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Information Aggregation in a Prediction Market for Climate Outcomes

Elmira Aliakbari and Ross McKitrick ()

No 1702, Working Papers from University of Guelph, Department of Economics and Finance

Abstract: Two forms of uncertainty in climate policy are the wide range of estimated marginal costs and uncertainty over credibility of rival information sources. We show how a recently-proposed solution to the first problem also addresses the second. The policy is an emissions tax tied to average temperatures, coupled with permits that exempt the emitter from paying the tax in a future year. It has been shown that the resulting tax path will be correlated with future marginal damages. It has been conjectured that the permit prices will yield unbiased forecasts of the climate, which, if true, would address the second uncertainty. We confirm the conjecture by showing a trading mechanism that converges on unbiased forecasts if traders are risk-neutral. Risk aversion slows down but does not prevent convergence. We also show that the forecasts are more likely to be sufficient statistics the stronger the consensus on climate science.

Keywords: Climate change; uncertainty; carbon tax; tradable permits; state-contingent pricing; prediction markets (search for similar items in EconPapers)
JEL-codes: G13 H23 Q54 Q58 (search for similar items in EconPapers)
Pages: 33 pages
Date: 2017
New Economics Papers: this item is included in nep-ene and nep-env
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Journal Article: Information aggregation in a prediction market for climate outcomes (2018) Downloads
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