships within

systems, with some efforts to make connections among them (such as with cost-benefit analysis and

ecosystem valuations). A field of sustainability analysis would aim for a systematic understanding and

explanations of issues and relationships among them. The normative aspect is to determine the priority to

be accorded to these systems and the value attached to each. The core objective of a field and practice of

sustainability policy analysis should be to maximize the opportunities for complementarities and

synergies among the four systems. Although trade-offs are inevitable, at least in the short term, the

challenge of this field is to be able to identify and evaluate trade-offs in the context of the four systems.

Consider the issue of the appropriate “balance” that should be maintained between the economic

and ecological systems. Environmental policy conflicts in the US have turned on the perceived conflicts

between these two systems. In the past, this has often been framed as a matter of values: Should we allow

more pollution, resource development, or ecosystem damage in the interests of growth? An advocate for

ecological values would say no; a growth proponent would argue yes. In John Dryzek’s (1997) terms,

they are advocating competing discourses, based on diverging values, interests, and world views.

The empirical aspect of these systems adds another dimension, one that is susceptible to analysis.

It is increasingly obvious that economic growth, population increases, and technology development pose

stresses that threaten the survival of subsystems within the global ecological system. Climate change,

water scarcity, and localized problems like overfishing or desertification are clear examples. As Peter

Victor (2008) argues in Managing without Growth, the economic system is dynamic and may expand

indefinitely while the ecological system is more static and subject to fixed limits (its carrying capacity).

The choice thus turns from one of competing values to one of collective survival. Whatever one’s views

on the inherent value of nature, at some point ecological degradation threatens economic success while

undermining capacities for governance and social progress. For this reason, Lafferty and Hoven (2003)

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accord the ecological system a “principled priority.” They argue: “the fact that we are facing potentially

irreversible damage to life support systems clearly implies that, as far as some environmental objectives

are concerned, these cannot simply be ‘balanced’ with the objectives of other policy sectors.” (11)

What a sustainability analysis brings to this discussion is that it may help to establish empirically

the points at which the demands of the economic system threaten the carrying capacity of the ecological

system. That is, the ecological system poses unalterable limits on the expansion of the economic system.

At the same time, certain degrees and forms of economic growth are not only consistent with but may

enhance ecological protection. More affluent societies are more likely to invest in pollution control, seek

cleaner production methods, and use energy efficiently. They also exhibit slower rates of population

growth, better education and health care, better status for women, and improved governance capacities.

Likewise, the social system supports norms of behavior and networks of trust that contribute to the

vitality of the other systems. In particular, both business transactions within the economic system and

community initiatives to manage natural resources within the ecological system are more likely to

succeed where social connectedness is higher. To the extent that the social system is threatened by

distortions in the economic or environmental spheres, virtuous cycles of social capital creation and

reinforcement may deteriorate.

Because these systems are crucial, yet interconnected, they define three long-term imperatives for

any society. The ecological imperative is “to remain within planetary biophysical capacity.” This includes

most environmental issues. The economic imperative is “to ensure and maintain adequate standards of

living for all people.” The focus here is on material sell-being and security. The social imperative is “to

provide social structures, including governance systems, which effectively propagate the values people

wish to live by” (77). The larger imperative may be seen as the need to sustain each of these systems

while maintaining an appropriate balance among them. Just what this “appropriate balance” means is an

issue that has to be worked out through the political process. A frequent criticism of the sustainability

concept is that it does not provide clear criteria for making such choices. There is no reason that it should.

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It does, however, offer an analytical framework for making choices. The definition and application of that

framework constitutes the field of sustainability policy analysis and is the basis for the arguments here.

Still, there are limits to any system’s ability to adapt. Systems fail, whether they are classified as

ecosystems (think of the Aral Sea or New England fisheries), political systems (consider the literature on

failed states), or economic systems (the Great Depression, Weimar Germany). Systems may fail as a

result of internal pressures or stresses from other systems. Unabated economic growth without attention to

limits of the biosphere, for example, leads to long-term, irreversible climate change and affects the

survival of ecosystems, economies, and states. Persistently high unemployment and rampant inflation

cause political instability. The environmental security literature argues that resource scarcity and

ecosystem degradation will be a major source of political conflict and instability (Matthew 2009).

This paper proposes an addition to the conceptual, systems-based model and to writing on

sustainability generally—that politics and governance be separated from the social system. In the

literature, the social component (the social system) has been defined vaguely, with a strong normative

content. The goals of equity, fairness, participation, transparency, access to health care and education, and

gender rights define the principles on which the social system is based. The vital tasks of governance as

an essential underpinning for the survival of the other systems are typically given short shrift in the

sustainability literature. This is a significant omission, given the role of stable and effective governance to

managing the other three systems. The governance system describes the societal capacity to make and

carry out collective decisions that maintain the values people wish to live by. In our approach, governance

is a precondition for successfully meeting the imperatives of the other three systems. Simply stated,

political instability or corrupt and ineffective governments make it nearly impossible to sustain the other

systems, let alone to maintain an appropriate balance among them (Fiorino 2010).

In sum, we propose a framework for sustainability policy analysis consisting of:

1. four overlapping, interdependent systems: economic, ecological, social, and governance (see

Table 1 for examples of likely indicators that could be used for each of the systems)

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2. the tensions among them, the most compelling and immediate of which is the expansion of

the economic system against the fixed limits of the ecological system

3. the existence of and search for complementarities and synergies among them, such as stable

and effective governance as a precondition for success in the other three

a. the effects of income growth on social well-being

b. the ability of democratic systems to respond to health-related pollutants

c. the value of ecosystem services for economic stability and growth

Sustainability policy analysis may be defined as a systematic effort to identify, explain, and

predict the relationships among the four systems. Although intra system relationships are important for

this discussion, our focus is the analysis of relationships across the four systems. The goals of this form of

analysis are (1) to identify the constraints in one system that affect choices made for the others (2) to

develop and apply the analytical tools for making inter as well as intra-system choices and (3) to provide

an analytical framework as well as an empirical base for informing policy debates and decision making.

Levels of Sustainability Analysis

We propose three levels of analysis as a starting point. The macro focuses on a national or global

level (e.g., the European Union, China, or Germany). A meso level could describe a geographical region

(the Great Lakes, Chesapeake Bay) or economic sector (steel, chemicals, autos, power generation, or

semiconductors). A micro level refers to applications of the sustainability concept in communities or

organizations (e.g., Boulder and Austin or Intel and Honda). Indeed, an advantage of the sustainability

concept is that it may be applied usefully at multiple levels of analysis. We illustrate this with examples

from macro and micro levels, where more of what we term sustainability analysis has been done.

In our framework, sustainability policy analysis involves an explicit focus on the relationships

among the four systems. Although “within-system” analysis will contribute to the understanding of inter-

system relationships, our primary concern is with relationships among rather than within systems. To

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illustrate the possible macro-level applications, this section briefly considers research that aims to explain

differences in national environmental performance. This research is only a subset of a much larger body

of work that studies the links among governance and economic development or other inter-system

relationships. For example, political science research has studied many of the relationships among

governance and economic growth indicators (Przeworski, et al. 2000). This includes studies of regime

type (democracy or authoritarian) as well as specific institutional characteristics, such as parliamentary

compared to presidential systems (Li and Reuveny 2006; Scruggs 2003; Payne 1995). Some of it

examines the relationships between such factors as per capita income, economic structure, regime type,

trade openness, social equity and quality of governance, as well as institutional characteristics (Scruggs

2003; Dasgupta, at al. 2005; Stern 2004). Although the findings regarding the correlates of environmental

performance are not definitive, they do suggest an empirical basis for the sustainability concept.

This research examines the most contested set of relationships, those among the ecological and

economic systems. The conventional view held that the links among these two systems constituted a

nearly inevitable zero-sum; any action to protect or improve the environment reduces growth,

competitiveness, jobs, income, and so on. Similarly, it was assumed that growth would increase pollution,

threaten health, degrade habitat, and impose other forms of ecological damage in an almost linear

relationship. A great deal of empirical research suggests this relationship is more complicated than was

assumed. For example, many studies have found that income growth is first associated with increasing

pollution levels that later decline as further growth occurs. As societies industrialize and urbanize, they

first experience more air and water pollution and waste. At some level of income, however, voters begin

to demand that government intervene to control pollution—particularly when it threatens health or valued

ecological resources—at the same time that the financial and technical capacities are expanding. Although

these findings regarding an “Environmental Kuznets Curve” should be qualified, particularly when it

comes to long-term, global pollutants such as carbon dioxide, they at least provide evidence that the

economy-environmental relationships is not always negative and may be complementary (examples of

this research are Stern 2004; Dasgupta, et al. 2005; Esty et al. 2008; Dasgupta et al. 2006).

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This research also confirms the existence of positive relationships among the other systems.

Democratic governance and economic growth appear to be positively linked. Similarly, higher incomes

have been linked to improved status for women, better health care and education, and other aspects of the

social system. Findings on any specific relationships between specific institutional characteristics (e.g.,

presidential compared to parliamentary systems) are inconclusive, although there is evidence that systems

with a greater capacity to integrate policies across the systems demonstrate better environmental

performance on some measures. This research illustrates the value of sustainability analysis and research.

The micro level of sustainability policy analysis focuses on communities and organizations. This

is the level at which policy debates, planning, and indicators development has been most active. Although

applications of sustainability at this level are uneven and far from universal, there is research on both the

business case and the community development (local government) case for sustainability. This discussion

focuses on the corporate setting, where the sustainability concept has been used widely.

In the United States, the early applications of the sustainability concept occurred in a corporate

context. The literature on corporate sustainability depicts leading firms as moving in stages from reluctant

compliance, to acceptance and compliance, to proactive pollution prevention, to a strategic focus on

sustainability. The 1992 Rio Earth Summit served as a catalyst for many in business, leading among other

initiatives to the 14001 series of ISO (International Standards Organization) environmental management

systems and formation of the World Business Council for Sustainable Development (Schmidheiny 1997).

In practice, firms like Johnson & Johnson, IBM, Intel, and 3M regularly engage in sustainability analysis.

An application of an inter-systems approach is a standard return on investment (ROI) analysis of energy,

water, or materials efficiency measures. Others are analysis of the business value of increased market

share for green products or actions to manage regulators and competitors (Reinhardt 2000). A substantial

literature has stressed the business value of a sustainability strategy (e.g., Porter and van der Linde 1995).

Firms may even accord principled priority to sustainability investments. An example is Johnson

& Johnson’s capital relief fund for CO2 projects (World Wildlife Fund 2009). The company had

previously committed to absolute reductions in emissions. It found, however, that energy efficiency and

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CO2 reduction was competing against investments in such areas as marketing and product innovation,

which often had higher return rates. Its solution was to create a fund allowing business units to spend up

to a total of $40 million annually on emission reduction projects. To qualify, projects had to be financially

viable and provide a minimum 15 percent internal rate of return, although lower rates were justified when

“clear and definable other benefits” were documented. In its first few years, the fund provided $86 million

of capital for 49 projects, an average return of 16.3 percent, and 88,500 tons of CO2 reductions.

Researchers also have investigated the links between strong environmental performance and

financial success. Studies have found positive relationships, in which firms ranking high in environmental

performance and policies are more profitable (King and Lenox 2001). They conclude that strong

environmental performance is not inconsistent with financial success. However, the direction of causality

is difficult to determine. Are the more environmentally progressive firms in a better position to succeed

financially? Or do greater profits allow firms to invest in more progressive and strategic policies? Or are

innovative and proactive policies a sign of strategic leadership and thus an overall indicator of likely

business success? It is difficult to answer these questions (although the authors lean toward the third

explanation), in light of the research challenges. What matters is that research on corporate sustainability

has explored relationships among the economic, ecological, and social systems in business settings.

Our vision of sustainability policy analysis thus is based on a reconceptualization of the concept

around four interconnected, interdependent systems. The challenge for contemporary governance is to

sustain each while maintaining a balance among them. The concept of sustainability cannot determine

those choices, but it may be used to define a framework for making them. The analytical foundation for

making those choices is what is proposed here as a field of sustainability policy analysis. Given the

centrality of complex systems in our approach to sustainability, the discussion turns now to that topic.

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Part Two: Complex Systems as the Foundation for Sustainability Policy Analysis

The first part of the discussion set out a framework for sustainability that emphasizes the existence of four

systems and the need to sustain each and to balance the relationships among them. This second part

explores the characteristics of complex systems and their implications for policy analysis, specifically as

this would influence the conceptual foundations for a field of sustainability policy analysis.

Conventional methods of policy analysis and program evaluation rest on the assumption that

cause-and-effect relationships in the systems of interest are reasonably stable, linear, and knowable. Cost-

benefit analysis, for example, begins with the assumption that the program or project under study will

generate the impacts for which the costs and benefits are being calculated. Risk assessment assumes that

the contribution of a given level of chemical exposure to the probability of contracting a particular

disease, for example, can be separated out from other causative factors and their interacting effects.

Environmental impact statements, economic impact analyses, and any other “impact” analysis depend

upon a basic input-output model of causation, which in turn reflects the idea of public policy as purposive

activity by government to transform inputs (the resources marshaled by a program) to generate outputs

(the goods or services produced by the program) to achieve desired ends (impacts our outcomes). Dave

Snowden (2003, p 462) refers to this idea as “the assumption of order … [which] implies that an

understanding of the causal links in past behavior allows us to define ‘best practice’ for future behavior.”

In many instances, this type of order is probably quite a reasonable assumption to make, but there

are other instances in which omitted variables, unseen interactions among variables, and non-linear

dynamics (such as tipping points) are likely to be important explanatory factors. Awareness of this

problem has driven a steady stream of methodological advances in statistical modeling and econometrics

for decades. Where highly complex systems are concerned, it seems unlikely that the models will ever

catch up with the complexity of the phenomena being modeled. If they did, they could cease to be useful

as simplified representations of reality.

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If sustainability policy analysis simply amounted to a bigger, better input-output model with a

larger number of variables, more connections among variables, and greater interactivity, it would make a

contribution to the field. But it probably would not warrant its own subfield niche. As this section will

attempt to show, sustainability analysis represents a more radical departure from conventional policy

analysis than any expansion of existing models. It begins with an effort to work with complexity rather

than “taming” it (Rittel and Webber (1973). Complexity must be the focus at present, because in the

environmental sphere, as in many others, the low-hanging fruit has been picked. The more

straightforward problems, such as highly visible point-source pollution, have been at least managed (but

not solved) through the hard work of scientists, activists, and policymakers. The sources of these

problems are relatively well understood, technology exists for addressing them, and policymakers have a

wide variety of attractors, resistors, and prompts for spurring action (e.g., regulations, subsidies, civil and

criminal penalties, tradable permits, and covenants). Of course, politicians debate ways of addressing a

problem such as toxic waste, but few view toxics as a myth perpetuated by scientists seeking research

grants. Thanks to the obvious links between toxics exposure and health, “toxic waste deniers” are scarce.

With the relatively clear cause-and-effect problems somewhat under control, what is left are the

“wicked problems” (Rittel and Webber 1973).1

1 Rittel and Webber were talking largely about social problems, but the label can be applied to other spheres of policy and planning, all of which touch the social systems at some point.

These include non-point-source pollution, climate change,

the effects of emerging technologies, ecosystem protection, and the cumulative harm caused by economic

hardship and environmental injustice in vulnerable communities, among others. Most are characterized

not only by complex and ambiguous causation but also sharply competing interests and disagreements

over values (Balint et al 2011). To address these problems, sustainability analysis begins with the basic

tenets of causation found in the literature on complex systems. Rittel and Webber describe that challenge

(1973, p. 159):

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“We have been learning to see social processes as the links tying open systems into large and

interconnected networks of systems, such that outputs from one become inputs to others. In that structural

framework it has become less apparent where problem centers lie, and less apparent where and how we

should intervene even if we do happen to know what aims we seek. We are now sensitized to the waves

of repercussions generated by a problem-solving action directed to any one node in the network, and we

are no longer surprised to find it inducing problems of greater severity at some other node. And so we

have been forced to expand the boundaries of the systems we deal with, trying to internalize those

externalities.”

Thus, wicked problems arising from complex systems share the defining characteristic of emergence:

1. Any whole complex system is greater than, and often different from, the sum of its parts.

This is because agents within the system (human or not) do not act independently all of the

time, and their interactions can generate surprising behaviors–everything from a standing

ovation to political revolution–that are impossible to predict in advance. This nonlinear, self-

reinforcing phenomenon is known as “emergence.”

2. Although higher-level, emergent behaviors may be difficult to predict, they often follow

identifiable patterns characterized by “retrospective coherence,” meaning that “[e]mergent

patterns can be perceived [in retrospect] but not predicted [in advance]” (Snowden 2003,

469). Thus, complexity should not be conflated with chaos, which is unpatterned even in

hindsight. But neither should analysts be less than humble about their ability to detect these

patterns at all, let alone anticipate them.

3. Systems continually change and evolve as agents interact and new patterns emerge. It is risky

to assume that cause-and-effect relationships will persist over time, which makes it difficult

to assess policy interventions based on expected impacts.

The emergent view of causation is not particularly radical; nor is it new. Many practicing policy

analysts would probably endorse the concept, at least in principle, as a good description of many

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phenomena. The problem is that emergence simply cannot be incorporated into the usual techniques of

policy analysis. Indeed, scholars seeking to model emergence have had to invent their own computer-

aided methods, under the banner of computational agent-based models, which resemble the simulations

used in computer games (see Miller and Page 2007). Agent-based simulation modeling offers many

benefits for sustainability-based policy analysts, but this paper speaks to a broader, less technically

oriented audience and aims to provide a broader set of concepts and practices beyond computer modeling.

Because systems-based thinking is a defining characteristic of our view of sustainability analysis,

the complex systems/emergence paradigm offers criteria for judging any proposal as being “sustainable.”

These criteria are organized according to the three characteristics of complex systems noted earlier:

stability, resilience, and self-organization; all contain pairs of contrasting forces that must be balanced.

The Tension Between Stability and Instability

Merriam-Webster’s on-line dictionary defines stability as 1a “the strength to stand or endure,”

and 1b “the property of a body that causes it when disturbed from a condition of equilibrium or steady

motion to develop forces or moments that restore the original condition.” Kenneth Boulding (1956) noted

that most physical and human systems tend toward equilibrium, with one type of force inducing a

counter-force to balance it. For example, a rise in average temperature induces species to migrate to

higher altitudes and latitudes where temperatures are cooler; a rise in oil prices induces increased demand

for fuel-efficient cars. Most systems contain components with this cybernetic or thermostatic control.

Complex systems also may exhibit dramatic instability, in the form of riots, stock market crashes,

political revolutions, bank failures, forest fires, epidemics, and sudden declines in species.2

2It is precisely these types of phenomena that inspired much of the inquiry that we now call complex adaptive systems research (Miller and Page 2007).

Even at times

of relative stability, new patterns of emergence are constantly in process, transforming the system bit by

bit or laying the foundation for a sudden, discontinuous change in the future. Thus, it can be said that

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complex systems are characterized by both stability and instability. In other words, most systems tend

toward equilibrium and possess a capacity to restore equilibrium when it is disturbed (stability), but they

also hold the potential for abrupt change at almost any time if reinforcing behaviors are triggered and

balancing forces suppressed (instability). Often, the abrupt system changes qualify as corrections in a

system that had become unbalanced, such as an over-valued stock market in the case of a crash. In other

situations, a systemic correction may go too far, leading to a wildly swinging pendulum. The ability of

systems to self-manage and adapt through small and large corrections is discussed below under resilience.

Baumgartner and Jones (1993) borrowed the term “punctuated equilibrium” from evolutionary

biology to describe the interactive relationship between stability and instability in policymaking.

According to their theory, forces of stability (entrenched interests, institutional inertia, and limited

horizons of policymakers) hold policies in place and resist anything more than incremental changes to

those policies until the policies become so out of date, and the need for change so obvious, that the forces

of reform finally break through (punctuation). The oscillations between stability and sudden bursts of

reform occur largely because key players tend to focus disproportionate attention on particular stimuli that

cross their path at particular times (Jones and Baumgartner 2005). The habit of locking into some ideas

and not others causes policymakers to neglect the need for changes in some areas, leading to periods of

stability. As these information processing errors accumulate, they become harder to ignore. As a result,

“Decisions are always catching up to reality; generals are always fighting the last war” (Jones and

Baumgartner 2005, p. 334). The concept of punctuated equilibrium can apply to phenomena across all

systems and even overlaps to some degree with the recently popularized idea of tipping points. It poses a

formidable challenge to policy analysts to find ways of challenging conventional wisdom when policy

gets stuck in an error-induced equilibrium. Cross-systems analysis may provide some leverage.

The tensions between stability and instability also apply to the implementation phase of policy.

Policy stability contributes to the phenomenon of regulatory capture, in which representatives of a

regulated industry (such as oil rig operators in the Gulf of Mexico) form inappropriately cozy

relationships with regulators (such as the Department of the Interior’s Minerals Management Service).

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The result is a dangerous equilibrium in which lax monitoring, assessment, and enforcement contribute to

a culture of negligence and risk-taking in a self-reinforcing, vicious cycle. One month after the Deepwater

Horizon oil rig exploded, the Minerals Management Service was restructured to clarify roles and separate

conflicting functions. It remains to be seen whether this deliberate destabilizing is sufficient to break the

old patterns of capture and nurture more desirable attitudes and behaviors among regulators and industry.

Those who seek to design policies that are more resistant to regulatory capture face another formidable

challenge at the institutional intersection between the economic and governance systems.

The lesson from all of this for sustainability policy analysts is clear but challenging: Policy needs

to be designed to strike a balance between reasonable stability and predictability of institutions and

programs, on one hand, and adaptive mechanisms that can respond effectively to inevitable instability in

the social, economic, and ecological spheres on the other. Striking such a balance requires deep

understanding of the forces that generate stability and instability within complex systems, which in turn

should suggest interventions that have the potential to nurture productive forms of stability while

disrupting unproductive forms. However, the uncertain nature of causation in these systems means that

the forces of stability and instability, and the emergent patterns that they create, are not apparent using

conventional analytical techniques.

An example is risk assessment, which focuses on harms that have already been identified; it does

not tell the analyst how to search for other risks, emergent or fully developed, that may have gone

unnoticed. Cost-benefit analysis and impact assessments also tend to focus on identifying and measuring

the likely efficiency and effectiveness of proposed actions, rather than devising new ideas for action that

work with the systems in question. Even basic research into system dynamics can promise only so much,

due to the lack of reliable connections between past experience and future expectations.

For these reasons, understanding systems often requires methods that do not generally fall into

the category of “analysis” at all. The “adaptive management” approach to natural resources, for example,

qualifies more as a blend of action research and responsive decision-making than policy analysis. The

basic idea is to design policies that can function both as experiments and as evolving solutions, with the

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goal of testing and comparing the effects of different management approaches by putting them in place,

monitoring results, and adjusting practices as learning occurs (Williams et al 2007, Walters 1986, Holling

1978). It has been used for decades to manage forests, wildlife populations, river systems, watersheds,

fisheries, and other resources. The lessons learned from adaptive management experiments may gradually

expand understanding of how policy intervention affects complex social and ecological systems. Adaptive

management works with rather than dispels uncertainty.

Adaptive co-management goes further; it brings together the idea of policies as experiments with

a commitment to local empowerment. Per Olsson and colleagues (2004) tell two stories in which local

people sounded an early alarm about deterioration of a natural resource and then led initiatives to address

the problems. One involved property owners, fishers, and a lake’s catchment area in Sweden, and the

second indigenous peoples and a dam-building project in Canada. In each case, the authors noted that

self-organization was critical to success: Only local people had the deep knowledge of the ecosystem and

the existing social ties needed to get things done in the first phases of activation. At the same time, local

players found it necessary to work closely with governments and private firms. The geographic and

institutional scope of their work expanded as they were drawn to consider larger ecosystem interactions

and their impacts on the local problem. Learning occurred at a rapid rate through adaptive management

and was captured in social networks. Scientific methods integrated well with traditional sources of

knowledge. In both cases, legislation facilitated the emergence and development of local organizations,

showing that government can play a constructive, non-controlling role in self-organization processes.

Resilience: Fostering Heterogeneity in Policy Analysis

Stability and resilience stand in tension (Snowden 2005). Stability implies continuation of a

relatively steady and unchanging equilibrium; resilience implies a capacity to survive by changing in

response to external stress. When a system responds resiliently to stress, the result is a new equilibrium

that is different from the pre-stress one.

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C.S. Holling and Gary Meffe (1996) refer to the use of command-and-control policies to stabilize

ecosystem equilibria as a form of “pathology.” When governments seek to shape ecosystems for their

own purposes, they often reduce natural variation. The result is reduced resilience in the ecosystems. For

example, ecologists have found that high flow variation in natural rivers (i.e., flash flooding) helps clear

away exotic species before they become established but does not hurt native species, which have evolved

to withstand these extreme events. When dams are built and water flows stabilized, exotic species move

in and wipe out native ones, thereby reducing biodiversity. Similar perverse effects follow forest fire

suppression efforts and the conversion of multi-species prairies and meadowlands to monocultural

farming.

Ecologists know that heterogeneity is basic to resilience. Holling and Meffe (1996) explain that

ecosystems with a greater variety of species are better able to flourish in the face of climate change,

because the probability of having species that can withstand the change increases as genotype variation

increases. Likewise, Miller and Page (2007) explain that smooth thermostatic control in beehives depends

on genetic variability among the bees: When the hive temperature drops below a comfortable level, bees

huddle to generate heat, starting first with the bees that are more sensitive to cold, and then involving

others if the temperature continues to drop. Temperature swings are greater in hives with low genetic

variability because all the bees respond to a drop in temperature simultaneously, which tends to generate

too much heat, which then requires all of the bees to spread out and flap their wings to cool things down,

which in turn may drop the temperature too low, and so on. Such a hive will expend far more total energy

on thermostatic control than one with bees that vary in temperature sensitivity.

Cybernetics experts offer further support for Holling and Meffe’s “pathology of command and

control” through the application of W. Ross Ashby’s (1956) “Law of Requisite Variety.” Ashby’s Law

holds that a system with greater variety (measured by a larger number of possible different states that can

be attained) cannot be controlled effectively by a mechanism with less variety. The implications of

Ashby’s Law for policy are enormous. In concert with Holling and Meffe’s pathology label, it explains

that a command-and-control approach to natural resources management will nearly always fail to exert

18

real control because the alterations made in the ecosystem are generally designed to operate under a

narrow set of conditions. When those conditions diverge from expected levels, ecosystems will respond in

unpredictable ways that often subvert the policy intent. Ashby’s Law predicts that many regulations will

fail to control the phenomena that they target because laws can never cover every possible variation in

natural or human behavior. Efforts to reduce overfishing, for example, often take the form of restricting

access by limiting the availability of licenses. Evaluations show that license restrictions had the

unintended effect of creating incentives for license holders to increase efforts per license by expanding

the capacity of vessels through better equipment (when licenses adhered to vessels) and purchasing more

vessels (when licenses adhered to operators), known as capital stuffing (Townsend 1990). Later efforts to

fight capital stuffing focused on regulating specifics such as boat length and the use of multiple nets and

rigs. In response, operators invested in other enhancements, such as electronics, engine size, and boat

width (Hilborn et al 1995).

The same story can be told about virtually any form of command-and-control policy. As long as

regulatees maintain a larger repertoire of activities than the regulations can cover they maintain the upper

hand within the system. As policy makers scramble to update laws and regulations to cover as many

undesirable activities as possible, the list of rules can grow so lengthy that no one understands them and

the regulators struggle to enforce them (Bardach and Kagan 1982). Meanwhile, many regulatees will have

focused on meeting the bare expectations of the law and avoiding detection, rather than meeting the larger

goals of the law, such as safety, sustainability, or fairness. For these reasons, Snowden (2005, p 25)

concludes that “[c]ore habits and sound ethical training beat rules and rule compliance any day,” because

good habits and internalized norms, once developed, can be applied to a variety of situations, including

ones that no one could have anticipated. The truth in Snowden’s conclusion stems from the greater variety

inherent in habits and ethical norms, compared to rules, and the benefits of variety for system resilience.

Running through the rapidly expanding literature on complexity theory’s relevance to public

policy and administration is a general preference for decentralized decision making, empowerment of

agents to organize their own solutions, and light-handed interventions aimed at facilitating desirable

19

rather than prohibiting undesirable behavior. This is a manifestation of what Snowden (2009, p. 3) calls

“distributed cognition,” or the capacity of networks to influence policy design and decision making based

on a deep understanding of local context and the power of existing social relationships. The paradigm of

adaptive co-management, described in the previous section, follows in this general school of thought.

The strong pro-decentralization view is well represented in Emery Roe’s 1998 book, entitled

Taking Complexity Seriously: Policy Analysis, Triangulation and Sustainable Development, which favors

local decision-making processes over global ones for their ability to differentiate more finely among the

facts of each local case and their propensity to develop diverse, customized management strategies that

respect local people’s perspectives. Global approaches, by contrast, tend to reduce the richness of local

wisdom to a generic set of unrealistic demands for reducing population and consumption and elevating

ecosystem over other values via the precautionary principle and the principle of intergenerational equity.

We should therefore look forward to an enlightened period when all top-down, command-and-

control policies are replaced by highly effective, locally customized, self-organizing solutions to complex

policy problems, right? Wrong. Scholars and practitioners enamored of complexity theory should

consider the synergies that often exist between top-down and bottom-up approaches. Variation-rich

approaches to environmental policy, such as self-regulatory arrangements by industries, environmental

covenants, and other voluntary approaches, are more effective when the players involved perceive a threat

of centralized regulation if voluntary efforts do not suffice (Fiorino 2010). A reviewer of Roe’s book

noted that his example of community-based resources planning in California (for spotted owl)–a potential

model for good practice in complexity-aware, locally driven, participative policy and management

design–owes at least part of its existence to the “stimulus” of the Endangered Species Act (Ralls 1998, p.

2576). Similarly, Olsson, at al. (2004) note the role of legislative requirements in improving the position

of local and indigenous organizations in the Swedish and Canadian adaptive co-management cases.

Acknowledgment of complexity, therefore, often requires a similarly complex portfolio approach

to policy, one with both horizontal and vertical dimensions. On the horizontal plane, multiple different

instruments or mechanisms may be used to influence a system. On the vertical plane, those instruments

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may be designed and implemented at different levels–macro, meso, or micro. The question is how to

decide which levels to include in a policy and in a policy analysis. That question relates to the substantive

question of where to draw the boundaries of the systems of interest.

Rittel and Webber neatly summarize the trade-offs involved (1973, p. 165),

“There is nothing like a natural level of a wicked problem. Of course, the higher the level of a

problem’s formulation, the broader and more general it becomes: and the more difficult it

becomes to do something about it. On the other hand, one should not try to cure symptoms: and

therefore one should try to settle the problem on as high a level as possible.”

Similarly, Kenneth Boulding (1956, p. 197) warned that “we always pay for generality by sacrificing

content, and all we can say about practically everything is almost nothing.” In other words, as we draw

the systems boundary wider and wider, we include more potentially critical factors and interconnections

into our frame. Perversely, we may be less able to see these critical things because they get lost in the big

picture, like dots in an impressionist painting. A serious drawback to high-level problem formulation of

sustainability problems is the difficulty of doing rigorous analysis at that level and scarcity of tools. Have

things changed much since Lindblom (1959, p. 80) quoted the head of RAND’s economics division,

Charles Hitch, as follows?

“I would make the empirical generalization from my experience at RAND and elsewhere that

operations research is the art of sub-optimizing, i.e., of solving some lower-level problems, and

that difficulties increase and our special competence diminishes by an order of magnitude with

every level of decision making we attempt to ascend.”

This lament applies to impact assessment, economic analysis, and cost-benefit analyses as clearly

as to parallel programming and other optimization methods. The quotation can be used to justify a more

severely self-limited approach to policy analysis, as Lindblom did in his embrace of “successive limited

comparisons,” or to spur the search for better tools of system-level analysis, as the authors of this paper

prefer. Either way, Hitch’s assessment of analytical competency should be taken as a warning against

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easy answers to the fundamental question–how to attack policy problems at a higher level of system

generality without abandoning analytical rigor.

The EPA’s sustainability framework advises that depth of analysis should reflect problem scale,

with geographically narrow permitting decisions at one end of a continuum and national strategic policy

choices–such as fuel choices–at the other (National Academy of Sciences 2011). Where routine decisions

need to be made about permitting, for example, analysts need not undertake full-blown systems analyses.

That is a good rule of thumb, but a complex systems–based approach adds an additional component,

which we will call opportunism. In the permitting case, the agency should periodically review random

samples of applications–not to assess how they were processed or analyze the decisions made, but to look

for underlying patterns. Who are the applicants? What types of activities are they engaged in, where, and

at what scale? What technologies are they using? Are any features of the systems producing these

applications changing? Permit applications are a rich source of dynamic system information.

Some issues are so sprawling, with such potentially grave consequences for all systems, that they

merit attention at all levels. Climate change offers the example of a highly complex global (macro-level)

problem caused by millions of local (micro-level) activities, with patchwork management efforts by

institutions at all levels (micro, meso, and macro), including the U.N., nations, states and provinces,

counties and municipalities, groups of states, industry associations, non-profit organizations, firms, and

communities. Local efforts to restrict or discourage emission-producing activities are not necessarily ill-

conceived, particularly if they achieve high levels of buy-in, but at the same time, local efforts are likely

to fall into the too-little-too-late basket because of the scale and urgency of the overall problem.

The case of climate change is unusual because of the availability of a single mechanism–setting a

price on carbon–with the power to control a vast array of activities. This is the rare wicked problem that

lends itself to a conceptually simple solution! Why has this solution not been implemented? Answering

that question requires analysis of the social, economic, and governance systems and their interactions.

Start with the economic-social system interchange. A central principle of the social system–fair

distribution of harms and benefits–explains one of the looming obstacles to global agreement on carbon

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pricing. The problem arises due to differences of opinion about what constitutes a fair distribution of the

economic costs of reducing carbon emissions. Should less developed countries face lower expectations

for emissions control (and lower prices for emissions) because of historically lower contribution to the

problem and their natural “right” to catch up to the more developed countries by rapidly industrializing?

The answer to that question lies partly in the economic sphere, where it depends on achieving consensus

around the question of whether developing countries must pass through the smokestack phase that

characterized much of Europe’s and North America’s economic growth in the past, and China’s rapid

growth now, or whether today’s economy offers other, less destructive developmental trajectories. The

answer to the burden-sharing question also lies in the social sphere. There the clash of values and interests

among nations, groupings of nations, and sub-national groupings that cross borders (indigenous peoples,

the international trade/labor union movement, and so on), needs to be resolved at a macro level, unless a

mechanism are designed for facilitating multiple bilateral and multilateral arrangements at the meso level.

The planet’s inability to resolve these clashes of values and interests results in part from lack of

capability in the governance system as well, where neither nation-states nor civil society institutions have

devised effective mechanisms for settling the disputes around burden-sharing for carbon reduction.

Treaties signed under the auspices of the United Nations have made some progress, but far more is

needed. It is not clear whether the most effective mechanisms for coordinating international activity lie at

the macro-, meso-, or micro-level. Nor is it clear which types of institutions–governments, civil society

organizations, commercial organizations, or hybrids –should take the lead. What is clear is that “the

institutional and organizational landscape should be approached as carefully as the ecological in order to

clarify the features that contribute to the resilience of social-ecological systems” (Olsson 2004, p. 87).

Turning attention to social and governance issues will take many policy analysts outside their

comfort zones, because much of the progress to date in cross-system analytical methods has focused on

the boundary between the economic and environmental systems. Consider the widespread use of cost-

benefit analysis and macro studies of the environmental Kuznets curve discussed earlier. As paradigms

such as adaptive management become understood, this imbalance can begin to correct itself.

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The complexity literature does the policy analysis field a valuable service when it encourages us

to deepen our understanding of community-led, self-organizing, co-managed policy solutions and expand

our portfolio of methods for “top-down stimulation of bottom-up activity” (Snowden 2009, p 3). But fans

of complexity science need not leap from the view that more of these types of activities are needed to the

view that only these types of activities are needed. With highly complex problems, the best arrangement

may consist of an integration of macro- or meso-level boundaries with micro-level collaboration to design

and implement local solutions. Adaptability, heterogeneity, and collective learning are key; such learning

nurtures open-mindedness in social networks, and learning-oriented networks are vital to sustainability.

The Capacity for Self-Organization

A beguiling quality of complex systems is their capacity to organize often massive initiatives

without central authority or management. Beehives and ant nests are classic examples, but drawing

analogies from them to human systems is risky. Human systems are vastly more complex and difficult to

predict than natural ones; humans have the unique ability to devise long-term plans, reflect on their own

behavior, control and modify their instincts, and deliberately try to alter systems in which they function.

Among human self-organizing systems, markets are probably the best understood. Even well-

functioning, integrated markets, however, do not operate without central management. They rely on a

central authority to enforce property rights and contracts. In traditional societies, property rights and

contracts are tacitly recognized and enforced via social norms more than laws. In modern societies,

reliance on rule of law is a leading indicator of how markets function and how well economies will grow.

The theme of synergies between micro, meso, and macro institutions arose in the earlier sections

of this paper in the contexts of climate change and the Swedish and Canadian resources cases. It surfaces

again with respect to self-organizing markets. Institutions like the World Trade Organization play a

valuable role in setting a framework, including international standards of good practice supporting both a

rule of law and economic growth. Still, individuals and organizations need to develop the reinforcing

patterns of activity that lead to healthy markets in a context of open, transparent, and reliable governance.

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As noted earlier, these two developments also bode well in the long run. Rules imposed from above

without awareness of local self-organization may descend into pathology. Used judiciously to set targets

for local and national self-organization, however, they may contribute to an effective multi-level strategy.

How policies affect the dominant form of self-organization–markets–is a topic of great interest,

and this paper cannot begin to do it justice. We want to raise just one question here: If market failure in

the form of negative externalities is the rationale for much of what government does in environmental

policy, then why complete full economic and cost-benefit analyses of policies designed to internalize

these externalities? The theory of market failure calls for externalities to be internalized in order to make

market prices more reflective of the true costs of production and consumption, thereby improving overall

efficiency. Any intervention that shifts the costs of a negative externality from the innocent bystander or

involuntary participant to producers and/or consumers qualifies as internalization. Standard approaches to

addressing a negative externality include writing a rule which requires a producer or consumer to reduce

the amount of the externality being generated (e.g., mandatory installation of scrubbers on manufacturing

plants to eliminate air pollutants or smoking bans in workplaces) or imposing fees that discourage

production of the externality (e.g., a tax on carbon emissions or excise taxes on alcohol and cigarettes).

When government intervenes, costs to producers and consumers go up. Indeed, the purpose of the

policy is to correct the earlier price, which omitted certain costs of the negative externality. Economic

impact analysis estimates the size and distribution of these additional costs in advance to inform policy

makers about likely impacts. Economic impact analysis can be seen as the companion to environmental

impact statements. While the latter predicts the environmental costs generated by economic projects, the

former predicts the economic costs of protecting the environment. Economic impact analysis asks such

questions as: Will plants be shuttered? Will jobs be lost? At first glance, these impacts look like perverse

side effects generated by government activity. On closer inspection, they are more accurately described as

short-run transitional costs associated with internalizing the alleged externalities. That description is not

meant to diminish the importance of these policy-induced transition costs; nor are we suggesting that such

25

costs should be ignored. Indeed, in selecting policies for negative externalities, decision makers should

aim for policies that minimize transition costs without sacrificing overall policy effectiveness.

This is different from saying that government should not intervene unless economic impacts are

minimal, period. The latter approach makes two mistakes: It views a policy’s impact on industry as a side

effect, when in fact the purpose of the policy may be to internalize a market-based externality which, by

definition, will change costs and prices within the industry. When policy alters prices within an industry

to more accurately reflect real costs, this inevitably entails unhappy short-term impacts for someone.

Second, those who reject policies if they appear to alter the distribution of goods and burdens within an

industry make the mistake of assuming that the market operated optimally prior to the intervention. But

this cannot be true if market failure provides the rationale for intervention. When government intervenes

to correct a market failure, it is acting in accord with the criterion stated above: It is enhancing self-

organization capacities, in this case through well-functioning markets in which prices reflect true costs.

Misunderstanding the basic purpose of internalization policies runs the risk of producing a lose-lose

situation in which markets continue to operate sub-optimally and third parties continue to suffer harms.

Like any policy, those designed to correct market failure may have collateral effects on other

systems. The purpose of cost-benefit (CBA) analysis is to calculate the net effect of all impacts associated

with a policy. It does this by monetizing the impacts, categorizing them as either costs or benefits, and

subtracting costs from benefits. CBA should be the ultimate in cross-system analytic methods, but

practicalities often limit its scope. A systems-oriented perspective should take account of the fact that the

ultimate impacts of a system change (in the form of a policy intervention, for example) depend on a series

of reactions to that change, and reactions to those, ad infinitum, reaching as far as the analyst’s informed

imagination can stretch. Rittel and Webber (1973) call these the “waves of repercussions that ripple

through such systemic networks” and explain that these multiple vectors of impact make complex

problems virtually impossible to analyze and resolve with standard techniques, because “there are no ends

to the causal chains that link interacting open systems.” To conduct a cost-benefit analysis of such

26

problems, the analyst has to choose an arbitrary point at which further waves of impacts will not be

included.

Internalizing negative or positive externalities may improve efficiency even if the costs of an

intervention exceed the calculable benefits in the long run. This is because of the intangible and hard to

measure benefits associated with demonstration effects, awareness-raising, and other distant and indirect

impacts, some of which occur in other systems. Rittel and Webber note the difficulties associated with

internalizing externalities that occur across, rather than within, systems. A major difficulty stems from the

challenges of proving the causal link between the externality’s source and point of impact and measuring

the impact. This is a chief barrier to progress in climate policy in the U.S.; a scientific consensus has not

been enough to persuade many of the links between greenhouse gas emissions and rising temperatures.

Where analysis is most needed is not in measuring who will bear the pain, but in calculating the

correct dollar amount of the negative externality that needs to be internalized. This analytical challenge is

fraught with technical and political difficulties. One approach that deserves more consideration uses the

adaptive management paradigm discussed briefly above to experiment with various dollar values of a fee

or tax, or various designs of a regulation, to determine which levels produce the best result. The adaptive

management paradigm also allows those involved to monitor the pain associated with a policy in order to

see who suffers the most and then to try to ease their pain without undermining the policy–for example,

by slowing the rollout of a fee or providing cushions for firms as they transition to other technologies or

business models. A sustainability approach would shift some resources currently spent on economic and

cost-benefit analyses toward adaptive, active-learning approaches beyond natural resources management.

Some policies use the principle of self-organization to seed a positively reinforcing trend, leading

to win-win policy ideas. For example, evidence suggests that social norms play a vital role in influencing

behavior, as demonstrated in two contexts: towel reuse in hotels and residential energy consumption. In a

recent experiment, hotel guests were given one of three cards encouraging the reuse of towels to save

water and energy (Griskevicius et al 2008). One card appealed to guests’ environmental sympathies

(“Help save the environment”) and provided information about respect for nature; the second touted the

27

hotel’s commitment to the cause (“Partner with us to help save the environment”); and the third appealed

to the guests’ herd instincts by noting that most customers reuse towels (“Join your fellow guests to help

save the environment”). Guests who received the third type of card reused 34 percent more towels than

the other two groups.

A second experiment showed similarly powerful effects of peer influence on residential energy

customers, many of whom altered their habits in response to information regarding how their use

compared to others in the neighborhood (Schultz et al 2007). When given information about how their

energy use ranked relative to similar houses, both above and below-average consumption households

adjusted their usage toward the average. When given the same comparative information as above, along

with emotive feedback ( a smiling or frowning face to signal better or worse-than-average consumption),3

good performers regressed less, leaving a larger net positive impact on conservation.4

To understand the system in which individuals make choices about resource use, policy analysts

need to understand not only the general power of social-psychological incentives to alter daily habits but

the circumstances in which they are activated. Some policies are based on large-scale structural changes

that lead to wholly new forms of self-organization. Individual transferable quotas on fishing operators, for

example, create new and complex sets of incentives designed to improve the sustainability of fish stocks

and encourage lowest-cost harvesting of fish to keep consumer prices down. Weimer and Vining (2011)

recommend 25-year monopolies on salmon-fishing in Canada, with each river system managed and fished

by a different single owner (except the Fraser River, where they recommend that rights be exercised by a

carefully designed cooperative). Although transferable quotas and long-term, river-specific monopolies

differ, they address the same systemic weakness–lack of property rights and incentives for long-term

stewardship and the perverse effects of restrictions (taking as many fish as possible while access lasts).

3 Some utilities that employed these techniques dropped the frowning faces after customers complained (Kaufman 2009). 4 It is worth noting that a similar technique was tried in the Washington, DC area in 1978, with positive impacts on conservation (Omang 1978).

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Taking Inventory: Do We Have the Tools Needed for Sustainability Policy Analysis?

What are the implications of this discussion for the common forms of environmental policy

analysis? Economic impact analysis has been central to EPA’s analytical toolkit since its founding.

Nearly all standard-setting authorities require an assessment of impacts, such as costs, plant closures, job

losses, effects on consumers and small businesses. These analyses follow the classic mechanistic, linear,

cause and effect model. When an agency requires a pollution control technology to be installed and

maintained, it assesses the effects on targeted organizations. There typically is no consideration of

adaptations over time (such as changes in raw materials or production processes) and other innovations

that reduce costs. In their classic analysis of the relationship between regulation and efficiency, Porter and

van der Linde (1995) fault technology-based regulation for its failure to take into account the dynamic

environment in which firms operate. The same criticism may be applied to economic impact analysis. It

fails account for the responses that firms and others make over time to reducing the costs of compliance.

Cost-benefit analysis is a variant of economic analysis but far more ambitious. Its aim is not just

to estimate costs, broadly defined, but also to calculate and monetize the social benefits of an action. It

has a strong normative component by emphasizing that actions should be taken only when they result in

net social benefits. The literature both in support and critical of cost-benefit is extensive. For purposes of

this discussion, the issue is its fit with the dynamic systems model of sustainability discussed above. In

one sense, cost-benefit may be seen as an integrating framework, because it explicitly links the economic

and ecological systems. In making this link, however, it elevates the first over the second by converting

the choices being made into the currency of the economic system. The value of the ecological system to

society is based in the monetized value consumers preferences assign to it, either directly in markets (e.g.,

health care costs or lost shellfish) or indirectly in willingness-to-pay (value of life). In theory, cost-benefit

would justify ecological destruction if such an outcome is consistent with consumer preferences.

Since the late 1970s, risk assessment has been central in environmental policy analysis. Its

purpose is to establish a quantitative estimate of the likelihood and severity of harm from a substance or

29

activity. The EPA’s early uses of risk assessment focused almost entirely on health risks; since the 1980s

it has also given attention to ecological risks. As a tool, risk assessment is focused entirely on the

ecological system, although its use in assessing differences in exposures and risks among groups, such as

for environmental justice, extends to the social system as well. Risk assessments are used most commonly

to identify potential sources of risk for action, such as for determining cleanup standards for hazardous

waste sites or setting emission limits for air pollution sources. At times, the tool has also been used to set

priorities for regulation. In practice, risk assessment is used by regulators most often to establish the

scientific basis for policy decisions, in the risk management stage of decision making.

The third tool, environmental impact assessment (EIA), is the most well-established of the three

within the field of environmental decision making. It was a cornerstone of the National Environmental

Policy Act of 1969, which required federal agencies to evaluate the impacts of actions affecting the

environment. The EIA has become a standard form of analysis for national and global environmental

agencies. As the title suggests, an EIA follows the linear, cause and effect model. If government or others

take an action (build a highway, alter a wetland), the EIA is used to estimate the adverse environmental

impacts and determine if they are acceptable and/or how to reduce them. EIA focuses largely on the

ecological system, but it also may encompass social impacts, such as effects on communities,

demographic groups, and cultural amenities.

A more complete application of the sustainability concept is the OECD’s guidance on

“Sustainability Impact Assessment,” issued in 2008. It defines sustainability impact assessment (SIA) as

“an approach for exploring the combined economic, environmental and social impacts of a range of

proposed policies, programmes, strategies, and action plans.” (4) It specifies that the standard three

“pillars” of sustainable development should be “fully integrated” into the assessment. (4) Like the

standard environmental analytic tools, it reflects the limitations of an impact orientation and the difficulty

of incorporating a systems perspective. It does, however, include a useful discussion of synergies and

trade-offs that may be identified and analyzed across the three domains of sustainability (economic,

environmental, and social). The aim of the assessment is “to compare the positive and negative impacts in

30

the different domains and to tease out potential conflicts.” (23) Although this guidance does not develop

and apply the systems perspective which we see as essential for a dynamic, integrated approach, it does

suggest criteria and principles for a field of sustainability policy analysis.

The recently released sustainability framework document for the EPA makes huge strides toward

a systems-based approach to policy analysis by advocating for “a broadening of disciplinary approaches

toward understanding underlying processes,” a greater capacity for considering long-term as well as

short-term consequences of policy interventions, and a more integrated analysis of the economic, social,

and environmental impacts of proposed policies (National Research Council 2011, p. 85). The document

describes cutting-edge analytical tools and methods that can help to achieve these goals, including life-

cycle analysis, ecosystem services valuation, integrated assessment models, sustainability impact

assessments, economic justice analysis, and scenario writing. Tools such as life-cycle analysis and

ecosystem services valuation can be combined with new approaches to discounting to improve the

capacity of cost-benefit analysis to acknowledge natural capital and its value over time. The

sustainability framework also emphasizes the importance of engaging and collaborating with stakeholders

throughout the stages of sustainability assessment and management.

Nonetheless, the new framework clearly continues to embrace the assumption of orderly cause-

and-effect relationships, with just a bit more acknowledgement of uncertainty than is usually found.

Moreover, the framework extends the assumption of order to include cause-and-effect relationships

stretching across longer periods of time as well as cumulative effects. It aims for a “comprehensive”

understanding of each relevant policy’s potential impacts across all of the system pillars. Along the

analytical journey, there is an exhortation to keep stakeholders informed and engaged, but the overall

process tends to be centrally driven. The impression is of a bigger, more encompassing, longer duration

cost-benefit analysis or risk assessment exercise, using a larger number of variables than usual. The

document notes that analytical projects based on the framework “can be quite involved and may require

EPA to devote significant staff time and resources to the task” over long time periods (National Research

Council 2011, p 52).

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Such an approach seeks to blend the conventional, step-by-step approach to policy analysis à la

Eugene Bardach (2009) – define the problem, sketch some options for addressing the problem, establish

criteria (indicators) for assessing the options, weight trade-offs between options, etc – with an

acknowledgment of the interconnected and uncertain nature of systemic risk and the “waves of

repercussions” that may be set off by a policy intervention designed to mitigate those risks. The

sustainability framework represents a significant advancement in policy analysis conceptualization and

practice, and confirms the environmental sector’s leadership role when it comes to policy innovation.

Where the sustainability framework falls short is in its failure to launch a new policy analysis

paradigm designed to fit the realities of a world in which the assumption of orderly cause and effect is

replaced by the phenomena of emergence and abrupt, unpredictable, large-scale systemic change. In the

world of complex systems, mega-studies of sustainability will provide some useful insights, but there is a

danger that the systems being studied will change so dramatically during the period of study that the

results will be obsolete by the time they arrive. In this world, what is needed is a combination of close

surveillance in order to spot patterns of emergence (including monitoring of the sustainability indicators

mentioned in the framework document and in Table 1), on one hand, and carefully designed experimental

interventions run by key actors who are close to the problems, on the other hand. This paradigm of policy

analysis, which bears close resemblance to the adaptive co-management techniques described earlier,

goes well beyond either scenario writing as a tool or stakeholder engagement as an add-on to the main

analysis, although it does not necessarily supplant these often useful techniques. An adaptive management

approach to policy analysis focuses more on real demonstrations of policies, with close scrutiny of

responses and impacts, and real partnerships between the analysts and the local people tasked with

management and decision-making. There is room in the EPA sustainability framework for strengthening

linkages between four core activities: close surveillance of system indicators (across multiple systems),

implementation of demonstration policies, learning by local actors, and adaptation of policy designs.

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Conclusion: Why Even Think about a Field of Sustainability Policy Analysis?

A great deal of sustainability policy analysis (SPA) is being conducted, although not described in those

terms. Like the character in the Moliere play who at some point discovered that all his life he had been

speaking prose, analysts have been doing SPA without realizing it. What is the value of defining such a

field when in many senses is being now? What value does such a framework bring to what exists?

To conclude, we propose six contributions that SPA can make to policy studies and analysis.

First, it can define a framework for understanding the constraints of each system, based on a continually

evolving understanding of characteristic patterns of emergence and self-organization. Second, it can

define a clearer foundation for making and comparing choices with each system’s distinctive constraints.

Third, SPA can help in identifying and analyzing the threats that one or more systems may pose for the

others. Fourth, it may aid in mapping the interrelationships and interdependencies among the four

systems, including the opportunities for synergy. Fifth, SPA may be useful in identifying the clearer win-

win and lose-lose relationships among the four systems, such as the relationships (to take an example)

among alternative energy paths and employment in an economy. And sixth, a field of sustainability policy

analysis may to clarify the normative choices we face and distinguish them from the more empirical ones.

Such issues as the ecological consequences of social inequities raise important normative questions that

may be at least informed by analysis. These propositions are clearly open for debate as we evaluate the

role of policy analysis in a possible but by no means certain transition to a more sustainable world.

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Table 1. Examples of Indicators from the Four Systems

Economic Indicators Ecological Indicators Social Indicators Governance Indicators

gross domestic product per capita income growth rates unemployment rates competitiveness index trade measures savings rates

pollutant releases/levels energy efficiency wetlands loss protected forest species diversity water stress CO2 emissions per capita

education levels lead blood levels genie index (inequality) annual birthrates nutrition levels life expectancy PCB body burden

political participation quality of governance corruption index freedom of speech civil liberties turnover in office professionalization