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Empirically-Constrained Climate Sensitivity and the Social Cost of Carbon

Listed author(s):
  • Kevin Dayaratna

    (Heritage Foundation, Washington DC.)

  • Ross Mckitrick

    ()

    (Department of Economics and Finance, University of Guelph)

  • David Kreutzer

    (Heritage Foundation, Washington DC.)

Integrated Assessment Models (IAMs) require parameterization of both economic and climatic processes. The latter includes Equilibrium Climate Sensitivity (ECS), or the temperature response to doubling CO2 levels, and Ocean Heat Uptake (OHU) efficiency. ECS distributions in IAMs have been drawn from climate model runs that lack an empirical basis,and in Monte Carlo experiments may not be constrained to consistent OHU values. EmpiricalECS estimates are now available, but have not yet been applied in IAMs. We incorporate a new estimate of the ECS distribution conditioned on observed OHU efficiency into two widely-used IAMs. The resulting Social Cost of Carbon (SCC) estimates are much lower than those from models based on simulated parameters. In the DICE model the average SCC falls by 30-50% depending on the discount rate, while in the FUND model the average SCC falls by over 80%. The span of estimates across discount rates also shrinks substantially

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File URL: http://www.uoguelph.ca/economics/repec/workingpapers/2016/2016-08.pdf
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Paper provided by University of Guelph, Department of Economics and Finance in its series Working Papers with number 1608.

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Length: 23 pages
Date of creation: 2016
Handle: RePEc:gue:guelph:2016-08
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Guelph, Ontario, N1G 2W1

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Web page: https://www.uoguelph.ca/economics/

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  1. Marten, Alex L., 2011. "Transient temperature response modeling in IAMs: The effects of over simplification on the SCC," Economics - The Open-Access, Open-Assessment E-Journal, Kiel Institute for the World Economy (IfW), vol. 5, pages 1-42.
  2. Detlef Vuuren & Jason Lowe & Elke Stehfest & Laila Gohar & Andries Hof & Chris Hope & Rachel Warren & Malte Meinshausen & Gian-Kasper Plattner, 2011. "How well do integrated assessment models simulate climate change?," Climatic Change, Springer, vol. 104(2), pages 255-285, January.
  3. Robert S. Pindyck, 2013. "Climate Change Policy: What Do the Models Tell Us?," Journal of Economic Literature, American Economic Association, vol. 51(3), pages 860-872, September.
  4. Magne Aldrin & Marit Holden & Peter Guttorp & Ragnhild Bieltvedt Skeie & Gunnar Myhre & Terje Koren Berntsen, 2012. "Bayesian estimation of climate sensitivity based on a simple climate model fitted to observations of hemispheric temperatures and global ocean heat content," Environmetrics, John Wiley & Sons, Ltd., vol. 23(3), pages 253-271, May.
  5. Crost, Benjamin & Traeger, Christian P., 2013. "Optimal climate policy: Uncertainty versus Monte Carlo," Economics Letters, Elsevier, vol. 120(3), pages 552-558.
  6. Gerard Roe & Yoram Bauman, 2013. "Climate sensitivity: should the climate tail wag the policy dog?," Climatic Change, Springer, vol. 117(4), pages 647-662, April.
  7. Kelly, David L. & Kolstad, Charles D., 1999. "Bayesian learning, growth, and pollution," Journal of Economic Dynamics and Control, Elsevier, vol. 23(4), pages 491-518, February.
  8. Ackerman, Frank & Stanton, Elizabeth A. & Bueno, Ramón, 2010. "Fat tails, exponents, extreme uncertainty: Simulating catastrophe in DICE," Ecological Economics, Elsevier, vol. 69(8), pages 1657-1665, June.
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