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US long-term energy intensity: Backcast and projection

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  • Dowlatabadi, Hadi
  • Oravetz, Matthew A.

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  • Dowlatabadi, Hadi & Oravetz, Matthew A., 2006. "US long-term energy intensity: Backcast and projection," Energy Policy, Elsevier, vol. 34(17), pages 3245-3256, November.
    • Handle: RePEc:eee:enepol:v:34:y:2006:i:17:p:3245-3256
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    2. Li, Yi & Sun, Linyan & Feng, Taiwen & Zhu, Chunyan, 2013. "How to reduce energy intensity in China: A regional comparison perspective," Energy Policy, Elsevier, vol. 61(C), pages 513-522.
    3. Shahiduzzaman, Md & Layton, Allan, 2017. "Decomposition analysis for assessing the United States 2025 emissions target: How big is the challenge?," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 372-383.
    4. Keller, Klaus & Miltich, Louise I. & Robinson, Alexander & Tol, Richard S.J., 2007. "How Overconfident are Current Projections of Anthropogenic Carbon Dioxide Emissions?," Climate Change Modelling and Policy Working Papers 9321, Fondazione Eni Enrico Mattei (FEEM).
    5. Kialashaki, Arash & Reisel, John R., 2014. "Development and validation of artificial neural network models of the energy demand in the industrial sector of the United States," Energy, Elsevier, vol. 76(C), pages 749-760.
    6. An, Kangxin & Wang, Can & Cai, Wenjia, 2023. "Low-carbon technology diffusion and economic growth of China: an evolutionary general equilibrium framework," Structural Change and Economic Dynamics, Elsevier, vol. 65(C), pages 253-263.
    7. Ahlroth, Sofia & Hojer, Mattias, 2007. "Sustainable energy prices and growth: Comparing macroeconomic and backcasting scenarios," Ecological Economics, Elsevier, vol. 63(4), pages 722-731, September.
    8. Jean-Francois Mercure, 2012. "On the changeover timescales of technology transitions and induced efficiency changes: an overarching theory," Papers 1209.0424, arXiv.org.
    9. FU, Yunyun & SHEN, Yongchang & SONG, Malin & WANG, Weiyu, 2024. "Does artificial intelligence reduce corporate energy consumption? New evidence from China," Economic Analysis and Policy, Elsevier, vol. 83(C), pages 548-561.
    10. Sue Wing, Ian, 2008. "Explaining the declining energy intensity of the U.S. economy," Resource and Energy Economics, Elsevier, vol. 30(1), pages 21-49, January.
    11. Sue Wing, Ian & Eckaus, Richard S., 2007. "The implications of the historical decline in US energy intensity for long-run CO2 emission projections," Energy Policy, Elsevier, vol. 35(11), pages 5267-5286, November.
    12. Zha, Jianping & Tan, Ting & Fan, Rong & Xu, Han & Ma, Siqi, 2020. "How to reduce energy intensity to achieve sustainable development of China's transport sector? A cross-regional comparison analysis," Socio-Economic Planning Sciences, Elsevier, vol. 71(C).
    13. Bibas, Ruben & Méjean, Aurélie & Hamdi-Cherif, Meriem, 2015. "Energy efficiency policies and the timing of action: An assessment of climate mitigation costs," Technological Forecasting and Social Change, Elsevier, vol. 90(PA), pages 137-152.
    14. Livas-García, A. & Bonilla, D. & Escalante Soberanis, M.A. & Bassam, A., 2019. "Projecting the energy pathway using a methodological sequence: The case of Mexico," Energy Policy, Elsevier, vol. 135(C).
    15. Kemp-Benedict, Eric, 2014. "The inverted pyramid: A neo-Ricardian view on the economy–environment relationship," Ecological Economics, Elsevier, vol. 107(C), pages 230-241.
    16. Okushima, Shinichiro & Tamura, Makoto, 2011. "Identifying the sources of energy use change: Multiple calibration decomposition analysis and structural decomposition analysis," Structural Change and Economic Dynamics, Elsevier, vol. 22(4), pages 313-326.

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