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End use technology choice in the National Energy Modeling System (NEMS): An analysis of the residential and commercial building sectors

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  • Wilkerson, Jordan T.
  • Cullenward, Danny
  • Davidian, Danielle
  • Weyant, John P.

Abstract

The National Energy Modeling System (NEMS) is arguably the most influential energy model in the United States. The U.S. Energy Information Administration uses NEMS to generate the federal government's annual long-term forecast of national energy consumption and to evaluate prospective federal energy policies. NEMS is considered such a standard tool that other models are calibrated to its forecasts, in both government and academic practice. As a result, NEMS has a significant influence over expert opinions of plausible energy futures. NEMS is a massively detailed model whose inner workings, despite its prominence, receive relatively scant critical attention.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:eneeco:v:40:y:2013:i:c:p:773-784
    DOI: 10.1016/j.eneco.2013.09.023
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    3. Frédéric Branger & Louis-Gaëtan Giraudet & Céline Guivarch & Philippe Quirion, 2014. "Sensitivity analysis of an energy-economy model of the residential building sector," Working Papers hal-01016399, HAL.
    4. Wilkerson, Jordan T. & Leibowicz, Benjamin D. & Turner, Delavane D. & Weyant, John P., 2015. "Comparison of integrated assessment models: Carbon price impacts on U.S. energy," Energy Policy, Elsevier, vol. 76(C), pages 18-31.
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    6. Zhu, Tong & Curtis, John & Clancy, Matthew, 2023. "Modelling barriers to low-carbon technologies in energy system analysis: The example of renewable heat in Ireland," Applied Energy, Elsevier, vol. 330(PA).
    7. Brown, Marilyn A. & Kim, Gyungwon & Smith, Alexander M. & Southworth, Katie, 2017. "Exploring the impact of energy efficiency as a carbon mitigation strategy in the U.S," Energy Policy, Elsevier, vol. 109(C), pages 249-259.
    8. Xiang, Xiwang & Ma, Minda & Ma, Xin & Chen, Liming & Cai, Weiguang & Feng, Wei & Ma, Zhili, 2022. "Historical decarbonization of global commercial building operations in the 21st century," Applied Energy, Elsevier, vol. 322(C).
    9. Langevin, J. & Reyna, J.L. & Ebrahimigharehbaghi, S. & Sandberg, N. & Fennell, P. & Nägeli, C. & Laverge, J. & Delghust, M. & Mata, É. & Van Hove, M. & Webster, J. & Federico, F. & Jakob, M. & Camaras, 2020. "Developing a common approach for classifying building stock energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    10. Fais, Birgit & Sabio, Nagore & Strachan, Neil, 2016. "The critical role of the industrial sector in reaching long-term emission reduction, energy efficiency and renewable targets," Applied Energy, Elsevier, vol. 162(C), pages 699-712.
    11. Zakerinia, Saleh, 2018. "Understanding the Role of Transportation in Meeting California’s Greenhouse Gas Emissions Reduction Target: A Focus on Technology Forcing Policies, Interactions with the Electric Sector and Mitigation," Institute of Transportation Studies, Working Paper Series qt0r69m651, Institute of Transportation Studies, UC Davis.
    12. Marilyn A. Brown & Matt Cox & Ben Staver & Paul Baer, 2016. "Modeling climate-driven changes in U.S. buildings energy demand," Climatic Change, Springer, vol. 134(1), pages 29-44, January.
    13. Gillingham, Kenneth & Huang, Pei, 2021. "Racial disparities in the health effects from air pollution: Evidence from ports," ZEW Discussion Papers 21-058, ZEW - Leibniz Centre for European Economic Research.
    14. 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.
    15. Robinson, Scott A. & Rai, Varun, 2015. "Determinants of spatio-temporal patterns of energy technology adoption: An agent-based modeling approach," Applied Energy, Elsevier, vol. 151(C), pages 273-284.
    16. Kenneth Gillingham & Marten Ovaere & Stephanie Weber, 2021. "Carbon Policy and the Emissions Implications of Electric Vehicles," CESifo Working Paper Series 8974, CESifo.
    17. Baer, Paul & Brown, Marilyn A. & Kim, Gyungwon, 2015. "The job generation impacts of expanding industrial cogeneration," Ecological Economics, Elsevier, vol. 110(C), pages 141-153.
    18. Kaixin Huang & Matthew J. Eckelman, 2022. "Appending material flows to the National Energy Modeling System (NEMS) for projecting the physical economy of the United States," Journal of Industrial Ecology, Yale University, vol. 26(1), pages 294-308, February.
    19. Marina Economidou & Paolo Zangheri & Andreas Müller & Lukas Kranzl, 2018. "Financing the Renovation of the Cypriot Building Stock: An Assessment of the Energy Saving Potential of Different Policy Scenarios Based on the Invert/EE-Lab Model," Energies, MDPI, vol. 11(11), pages 1-25, November.
    20. Cullenward, Danny & T. Wilkerson, Jordan & Wara, Michael & Weyant, John P., 2016. "Dynamically estimating the distributional impacts of U.S. climate policy with NEMS: A case study of the Climate Protection Act of 2013," Energy Economics, Elsevier, vol. 55(C), pages 303-318.
    21. Frédéric Branger & Louis-Gaëtan Giraudet & Céline Guivarch & Philippe Quirion, 2015. "Global sensitivity analysis of an energy-economy model of the residential building sector," Policy Papers 2015.01, FAERE - French Association of Environmental and Resource Economists.
    22. Marilyn Brown & Matt Cox & Ben Staver & Paul Baer, 2016. "Modeling climate-driven changes in U.S. buildings energy demand," Climatic Change, Springer, vol. 134(1), pages 29-44, January.
    23. Radpour, Saeidreza & Hossain Mondal, Md Alam & Kumar, Amit, 2017. "Market penetration modeling of high energy efficiency appliances in the residential sector," Energy, Elsevier, vol. 134(C), pages 951-961.

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    More about this item

    Keywords

    Energy models; Consumer preferences; Behavior; Energy forecasting;
    All these keywords.

    JEL classification:

    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
    • Q48 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Government Policy
    • D14 - Microeconomics - - Household Behavior - - - Household Saving; Personal Finance
    • D23 - Microeconomics - - Production and Organizations - - - Organizational Behavior; Transaction Costs; Property Rights

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