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Declining CO(2) price paths

Pricing greenhouse-gas (GHG) emissions involves making trade-offs between consumption today and unknown damages in the (distant) future. While decision making under risk and uncertainty is the forte of financial economics, important insights from pricing financial assets do not typically inform stan...

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Autores principales: Daniel, Kent D., Litterman, Robert B., Wagner, Gernot
Formato: Online Artículo Texto
Lenguaje:English
Publicado: National Academy of Sciences 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6800385/
https://www.ncbi.nlm.nih.gov/pubmed/31575747
http://dx.doi.org/10.1073/pnas.1817444116
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author Daniel, Kent D.
Litterman, Robert B.
Wagner, Gernot
author_facet Daniel, Kent D.
Litterman, Robert B.
Wagner, Gernot
author_sort Daniel, Kent D.
collection PubMed
description Pricing greenhouse-gas (GHG) emissions involves making trade-offs between consumption today and unknown damages in the (distant) future. While decision making under risk and uncertainty is the forte of financial economics, important insights from pricing financial assets do not typically inform standard climate–economy models. Here, we introduce EZ-Climate, a simple recursive dynamic asset pricing model that allows for a calibration of the carbon dioxide ([Formula: see text]) price path based on probabilistic assumptions around climate damages. Atmospheric [Formula: see text] is the “asset” with a negative expected return. The economic model focuses on society’s willingness to substitute consumption across time and across uncertain states of nature, enabled by an Epstein–Zin (EZ) specification that delinks preferences over risk from intertemporal substitution. In contrast to most modeled [Formula: see text] price paths, EZ-Climate suggests a high price today that is expected to decline over time as the “insurance” value of mitigation declines and technological change makes emissions cuts cheaper. Second, higher risk aversion increases both the [Formula: see text] price and the risk premium relative to expected damages. Lastly, our model suggests large costs associated with delays in pricing [Formula: see text] emissions. In our base case, delaying implementation by 1 y leads to annual consumption losses of over 2%, a cost that roughly increases with the square of time per additional year of delay. The model also makes clear how sensitive results are to key inputs.
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spelling pubmed-68003852019-10-24 Declining CO(2) price paths Daniel, Kent D. Litterman, Robert B. Wagner, Gernot Proc Natl Acad Sci U S A Social Sciences Pricing greenhouse-gas (GHG) emissions involves making trade-offs between consumption today and unknown damages in the (distant) future. While decision making under risk and uncertainty is the forte of financial economics, important insights from pricing financial assets do not typically inform standard climate–economy models. Here, we introduce EZ-Climate, a simple recursive dynamic asset pricing model that allows for a calibration of the carbon dioxide ([Formula: see text]) price path based on probabilistic assumptions around climate damages. Atmospheric [Formula: see text] is the “asset” with a negative expected return. The economic model focuses on society’s willingness to substitute consumption across time and across uncertain states of nature, enabled by an Epstein–Zin (EZ) specification that delinks preferences over risk from intertemporal substitution. In contrast to most modeled [Formula: see text] price paths, EZ-Climate suggests a high price today that is expected to decline over time as the “insurance” value of mitigation declines and technological change makes emissions cuts cheaper. Second, higher risk aversion increases both the [Formula: see text] price and the risk premium relative to expected damages. Lastly, our model suggests large costs associated with delays in pricing [Formula: see text] emissions. In our base case, delaying implementation by 1 y leads to annual consumption losses of over 2%, a cost that roughly increases with the square of time per additional year of delay. The model also makes clear how sensitive results are to key inputs. National Academy of Sciences 2019-10-15 2019-10-01 /pmc/articles/PMC6800385/ /pubmed/31575747 http://dx.doi.org/10.1073/pnas.1817444116 Text en Copyright © 2019 the Author(s). Published by PNAS. https://creativecommons.org/licenses/by-nc-nd/4.0/ https://creativecommons.org/licenses/by-nc-nd/4.0/This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) .
spellingShingle Social Sciences
Daniel, Kent D.
Litterman, Robert B.
Wagner, Gernot
Declining CO(2) price paths
title Declining CO(2) price paths
title_full Declining CO(2) price paths
title_fullStr Declining CO(2) price paths
title_full_unstemmed Declining CO(2) price paths
title_short Declining CO(2) price paths
title_sort declining co(2) price paths
topic Social Sciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6800385/
https://www.ncbi.nlm.nih.gov/pubmed/31575747
http://dx.doi.org/10.1073/pnas.1817444116
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