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Electricity bill savings and the role of energy efficiency improvements: A case study of residential solar adopters in the USA

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  • Fikru, Mahelet G.

Abstract

This study measures the actual value of electricity bill savings for residential solar adopters and examines how decisions related to the timing and type of energy-efficiency improvements affect a solar adopter's electricity bill savings. Among different types of efficiency upgrades, solar adopters with an efficient heating/cooling system and efficient lighting have higher savings than those without. Among solar adopters who made certain efficiency improvements, those who upgraded efficiency a few months before or after installing solar panels have higher savings than those who upgraded over three years before installing solar panels.

Suggested Citation

  • Fikru, Mahelet G., 2019. "Electricity bill savings and the role of energy efficiency improvements: A case study of residential solar adopters in the USA," Renewable and Sustainable Energy Reviews, Elsevier, vol. 106(C), pages 124-132.
  • Handle: RePEc:eee:rensus:v:106:y:2019:i:c:p:124-132
    DOI: 10.1016/j.rser.2019.02.028
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    References listed on IDEAS

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    1. Keirstead, James, 2007. "Behavioural responses to photovoltaic systems in the UK domestic sector," Energy Policy, Elsevier, vol. 35(8), pages 4128-4141, August.
    2. Herche, Wesley, 2017. "Solar energy strategies in the U.S. utility market," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 590-595.
    3. Pillai, Unni, 2015. "Drivers of cost reduction in solar photovoltaics," Energy Economics, Elsevier, vol. 50(C), pages 286-293.
    4. Lee, Minhyun & Hong, Taehoon & Koo, Choongwan, 2016. "An economic impact analysis of state solar incentives for improving financial performance of residential solar photovoltaic systems in the United States," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 590-607.
    5. Kavousian, Amir & Rajagopal, Ram & Fischer, Martin, 2013. "Determinants of residential electricity consumption: Using smart meter data to examine the effect of climate, building characteristics, appliance stock, and occupants' behavior," Energy, Elsevier, vol. 55(C), pages 184-194.
    6. Berry, Stephen & Davidson, Kathryn, 2016. "Improving the economics of building energy code change: A review of the inputs and assumptions of economic models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 157-166.
    7. Severin Borenstein, 2017. "Private Net Benefits of Residential Solar PV: The Role of Electricity Tariffs, Tax Incentives, and Rebates," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 4(S1), pages 85-122.
    8. Jones, Rory V. & Fuertes, Alba & Lomas, Kevin J., 2015. "The socio-economic, dwelling and appliance related factors affecting electricity consumption in domestic buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 901-917.
    9. Darghouth, Naïm R. & Barbose, Galen & Wiser, Ryan, 2011. "The impact of rate design and net metering on the bill savings from distributed PV for residential customers in California," Energy Policy, Elsevier, vol. 39(9), pages 5243-5253, September.
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    Cited by:

    1. Emily K. Schwartz & Moncef Krarti, 2022. "Review of Adoption Status of Sustainable Energy Technologies in the US Residential Building Sector," Energies, MDPI, vol. 15(6), pages 1-18, March.

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