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Page 133 3 Exploring the Future The previous chapter examined past trends and ongoing transitions that will need to be confronted in efforts to navigate a transition toward sustainability. This chapter looks to the future. We recognize that much of the continuing interaction between human development and the environment will be a process of ''muddling through." The inevitable trial-and-error of selecting a course, learning, and correction will be carried out less by efforts to think through our futures than by the necessity of acting them out. The decisive factor in determining how effective, fair, and efficient this muddling will be is in our choices not of analytic tools, but rather of the social institutions that help to provide the incentives and feedbacks necessary for social learning. We nonetheless believe that the inevitable trials may be made more productive, and the likelihood of costly and irreversible errors may be reduced through organized efforts to assess the possible future implications of present trends, relying on growing understanding of earth system processes and social goals. The international efforts in recent years to address threats to the stratospheric ozone layer is a case in point. Understanding as much as possible about what the future may hold is important. It can identify things societies should try to avoid. It can give useful insights about what societies should do now to prepare for plausible contingencies. It can even help societies to learn what they ought to want for the future, by helping to illuminate the alternatives before them, and some of the implications of trying to achieve alternative futures. In some respects, the future is known. Using the laws of physics, the
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Page 134 orbital location of the planets 100 years from now can be predicted with considerable precision. While prediction is often possible, however, in many cases it is difficult, impossible, or irrelevant. This may be true because of incomplete causal knowledge, system complexity, insufficient data about current conditions, the engagement of reflective humans in the system, or combinations of all of these factors. Some physical systems are inherently chaotic. At least within broad boundaries, their future performance can not be known. Social systems add another level of complication. People react to their environments. Their preferences and values change, in part because of what they experience, in part because of what their imperfect efforts to look into the future have revealed to them. People and their organizations act strategically, based on what they think others may do in response to different interpretations of the future. Since many of these reactions cannot be predicted, over time they impose progressively more serious limits on our ability to see the shape of possible futures. Even when the future performance of a system can only be described in the most general terms, however, "what if" analysis can be useful. Such analysis can help societies to explore what contingencies they may face, determine how well they are prepared to deal with those contingencies, and identify indicators for which they should be watchful. If we can find ways to generate a range of plausible alternative futures, we can use them to evaluate different behavioral strategies for their likely efficacy and robustness in the face of a range of alternatives, and for how easily these strategies can be adapted to deal with unanticipated developments. Efforts to structure and discipline our thinking about future possibilities in the light of present knowledge and intentions may therefore have an important role to play in shaping strategies for a sustainability transition. This chapter explores various approaches that have been used to explore the future toward addressing sustainability concerns. It seeks to evaluate their respective strengths and weaknesses as tools to aid in navigating a sustainability transition, to illustrate the sorts of insights that can emerge from their use, and to identify priorities for improving their performance and practical utility. Strategies for Exploring the Future Strategies for using science to explore possible futures in policy contexts may be evaluated on at least four criteria: scientific credibility, political legitimacy, practical utility, and effectiveness.1 Scientific credibility: Such analytic strategies can make systematic but skeptical use of available scientific knowledge in laying out not only the likely conditions that might be encountered ahead, but also the pos-
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Page 135 sible and the impossible ones. Especially important would be these strategies' treatment of uncertainty. Debates over what is known "for sure" are unscientific and not particularly productive. An overemphasis on "consensus" assessments can clearly suppress the discussion of unlikely but not impossible outcomes. Needed as well are tools that can help to structure the inevitable uncertaintiesincluding the possible low-probability, high-consequence events and ''surprises"such that their implications can be critically evaluated and addressed. Also important will be the ways in which known and hypothesized long chains of causal links are concatenated across multiple disciplines and multiple scales of analysis. These issues pose substantial technical challenges. They also raise fundamental questions about who should count as an expert, what should be the meaning and nature of peer review, and how critical evaluations of exploratory tools and the possible futures they illuminate can be most helpfully conducted. Political legitimacy: Efforts to navigate a transition toward sustainability are inherently social enterprises. Individuals are, of course, free to shape their own private images of the future and may use the results in crafting their own policies. But to the extent that societies seek scientifically based explorations of possible futures to provide a common foundation for collective action, it is crucial that the explorations be viewed as fair and legitimate by those whose futures they might affect. The credibility of future assessments to users is therefore also critical. This type of credibility may be related to, but is almost never identical to, scientific credibility. Issues about participation in the design and use of exploratory tools, about transparency and openness in embodied values and assumptions, and about the embedding of assessments in appropriate institutional settings all come to the fore in efforts to satisfy this criterion of political legitimacy. Practical utility: Tools for exploring the future should also be usable, and used. Above all, this means that they must be relevant to real choices faced by real individuals and institutions. They need to be available to potential users in a timely manner and sufficiently flexible that they encourage exploration of a wide range of possible goals and choices. Often, they will need to enable users to perform "what if" analyses of the possible future consequences of present actions. Since the realm of possible actions is often large, and the range of possible futures so wide, practical utility may also require means for sorting through alternative actions in light of users' values and preferences. Finally, useful assessments must be sparing in their demands for time and other resources that choice makers may find in short supply. Effectiveness: Finally, tools needed for exploring the sustainability transition should be effective in actually illuminating pitfalls and oppor-
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Page 136 tunities in the roads ahead. This is admittedly a post hoc evaluation criterion. But individuals, institutions, and societies have been facing the challenges of grappling with uncertain futures for a long time indeed. It should not be too much to ask of tools for exploring sustainable futures that they be designed, and chosen, at least partially on the basis of their past performances in analogous circumstances. Various approaches to satisfy these criteria in exploring the future have been adopted in forms that could be applied to sustainability issues. These include (1) qualitative consultation among "knowledgeable" people as in study panels; (2) formal elicitation of expert judgment in forms such as subjective probability distributions; (3) creation of structured and internally consistent narratives or scenarios; (4) various forms of strategic gaming; (5) formal extrapolation of past trends using statistical methods; and (6) a wide variety of different kinds of causal modeling. Often, several of these methods are used together. None of them provide more than partial illuminations of the futures before us. Each is limited in particular ways. Each, howeverwhen used critically, skeptically, and carefullycan make a useful contribution. Study panels such as those organized under the auspices of the Brundtland Commission, the Intergovernmental Panel on Climate Change (IPCC), the International Council for Science (ICSU) (e.g., the Scientific Committee on Problems of the Environment), the U.S. National Research Council (NRC), and the German Enquette Commissions are common strategies for exploring possible future implications of our current understanding. Such panels often make use of the other strategies outlined below in various combinations. The great strengths of these panels include the ability to draw on a wide range of expertise and stakeholders; to build from data but to tap understanding as well; and to provide environments in which experts can challenge and learn from one another. Common weaknesses include difficulties in quality control; a tendency to exclude disenfranchised stakeholder groups; a vulnerability to group-think; and the tyranny of consensus-seekinga special problem in areas as uncertainty-laden as those encountered in efforts to navigate a transition to sustainability. Of the tools used by study panels and other methods in exploring the future, one extreme consists of causal process models, such as those used to simulate fishery yields and the general circulation of the atmosphere.2 The strength of approaches to modern modeling lies in their explicit incorporation of scientifically verifiable relationships, and in their ability to make quantitative, if still conditional, forecasts of the implications of those relationships. Among their weaknesses remain their insatiable demands for data, difficulties in incorporating the different types and levels of
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Page 137 knowledge and understanding that characterize different disciplines, and a host of computational problems. We turn to recent developments in modeling and integrated assessment that have begun to confront some of these shortcomings in the following section. At another extreme are strategies built around the use of narratives or scenarios that tell a plausible and coherent story while relying on particular examples to provide context and details.3 A seminal example is Rachel Carson's account of widespread and enduring ecological damage from some pesticides and other common substances, in her 1962 Silent Spring. At their best, these approaches can do a relatively good job at addressing complexity, context, and contingency. A special form of narrative is future history. Future histories have been used effectively to explore surprising futures beyond the normal range of extrapolation or projection.4 They are also receptive to the explicit incorporation of norms and values. But they tend to be idiosyncratic, only partially constrained by scientific knowledge, and lacking in the precision that many would like to have in a navigational tool. In this chapter, we turn to recent developments that avoid some of these shortcomings under the discussion of scenario-based approaches to exploring sustainability futures. An intermediate strategy that has proven helpful for exploring the future has been the use of extrapolation, drawing both on past trends and on analogous circumstances elsewhere. Relatively sophisticated examples include work on trends such as decarbonizationthe long-term reduction in the amount of carbon produced per unit of energyand the demographic transition discussed in Chapter 2, the cataloging of environmental degradation syndromes advocated by the German Advisory Council on Global Change, and econometric forecasts of energy use.5 These approaches work well to the extent that they capture deep underlying forces not readily subject to deflection. Their great weakness is that, in the absence of accompanying causal understanding, the limits to their applicability are unknowable and their visions of the future are thus particularly vulnerable to surprise. When uncertainty precludes conventional scientific analysis, yet quantitative estimates are needed for use in analysis, it is sometimes possible to obtain the judgments of experts in the form of subjective probability distributions. Such judgments are no substitute for solid understanding of the relevant science. But when decision makers cannot wait for better science, expert judgments can be used on an interim basis to provide some grounds for more informed policy choices. The decision analysis community has developed these methods and employed them in a variety of applications.6 Formulating interview procedures and obtaining expert judgments relating to large, complex natural and social systems pose significant challenges. However, there are a number of examples of
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Page 138 successful applications in such contexts as depletion of stratospheric ozone, long-range transport of sulfur air pollution, the assessment of earthquake structural risks, possible climate change in the face of increased atmospheric carbon dioxide, and energy modeling.7 Expert elicitation often reveals a richer and more diverse array of expert opinion than is typically captured in the reports of traditional consensus expert panels.8 But subjective probability distributions can be wrong as often as expert opinion. For example, an elicitation of estimated probabilities of weather modification success from 113 atmospheric scientists in 1968 found universal optimism about the expected success of modifications that 30 years later have either been abandoned or never scientifically validated.9 Similarly, there is strong evidence that scientists have been overconfident in the past about the accuracy with which they know the value of basic physical constants.10 Additionally, there is strong evidence of consistent overconfidence in the literature on behavioral decision theory.11 A strategy complementary to several of those described above is based on the creation of comprehensive accounts for resource use and pollutant emissions associated with particular futures. Such accounts are important because the multisectoral character of environment-development interactions makes it difficult to avoid analytic blunders such as double-counting the same water in independent agricultural and industrial analyses, or the same land in separate studies of energy and food production. Similarly, in the absence of comprehensive accounting frameworks, emissions of large-scale pollutants such as carbon dioxide can be underestimated when only some sectoral sources are considered. Starting with pioneering work by Resources for the Future in regional environmental management, reflecting integrated studies of the basins of the Potomac, Delaware, and Ruhr rivers, comprehensive accounting frameworks have helped to minimize such errors in careful efforts to explore alternative futures.12 Such contributions notwithstanding, it is important to realize that accounting strategies provide a tool for exploring the future only when used in conjunction with other approaches. Finally, a number of assessment methods have begun to emerge that combine elements of representation and deliberation.13 The most developed of these methods, strategic gaming, is a special form of study panel that developed in military contexts seeking to address major uncertainties in future environments.14 Military approaches have been adapted for use in civilian contexts, in both corporate planning and a broad range of public policy analyses germane to sustainable development.15 Strategic gaming has proven an excellent way to integrate scientific models and human ingenuity into evaluations of possible future implications of present decisions. The weakness of this approach is that it is very good at teaching lessons that have little to do with the real world, and that it
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Page 139 makes extraordinary demands on the time and resources of the analytic community. In practice, some of the most interesting and potentially helpful efforts to explore possible futures relevant to a transition toward sustainability have entailed mixed strategies drawing on a combination of those outlined above. The following sections therefore discuss in more detail the present and potential contributions of three mixed strategies that seem particularly promising for exploring such possible futures: integrated assessment models, scenarios building, and institutionally oriented efforts to incorporate such tools into regional systems of policy development and adaptive management. Integrated Assessment Models Integrated assessment models seek to link in a consistent fashion formal models of the environment and society.16 Examplessome discussed in more detail belowinclude the Club of Rome's Limits to Growth, the International Institute for Applied Systems Analysis' RAINS (Regional Air Pollution Information and Simulation) model of acidification in Europe, the Latin American World Model, and the TARGETS (Tools to Assess Regional and Global Environmental and Health Targets for Sustainability) model of regional and global environment and health for sustainability in the Netherlands.17 Early Efforts Early efforts in developing integrated assessment models included systems dynamics studies and, at the global scale, the Club of Rome's Limits to Growth.18 This work helped to draw attention to sustainability issues, but largely failed to satisfy criteria of scientific credibility. A second round of integrated modeling took place in the context of the energy crises of the 1970s.19 Again, while detailed predictions were not the strong points of these models, they did manage to provide insight into the structure of problems at the interface of society and environment. Lessons learned from these early efforts included the importance of building models to explore a specific set of futures rather than general ones, the need to specify realistic model structures and parameter values, the critical role of feedback loops in stabilizing complex systems, and the place of sensitivity analysis in evaluating model results.20 More generally, experienced assessors began to question the preeminent focus of the early enterprise on outputs consisting of relatively unconditional predictions. Consistent with trends in the modeling of large-scale economic systems,21 the most used and useful work began to
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Page 140 emphasize instead the role of integrated assessments in providing conditional answers to "what if" policy questions. At the same time, integrated assessment practitioners began to emphasize less the predictions of their models and more the basic insights and understanding that those models could offer about the complex interplay of social and natural processes in shaping possible futures.22 The reorientation of integrated assessors away from prediction as an end in itself and toward prediction as a means of enhancing and calibrating understanding sometimes seems to be the field's own coming-of-age passage, recapitulated by each generation of modelers on their way to mastery of an important and difficult craft.23 Contemporary Efforts Contemporary integrated assessment modeling has been strongly shaped by the need to address problems of large-scale interactions between economic development and the atmospheric environment. One of the most successful and widely known efforts has been the RAINS model of acidification in Europe developed by the International Institute for Applied Systems Analysis (IIASA) beginning in the mid 1980s.24 As developed and applied over a decade and more, RAINS now provides a spatially distributed modeling framework linking emissions and deposition patterns, and estimating local ecological impacts at deposition sites. In "what-if" mode, it allows exploring the ecological consequences of alternative policies for emission reductions. In optimization mode, it allows computation of minimal cost emission reduction schedules for satisfying specified impact constraints. The model, along with the processes of consultation in the science and policy communities in which it is embedded, has been widely credited with influencing policies for the most recent protocols for sulfur dioxide emissions in Europe as negotiated under the Convention on Long-Range Transboundary Air Pollution (LRTAP).25 Integrated assessment modeling is now being extensively applied in national and international efforts to address the risk of global climate change. The phenomena of climate change are manifestly complex, involving large-scale socioeconomic forces and the coupled ocean-atmosphere-biosphere system. Seeking to engage with this complicated array of interacting and intersecting phenomena, modelers have created a large variety of integrated assessments linking energy use and other human activities to changes in climate and, more recently, to impacts of climate change on ecosystems and society. In its 1995 report, the IPCC reviewed the 22 such models listed in Table 3.1 and classified them according to the scheme shown in Table 3.2.26 Some such classification is necessary to sort through the increasing variety of integrated assessment models being applied in explorations of
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Page 141 Table 3.1 Integrated Assessment Models Model Modellers AS/ExM (Adaptive Strategies/ Exploratory Model) R. Lempert, S. Popper (Rand); M. Schlesinger (U. of Illinois) AIM (Asian-Pacific Integrated Model) T. Morita, M. Kainuma (National Inst. for Environmental Studies, Japan); Y. Matsuoka (Kyoto U.) CETA (Carbon Emissions Trajectory Assessment) S. Peck (Electric Power Research Institute); T. Teisberg (Teisberg Assoc.) Connecticut (also known as the Yohe model) G. Yohe (Wesleyan U.) CRAPS (Climate Research and Policy Synthesis model) J. Hammitt (Harvard U.); A. Jain, D. Wuebbles (U. of Illinois) CSERGE (Centre for Social and Economic Research on the Global Environment) D. Maddison (University College of London) DICE (Dynamic Integrated Climate and Economy model) W. Nordhaus (Yale University) FUND (The Climate Framework for Uncertainty, Negotiation, and Distribution) R.S.J. Tol (Vrije Universiteit Amsterdam) DIAM (Dynamics of Inertia and Adaptability Model) M. Grubb (Royal Institute of International Affairs); M. H. Dong, T. Chapuis (Centre Internationale de recherche sur l'environnement et développement) ICAM-2 (Integrated Climate Assessment Model) H. Dowlatabadi, G. Morgan (Carnegie Mellon U.) IIASA (International Institute for Applied Systems Analysis) L. Schrattenholzer, Arnulf Grübler (IIASA) IMAGE 2.0 (Integrated Model to Assess the Greenhouse Effect) J. Alcamo, M. Krol (Rijksinstitut voor Volksgezondheid Milieuhygiene, Netherlands) MARIA (Multiregional Approach for Resource and Industry Allocation) S. Mori (Sci. U. of Tokyo) (table continued on next page)
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Page 142 (table continued from previous page) Table 3.1 Continued Model Modellers MERGE 2.0 (Model for Evaluating Regional and Global Effects of GHG Reductions Policies) A. Manne (Stanford U.); R. Mendelsohn (Yale U.); R. Richels (Electric Power Research Institute) MiniCAM (Mini Global Change Assessment Model) J. Edmonds (Pacific Northwest Lab), R. Richels (Electric Power Research Institute), T. Wigley (University Consortium for Atmospheric Research [UCAR]) MIT (Massachusetts Institute of Technology) H. Jacoby, R. Prinn, Z. Yang (MIT) PAGE (Policy Analysis of the Greenhouse Effect) C. Hope (Cambridge U.); J. Anderson, P. Wenman (Environmental Resources Management) PEF (Policy Evaluation Framework) J. Scheraga, S. Herrod (EPA); R. Stafford, N. Chan (Decision Focus Inc.) ProCAM (Process Oriented Global Change Assessment Model) J. Edmonds, H. Pitcher, N. Rosenberg (Pacific Northwest Lab); T. Wigley (UCAR) RICE (Regional DICE) W. Nordhaus (Yale U.); Z. Yang (MIT) SLICE (Stochastic Learning Integrated Climate Economy Model) C. Kolstad (U. of California, Santa Barbara) TARGETS (Tools to Assess Regional and Global Environmental and Health Targets for Sustainability) J. Rotmans, M.B.A. van Asselt, A. Beusen, M.G.J. den Elzen, M. Janssen, H.B.M. Hilderink, A.Y. Hoekstra, H.W. Koster, W.J.M. Martens, L.W. Niessen, B. Strengers, H.J.M. de Vries (Rijksinstitut voor Volksgezondheid en Milieuhygiene, Netherlands) Source: Weyant et al. (1996). Courtesy of the IPCC (Intergovernmental Panel on Climate Change).
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Page 143 Table 3.2 Summary Characterization of Integrated Assessment Models Model Forcings 0. CO2 1. other GHG 2. aerosols 3. land use 4. other Geographic Specificity 0. global 1. continental 2. countries 3. grids/basins Socioeconomic Dynamics 0. exogenous 1. economics 2. technology choice 3. land use 4. demographic Geophysical simulation1 0. Global DT 1. 1-D DT, DP 2. 2-D DT, DP 3. 2-D Climate Impact Assessment2 0. DT 1. Dsea level 2. agriculture 3. ecosystems 4. health 5. water Treatment of Uncertainty 0. None 1. Uncertainty 2. Variability 3. Stochasticity 4. Cultural Perspectives Treatment of Decision Making 0. optimization 1. simulation 2. simulation with adaptive decisions AS/ExM 0 0 0 0 0 1 2 AIM 0,1,2,3 2,3 1,2,3,4 1,2 0,1,2,3,5 0 1 CETA 0,1 0 1,2 1,2 0 0 0 or 1 Connecticut 0 0 1 0 0 1 0 CRAPS 0 0 1 0 0 1 2 CSERGE 0 0 1 0 0 1 0 DICE 0 0 1 0 0 0 or 1 0 FUND 0,1 1 1,4 0 0,1,2,3,4 0 or 1 0 DIAM 0 0 1,2 0 0 0 or 1 0 ICAM-2 0,1,2,3 1,2 1,3,4 1,2 0,1,3 1,2,3 1,2 IIASA 0 0 1 1 2 0 0 IMAGE 2.0 0,1,2,3 3 0,2,3 2 1,2,3 1 1 MARIA 0 0,1 1 0 0 0 0 MERGE 2.0 0,1 1 1,2 0 0 0 or 1 0 MiniCAM 0,1,2,3 2,3 1,2,3 2 0 0 1 MIT 1,2,3,4 2,3 1 2,3 0,2,3 1 0,1 PAGE 0,1 1,2 1 0 0,1,2,3,4 2 1 PEF 0,1 1,2 1 0 0 2 1 ProCAM 0,1,2,3 2,3 1,2,3,4 2 0,2,3,5 1 1 RICE 0 1 1 0 0 0 0 SLICE 0 1 1 0 0 1 2 TARGETS 0,1,2,3,4 0 1,2,3,4 2 1,2,3,4 4 1,2 Source: Weyant et al. (1996). Courtesy of the IPCC (Intergovernmental Panel on Climate Change). 1 TARGETS includes ozone depletion, soil erosion, acid rain, and toxic and hazardous pollutant releases. 2 In AIM, FUND, IMAGE, PAGE, and ProCAM, the impacts are calculated separately for each sector.
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Page 174 Sheet 6: Primary Energy Requirements by Region Current Forces and Trends Hunger and Carbon Reduction
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Page 175 Sheet 7: Primary Energy Requirements by Source Current Forces and Trends Hunger and Carbon Reduction
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Page 176 Sheet 8: Carbon Emissions Current Forces and Trends Hunger and Carbon Reduction
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Page 177 References and Bibliography Achebe, Chinua, Goran Hyden, Achola Pala Okeyo, and Christopher Magadza, eds. 1990. Beyond hunger in Africa: Conventional wisdom and a vision of Africa in 2057. Nairobi: Henemann Kenya. Ackerman, B.A., S. Rose-Ackerman, J.W. Sawyer, Jr., and D.W. Henderson. 1974. The uncertain search for environmental quality. New York: The Free Press. Alcamo, J., R. Shaw, and L. Hordijk, eds. 1990. The RAINS model of acidification: Science and strategies in Europe. Dordrecht: Kluwer Publishers. Benedick, Richard Elliott. 1988. Ozone diplomacy: New directions in safeguarding the planet. Cambridge, MA: Harvard University Press. Especially Chapter 2. Blinder, Alan. 1988. Economic policy and economic science: The case of macroeconomics. In Perspective 2000: Proceedings of a conference sponsored by the Economic Council of Canada, December 1998, eds. K. Newton, T. Schweitzer, and J-P. Voyer. Ottawa: Canadian Government Publishing Centre. Bossel, Harmut. 1998. Earth at a crossroads: Paths to a sustainable future. Cambridge: Cambridge University Press. Brewer, G.D. 1986. Methods for synthesis: Policy exercises. Chap. 17 of Sustainable development of the biosphere, eds. W. C. Clark and R. E. Munn. Cambridge, UK: Cambridge University Press. Brewer, G.D., and M. Shubik. 1979. The War game: A critique of military problem solving. Cambridge, MA: Harvard University Press. Budnitz, R.J., P.R. Davis, M.K. Ravindra, and W.H. Tong. 1995. Seismic risk of nuclear power plants under shutdown conditions. In International conference on structural mechanics in reactor technology 4. Conference organized by the International Association for Structural Mechanics in Reactor Technology and Universidade Federal do Rio Grande do Sul. Porto Alegre, Brazil: Editora da Universidade Federal do Rio Grande do Sul. Carson, Rachel. 1962. Silent spring. New York: Fawcett Crest. Cebon, P., U. Dahinden, H.C. Davies, D. Imboden, and C.C. Jaeger. 1998. Views from the Alps: Regional perspectives on climate change. Cambridge, MA: MIT Press. Clark, William C., and Giandomenico Majone. 1985. The critical appraisal of scientific inquiries with policy implications. Science Technology and Human Values 10 (Summer 1985): 6–19. Clark, William C. 1988. Visions of the 21st century: Conventional wisdom and other surprises in the global interactions of population, technology and environment. In Perspective 2000: Proceedings of a conference sponsored by the Economic Council of Canada, December 1998, eds. K. Newton, T. Schweitzer, and J-P. Voyer, 7–32. Ottawa: Economic Council of Canada. Commission on Risk. See Presidential/ Congressional Commission on Risk Assessment and Risk Management. Cronon, William. 1992. A place for stories: Nature, history, and narrative. Journal of American History 78: 1347–1376. Cukier, R.I., H.B. Levine, and K.E. Schuler. 1978. Nonlinear sensitivity analysis of multi-parameter model systems. Journal of Computational Physics 26: 1–42. Dowlatabadi, Hadi, and M. Granger Morgan. 1993. Integrated assessment of climate change. Science 259, no. 5013: 1813, 1932. Epple, Dennis, and Lester Lave. 1985. Scenario analysis. In Climate Impact Analysis, eds. R. Kates, J. Ausubel, and M. Berberian. London: Wiley. Fishbone, L.G., and H. Abilock. 1981. MARKAL, a linear-programming model for energy systems analysis: Technical description of the BNL version. Energy Research 5: 353–375. Forrester, J.W. 1968. Principles of systems. Cambridge, MA: Wright-Allen Press.
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Page 182 Watt, K.E.F. 1966. Systems analysis in ecology. New York: Academic Press. Weyant, J., O. Davidson, H. Dowlatabadi, J. Edmonds, M. Grubb, E.A. Parson, R. Richels, J. Rotmans, P.R. Shukla, R.S.J. Tol, W. Cline, S. Fankhauser. 1996. Integrated assessment of climate change: An overview and comparison of approaches and results. Chap. 10 of Climate change 1995, ed. James P. Bruce, Hoesung Lee, and Erik F. Haites. Contribution of Working Group III to the Second Assessment Report of the Intergovernmental Panel on Climate Change (IPCC). Cambridge, UK: Cambridge University Press. Published for the IPCC. World Bank. 1990. World development report 1990. New York: Oxford University Press. Published for the World Bank. Worster, Donald. 1985. Rivers of empire: Water, aridity, and the growth of the American West. New York: Pantheon. Endnotes 1 See, e.g., Guston (1997); Brewer (1986); Clark and Majone (1985). 2 E.g., Washington and Parkinson 1986; NRC 1994, NRC (1998). 3 See Cronon (1992). 4 Svedin and Aniansson (1987); Clark (1988); Achebe et al. (1990); Hammond (1998). 5 Environmental degradation syndromes, WBGU (1997), see Box 6.2 in Chapter 6; eonometric forecasts of energy use, WEFA (1998), Standard and Poors (1998), Jorgenson and Wilcoxen (1993), Nakicenovic et al. (1998). 6 Spetzler and von Holstein (1975); Morgan et al. (1990). 7 Depletion of stratospheric ozone, NRC (1979), Morgan et al. (1990); long-range transport of sulfur air pollution, Morgan et al. (1986); the assessment of earthquake structural risks, Budnitz et al. (1995); possible climate change in the face of increased atmospheric carbon dioxide, Morgan and Dowlatabadi (1996); energy modeling, e.g., NRC (1983). 8 Morgan and Keith (1995); Morgan (1998). 9 Julian et al. (1969). 10 Henrion and Fischhoff (1986). 11 Morgan et al. (1990). 12 Integrated studies of river basins, Kneese and Bower (1968); comprehensive accounting frameworks, e.g., Toth (1988a, 1998b). 13 Parson (1997); NRC (1996a); Commission on Risk (1997). 14 e.g., Brewer and Shubik, (1979). 15 Brewer (1986); Jaeger et al. (1991); Toth (1988a,b). 16 We distinguish here between integrated assessment models and integrated assessment processes such as some might call the IPCC or the production of an NRC report on policy responses to climate change. As will become clear later in the chapter, our emphasis on models here is not because we underestimate the importance of the process component of successful strategies for exploring the future. Rather, it is because "integrated assessment" as a process has, in the arena of global environmental change policy, come to be used synonymously with virtually any form of science-based assessment or evaluation. This is not necessarily a bad thing. But it does not make for a useful analytic category. Integrated assessment models, in contrast, constitute an evolving methodology conducive to critical appraisal, application, and improvement. 17 Club of Rome, Meadows et al. (1972); RAINS, Alcamo et al. (1990), Hordijk and Kroeze (1997); Latin American World Model, Herrera et al. (1976); TARGETS, Rotmans, and de Vries (1997). 18 Systems studies, Forrester (1968); Club of Rome, Meadows et al. (1972).
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Page 183 19 E.g., Haefele (1981), Fishbone and Abilock (1981). 20 See for example, Meadows et al. (1982); Greenburger (1983); Brewer (1986); Meadows and Robinson (1985); Keepin and Wynne (1987). 21 See Blinder (1988). 22 E.g., Holling (1978). 23 Hourcade and Robinson (1996). 24 Alcamo et al. (1990); Hordijk and Kroeze (1997). 25 Levy (1993). 26 Weyant et al. (1996). 27 Dowlatabadi and Morgan (1993); Parson and Fisher-Vanden (1997). 28 Weyant et al. (1996). 29 Cukier et al. (1978); Tatang et al. (1997). 30 See Robinson and Rothman (1997); Hourcade and Robinson (1996). 31 Holling et al. (1978). 32 See http://www.nacc.usgcrp.gov. 33 Weyant et al. (1996); Parson (1995); Dowlatabadi and Morgan (1993). 34 Benedick (1988). 35 Hourcade et al. (1996). 36 See GEA (1997). 37 Hourcade et al. (1996); Hourcade and Robinson (1996). 38 E.g., Holling (1978). 39 Epple and Lave (1985). 40 Wack (1985ab). 41 WBCSD (1997). 42 Meadows et al. (1992). 43 Kinsman (1990). 44 Gallopin et al. (1997). 45 The Global Scenario Group (GSG), part of the Stockholm Environment Institute's Polestar Project, was established to engage a diverse group of development professionals in a long-term commitment to examining the requirements for sustainability. GSG is an independent, international, and interdisciplinary body that represents a variety of geographic and professional experiences and engages in an ongoing process of global and regional scenario development, policy analysis, and public education. Individuals particularly active in the Group's publications have included Gilberto Gallopin, Pablo Gutman, Al Hammond, Paul Raskin, and Rob Swart. Raskin is a member of the Board responsible for this report. Hammond (1998) has published a scenario study of his own, drawing on the GSG work. The GSG is supported by the Nippon Foundation, the Rockefeller Foundation, the UN Environment Program, and the Stockholm Environment Institute. See http:// www.gsg.org for more details. 46 Paul Raskin 47 Raskin et al. (1998). 48 National Assessment Synthesis Team (1998); Gallopin et al. (1997); Walsh et al. (1999). 49 Watersheds, e.g., Maass (1962); airsheds, i.e., Hordijk (1988). 50 E.g., Ricker (1954); Morris (1963). 51 E.g., Maass (1962); Watt (1966); Mar (1974); Ackerman et al. (1974); SCOPE (1978). 52 Holling (1978). 53 See Walters (1986). 54 E.g., Gunderson et al. (1995); Cebon et al. (1998). 55 Lansing (1991). 56 Stephen Lansing and James Kremer, a systems ecologist. 57 NRC (1996b); Lee (1993). 58 NPPC (1994).
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Page 184 59 In a wide-ranging examination, an NRC panel concluded, "We found no easy answers for institutional change, but many constructive possibilities can be identified." (NRC 1996b), p.325. 60 Miles (1998). 61 cf. Kennedy (1993). 62 As reported in Raskin et al. (1998). 63 See Raskin et al. (1998) for a discussion. 64 This scenario assumes that trends such as those toward more efficient technologies continue. The scenario is therefore not simple extrapolations of current data. 65 Expressed as GDPPPP per capita. In this report, GDP adjusted for purchasing power parity is denoted by GDPPPP, to distinguish it from the more common GDP conversion in market exchange rates (GDPMER). 66 UN (1998). 67 Specifically, hunger lines increase to $3,670, the current inferred value for North America, as mean income approaches $21,880 (the value where the linear fit to the national data intersects the constant line at $3,670). This is analogous to the observation that absolute poverty lines tend to rise as average incomes do (Ravallion et al. 1991; World Bank 1990). 68 IPCC (1992). 69 UNDP (1997). 70 Alternative scenarios can be represented as trajectories in a space defined by three coordinates: the size of the world economy, international equity (ratio of non-OECD to OECD average incomes), and national equity average (ratio of incomes of the poorest 20 percent to the richest 20 percent). The sector of the space over which the scenarios can move plausibly is limited. For example, let us require that both the OECD and non-OECD regions exhibit positive economic growth. Let us also assume that, consistent with convergence assumptions of the scenario, incomes grow faster in non-OECD regions than in OECD regions (implying international equity of income should increase throughout the scenario time frame). Finally, based on historical patterns, let us assume a maximum plausible growth rate of GDPPPP per capita of about 4 percent over the large regions and long time periods we are considering. These plausibility constraints define a possible scenario space. Both the Current Forces and Trends scenario and the Hunger and Carbon Reduction scenario lie within this scenario space. 71 This national equity value would correspond to an average Gini coefficient of only 0.21 in 2025, about two-thirds the current average value in Western Europe. Gini coefficient is a measure of the degree of inequality in a given society. The coefficient is defined with reference to the Lorenz curve, a plot of the fraction of total income held by a given fraction of the population, beginning with the lowest income populations. The coefficent can take values from zero (complete equality) to one (extreme inequality). See Raskin et al. (1998). 72 This corresponds to an average regional Gini coefficient between 0.35 and 0.41 in 2050. 73 IPCC (1996). 74 Rijsberman and Swart (1990). 75 In addition to these energy-related carbon emissions, about 30 Gt C is emitted in the scenario from land changes over this period, mostly due to deforestation. It is assumed that policies for forest sustainability succeed in decreasing net emissions to zero by the year 2050. 76 The patterns of energy-efficiency improvement and energy mix change in Hunger and Carbon Reduction scenario are comparable to the "ecologically driven" scenario of a recent energy scenario exercise of the World Energy Council and the International Institute for Applied Systems Analysis (WEC/IIASA 1995). However, an important difference is that the WEC/IIASA scenario assumes much lower economic growth rates in developing regions (OECD and transitional region assumptions are comparable) so that developing country GDPPPP in 2050 is only half that of the Hunger and Carbon Reduction scenario. 77 See Raskin et al. (1998) for details.
Representative terms from entire chapter: