Innovative Approaches to Estimating the Social Cost of Carbon: New Perspectives and Methodologies
Paper Session
Saturday, Jan. 6, 2024 8:00 AM - 10:00 AM (CST)
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Chairs:
Frances C. Moore, University of California-Davis - Mortiz Drupp, University of Hamburg
- James Rising, University of Delaware
Structural Change in the Social Cost of Carbon
Abstract
The literature on the social cost of carbon (SCC) is large and growing, with substantial differences in underlying assumptions across and at times within studies. Of particular interest are changes to the structure of integrated assessment models (IAMs) -- structural changes -- including Earth system processes, tipping points, substitutability of ecosystem services, persistence of damages to economic output, preferences over societal risk and inequality, and how learning new information affects policy. These structural changes have often been analyzed in piecemeal, uncoordinated fashion, leaving their relative importance unclear. We here analyze 1823 estimates of the SCC from 147 studies published between 2000 and 2020 to isolate the effects of 9 structural changes, 6 preference parameters, and 14 dimensions of uncertainty. To further understand gaps in the literature, we survey the authors of these studies, and analyze the perceived role of structural changes across 66 respondents. We find that the distribution of published SCCs and the distribution of experts' comprehensive estimates of SCCs have diverged from the formal US SCC estimate, particularly in the right tail. The structural changes with the greatest effects on the SCC are persistent damages to economic output, limited substitutability of ecosystem services, and changes to Earth system processes. In comparison, discounting and standard damage function parameters appear to play a smaller role in final SCC values than structural changes, per both the literature and surveyed experts. Finally, we calibrate a Random Forest model of SCC values, and use expert valuations of the structural changes to construct a meta-analytic SCC and its distribution. The mean of experts' central best SCC estimate and the mean meta-analytic SCC amount to $160 and $210 per tonne of CO2 (2020 US dollars), respectively, which are more than three to four times larger than the current formal US SCC estimate.The Welfare Economics of a Data Driven Social Cost of Carbon
Abstract
This paper presents the first estimates of the social cost of a marginal ton of carbon dioxide emissions that combine theoretical insights on the welfare economics of climate change with a rich set of empirically grounded, probabilistic projections of future climate damages across five impact categories: human mortality, agricultural productivity, energy consumption, labor disutility, and coastal flooding. We find that accounting for the full welfare effects of uncertain and unequal climate damages raises the social cost of carbon (SCC) substantially relative to previous prevailing estimates based on deterministic projections and a global representative agent. The risk premium to avoid uncertainty in damages and realized global temperature change raises the SCC by over 50% in low emissions scenarios, and by up to an order of magnitude or more in high emission scenarios, depending on a choice parameter that governs the valuation of a small proportion of extreme temperature draws that imply catastrophic losses. In high emission scenarios, the discount rate implied by standard intertemporal optimization can be close to zero as the possibility of severe damages reduces future welfare relative to present welfare. Finally, applying a welfare metric that accounts for differential marginal utility of damages incurred by poorer individuals can raise the SCC by up to an order of magnitude, as projected damages are heavily concentrated in poorer regions of the world.Pricing an Unknown Climate
Abstract
Anthropogenic climate change is subject to a multitude of highly uncertain feedback processes making the long-run impact of current emissions also highly uncertain. At present, we cannot reliably quantify the likelihood of differing global warming scenarios. Decision theory distinguishes between known, quantifiable risks and situations of ambiguity or deep uncertainty. A fully rational decision maker can respond differently to ambiguity and to risk, and real-world decision makers frequently do. We show how aversion to ambiguity affects optimal climate policy in an integrated assessment of climate change. We derive an analytic social cost of carbon formula for an ambiguity averse decision maker in a generic integrated assessment model. We also quantify the impact of recursive smooth ambiguity aversion for a stochastic dynamic programming implementation of DICE. Previous and paralleling approaches suggest substantial ambiguity premia on the optimal carbon tax. Our results show that the ambiguity premium is very small and optimal policy deriving from the standard Bayesian model is robust to ambiguity concerns under moderately large ambiguity aversion if climate policy is endogenous and policy maker’s have rational foresight.Discussant(s)
Ivan Rudik
,
Cornell University
Ben Groom
,
University of Exeter
Brian Prest
,
Resources for the Future
Tamma Carleton
,
University of California-Santa Barbara
JEL Classifications
- Q5 - Environmental Economics
- D6 - Welfare Economics