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Unintended anchors: Building rating systems and energy performance goals for U.S. buildings

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  • Klotz, Leidy
  • Mack, Daniel
  • Klapthor, Brent
  • Tunstall, Casey
  • Harrison, Jennilee

Abstract

In the U.S., where buildings account for 40% of energy use, commercial buildings use more energy per unit area than ever before. However, exemplary buildings demonstrate the feasibility of much better energy performance at no additional first cost. This research examines one possible explanation for this inconsistency. The aim is to investigate whether the anchoring bias, which refers to our tendency to gravitate towards a pre-defined standard regardless of its relevance, influences energy performance goals in building design. The scope examines professionals who help set energy performance goals for U.S. buildings. Prior to being asked to set an energy performance goal, these professionals were randomly directed to one of three series of questions. One series set an anchor of 90% energy reduction beyond standard practice, one set a 30% anchor, and one set no anchor. Respondents exposed to the 90% anchor, and respondents exposed to no anchor at all, set higher energy performance goals than respondents exposed to the 30% anchor. These results suggest that building rating systems that only reward incremental energy improvements may inadvertently create anchors, thereby discouraging more advanced energy performance goals and inhibiting energy performance that is technically and economically feasible.

Suggested Citation

  • Klotz, Leidy & Mack, Daniel & Klapthor, Brent & Tunstall, Casey & Harrison, Jennilee, 2010. "Unintended anchors: Building rating systems and energy performance goals for U.S. buildings," Energy Policy, Elsevier, vol. 38(7), pages 3557-3566, July.
  • Handle: RePEc:eee:enepol:v:38:y:2010:i:7:p:3557-3566
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    Cited by:

    1. 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, vol. 9(1), pages 1-16, December.
    2. Melo, A.P. & Cóstola, D. & Lamberts, R. & Hensen, J.L.M., 2014. "Development of surrogate models using artificial neural network for building shell energy labelling," Energy Policy, Elsevier, vol. 69(C), pages 457-466.
    3. Dalya Ismael & Tripp Shealy, 2018. "Sustainable Construction Risk Perceptions in the Kuwaiti Construction Industry," Sustainability, MDPI, vol. 10(6), pages 1-17, June.
    4. Loonen, R.C.G.M. & Trčka, M. & Cóstola, D. & Hensen, J.L.M., 2013. "Climate adaptive building shells: State-of-the-art and future challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 25(C), pages 483-493.
    5. Nora Harris & Tripp Shealy & Leidy Klotz, 2016. "How Exposure to ”Role Model” Projects Can Lead to Decisions for More Sustainable Infrastructure," Sustainability, MDPI, vol. 8(2), pages 1-9, January.
    6. Heather Klemick & Elizabeth Kopits & Ann Wolverton, 2015. "The Energy Efficiency Paradox: A Case Study of Supermarket Refrigeration System Investment Decisions," NCEE Working Paper Series 201503, National Center for Environmental Economics, U.S. Environmental Protection Agency, revised Jun 2015.
    7. Delgado, Laura & Shealy, Tripp, 2018. "Opportunities for greater energy efficiency in government facilities by aligning decision structures with advances in behavioral science," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 3952-3961.

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    Keywords

    Buildings Anchoring Cognitive Bias;

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