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Energy Intensity and Carbon Emission Responses to Technological Change: The U.S. Outlook

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  • Andy S. Kydes

Abstract

Technological progress, energy use, energy intensity, and carbon mitigation are tightly intertwined concepts within the worldwide climate change debate. The state-of-the-art National Energy Modeling System (NEMS) is used to examine, for the United States: (a) the potential role of technological progress on energy supply, consumption, and prices in U.S. energy markets and their impact on carbon emissions; (b) how "success" on one side of the supply or demand equation may reduce the potential benefits of technological progress on the other side; and (c) the sensitivity of energy intensity in the U.S. to technological change and adoption. Some of the key findings of the analysis include: (a) technological progress alone (without significant and effective new policies) is insufficient to achieve reduction of carbon emissions at or near 1990 levels by 2010; (b) successful R&D programs that improve the availability and market acceptance of cost-efficient transportation technologies, coupled with successful oil and gas supply R&D programs, could have a significant impact on reducing U.S. dependence on imported oil; (c) the annual rate of decline of energy intensity (primary energy used per dollar of GDP) between 1996 and 2015 appears to be bounded by 1.25 percent when real energy prices are relatively stable or gradually rising, even when more advanced technologies are made available to the market.

Suggested Citation

  • Andy S. Kydes, 1999. "Energy Intensity and Carbon Emission Responses to Technological Change: The U.S. Outlook," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3), pages 93-121.
    • Handle: RePEc:aen:journl:1999v20-03-a04
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    Cited by:

    1. Wilkerson, Jordan T. & Cullenward, Danny & Davidian, Danielle & Weyant, John P., 2013. "End use technology choice in the National Energy Modeling System (NEMS): An analysis of the residential and commercial building sectors," Energy Economics, Elsevier, vol. 40(C), pages 773-784.
    2. Auffhammer, Maximilian, 2005. "The Rationality of EIA Forecasts under Symmetric and Asymmetric Loss," CUDARE Working Papers 25017, University of California, Berkeley, Department of Agricultural and Resource Economics.
    3. Steven A. Gabriel & Andy S. Kydes & Peter Whitman, 2001. "The National Energy Modeling System: A Large-Scale Energy-Economic Equilibrium Model," Operations Research, INFORMS, vol. 49(1), pages 14-25, February.
    4. Auffhammer, Maximilian, 2007. "The rationality of EIA forecasts under symmetric and asymmetric loss," Resource and Energy Economics, Elsevier, vol. 29(2), pages 102-121, May.
    5. Xiongtian Shi & Yan Liu & Zhengyong Yu, 2024. "Unveiling the Catalytic Role of Digital Trade in China’s Carbon Emission Reduction under the Dual Carbon Policy," Sustainability, MDPI, vol. 16(12), pages 1-25, June.
    6. Laitner, J. A. & DeCanio, S. J. & Koomey, J. G. & Sanstad, A. H., 2003. "Room for improvement: increasing the value of energy modeling for policy analysis," Utilities Policy, Elsevier, vol. 11(2), pages 87-94, June.
    7. Morakinyo O. Adetutu & Anthony J. Glass & Thomas G. Weyman-Jones, 2016. "Economy-wide Estimates of Rebound Effects: Evidence from Panel Data," The Energy Journal, , vol. 37(3), pages 251-270, July.
    8. Wu, Wei & Zhang, Tingting & Xie, Xiaomin & Huang, Zhen, 2021. "Regional low carbon development pathways for the Yangtze River Delta region in China," Energy Policy, Elsevier, vol. 151(C).
    9. Webster, Mort & Paltsev, Sergey & Reilly, John, 2008. "Autonomous efficiency improvement or income elasticity of energy demand: Does it matter?," Energy Economics, Elsevier, vol. 30(6), pages 2785-2798, November.

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    JEL classification:

    • F0 - International Economics - - General

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