Climate Policy Under Fat-Tailed Risk: An Application of Dice

Hwang, Ingyu; Reynès, Frédéric; Tol, Richard S.J.

doi:10.1007/s10640-013-9654-y

Cited by 27 publications

(14 citation statements)

References 43 publications

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“…Many papers expand the DICE framework to investigate the impact of particular aspects of the climate-economy system on the optimal timing of climate mitigation. Examples include Kolstad (1996) and Keller et al (2004) on learning that reduces climate uncertainty over time; Bruin et al (2009) and Bosello et al (2010) on how considering adaptation to climate change impacts may affect optimal mitigation; Hwang et al (2013) and Lemoine and Traeger (2014) on the impact of fat-tailed risks and the role of tipping points; and Heal and Millner (2014) on the choice of the appropriate discount rate for climate policy. Dietz and Stern (2014) propose several modifications to DICE, including the use of a lower discount rate, and a different modelling of climate-change-related damages.…”

Section: Resultsmentioning

confidence: 99%

When starting with the most expensive option makes sense: Optimal timing, cost and sectoral allocation of abatement investment

Vogt-Schilb

Meunier

Hallegatte

2018

Journal of Environmental Economics and Management

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This paper finds that it is optimal to start a long-term emission-reduction strategy with significant short-term abatement investment, even if the optimal carbon price starts low and grows progressively over time. Moreover, optimal marginal abatement investment costs differ across sectors of the economy. It may be preferable to spend $25 to avoid the marginal ton of carbon in a sector where abatement capital is expensive, such as public transportation, or in a sector with large abatement potential, such as the power sector, than $15 for the marginal ton in a sector with lower cost or lower abatement potential. The reason, distinct from learning spillovers, is that reducing greenhouse gas emissions requires investment in long-lived abatement capital such as clean power plants or public transport infrastructure. The value of abatement investment comes from avoided emissions, but also from the value of abatement capital in the future. The optimal levelized cost of conserved carbon can thus be higher than the optimal carbon price. It is higher in sectors with higher investment needs: those where abatement capital is more expensive or sectors with larger abatement potential. We compare our approach to the traditional abatement-cost-curve model and discuss implications for policy design. HighlightsThe same carbon price translates into different abatement investment costs in different sectors Sectors with higher emissions and more expensive abatement capital should invest more dollars per abated ton Abatement cost curves cannot be used to model abatement options that require investment in low-carbon capital * Corresponding author: Adrien Vogt-Schilb, avogtschilb@iadb.org, +1 202 623 2564Email addresses: avogtschilb@iadb.org (Adrien Vogt-Schilb), guy.meunier@ivry.inra.fr (Guy Meunier), shallegatte@worldbank.org (Stéphane Hallegatte) Preprint submitted to ElsevierNovember 10, 2017Keywords: climate change mitigation, transition to clean capital, path dependence, social cost of carbon, marginal abatement cost, timing JEL classification: Q54, Q52, Q58National governments committed to stabilize climate change to mitigate subsequent damages (G7, 2015; UNFCCC, 2016). This will require transitioning from an economy based on polluting capital, such as inefficient buildings and polluting cars, to an economy based on clean capital, such as retrofitted buildings or electric vehicles. A critical question for public policy is to determine the optimal cost and timing of such abatement investment. Is action as urgent as frequently advocated? A second important issue is the optimal allocation of abatement. There are many options to reduce greenhouse gas (GHG) emissions, from renewable power plants to improved building insulation and more efficient cars. Each of these options has a cost, and would reduce emissions by a certain amount. Should mitigation start with the least expensive options and progressively clean the economy by ascending cost order?To shed light on these questions, we study the optimal timing, cost, and sectoral...

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Section: Resultsmentioning

confidence: 99%

When starting with the most expensive option makes sense: Optimal timing, cost and sectoral allocation of abatement investment

Vogt-Schilb

Meunier

Hallegatte

2018

Journal of Environmental Economics and Management

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“…The law of motion given by equation (12) is different from the one specified by AABH. This is to facilitate the common assumption in integrated assessment models that the rate of CO 2 depreciation in the atmosphere is increasing in the stock of CO 2 ; see, for example, Hwang et al (2013), who use a simplified version of the DICE model. See Appendix A5 for further discussion of this issue and for some other details about the numerical model.…”

Section: Consumers and The Environmentmentioning

confidence: 99%

Environmental Policy and the Direction of Technical Change

Greaker

Heggedal

Rosendahl

2018

Scandinavian J Economics

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Should governments direct research and development (R&D) away from “dirty” technologies towards “clean” ones? How important is this compared to carbon pricing? We address these questions with the introduction of two model features to the literature on directed technological change and the environment. We introduce decreasing returns to R&D, and allow future carbon taxes to influence current R&D decisions. Our results suggest that governments should prioritize clean R&D. Dealing with major environmental problems requires an R&D shift towards clean technology. However, in the case where most researchers are working with clean technology, both productivity spillovers and the risks of future replacement increase. Consequently, the gap between the private and social values of an innovation is greatest for clean technologies.

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“…We defer the application of a flexible savings rate to future research. 7 As Hwang et al (2013;2016) show, the application of fat-tailed distribution of the climate sensitivity with CRRA utility function into the DICE model (as in our model) may lead to a catastrophic consumption loss. It would be optimal for the decision maker to reduce carbon emissions totally (i.e.…”

Section: The Revised Dice Modelmentioning

confidence: 86%

Active Learning and Optimal Climate Policy

Hwang

Tol

Hofkes

2018

Environ Resource Econ

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This paper develops a climate-economy model with uncertainty, irreversibility, and active learning. Whereas previous papers assume learning from one observation per period, or experiment with control variables to gain additional information, this paper considers active learning from investment in monitoring, specifically in improved observations of the global mean temperature. We find that the decision maker invests a significant amount of money in climate research, far more than the current level, in order to increase the rate of learning about climate change. This helps the decision maker make improved decisions. The level of uncertainty decreases more rapidly in the active learning model than in the passive learning model with only temperature observations. As the uncertainty about climate change is smaller, active learning reduces the optimal carbon tax. The greater the risk, the larger is the effect of learning. The method proposed here is applicable to any dynamic control problem where the quality of monitoring is a choice variable, for instance, the precision at which we observe GDP, unemployment, or the quality of education.

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Climate Policy Under Fat-Tailed Risk: An Application of Dice

Cited by 27 publications

References 43 publications

When starting with the most expensive option makes sense: Optimal timing, cost and sectoral allocation of abatement investment

When starting with the most expensive option makes sense: Optimal timing, cost and sectoral allocation of abatement investment

Environmental Policy and the Direction of Technical Change

Active Learning and Optimal Climate Policy

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