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Technology diffusion and energy intensity in US commercial buildings

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  • Andrews, Clinton J.
  • Krogmann, Uta

Abstract

This paper analyzes the 1992 and 2003 US Commercial Buildings Energy Consumption Survey microdata files to show the extent to which certain heating, cooling, lighting, and window technologies are entering use, and the resulting impacts on the intensity of energy use. Excepting the case of fluorescent lights, no technology dominates the entire market but instead each conquers a specific niche. Most of the buildings in which these technologies are installed do not have lower-than-average energy intensity, measured as annual energy use per square meter of floor space. The exceptional technology that does measurably correlate with reduced energy intensity is daylighting. These results suggest that technologies are adopted to serve comfort or quality objectives rather than to save energy, or that buildings' users confound the designers' intentions. Decision makers thus should improve operating and maintenance practices, invest in building commissioning, and rely more heavily on passive design features to save energy.

Suggested Citation

  • Andrews, Clinton J. & Krogmann, Uta, 2009. "Technology diffusion and energy intensity in US commercial buildings," Energy Policy, Elsevier, vol. 37(2), pages 541-553, February.
  • Handle: RePEc:eee:enepol:v:37:y:2009:i:2:p:541-553
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Nelson, Hal T., 2012. "Lost opportunities: Modeling commercial building energy code adoption in the United States," Energy Policy, Elsevier, vol. 49(C), pages 182-191.
    2. Chai, Jian & Guo, Ju-E & Wang, Shou-Yang & Lai, Kin Keung, 2009. "Why does energy intensity fluctuate in China?," Energy Policy, Elsevier, vol. 37(12), pages 5717-5731, December.
    3. Heeren, Niko & Jakob, Martin & Martius, Gregor & Gross, Nadja & Wallbaum, Holger, 2013. "A component based bottom-up building stock model for comprehensive environmental impact assessment and target control," Renewable and Sustainable Energy Reviews, Elsevier, vol. 20(C), pages 45-56.
    4. Egging, Ruud, 2013. "Drivers, trends, and uncertainty in long-term price projections for energy management in public buildings," Energy Policy, Elsevier, vol. 62(C), pages 617-624.
    5. Juaidi, Adel & AlFaris, Fadi & Montoya, Francisco G. & Manzano-Agugliaro, Francisco, 2016. "Energy benchmarking for shopping centers in Gulf Coast region," Energy Policy, Elsevier, vol. 91(C), pages 247-255.
    6. Liu, Pei & Pistikopoulos, Efstratios N. & Li, Zheng, 2010. "An energy systems engineering approach to the optimal design of energy systems in commercial buildings," Energy Policy, Elsevier, vol. 38(8), pages 4224-4231, August.
    7. Constantine Kontokosta, 2015. "A Market-Specific Methodology for a Commercial Building Energy Performance Index," The Journal of Real Estate Finance and Economics, Springer, vol. 51(2), pages 288-316, August.
    8. Reiche, Danyel, 2013. "Climate policies in the U.S. at the stakeholder level: A case study of the National Football League," Energy Policy, Elsevier, vol. 60(C), pages 775-784.
    9. Jennings, Mark G., 2013. "A smarter plan? A policy comparison between Great Britain and Ireland's deployment strategies for rolling out new metering technologies," Energy Policy, Elsevier, vol. 57(C), pages 462-468.
    10. Nora Harris & Tripp Shealy & Leidy Klotz, 2016. "Choice Architecture as a Way to Encourage a Whole Systems Design Perspective for More Sustainable Infrastructure," Sustainability, MDPI, Open Access Journal, vol. 9(1), pages 1-16, December.

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