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Calculating impacts of energy standards on energy demand in U.S. buildings with uncertainty in an integrated assessment model

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  • Scott, Michael J.
  • Daly, Don S.
  • Hathaway, John E.
  • Lansing, Carina S.
  • Liu, Ying
  • McJeon, Haewon C.
  • Moss, Richard H.
  • Patel, Pralit L.
  • Peterson, Marty J.
  • Rice, Jennie S.
  • Zhou, Yuyu

Abstract

In this paper, an IAM (integrated assessment model) uses a newly-developed Monte Carlo analysis capability to analyze the impacts of more aggressive U.S. residential and commercial building-energy codes and equipment standards on energy consumption and energy service costs at the state level, explicitly recognizing uncertainty in technology effectiveness and cost, socioeconomics, presence or absence of carbon prices, and climate impacts on energy demand. The paper finds that aggressive building-energy codes and equipment standards are an effective, cost-saving way to reduce energy consumption in buildings and greenhouse gas emissions in U.S. states. This conclusion is robust to significant uncertainties in population, economic activity, climate, carbon prices, and technology performance and costs.

Suggested Citation

  • Scott, Michael J. & Daly, Don S. & Hathaway, John E. & Lansing, Carina S. & Liu, Ying & McJeon, Haewon C. & Moss, Richard H. & Patel, Pralit L. & Peterson, Marty J. & Rice, Jennie S. & Zhou, Yuyu, 2015. "Calculating impacts of energy standards on energy demand in U.S. buildings with uncertainty in an integrated assessment model," Energy, Elsevier, vol. 90(P2), pages 1682-1694.
    • Handle: RePEc:eee:energy:v:90:y:2015:i:p2:p:1682-1694
    DOI: 10.1016/j.energy.2015.06.127
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    2. Szodrai, Ferenc & Lakatos, Ákos & Kalmár, Ferenc, 2016. "Analysis of the change of the specific heat loss coefficient of buildings resulted by the variation of the geometry and the moisture load," Energy, Elsevier, vol. 115(P1), pages 820-829.
    3. Soares, João & Borges, Nuno & Fotouhi Ghazvini, Mohammad Ali & Vale, Zita & de Moura Oliveira, P.B., 2016. "Scenario generation for electric vehicles' uncertain behavior in a smart city environment," Energy, Elsevier, vol. 111(C), pages 664-675.

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