Climatic Change

, Volume 117, Issue 3, pp 433–438

Improving the assessment and valuation of climate change impacts for policy and regulatory analysis

Authors

    • National Center for Environmental Economics, U.S. Environmental Protection Agency
  • Robert E. Kopp
    • Department of Earth & Planetary Sciences and Rutgers Energy InstituteRutgers University
  • Kate C. Shouse
    • Office of Air and Radiation, U.S. Environmental Protection Agency
  • Charles W. Griffiths
    • National Center for Environmental Economics, U.S. Environmental Protection Agency
  • Elke L. Hodson
    • U.S. Department of EnergyOffice of Climate Change Policy & Technology
    • AAAS Science & Technology Policy Fellow, American Association for the Advancement of Science
  • Elizabeth Kopits
    • National Center for Environmental Economics, U.S. Environmental Protection Agency
  • Bryan K. Mignone
    • U.S. Department of EnergyOffice of Climate Change Policy & Technology
  • Chris Moore
    • National Center for Environmental Economics, U.S. Environmental Protection Agency
  • Steve C. Newbold
    • National Center for Environmental Economics, U.S. Environmental Protection Agency
  • Stephanie Waldhoff
    • Joint Global Change Research Institute, Pacific Northwest National Laboratory
  • Ann Wolverton
    • National Center for Environmental Economics, U.S. Environmental Protection Agency
Article

DOI: 10.1007/s10584-012-0608-0

Cite this article as:
Marten, A.L., Kopp, R.E., Shouse, K.C. et al. Climatic Change (2013) 117: 433. doi:10.1007/s10584-012-0608-0

The social cost of carbon (SCC) is a monetized metric for evaluating the benefits associated with marginal reductions in carbon dioxide (CO2) emissions. It represents the expected welfare loss from the future damages caused by the release of one tonne of CO2 in a given year, expressed in consumption equivalent terms.1 It is intended to be a comprehensive measure, taking into account changes in agricultural productivity, human health risks, loss of ecosystem services and biodiversity, and the frequency and severity of flooding and storms, among other possible impacts. Estimating the SCC requires long-term modeling of global economic activity, the climate system, and the linkages between the two through anthropogenic greenhouse gas (GHG) emissions and the effects of changing climatic conditions on economic activity and human well-being.

The United States government currently uses the SCC in regulatory benefit-cost analyses to assess the welfare effects of changes in CO2 emissions (Kopp and Mignone 2012). Consistent application of the SCC to federal rulemaking analyses began in 2009–2010 with the development of a set of global SCC estimates that employed three prominent integrated assessment models (IAMs)—DICE, FUND, and PAGE2 (Interagency Working Group on the Social Cost of Carbon, United States Government 2010). These models were run with a standardized set of input assumptions, based on the existing scientific and economic literature, for the reference socio-economic-emission scenarios, discount rates, and a probability distribution for equilibrium climate sensitivity. The differences in the outputs between the models therefore reflected differences in the models’ estimates of climate change damages and in their physical climate and carbon cycle components.

The IAMs that are used to estimate the SCC include representations of earth system processes, economic behavior and some of the critical feedbacks between them. They provide an integrated framework that links future socio-economic pathways, technological development, GHG emissions, climate changes, and the resulting physical and societal impacts. Each of these elements represents an area of active research with its own limitations and uncertainties, and the development of a single model that combines these components introduces additional technical and scientific challenges. IAM-based projections, including estimates of the SCC, are therefore characterized by numerous limitations and uncertainties, many of which are being actively investigated by researchers.

The U.S. government report identified a number of limitations associated with SCC estimates in general and its own assumptions in particular: an incomplete treatment of damages, including potential “catastrophic” impacts; uncertainty regarding the extrapolation of damage functions to high temperatures3; incomplete treatment of adaptation and technological change; and the evaluation of uncertain outcomes in a risk-neutral fashion. External experts have identified other potential issues, including how best to model long-term socio-economic and emissions pathways (O’Neill 2010), oversimplified physical climate and carbon cycle modeling within the IAMs (Warren et al. 2010; Hof et al. 2011; Marten, 2011; van Vuuren et al. 2011), and an inconsistency between non-constant economic growth scenarios and constant discount rates (as used in the U.S. government analysis, among others) (O’Neill 2010; Kopp and Mignone 2012). The U.S. government has committed to updating the estimates regularly as modeling capabilities and scientific and economic knowledge improves.

To help foster further improvements in estimating the SCC, the U.S. Environmental Protection Agency and the U.S. Department of Energy hosted a pair of workshops on “Improving the Assessment and Valuation of Climate Change Impacts for Policy and Regulatory Analysis.” The first workshop, which took place in November 2010, focused on conceptual and methodological issues related to integrated assessment modeling. Presentations from leading experts reviewed the state of the art represented by more complex IAMs and investigated critical modeling challenges: sectoral and regional disaggregation, adaptation and technological change, multi-century scenario development, climate system uncertainty, damages at high temperatures, non-market impacts, tipping points and potential “catastrophes” (proceedings available at http://go.usa.gov/426). The workshop also covered the role of IAMs in policy analysis and design. The second workshop, held in January 2011, brought together natural and social scientists to explore methods for improving damage assessment for multiple sectors. Specifically, the workshop examined: the availability of water resources; agriculture; storms and extreme weather events; human health; sea level rise; marine ecosystems and resources; terrestrial ecosystems and forestry; energy production and consumption; and socio-economic and geopolitical impacts (proceedings available at http://go.usa.gov/42F).

These two workshops provide the basis for the 13 papers in this special issue. The first five papers address overarching conceptual and methodological issues around climate change impact assessment and valuation, including concerns about the interaction of effects within models (Oppenheimer; Yohe & Hope), uncertainty analysis (Cooke), a framework to incorporate adaptation (Sue Wing and Fisher-Vanden), and a review of empirical impacts and adaptation estimates to assess their incorporation into IAMs (Fisher-Vanden et al.). Two subsequent papers (Anthoff & Tol, Hope) examine SCC estimates based on updated versions of two of the models used in the original U.S. government SCC analysis. In both papers the authors consider detailed sensitivity analyses to explore the components of the system that have the largest impact on the SCC to continue exploring what future research may be most fruitful. Another pair of articles examines the contributions that more detailed and disaggregated IAMs, such as the Global Change Assessment Model (GCAM) and the Integrated Global System Modeling framework (IGSM), may add to the SCC discussion (Calvin et al., Paltsev et al.). The authors discuss developments in GCAM and IGSM that may help introduce feedback effects of emissions on human welfare. A final set of four papers examines three specific types of impacts—water availability (Hurd & Rouhi-Rad), climate-induced armed conflict (Theisen et al.), and climate-induced human migration (McLeman)—as well as the potential economic effects of crossing Earth system tipping points (Lenton and Ciscar). We proceed with a brief summary the papers within this.4

While the current generation of IAMs has made progress in a variety of key areas, some of the articles in this issue point out that there is still much room for improvement. In their essay, Yohe and Hope use valuation of cyclone impacts to demonstrate the importance of avoiding an excessively narrow scope in terms of both the climate variables considered and the types of impacts examined. They suggest that, rather than focusing on untangling the complex web of uncertainties that envelop SCC estimates, it may be more useful to prioritize development of a medium-term carbon cap that maintains future flexibility and then to place a value on carbon consistent with meeting this constraint.

In his contribution, Oppenheimer emphasizes the importance of including human responses to climate change in assessing its net impacts. He highlights two contexts as examples: the extended effects of climate impact-driven migration on both ecosystems and economies, and changes in the availability of land and water for adaptation purposes due to the use of biofuels as part of a mitigation strategy. In both of these contexts, human responses both modify the net costs of climate change impacts and adaptation and shift costs between regions.

Sue Wing and Fisher-Vanden find that most studies have suffered from an incomplete representation of adaptation. In response they provide insight into how IAMs may better handle the issue by carefully differentiating between types of adaptation (e.g., passive response, reactive response, and proactive) and develop a conceptual framework that includes adaptation as an integral component of impact analysis. In a separate paper, Fisher-Vanden et al. use this framework to examine the impacts and adaptation modeling literature and demonstrate the ways in which the various types of adaptation should be incorporated theoretically.

Another key overarching issue is uncertainty analysis, which Cooke examines by describing the evolution of uncertainty analysis and drawing upon lessons from three and a half decades of experiences in the nuclear safety arena. He notes the importance of stress testing IAMs and of taking multiple pathways to bracket the calibration of climate change damage functions—combining top-down “outer measures” of damages, for example based on macroeconomic econometric analysis, with more conventional bottom-up “inner measures” that sum impacts in particular sectors to estimate damages. Cooke concludes that a “fourth generation” of uncertainty analysis is needed to adequately quantify the risks of climate change, including the careful combination of expert judgments to avoid bias in the variance, improved representations of the dependencies among uncertain parameters, and the development of a richer set of alternative models to represent model uncertainty.

The contributions by Anthoff and Tol, and by Hope examine SCC estimates from new versions of their respective IAMs, FUND and PAGE. Anthoff and Tol diagnose the most important uncertain parameters in FUND. They find that the temperature elasticity of demand for air conditioning plays the most important role in FUND, followed by climate sensitivity and impacts upon Chinese agriculture. Other important and uncertain parameters relate to CO2 fertilization, cold-related cardiovascular deaths in Southeast Asia, and interregional migration.

Hope presents new estimates of the SCC from an updated version of PAGE, finding a somewhat wider range and an increase in the mean SCC of 31% over the mean SCC of Stern (2007), which used a previous version of the PAGE model. The article reviews changes in the new model version that drive the difference in results, including choice of BAU scenario, inclusion of a range rather than one value for discounting parameters, updated distributions for climate sensitivity, and less optimistic assumptions about the effectiveness of adaptation. Based on the results from the two versions of PAGE, Hope suggests seven factors important to the interpretation of SCC estimates, including discount rate selection, equity weighting, socio-economic scenario, and assumptions about adaptation.5

Paltsev et al. and Calvin et al. examine issues related to the impacts of climate change using disaggregated, process-based IAMs (IGSM and GCAM, respectively). Because of the level of detail, these models have the potential to capture interactions among sectors when considering climate change impacts that are often missed in more highly aggregated IAMs (Warren 2011). Paltsev et al. introduce developments within the IGSM model to better capture the biological and physical impacts that ultimately affect human welfare, such as water availability, agricultural crop yields, marine food webs, and human mortality risk. They present a computable general equilibrium framework that allows traditional national accounting systems to be augmented by equivalent variation measures of non-market impacts from climate changes.

Calvin et al. describe the effort to couple the Community Earth System Model, a state-of-the-art physical climate model, to GCAM, a partial-equilibrium IAM with detailed representations of the energy, agriculture, and forestry, sectors. They explore pathways for incorporating agriculture and land use, energy use, ocean acidification, water resources, sea level rise, human health, extreme events, and biodiversity impacts into process-based IAMs. As an example, they consider the incorporation of climatically-induced crop-yield changes into GCAM and find that mitigation policy choices—in particular, whether to incorporate land-use emissions within a regulatory regime—have a considerably greater impact on corn prices than climate change directly.

The final four papers review issues related to specific impact sectors or areas. Lenton and Ciscar address the inclusion of Earth system tipping points in IAMs. They differentiate the way in which economists and natural scientists have discussed climate “catastrophes” and their relationship with the concept of tipping points in earth systems in order to shed light on how they might be better modeled in IAMs. Reviewing a number of possible tipping points, they emphasize that some of these may occur at relatively modest levels of warming and that some are quite probable: a fairly different perspective from the economic literature on climate change which has commonly treated tipping points as “high-impact, low-probability” events.

McLeman, Theisen et al., and Hurd and Rouhi-Rad examine additional climate change impacts that have generally been omitted, or at best, crudely modeled, in the current generation of IAMs. McLeman reviews the literature of environmentally-driven migration and the potential role that climate change may play in increasing its prevalence. He points out that a lack of adequate data has limited recent modeling efforts and that accurately accounting for vulnerability requires a careful consideration of institutional and economic structures, a point that mirrors the concerns presented by Oppenheimer.

Theisen et al. review the literature on the role of climate change and environmental stresses in exacerbating intrastate violent conflicts. They note that few studies exist and, taken as a group, these are inconclusive. They find that the policy debate in this field may be ahead of its academic foundation and the prevailing beliefs may run contrary to the best available evidence. They call for more research to fill knowledge gaps and recommend prioritizing investigation into plausible mechanisms linking environment stress with conflict, more geographically disaggregated analyses that also take into account short-term variability, a greater focus on the poorest, most economically stagnant regions, and the inclusion of small-scale violence.

Hurd and Rouhi-Rad consider the critical interaction between water availability and a changing climate and the associated economic damages. They examine empirical damage estimates at both the national level and regional level and observe that while there is concern that national level studies may operate on spatial resolution that misses the important aspects of the problem only realized in spatially detailed studies, the economic damages forecast in both national and regional studies are of a similar magnitude. However, they note that the spatial resolution of regional studies may still be too coarse and suffer from the same aggregation biases as national level studies. They conclude that although it is instructive to examine macro-level economic impacts, communities should examine water resource impacts at much higher levels of resolution than the regional studies that have been completed to date.

The complexities of the systems being modeled—future population and economic growth and the resulting emissions, the global climate and carbon cycle, the responses of natural systems to changing forcings, and the implications for human welfare—combined with the challenges of modeling linkages among these systems results in considerable uncertainty around estimates of mitigation benefits. Given the inherent uncertainties associated with this problem, IAMs will never be able to provide a single, precise estimate of the SCC.6 However, provided the uncertainties are well characterized, even uncertain estimates are useful for informing policymakers’ decisions regarding the design and stringency of regulations that alter CO2 emissions. Moreover, estimates of the SCC are improving; the articles in this issue reveal the research community’s progress in and continued commitment to improving valuation of climate change impacts.

These articles offer multiple perspectives on and approaches to addressing the challenges involved in modeling climate change damages. Together, they begin to map out a path for strengthening the research base upon which SCC estimates are built. Despite their limitations, these estimates have an important role in assessing the benefits associated with marginal reductions in carbon dioxide emissions in regulatory decision making. They are an important tool that enables more complete evaluation of the costs and benefits of regulation. As the modeling of the SCC continues to improve, the SCC estimates will more accurately monetize the benefits of climate change impacts into benefit-cost analysis.

Footnotes
1

The time horizon considered can range considerably from a century (Kandlikar 1996) to multiple millennia as in the FUND model. The recent United States government SCC exercise used a fixed terminal period of 2300.

 
2

The full names of the models are: Dynamic Integrated Climate and Economy (DICE), Policy Analysis of the Greenhouse Effect (PAGE), and Climate Framework for Uncertainty, Negotiations, and Distribution (FUND). These IAMs are highly aggregated, and can be distinguished from more disaggregated, process-based IAMs that traditionally have been used for mitigation policy analysis but have not generally attempted to represent comprehensively climate feedbacks upon human systems.

 
3

Many damages have only been studied at low to moderate levels of climate change (e.g. changes of 2.5° to 3°C global average temperature). However, even at these levels of warming the level of agreement among IAMs about specific sectoral impacts is low (e.g., Kopp and Mignone 2012). Multiple methods to extrapolate damages from temperature changes beyond this level have been proposed (e.g., Kopp et al. 2012a).

 
4

Parallel to but independent of this special issue, a special issue of Economics: The Open-Access, Open-Deliberation E-Journal contains additional related papers on the SCC (Kopp et al. 2012b)

 
5

Although not included in this special issue, Nordhaus (2011) has examined SCC estimates from his latest multi-regional model, RICE 2011, a regionalized and updated version of the DICE model used in the U.S. government analysis.

 
6

Though they are treated as accurate to the nearest dollar for U.S. regulatory analyses, unresolvable uncertainties will always prevent confidence in the SCC estimates to this degree of precision. The U.S. National Academy of Sciences (2009) concluded “only rough order-of-magnitude estimates of marginal climate damages are possible at this time.”

 

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© U.S. Government 2012