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Do intensity targets control uncertainty better than quotas ? Conditions, calibrations, and caveats

  • Marschinski, Robert
  • Lecocq, Franck

Among policy instruments to control future greenhouse gas emissions, well-calibrated general intensity targets are known to lead to lower uncertainty on the amount of abatement than emissions quotas (Jotzo and Pezzey 2004). The authors test whether this result holds in a broader framework, and whether it applies to other policy-relevant variables as well. To do so, they provide a general representation of the uncertainty on future GDP, future business-as-usual emissions, and future abatement costs. The authors derive the variances of four variables, namely (effective) emissions, abatement effort, marginal abatement costs, and total abatement costs over GDP under a quota, a linear (LIT) and a general intensity target (GIT)-where the emissions ceiling is a power-law function of GDP. They confirm that GITs can yield a lower variance than a quota for marginal costs, but find that this is not true for total costs over GDP. Using economic and emissions scenarios and forecast errors of past projections, the authors estimate ranges of values for key parameters in their model. They find that quotas dominate LITs over most of this range, that calibrating GITs over this wide range is difficult, and that GITs would yield only modest reductions in uncertainty relative to quotas.

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Paper provided by The World Bank in its series Policy Research Working Paper Series with number 4033.

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Date of creation: 01 Oct 2006
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Handle: RePEc:wbk:wbrwps:4033
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  1. A. Denny Ellerman & Ian Sue Wing, 2003. "Absolute versus intensity-based emission caps," Climate Policy, Taylor & Francis Journals, vol. 3(sup2), pages S7-S20, December.
  2. Aldy, Joseph & Barrett, Scott & Stavins, Robert, 2003. "Thirteen Plus One: A Comparison of Global Climate Policy Architectures," Working Paper Series rwp03-012, Harvard University, John F. Kennedy School of Government.
  3. Kolstad, Charles D., 2005. "The simple analytics of greenhouse gas emission intensity reduction targets," Energy Policy, Elsevier, vol. 33(17), pages 2231-2236, November.
  4. Jean-Charles Hourcade & Philippe Ambrosi & Stéphane Hallegatte & Franck Lecocq & Patrice Dumas & Minh Ha-Duong, 2003. "Optimal control models and elicitation of attitudes towards climate damages," Post-Print halshs-00000966, HAL.
  5. Pizer, William & Newell, Richard, 1998. "Regulating Stock Externalities Under Uncertainty," Discussion Papers dp-99-10-rev, Resources For the Future.
  6. Lester B. Lave, 1991. "Formulating Greenhouse Policies in a Sea of Uncertainty," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1), pages 9-22.
  7. Randall Lutter, 2000. "Developing Countries' Greenhouse Emmissions: Uncertainty and Implications for Participation in the Kyoto Protocol," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 93-120.
  8. Pizer, William A., 1999. "The optimal choice of climate change policy in the presence of uncertainty," Resource and Energy Economics, Elsevier, vol. 21(3-4), pages 255-287, August.
  9. Ravallion, Martin & Heil, Mark & Jalan, Jyotsna, 2000. "Carbon Emissions and Income Inequality," Oxford Economic Papers, Oxford University Press, vol. 52(4), pages 651-69, October.
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