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Carbon Mitigation Costs for the Commercial Sector: Discrete-Continuous Choice Analysis of Multifuel Energy Demand

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  • Pizer, William

    ()
    (Resources for the Future)

  • Newell, Richard

    ()
    (Resources for the Future)

Abstract

We estimate a carbon mitigation cost curve for the U.S. commercial sector based on econometric estimation of the responsiveness of fuel demand and equipment choices to energy price changes. The model econometrically estimates fuel demand conditional on fuel choice, which is characterized by a multinomial logit model. Separate estimation of end uses (e.g., heating, cooking) using the 1995 Commercial Buildings Energy Consumption Survey allows for exceptionally detailed estimation of price responsiveness disaggregated by end use and fuel type. We then construct aggregate long-run elasticities, by fuel type, through a series of simulations; own-price elasticities range from –0.9 for district heat services to –2.9 for fuel oil. The simulations form the basis of a marginal cost curve for carbon mitigation, which suggests that a price of $20 per ton of carbon would result in an 8% reduction in commercial carbon emissions, and a price of $100 per ton would result in a 28% reduction.

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Bibliographic Info

Paper provided by Resources For the Future in its series Discussion Papers with number dp-05-13.

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Date of creation: 06 Jun 2005
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Handle: RePEc:rff:dpaper:dp-05-13

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Related research

Keywords: commercial energy demand; carbon policy; climate change; discrete choice;

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References

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  1. Christopher Garbacz, 1984. "Residential Electricity Demand: A Suggested Appliance Stock Equation," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 151-154.
  2. Dubin, Jeffrey A & McFadden, Daniel L, 1984. "An Econometric Analysis of Residential Electric Appliance Holdings and Consumption," Econometrica, Econometric Society, vol. 52(2), pages 345-62, March.
  3. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 31(3), pages 129-137.
  4. E. Raphael Branch, 1993. "Short Run Income Elasticity of Demand for Residential Electricity Using Consumer Expenditure Survey Data," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 111-122.
  5. Dahl, Carol A., 1993. "A survey of energy demand elasticities in support of the development of the NEMS," MPRA Paper 13962, University Library of Munich, Germany.
  6. Baker, Paul & Blundell, Richard, 1991. "The Microeconometric Approach to Modelling Energy Demand: Some Results for UK Households," Oxford Review of Economic Policy, Oxford University Press, vol. 7(2), pages 54-76, Summer.
  7. Jean-Thomas Bernard & Denis Bolduc & Donald Belanger, 1996. "Quebec Residential Electricity Demand: A Microeconometric Approach," Canadian Journal of Economics, Canadian Economics Association, vol. 29(1), pages 92-113, February.
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Cited by:
  1. Su, Qing, 2011. "The effect of population density, road network density, and congestion on household gasoline consumption in U.S. urban areas," Energy Economics, Elsevier, vol. 33(3), pages 445-452, May.

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