Nidhi R. Santen

An approximate dynamic programming framework for modeling global climate policy under decision-dependent uncertainty

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 show that 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 the magnitude 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.

Should We Give Up after Solyndra? Optimal Technology R&D Portfolios Under Uncertainty

Climate change and other environmental challenges require the development of new energy technologies with lower emissions. In the near term, R&D investments, either by the government or the private sector, can reduce the costs of these lower-emitting technologies. However, the returns to R&D are uncertain, and there are many potential technologies that may emerge to play an important role in the future energy mix. In this paper, we address the problem of allocating scarce R&D resources across technologies when uncertainties exist. We develop a multistage stochastic dynamic programming version of an integrated assessment model of the climate and economy that represents endogenous technological change through R&D decisions for two substitutable noncarbon backstop technologies. We demonstrate that near-term R&D investment in the higher cost technology is justified and that the optimal R&D investment in the higher cost technology increases with both higher variance and higher skewness in the distribution of returns to R&D.