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‘Constant’ rebound effects in domestic heating: Developing a cross-sectional method

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  • Galvin, Ray

Abstract

Policymakers are increasingly concerned about rebound effects, which lead to lower savings than expected when energy-efficiency increases. There are difficulties in making coherent comparisons between magnitudes of rebound effects in different sectors, such as home heating, industry and transport. A barrier to this in domestic heating is the conceptual difficulty of estimating rebound effects as energy-efficiency elasticities, since the large, stepwise increases in energy-efficiency through thermal retrofits fit uneasily with differential calculus. This paper offers a solution. Firstly, it develops a ‘cross-sectional’ method in which elasticity-based rebound formulae are used to estimate implied average rebound effects in large datasets of energy consumption and energy efficiency. It then shows how a simple power curve can model these datasets, simplifying the mathematics, giving ‘constant’ rebound effect results that are immediately meaningful, and extending compatibility to rebound effect modelling in other sectors. The method is then extended to case studies of thermal retrofits of individual dwellings. Here the inherent inaccuracy of using linear approximations in rebound effect formulas is also illustrated. The cross-sectional, ‘constant’ rebound effect methodology is offered for wider use in estimating rebound effects in case studies and in domestic heating as more datasets of energy consumption and efficiency become available.

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  • Galvin, Ray, 2015. "‘Constant’ rebound effects in domestic heating: Developing a cross-sectional method," Ecological Economics, Elsevier, vol. 110(C), pages 28-35.
    • Handle: RePEc:eee:ecolec:v:110:y:2015:i:c:p:28-35
    DOI: 10.1016/j.ecolecon.2014.12.016
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    Cited by:

    1. Michelsen, Claus & El-Shagi, Makram & Rosenschon, Sebastian, 2016. "The diffusion of "green'' buildings in the housing market: empirics on the long run effects of energy efficiency regulation," VfS Annual Conference 2016 (Augsburg): Demographic Change 145534, Verein für Socialpolitik / German Economic Association.
    2. Benjamin Volland, 2016. "Efficiency in Domestic Space Heating: An Estimation of the Direct Rebound Effect for Domestic Heating in the U.S," IRENE Working Papers 16-01, IRENE Institute of Economic Research.
    3. Galvin, Ray & Sunikka-Blank, Minna, 2016. "Quantification of (p)rebound effects in retrofit policies – Why does it matter?," Energy, Elsevier, vol. 95(C), pages 415-424.
    4. Galvin, Ray, 2015. "The rebound effect, gender and social justice: A case study in Germany," Energy Policy, Elsevier, vol. 86(C), pages 759-769.
    5. Calvo, Rubén & Álamos, Nicolás & Huneeus, Nicolás & O'Ryan, Raúl, 2022. "Energy poverty effects on policy-based PM2.5 emissions mitigation in southern and central Chile," Energy Policy, Elsevier, vol. 161(C).
    6. Niemierko, Rochus & Töppel, Jannick & Tränkler, Timm, 2019. "A D-vine copula quantile regression approach for the prediction of residential heating energy consumption based on historical data," Applied Energy, Elsevier, vol. 233, pages 691-708.
    7. Galvin, Ray, 2016. "Rebound effects from speed and acceleration in electric and internal combustion engine cars: An empirical and conceptual investigation," Applied Energy, Elsevier, vol. 172(C), pages 207-216.
    8. Deng, Qianli & Jiang, Xianglin & Zhang, Limao & Cui, Qingbin, 2015. "Making optimal investment decisions for energy service companies under uncertainty: A case study," Energy, Elsevier, vol. 88(C), pages 234-243.
    9. Belaïd, Fateh & Bakaloglou, Salomé & Roubaud, David, 2018. "Direct rebound effect of residential gas demand: Empirical evidence from France," Energy Policy, Elsevier, vol. 115(C), pages 23-31.
    10. Li, Ke & Zhang, Ning & Liu, Yanchu, 2016. "The energy rebound effects across China’s industrial sectors: An output distance function approach," Applied Energy, Elsevier, vol. 184(C), pages 1165-1175.

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