期刊
COMPUTATIONAL MANAGEMENT SCIENCE
卷 9, 期 3, 页码 339-362出版社
SPRINGER HEIDELBERG
DOI: 10.1007/s10287-012-0147-1
关键词
Climate policy analysis; Approximate dynamic programming; Decision dependent uncertainty; Stochastic dynamic programming; Endogenous uncertainty
资金
- U.S. National Science Foundation [0825915]
- US Department of Energy Office of Science, Biological and Environmental Research Program, Integrated Assessment Research Program [DE-SC0005171, DE-SC0003906]
- Divn Of Social and Economic Sciences
- Direct For Social, Behav & Economic Scie [0929340, 0825915] Funding Source: National Science Foundation
- U.S. Department of Energy (DOE) [DE-SC0003906] Funding Source: U.S. Department of Energy (DOE)
Analyses of global climate policy as a sequential decision under uncertainty have been severely restricted by dimensionality and computational burdens. Therefore, they have limited the number of decision stages, discrete actions, or number and type of uncertainties considered. In particular, two common simplifications are the use of two-stage models to approximate a multi-stage problem and exogenous formulations for inherently endogenous or decision-dependent uncertainties (in which the shock at time t+1 depends on the decision made at time t). In this paper, we present a stochastic dynamic programming formulation of the Dynamic Integrated Model of Climate and the Economy (DICE), and the application of approximate dynamic programming techniques to numerically solve for the optimal policy under uncertain and decision-dependent technological change in a multi-stage setting. We compare numerical results using two alternative value function approximation approaches, one parametric and one non-parametric. We showt hat increasing the variance of a symmetric mean-preserving uncertainty in abatement costs leads to higher optimal first-stage emission controls, but the effect is negligible when the uncertainty is exogenous. In contrast, the impact of decision-dependent cost uncertainty, a crude approximation of technology R&D, on optimal control is much larger, leading to higher control rates (lower emissions). Further, we demonstrate that themagnitude of this effect grows with the number of decision stages represented, suggesting that for decision-dependent phenomena, the conventional two-stage approximation will lead to an underestimate of the effect of uncertainty.
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