Information aggregation in a prediction market for climate outcomes
| Autor: | Ross McKitrick, Elmira Aliakbari |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Marginal cost
Computer Science::Computer Science and Game Theory Economics and Econometrics Carbon tax 010504 meteorology & atmospheric sciences Risk aversion Financial economics Yield (finance) 05 social sciences Climate change Prediction market 01 natural sciences General Energy 0502 economics and business Credibility Econometrics Economics Damages 050207 economics Physics::Atmospheric and Oceanic Physics 0105 earth and related environmental sciences |
| Zdroj: | Energy Economics. 74:97-106 |
| ISSN: | 0140-9883 |
| DOI: | 10.1016/j.eneco.2018.06.002 |
| Popis: | 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 helps address 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 describing 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. |
| Databáze: | OpenAIRE |
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