John Weyant, professor of management science and engineering at Stanford University, discussed the progression of integrated assessment modeling (IAM) since 2000 and future research needs in the field. Dr. Weyant defined an integrated assessment as an analysis of two or more major earth system components and at least one natural and one human component. These assessments are not always models but often cover as much of a global earth system as possible. Integrative assessments can capture uncertainties and emergent behavior of systems that would otherwise “fall through the cracks” in interdisciplinary research performed in silos or subcomponents. During Dr. Weyant’s role in the 2000 Intergovernmental Panel on Climate Change (IPCC) climate assessment, a number of gaps were identified that related to understanding the linkages and feedbacks in the global climate system.1 Dr. Weyant noted that without a fuller systemic understanding of linkages and related data, many of the integrated assessments in the early 2000s, specifically simplistic cost-benefit models, were only practical at local levels and lost their relevance at the global scale.
Recently, modelers and analysts improved the relevance of integrated assessments by incorporating more governance, land, water, and food capabilities; developing shared social-economic pathway scenarios to system modeling; and gathering diverse actors to advance regional integrated assessment initiatives. Examples include the MIT Global Integrated Systems Model (IGSM); and the Potsdam Institute for Climate Impact Research’s (PIK’s) Integrated Assessment Modelling framework (PIAM); both of which are described further on. Dr. Weyant indicated, however, there is a need to continue addressing gaps in understanding various systemic linkages, feedbacks, and uncertainties.
He suggested that further analysis is needed of subsystems that exist and operate within larger systems to adequately understand higher-level interactions and feedbacks. Such a method was applied to the IPCC’s Third Assessment Report on sustainable development and international equity in 2001, which tried to model decisions made in climate policy.2 A birds-eye view of how climate policy fits within larger frames of sustainable development and international equity was applied instead of a dissected analysis of individual decisions and actors (Figure 2-1). The climate-policy system may not have direct control of movement and activity in other systems such as “Environmental and Socio-economic Impacts” and “Equity and Sustainable Development Policy,” but what
1 Intergovernmental Panel on Climate Change (IPCC). 1995. Chapter 10: Integrated assessment of climate change: An overview and comparison of approaches and results. In IPCC Second Assessment. Synthesis Report. Geneva, Switzerland: WMO 1.
2 Metz, Bert. 2001. Chapter 1 in Climate Change 2001: Mitigation: Contribution of Working Group III to the Third Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, UK: Cambridge University Press, 3.
occurs in these other systems may constrain and influence the decisions made in climate policy and, ultimately, the amount of emissions released.
The efforts of integrative assessment models to identify higher-level connections and feedbacks, however, have been met with criticism regarding mistaken attribution or oversimplification. Dr. Weyant addressed these critiques by referencing the models Dynamic Integrated Climate-Economy (DICE), Policy Analysis of the Greenhouse Effect (PAGE), and Climate Framework for Uncertainty, Negotiation, and Distribution (FUND) that quantify the social cost of carbon.3 These models, while integrating across human and physical earth systems, only use a few factors—economy and population for human systems and carbon cycle for physical earth systems—to identify a correlation between global mean temperature with global damages. These models also establish a single fixed social price on carbon, even though additional factors may influence the relationship (Figure 2-2). Dr. Weyant emphasized that such a simplistic approach to integrated assessment is not embodied in all IAMs, particularly for the task of quantifying the cost of carbon that can range from as little as $37 per ton of CO2 to as large as $1,000, depending on the factors assessed (Figure 2-2). Europe, for example, developed more complex IAMs for the cost of carbon by attempting to add input assumptions representative of different components of the earth system to capture more detailed costs and benefits, such as aggregate gross domestic product (GDP), GDP per capita, and gross climate outcomes.
The added complexities to these IAMs enable them to assess systems more holistically and to develop sustainability indicators that cover a diverse ground of human capital, economic, and environmental impacts and drivers, including land use, ecosystems, net primary productivity, water and heat stress, electric generation and capacity, and cost effectiveness. PBL’s4 Integrated Model to Assess the Global Environment (IMAGE) exemplifies diverse
3 Statistical models used by the EPA to estimate the social cost of carbon—DICE (Dynamic Integrated Climate-Economy), PAGE (Policy Analysis of the Greenhouse Effect), and FUND (Climate Framework for Uncertainty, Negotiation, and Distribution).
4 PBL Netherlands Environment Assessment Agency is the national institute for strategic policy analysis in the fields of the environment, nature and spatial planning of the Dutch government; PBL. 2016. http://www.pbl.nl/en/aboutpbl. Online. Available at: http://www.pbl.nl/en/aboutpbl. Accessed May 13, 2016.
capabilities of many of these models after including additional human system sectors such as agriculture, energy, and governance from an ecosystem model template created by past land-use accounting (Figure 2-3).5 The wide range of sectors and systems represented in this model allow for tracing pathways for actions, decisions, and activities across food, water, land, technology, and population sectors.
Despite progress made in integrating human and physical earth systems in assessments, Dr. Weyant noted a lack of social indicators for things such as equity, connectedness, culture, and health in the six “complex” IAMs presented—IGSM, GCAM, PIK, MESSAGE, PBL IMAGE, and MERGE.6,7, 8, 9, 10, 11 Integration could allow these assessments to factor in social indicators and costs, but only if the scientific community increases its flexibility and willingness to gather such information, as some important drivers for these models are inputs, not outputs. Other areas of need for IAMs include research on ocean acidification, irrigation potentials and aquifer net positions, black carbon, and subsurface carbon sinks.
During the brief question-and-answer session, Dr. Weyant was asked to describe if and how IAMs contribute to the policy landscape and practical work. He said many models could support decision making if downscaled to a regional context where policymakers act. These downscaled models, however, may leave out interactions and
5 Stehfest, E., et al. 2014. Integrated Assessment of Global Environmental Change with IMAGE 3.0. Model description and policy applications. The Hague, Netherlands: PBL Netherlands Environmental Assessment Agency.
6 MIT Global Integrated Systems Model (IGSM).
7 Joint Institute for Global Change Global Change Assessment Model (GCAM).
8 Potsdam Institute for Climate Impact Research (PIK), referring specifically to the Potsdam Integrated Assessment Modelling (PIAM) framework.
9 International Institute for Applied Systems Analysis (IIASA) Model for Energy Supply Strategy Alternatives and their General Environmental Impact (MESSAGE).
10 PBL Netherlands Environmental Assessment Agency Integrated Model to Assess the Global Environment (IMAGE).
11 Stanford University Model for Evaluating the Regional and Global Effects of GHG Reduction Policies (MERGE).
feedbacks occurring outside of the region that could heavily influence the region’s decision making. For example, regional models may not adequately predict global water and crop shortage trends that constrain or enable trade and agriculture in a specified region. Due to limitations of regional models, he noted that decision makers could use their results as a baseline of potential outcomes.
Joseph Arvai, Max McGraw Professor of Sustainable Enterprise and director of the Erb Institute for Global Sustainable Enterprise at the University of Michigan, discussed the progress made in developing models to support decision making for sustainability and additional efforts still needed to address remaining challenges. He opened his discussion by pointing to the increasing prevalence of research in decision sciences and the emergence of studies that provide relevant context to sustainability. One study by Baba Shiv et al. (1999) on emotion versus cognition argued that if processing resources become limited and individuals feel cognitively taxed by the decision, then individuals often instinctively choose options that appeal to them on an emotional level.12 This has direct application to individuals facing large sustainability decisions.
12 Shiv, B., and A. Fedorikhin. 1999. Heart and mind in conflict: The interplay of affect and cognition in consumer decision making. Journal of Consumer Research 26:278–292.
A study devised by Dr. Arvai and others on trade-offs compared consumer preferences at gas stations when prompted with information about a singular attribute of the gasoline versus information on multiple attributes.13 When provided with only information on the location of oil extraction, the majority of Canadian participants chose gasoline from the oil sands of Canada first, followed by American oil second, Saudi Arabian third, Venezuelan fourth, and Nigerian fifth. Using the decision science technique of swing weighting, however, Dr. Arvai and his team found that Canadian preferences changed when provided with additional attributes such as origin, cost per liter, greenhouse gas emissions, overall environmental impact, and human rights score of the country of origin (Figure 2-4). On average, Canadians preference for Canadian tar sand oil dropped from first to fourth, U.S. oil from second to first, Saudi Arabian oil from third to second, Venezuelan oil from fourth to third, and Nigerian oil stayed at fifth. A complete lack of calibration between values important to individuals and the decisions they made persisted in different variations of the study with the rate never eclipsing 50 percent. Dr. Arvai called for framing these questions of preference in a manner that does not incite individuals to make decisions based on an emotional appeal, but instead in the larger context of intergenerational well-being.
Another example of prevalent research in decision sciences with relevant context to sustainability is the increasingly popular decision science concept of “nudging,” which provides an option to encourage individuals to make rational decisions based on their values as opposed to emotional decisions. Proponents of nudging suggest that if one can identify the instinctive patterns of biased preferences, then one can reconfigure the world to help individuals make choices internally consistent with their values.14 Though such nudge studies have been highly successful, they suggest that individuals realize their internally consistent preferences in decision making without much effort, which does not completely align with the complexities associated with sustainability decision making. Using the concept of nudging to guide society in the right direction in making sustainability decisions may not make significant progress on some of the largest sustainability challenges.
Daniel Kahneman’s book Thinking Fast and Slow recognizes the need for more active cognition by advising individuals to slow down in high-stake decisions.15 Dr. Arvai counter argues Kahneman’s speed argument for high-quality decision making by noting that individuals will simply make bad decisions more slowly due to the difficulty of weighing preferences and trade-offs. Increasing consultation, improving accessibility to high-quality
13 Bessette, D., and J. Arvai. 2014. A lack of internal consistency plagues consumer and policy preferences. In prep.
14 Thaler, R. H., and C. R. Sunstein. 2008. Nudge: Improving Decisions about Health, Wealth, and Happiness. New Haven, CT: Yale University Press.
15 Kahneman, D. 2011. Thinking, Fast and Slow. New York: Farrar, Straus and Giroux.
information under science-based decision making, and creating platforms or environments that facilitate negotiation all constitute possible paths forward for decision support. Additionally, he said a missing essential element is imposing a structure that can decompose complex decision problems into cognitively manageable parts to help move society toward internally consistent choices that would lead to intergenerational well-being.
Dr. Arvai also discussed new efforts to address needs and opportunities related to sustainability and reiterated the importance of structure (Figure 2-5). Firstly, collaboration with communities and individuals may improve understanding, identification, and characterization of the decision problems and opportunities they face. In addition, identification of appropriate objectives and performance measures may be essential to decision making. In the sustainability context, dialogue often focuses on making more sustainable decisions without defining what “more sustainable” means. Regarding indicators, from a decision-making standpoint, some measurement challenges remain—a multitude of metrics for sustainability can be devised, yet an individual decision maker may only handle about five to seven at any one time. Thus, paring down the indicators most useful for decision making and developing a list of creative and substantially different alternatives may facilitate decision making. Confronting trade-offs directly is also a needed element in addressing sustainability decision making and finding pathways for individuals, whether sustainability decision makers or stakeholders, to make tough trade-off decisions. Further, an adaptive management and iterative decision-making process where individuals make decisions and then implement, evaluate, learn from, and remake those decisions can highly benefit sustainability decision making. As an example, he pointed to a research project with Michigan State University focused on decision making for an energy transition in the context of sustainability that developed a number of decision support tools to move individuals toward preferences more internally consistent with their values and sustainability goals.16
Dr. Arvai concluded his discussion by pointing to the need for further work in behavioral science dealing specifically with sustainability. There is also a need to scale up from purely behavior studies to prescriptive studies on how to move individuals to make decisions internally consistent with their values and objectives.
Wolfgang Lutz, founding director of the Wittgenstein Centre for Demography and Global Human Capital (a new collaboration between the International Institute for Applied Systems Analysis (IIASA), the Austrian Academy of Sciences and the WU-Vienna University of Economics and Business), examined global population trends in the context of sustainability. He discussed how human numbers and demographic differential vulnerability relate to sustainability challenges, as well as what well-being indicators and demography metrics offer for sustainability science. He began with a brief analysis of world population outlooks commenting that population metrics had varying degrees of uncertainty largely due to rapid fertility rate declines in Africa (Figure 2-6). He also presented
16 Bessette, D., J. Arvai, and V. Campbell-Arvai. 2014. Decision support framework for developing regional energy strategies. Environmental Science & Technology 48:1401–1408.
population outlooks based on level of education (Figure 2-7). Recent research at the IIASA predominantly focused on producing what Dr. Lutz termed “the human core of the shared socioeconomic pathways,” which includes population numbers, age structures, gender distributions, and educational attainment levels.
Regarding heterogeneity of the human population, education may be considered the single most important source of observable population heterogeneity after age and sex. In the context of population dynamics, the changing educational composition of the population directly influences changes in population growth and age distribution. Additionally, education is largely considered a crucial determinant of individual empowerment and human capital—driving socioeconomic development in public health, economic growth, quality of institutions, and democracy. As such, this type of analysis may provide important insights as related to sustainability.
Dr. Lutz elaborated on education as a demographic dimension by describing the education-cognition effect, where such variables as health, fertility, and other forms of behavior have an established functional causality to cognition and education. At the individual level, education increases cognitive skills, which may lead to less risky behavior by extending the planning horizon to enable a person to better plan ahead and learn from past damage at the individual and social level. Education can also positively affect health and physical well-being by acting as a growth enhancement. Better-educated societies typically tend to have a higher GDP, which may also decrease vulnerability. In one series of studies, IIASA asked the question, “What is more important for infant mortality, the mother’s income or education?” The mother’s education consistently proved the most important factor at the individual, household, and national levels.17
17 Pamuk, E. R., R. Fuchs, and W. Lutz. 2011. Comparing relative effects of education and economic resources on infant mortality in developing countries. Population and Development Review 37(4):637-664.
In the context of human capital, demographers focus on the quantity of formal education for data measurement. They also consider informal education, though it is more difficult to measure. “Education stocks” typically measured in mean years of school or full distribution of highest educational attainment largely determines human capital. A population pyramid study for Singapore in the 1970s, 1980s, and 1990s observed a process of demographic metabolism and intergenerational change where the educated population increased as the country transformed from developing to developed. Another study conducted by Dr. Lutz and others at IIASA calculated projections of population growth based on education’s relationship to fertility (Figure 2-8). In Kenya, highly educated populations had an average of two children compared with more than six children in uneducated populations.18,19
Dr. Lutz addressed the issue of the human population’s adaptive capacity to climate change. A recently completed 5-year project that forecasted society’s adaptive capacity to climate change illustrates that society’s capacity to innovate and develop green technologies and mitigation strategies is a function of education in society. Dr. Lutz and others pointed to a clear differential vulnerability in adaptive capacity to climate change where climate change does not affect the entire population, but rather affects livelihoods, health, and migration possibilities and depends on the individual’s empowerment.20 Similar results were summarized for vulnerability to natural disasters, where research established education as a key determinant.21 Thus, generally empowering the population to adequately respond to climate change challenges may be more effective than only developing concrete climate change infrastructure. Such data may also indicate that, in addition to universal primary education, near universal secondary education may also improve sustainable development.
18 Lutz, W., and S. KC. 2011. Global human capital: Integrating education and population. Science 333(6042):587-592.
19 Lutz, W., W. P. Butz, and S. KC, eds. 2014. World Population and Human Capital in the Twenty-first Century. Oxford: Oxford University Press.
20 Lutz, W., R. Muttarak, and E. Striessnig. 2014. Universal education is key to enhanced climate adaptation. Science 346(6213):1061-–1062.
21 Muttarak, R., and W. Lutz. 2014. Is education a key to reducing vulnerability to natural disasters and hence unavoidable climate change? Ecology and Society 19(1):42.
In identifying other indicators relating to human well-being, he said that being alive is a basic prerequisite often missed by sustainability indicators and metrics. However, Dr. Lutz argued that mere survival may not sufficiently achieve sustainability and pointed to empowered life years as a possible alternative measure. Empowered life years could encompass healthy life expectancy (how many years an individual can expect to be alive and in good health), or literate life expectancy, poverty life expectancy, happy life expectancy, or any combination of these factors. A possible sustainability metric based on demographic indicators would measure empowered life expectancy and account for whether it declined over time in any subpopulation or not.
The question-and-answer session raised the issue of conflict and sustainability and if any studies examined the relationship between levels of education and conflict within the boundaries of a country. Dr. Lutz referenced a body of research that related empowerment and the education of a population to political outcomes and processes—indicating there is a relationship between demography and democracy. He also mentioned global migration and how the choice to migrate may be essential in terms of sustainability and that migration should be incorporated into integrative modeling. It was noted that as an extremely complex and multifaceted issue, migration may not have a single solution, particularly with respect to global environmental change.
Kristie Ebi, professor, Department of Global Health and Department of Environmental and Occupational Health Sciences at the University of Washington, discussed drivers of indicators and metrics as they relate to frameworks for evaluating health systems and climate change. Many health indicators focus on children and often integrate many health issues, such as nutrition, vaccination of children, and childhood mortality. In 2015 the World Health Organization (WHO) released an operational framework composed of ten components related to building climate-resilient health systems (Figure 2-9).22
22 World Health Organization (WHO). 2015. Operational Framework for Building Climate Resilient Health Systems. Available at http://apps.who.int/iris/bitstream/10665/189951/1/9789241565073_eng.pdf.
One challenge Dr. Ebi noted for developing metrics around climate-informed health programs is the need to better account for information provided by models of climate systems. For example, the public health sector creates metrics around heat-related mortality, but those metrics do not take into account short-term forecasts about climate change. This would help public health officials to better set up early warning systems and make action plans for periods of decades or longer. Indicators and metrics are needed to better understand what has been learned about climate programs and assess other ways to learn those lessons.
Related to this learning process is the incorporation of theory of change, which is “a comprehensive description and illustration of how and why a desired change is expected to happen in a particular context. It is focused on mapping out or ‘filling in’ what has been described as the ‘missing middle’ between what a program or change initiative does (its activities or interventions) and how these lead to desired goals being achieved.”23 Dr. Ebi noted that many organizations have adopted this approach for development projects and that there will be upcoming opportunities to review their work to assess successes and failures. In conclusion, Dr. Ebi said that there are many potential indicators related to sustainability in the literature and being used in practice, but that there needs to
be a systematic way of integrating them all. There is also the need for an institutional and political climate that encourages the development of indicators that aim to be broader than single issue-based foundations.
Elena Bennett, associate professor at McGill University, discussed models that support decisions related to sustainability, gaps and critical barriers to models, and new efforts needed to address opportunities related to sustainability. Dr. Bennett framed the discussion around sustainability development goals, and identified successes in an integrated assessment model as moving the needle closer to achieving those goals (Figure 2-10). One of the first applications of integrated assessment models was to understand environmental and economic impacts of acid rain on biological systems. From there, they were further developed for the International Panel on Climate Change (IPCC) to integrate climate and economics into an understanding of global systems. They were also developed for the Millennium Ecosystem Assessment, which integrated ecological feedback into biogeophysical models. This integration was accomplished with output from biogeophysical models used by the IPCC as input into models with ecological feedback incorporated, such as InVEST or other ecosystem service models.24 Ongoing work with these models focuses on incorporating the needs and consideration of stakeholders and decision makers. The Madingley Model from Microsoft Research’s Computational Science Laboratory in Cambridge is the first General Ecosystem Model, which attempts to simulate all life on Earth. The model couples key biological processes underpinning life cycles and behaviors of all of the planet’s organisms.25
Dr. Bennett said integrated assessment models have become increasingly more robust and rigorous; however, successful models may result from increasing parameterization until desired outputs are achieved. The millennium assessment models were different, i.e., more robust, because they were more integrated and contained more feedbacks (e.g., climate, economics, ecology).
Remaining gaps include large scale models with limited focus that are not useful for decision making. For example, there is generally a single focus on the effect of a single intervention, such as food security with limited integration of energy systems. Ecology is often missing from models, and social systems are entirely nonevident except for economics. Feedbacks are rare and are limited in action within models, especially ecological feedbacks. There is limited model validation, so accuracy is unknown. Dr. Bennett said her research group ran InVEST on data for more than 190 counties with 12 different ecosystem services; however, there were only three services they could model using InVEST. There was also no correlation between the InVEST model output and actual data. This lack of correlation could be from inaccurate on-the-ground measurements or errors within the model. No amount of reparametrizing could correlate the model with data from the field. This raises questions about whether these models should be used for decision making when there is so much uncertainty and no means yet of addressing that uncertainty or risk.
Dr. Bennett discussed new efforts needed to further move toward well-being. Codesign and coproduction are key areas for development, which engages users not only at the end of the process but also as contributors to the development of scenarios and models. Dr. Bennett shared an example of a project that engaged shareholders in developing scenarios. The project focused on communities in a mostly agricultural region southeast of Montreal, Canada interested in land-use planning and designing better networks among forest patches to improve ecosystem services. Dr. Bennett’s group engaged mayors and land-use planners to develop a scientifically and theoretically interesting project around ecosystems services that provided land-use planners with useable information.
The project further focused on land use and cover, which are parameters planners can manipulate to increase biodiversity. These parameters were tied to a set of 12 ecosystem services in the region. The relationships among land use, ecosystem services, and biodiversity were evaluated for the past, from the year1900 on, for the present, and into the future. Stakeholders were engaged to develop future land-use scenarios, and models were built around those scenarios. Different ecosystem services would be expected from different landscapes determined by land-use decisions (Figure 2-11). Land-use planners wanted to optimize ecosystem services, represented by different petals on a diagram, by adjusting where forest patches were located on the landscape.
Another key area for future efforts is in better thinking about futures, as in scenarios for modeling future outcomes. Dr. Bennett discussed Seeds of a Good Anthropocene, which is a project focusing on finding pockets
of a better future that already exist today and using them to understand how, where, and why transitions occur. There are many utopian visions in classic literature and in modeled scenarios in scientific literature, but there is never an explanation of the transition needed to arrive at the scenario. Other key topics that are part of utopian narratives that need to be incorporated into integrative assessment models include cultural diversity, resilience, political economy, and urban centers. They collected 350 “seeds” from all around the world, which she described as not just good-news stories but examples that can lead to real transitions to sustainability and be used to rethink the future in entirely new ways.
Thomas Dietz, professor of sociology and environmental science and policy and assistant vice president for environmental research at Michigan State University, presented on informing sustainability science through advances in environmental decision making. The National Research Council (NRC) report Our Common Journey and the United Nations Millennium Ecosystem Assessment led to a body of scholarship on the relationships between human well-being and the environment. The focus on inclusive human well-being and the environment
allows researchers and policy makers to focus on a select number of indicators that are broad, have normative consensus, and for which relatively good data exist. A more select list of indicators helps to avoid a cacophony of hundreds of indicators and can supplement standard economic measures, such as gross domestic product per capita.
There is an increasing amount of empirical research on the factors that influence well-being. Measures of resources, for example, are used as inputs along with contextual information to determine the factors that shape resource use. Another example is the relationships among carbon dioxide emissions, measures of stress on the environment, and measures of well-being. These relationships can be used to identify countries that seem to be doing very well in terms of well-being and examine the factors that led to success in those countries. For example, one factor is governmental institutions, which could develop strategies that would help move other countries toward better human well-being while maintaining minimal impacts.
Dr. Dietz said that further work is need in several areas, including on whether the environment and other species are the only means to human well-being, what other ethical theories or values justify endpoints, how varying ethical theories are reconciled, and what other theories can shape research on how resources, institutions, and people influence endpoints. The identification of measurement properties about endpoints is also needed, as
well as an examination into how variables (indicators) that society conjectures drive endpoints. Lastly, the issue of discounting or substitutability of resources remains an outstanding challenge to address. Dr. Dietz said that ethical theories are a key area of investigation and that it has been empirically shown that peoples’ values and concern with the biosphere (including other species) is correlated with, but also distinct from, altruism directed toward other people. An examination of whether major changes in the biosphere, such as Glacier National Park without glaciers or Joshua Tree National Park without trees, matter intrinsically to society is an example of how new ethical theories need to be brought into natural resource decision making.
Key lessons have been learned in this research area. There is widespread acknowledgment that there is global environmental change of coupled human ecology and natural systems and not just climate change. Such systems are complex and evolving, and researchers have to be cautious about how subsystems are isolated for studying or modeling. Interdisciplinary work is essential, and social networks are fundamental to learning about and responding to change. Sustainability is about linking the conservation community with the development community, but in considering development and well-being, it is also important to consider what other 21st century issues may affect human well-being and the environment, such as globalization, the Internet of Things, robotics, artificial intelligence, and bio- and nanotechnology; however, 19th and 20th century challenges also still exist—poverty, violence, and discrimination.
These issues need to be integrated to determine how they affect each other in addition to well-being. For example, what response would be needed if developments in robotics and artificial intelligence over the next 50 years substantially reduced the demand for labor? What effect would that scenario have on poverty and human well-being, and how would that fit into the current thinking about sustainability? This leads to another key lesson learned, Dr. Dietz said, which is that uncertainty pervades. At best, uncertainty can be characterized as quantifiable risks, but typically it is characterized as meta-uncertainty. Meta-uncertainty is uncertainty about how to characterize a system, including how other subsystems influence the overall system. Methods are being developed and approaches framed to understand and deal with this uncertainty, which is a form of adaptive risk management. Institutions and networks that learn in the face of such uncertainty are needed to further develop this idea of adaptive risk management.
Dr. Dietz said that there has been a lot learned about values and their influence on decision making. Researchers are beginning to learn how to link scientific analysis to public deliberation about values. Acknowledging values and learning how to incorporate values into research are important. There is an iterative communication process between the public and those conducting scientific analysis, which links public deliberation with the analysis. A 2008 NRC report on public participation concluded that “when done well, public participation improves the quality and legitimacy of decisions and builds the capacity of all involved to engage in the policy process.”26 Improvement in this context means that the quality of decisions or assessments is better.
There is a challenge, Dr. Dietz said, when conducting an analysis in figuring out how to engage multiple standpoints, acknowledge different types of expertise, and take advantage of social learning on networks. Individuals who engage in an analysis may have several different types of expertise. Scientific expertise is at the center of this, but there are multiple types of scientific expertise:27
- Scientific expertise about substance: expert knowledge about the systems and processes that will be affected by decisions.
- Scientific expertise about process and decision making: expert knowledge about individual and collective decision making, including valuation.
- Community expertise: knowledge based on life experience and living in systems that will be affected—“traditional ecological knowledge.”
- Political expertise: knowledge about conflicts, assumptions, trust, and informal institutional arrangements based on engagement in policy systems.
26 National Research Council (NRC). 2008. Public Participation in Environmental Assessment and Decision Making. Washington, DC: The National Academies Press.
27 Dietz, T. 2013. Bringing values and deliberation to science communication. Proc. Natl. Acad. Sci. 110:14081–14087.
- Value expertise: everyone has legitimacy regarding values, but good processes and research may help articulate values and reduce value conflict.
Sustainability is about decisions and making trade-offs under uncertainty. There are many different theories on how to conduct trade-offs and address uncertainty, but there needs to be more work on learning how to integrate theories and determine which ones function best in which context. In general, more work is needed in being more attentive to context—context matters. There needs to be more consideration of individuals as being embedded in communities, which are embedded in nations. There is a tradition of place-based studies, but there is also a need to incorporate the individual and a need for the individual to be integrated into large macrocomparative (across nations, time, and/or institutions) analyses. Datasets are needed that provide comparable data on representative samples of individuals across large numbers of context and different nations. Such data would help to understand contextual- and individual-level effects. An example of how this can be accomplished is with the World Fertility Survey, where comparable surveys were conducted in a large number of countries.28 These surveys examined how women’s education affected fertility, as well as how the national context affected women’s education on fertility. More data are needed, though, as is the need to build more of a community of new scholars and practitioners.