Optimal Climate Policy for a Pessimistic Social Planner

Edilio Valentini and Paolo Vitale

Environmental & Resource Economics, 2019, vol. 72, issue 2, No 3, 443 pages

Abstract: Abstract This paper characterizes the preferences of a pessimistic social planner concerned with the potential costs of extreme, low-probability climate events. This pessimistic attitude is represented by a recursive optimization criterion à la Hansen and Sargent (IEEE Trans Autom Control 40:968–971, 1995) implying that a very sharp and early mitigation effort arises as the optimal climate policy. We find that for sufficiently high levels of risk-aversion an aggressive mitigation policy is chosen even when the discount factor is low. The dynamics of the optimal mitigation policy displays an inverted policy ramp with a sharp and immediate mitigation effort, followed by a gradual reduction until the pollution stock converges towards its long-run equilibrium. We also observe that the initial sharpness of the mitigation effort requires substantial capture and sequestration of carbon from the atmosphere. We extend our analysis showing that when the social planner observes the concentration and emission levels with a time lag, she undertakes a more aggressive policy to reduce the greater degree of uncertainty she faces. Finally, we show under which conditions the optimal mitigation policy dictated by our analysis coincides with that derived using the robustness approach of Hansen and Sargent (Robustness, Princeton University Press, Princeton, 2008).

Keywords: Climate change policy; Risk-aversion; Pessimism and precautionary principle; Linear exponential quadratic Gaussian; Carbon capture and sequestration (search for similar items in EconPapers)
JEL-codes: C61 Q54 (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (3)

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Working Paper: Optimal Climate Policy for a Pessimistic Social Planner (2014)
Working Paper: Optimal Climate Policy for a Pessimistic Social Planner (2014)
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DOI: 10.1007/s10640-017-0199-3

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