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Estimates of long-run energy savings and realization rates from a large energy efficiency retrofit program

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Abstract
Over 50 countries representing three quarters of global CO2 emissions have pledged to achieve a net-zero carbon economy by 2050, and energy efficiency improvements are a primary contributor in policy scenarios that attain this goal. However, uncertainties remain about the realized effectiveness of energy efficiency programs. This paper provides evidence on the realized savings from Canada's largest residential energy retrofit program. We use utility data from all single-family homes in a mid-sized Canadian city and detailed energy audit records from the EnerGuide for Homes database, which includes modeled predictions of energy savings from retrofit adoptions. The retrofit program reduces natural gas consumption in the average participating home by about 21%, representing 60% of predicted natural gas savings. Whole-envelope retrofits are predicted to reduce natural gas consumption by 67%, but in practice only half of these savings are realized. This underscores the importance of developing new modeling approaches that incorporate house-level data, which reflect the outcome of realized rather than predicted occupant behavior, to increase retrofit energy savings and return per subsidy dollar spent.

Suggested Citation

  • Maya Papineau & Nicholas Rivers & Kareman Yassin, 2022. "Estimates of long-run energy savings and realization rates from a large energy efficiency retrofit program," Carleton Economic Papers 22-09, Carleton University, Department of Economics.
    • Handle: RePEc:car:carecp:22-09
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    References listed on IDEAS

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    1. Nicholas Rivers & Leslie Shiell, 2016. "Free Riding on Energy Efficiency Subsidies: The Case of Natural Gas Furnaces in Canada," The Energy Journal, , vol. 37(4), pages 239-266, October.
    2. Goodman-Bacon, Andrew, 2021. "Difference-in-differences with variation in treatment timing," Journal of Econometrics, Elsevier, vol. 225(2), pages 254-277.
    3. Christensen, Peter & Francisco, Paul & Myers, Erica & Shao, Hansen & Souza, Mateus, 2024. "Energy efficiency can deliver for climate policy: Evidence from machine learning-based targeting," Journal of Public Economics, Elsevier, vol. 234(C).
    4. Sun, Liyang & Abraham, Sarah, 2021. "Estimating dynamic treatment effects in event studies with heterogeneous treatment effects," Journal of Econometrics, Elsevier, vol. 225(2), pages 175-199.
    5. Fiona Burlig & Christopher Knittel & David Rapson & Mar Reguant & Catherine Wolfram, 2020. "Machine Learning from Schools about Energy Efficiency," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 7(6), pages 1181-1217.
    6. Hancevic, Pedro I. & Sandoval, Hector H., 2022. "Low-income energy efficiency programs and energy consumption," Journal of Environmental Economics and Management, Elsevier, vol. 113(C).
    7. Boomhower, Judson & Davis, Lucas W., 2014. "A credible approach for measuring inframarginal participation in energy efficiency programs," Journal of Public Economics, Elsevier, vol. 113(C), pages 67-79.
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    Cited by:

    1. Marko Å ostar & Ines Å koko, 2024. "Unpacking the Complexities of Energy Renovation Programs for Family Houses: Case Study of Croatia," International Journal of Energy Economics and Policy, Econjournals, vol. 14(4), pages 12-25, July.

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    Keywords

    energy efficiency; energy retrofit program; energy savings; Canada;
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