Climate and energy policy modeling

Projects

A multi-level, agent-based model for identifying the factors that enable or constrain international climate change negotiations
Duration and funding source: 3 years (April 2010 – March 2013); U.S. National Science Foundation

Expert elicitation of the deep uncertainty surrounding the market and non-market damages of climate change
Duration and funding source: 1 year (October 2011 – September 2012.); U.S. Environmental Protection Agency

Implications of representing diverse sources of uncertainty in the integrated assessment of climate change
Duration and funding source: 3 years (June 2008 – May 2011); U.S. Environmental Protection Agency


A multi-level, agent-based model for identifying the factors that enable or constrain international climate change negotiations

Duration and funding source: 3 years (April 2010 – March 2013); U.S. National Science Foundation

Personnel: M. Borsuk (PI), M. Gerst (co-PI), R. Howarth , P. Ding, and P. Wang (Dartmouth); M. Mastrandrea (IPCC & Stanford); G. Dosi (Sant'Anna School of Advanced Studies, Italy); A. Roventini (University of Verona, Italy); G. Bang (CICERO, Norway)

Synopsis and progress: Climate policy represents a global, collective decision-making problem unprecedented in scale and complexity.  However, scientific methods for evaluating international policy have tended to follow two separate lines of analysis, neither of which is fully instructive for real world settings.  One approach, typically referred to as Integrated Assessment Modeling (IAM), is largely pursued by economists and decision theorists and focuses on assessment of the long-term costs and benefits of various greenhouse gas reduction scenarios.  A second approach originates with game theorists and focuses on evaluating international structures and conditions likely to lead to effective cooperative climate agreements.  Both types of analysis rely heavily on the simplifying assumption that national economies are orchestrated by perfectly rational central planners who have the information and ability to make optimal decisions despite the presence of pervasive uncertainty about mitigation costs, climate damages, and future states of the economy.  In reality, the outcome and implementation of any international climate agreement will be the net result of a complex interplay of stakeholders at multiple levels who have limited ability to make optimal decisions and have differing beliefs, power, and incentive structures.  Therefore, it is likely that the existing assessment tools overlook some important factors that may enable or constrain effective climate policy formation.

The goal of our research is to develop of new tool for international climate policy analysis based on the concept of agent-based modeling (ABM).  ABM facilitates a more realistic and simultaneous treatment of the diverse forces which influence multi-party decisions.  Our model will represent both the international climate negotiation process, as well as the key dynamics of domestic economies relevant to energy and climate change.  Some key questions to be explored with our model include: Are there patterns of innovation, adaptation, or climate damages that emerge from an ABM representation of an economy that are obscured by conventional assessments?  Does an ABM that accounts for heterogeneity of beliefs and incentives at the national level and heterogeneity of power and vulnerability at the international level explain the negotiation outcomes historically observed? Does the design of effective international negotiation structures depend on the degree of heterogeneity occurring either between or within national economies?

We have assembled an international, multi-disciplinary research team to address these questions and to disseminate results to target audiences.  Our objective is to assist these audiences, including citizens, interest groups, businesses, governments, and international organizations in understanding their role as influential participants in a globally connected network of decision-makers.


Expert elicitation of the deep uncertainty surrounding the market and non-market damages of climate change

Duration and funding source: 1 year (October 2011 – September 2012.); U.S. Environmental Protection Agency

Personnel: M. Gerst (PI), M. Borsuk, and R. Howarth (Dartmouth)

Synopsis and progress:  Society’s reaction to climate change is ultimately an exercise in assessing exposure to poorly-defined, long-term risk.  Therefore, consideration of uncertainty should be central, and not secondary, in cost-benefit analysis.  Making uncertainty central to cost-benefit analysis, however, is hindered by current methods of representing climate damages; namely, representation of uncertainty, if considered at all, is focused on weak (first-order) instead of strong (second-order, Knightian, or pure) uncertainty.  Building on advances in the climate change damages and expert elicitation literature, we have designed protocol to assess expert knowledge on damages of climate change in a manner that is consistent with representing strong uncertainty.  The protocol explicitly assesses expert knowledge on adaptation, damages due to the rate and level of climate change, and willingness-to-pay to prevent non-market damages, as well as differences in the aforementioned parameters between the developed and developing world.  The expert elicitation process is currently in progress.


Implications of representing diverse sources of uncertainty in the integrated assessment of climate change

Duration and funding source: 3 years (June 2008 – May 2011); U.S. Environmental Protection Agency

Personnel: M. Borsuk (PI), M. Gerst, R. Howarth, P Ding, A. Bernstein (Dartmouth)

Synopsis:  There are many uncertainties in forecasting the impacts of climate change, and choosing an appropriate mitigation strategy will be one of the major decisions of the century.  For this project we have approached studying the effects of uncertainty on climate policy by breaking the problem into three focal areas: (1) the policy implications of fat-tailed uncertainty about climate sensitivity; (2) individuals' aversion to uncertainty as revealed by the historical equity premium; and (3) implications of uncertainty aversion to climate policy.

(1) The effects of fat-tailed climate sensitivity uncertainty are investigated by constructing a stochastic version of the DICE climate policy model. In this model, the welfare effects of three mitigation and one reference scenarios are measured. Population growth, technological change, carbon intensity, mitigation cost, climate impacts, carbon cycle mass transfer, and climate sensitivity are all uncertain. The resulting analysis (published in Energy Policy) shows that more lenient emissions reductions strategies are preferred over more aggressive reduction strategies only if the uncertainty surrounding the climate system, carbon cycle, and climate impacts are ignored and the consumption discount rate is substantially higher than that observed by historical capital returns. The implication for climate policy is there is substantial value associated with reducing the risk of extreme economic damages.

(2) We adopt an empirical approach to risk preference description using international historical data on investment returns and the occurrence of rare economic disasters.  We improve on earlier analyses by employing a hierarchical Bayesian inference procedure that allows for nation-specific estimates of both disaster probabilities and preference parameters.  This provides a stronger test of the underlying investment model than provided by previous calibrations and generates some compelling hypotheses for further study.  Specifically, results (published in Risk Analysis) suggest that society is substantially more averse to risk than typically assumed in integrated assessment models. Additionally, there appear to be systematic differences in risk preferences among nations.  These results are likely to have important implications for policy recommendations: higher aversion to risk increases the precautionary value of taking action to avoid low probability, high impact outcomes.  However, geographically variable attitudes toward risk indicate that this precautionary value could vary widely across nations, thereby potentially complicating the negotiation of transboundary agreements focused on risk reduction.

(3) Most existing integrated assessment models of climate change (e.g., DICE, FUND, PAGE) rely on a model of decision-making (the ‘Ramsey model’) that assumes perfect foresight. In reality, the presence of scientific uncertainty invalidates such an assumption. By replacing the standard decision-making model with a model that accounts for uncertainty (the ‘Lucas model’), we show that this common shortcoming leads to policy prescriptions that undervalue the importance of risk aversion in weighing uncertain long-term costs and benefits. We employ a stochastic, dynamic integrated assessment model to evaluate a variety of emissions reduction scenarios. When attitudes toward time and risk are specified to conform to historically-revealed social preferences, clear support emerges for aggressive near-term emissions reduction. This is a very different conclusion than has been reached by many other models claiming consistency behavioral evidence.