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The Curious Role of "Learning" in Climate Policy: Should We Wait for More Data?

Mort Webster

Year: 2002
Volume: Volume23
Number: Number 2
DOI: 10.5547/ISSN0195-6574-EJ-Vol23-No2-4
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Abstract:
Given the large uncertainties regarding potential damages from climate change and the significant but also uncertain costs of reducing greenhouse emissions, the debate over a policy response is often framed as a choice of acting now or waiting until the uncertainty is reduced. Implicit in the "wait to learn" argument is the notion that the ability to learn in the future necessarily implies that less restrictive policies should be chosen in the near term. I demonstrate in the general case that the ability to learn in the future can lead to either less restrictive or more restrictive policies today. I also show that the initial decision made under uncertainty will be affected by future learning only if the actions taken today change the marginal costs or marginal damages in the future. Results from an intermediate-scale integrated model of climate and economics indicate that the choice of current emissions restrictions is independent of whether or not uncertainty is resolved before future decisions, because, like most models, the cross-period interactions are minimal. With stronger interactions, the effect of learning on initial period decisions can be more important.



Hedging Strategies: Electricity Investment Decisions under Policy Uncertainty

Jennifer Morris, Vivek Srikrishnan, Mort Webster, and John Reilly

Year: 2018
Volume: Volume 39
Number: Number 1
DOI: 10.5547/01956574.39.1.jmor
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Abstract:
Given uncertainty in long-term carbon reduction goals, how much non-carbon generation should be developed in the near-term? This research investigates the optimal balance between the risk of overinvesting in non-carbon sources that are ultimately not needed and the risk of underinvesting in non-carbon sources and subsequently needing to reduce carbon emissions dramatically. We employ a novel framework that incorporates a computable general equilibrium (CGE) model of the U.S. into a two-stage stochastic approximate dynamic program (ADP) focused on decisions in the electric power sector. We solve the model using an ADP algorithm that is computationally tractable while exploring the decisions and sampling the uncertain carbon limits from continuous distributions. The results of the model demonstrate that an optimal hedge is in the direction of more non-carbon investment in the near-term, in the range of 20-30% of new generation. We also demonstrate that the optimal share of non-carbon generation is increasing in the variance of the uncertainty about the long-term carbon targets, and that with greater uncertainty in the future policy regime, a balanced portfolio of non-carbon, natural gas, and coal generation is desirable.





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