IDEAS home Printed from https://ideas.repec.org/a/eee/enepol/v139y2020ics0301421520301087.html
   My bibliography  Save this article

Determinants of electricity bill savings for residential solar panel adopters in the U.S.: A multilevel modeling approach

Author

Listed:
  • Fikru, Mahelet G.

Abstract

This study provides a comprehensive examination of factors that affect the electricity bill savings of a sample of solar adopters in four U.S. states. A multilevel model is used to capture the role of variations across state policies and local regulations, and examine their effect on savings after controlling for household characteristics. We find that solar adopters located in zip codes with higher photovoltaics penetration have significantly higher summer savings. This suggests that local policies that remove barriers for the wide adoption of solar panels across multiple households would be in alignment with increasing the private value of solar panels. Furthermore, we find that solar adopters in zip codes with smaller installed capacity have higher summer savings. The analysis in this study suggests that, to achieve higher savings, local policies that regulate size are less effective compared to policies that remove barriers to a wider photovoltaics adoption. Finally, this study finds evidence for the role of certain household-level variables in explaining the electricity bill savings of solar adopters. Solar-savings-calculators can be customized to include some of these house and resident characteristics.

Suggested Citation

  • Fikru, Mahelet G., 2020. "Determinants of electricity bill savings for residential solar panel adopters in the U.S.: A multilevel modeling approach," Energy Policy, Elsevier, vol. 139(C).
  • Handle: RePEc:eee:enepol:v:139:y:2020:i:c:s0301421520301087
    DOI: 10.1016/j.enpol.2020.111351
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0301421520301087
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.enpol.2020.111351?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. Severin Borenstein & Lucas W. Davis, 2016. "The Distributional Effects of US Clean Energy Tax Credits," Tax Policy and the Economy, University of Chicago Press, vol. 30(1), pages 191-234.
    3. Makena Coffman & Scott F. Allen & Sherilyn Wee, 2018. "Determinants of Residential Solar Photovoltaic Adoption," Working Papers 2018-1, University of Hawaii Economic Research Organization, University of Hawaii at Manoa.
    4. 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.
    5. Borchers, Allison M. & Xiarchos, Irene & Beckman, Jayson, 2014. "Determinants of wind and solar energy system adoption by U.S. farms: A multilevel modeling approach," Energy Policy, Elsevier, vol. 69(C), pages 106-115.
    6. Lukanov, Boris R. & Krieger, Elena M., 2019. "Distributed solar and environmental justice: Exploring the demographic and socio-economic trends of residential PV adoption in California," Energy Policy, Elsevier, vol. 134(C).
    7. Orioli, Aldo & Di Gangi, Alessandra, 2013. "Load mismatch of grid-connected photovoltaic systems: Review of the effects and analysis in an urban context," Renewable and Sustainable Energy Reviews, Elsevier, vol. 21(C), pages 13-28.
    8. W. J. Browne & S. V. Subramanian & K. Jones & H. Goldstein, 2005. "Variance partitioning in multilevel logistic models that exhibit overdispersion," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 168(3), pages 599-613, July.
    9. Pillai, Unni, 2015. "Drivers of cost reduction in solar photovoltaics," Energy Economics, Elsevier, vol. 50(C), pages 286-293.
    10. 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.
    11. 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.
    12. Kurdgelashvili, Lado & Shih, Cheng-Hao & Yang, Fan & Garg, Mehul, 2019. "An empirical analysis of county-level residential PV adoption in California," Technological Forecasting and Social Change, Elsevier, vol. 139(C), pages 321-333.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Stewart, Fraser, 2021. "All for sun, sun for all: Can community energy help to overcome socioeconomic inequalities in low-carbon technology subsidies?," Energy Policy, Elsevier, vol. 157(C).
    2. Stewart, Fraser, 2022. "Friends with benefits: How income and peer diffusion combine to create an inequality “trap” in the uptake of low-carbon technologies," Energy Policy, Elsevier, vol. 163(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Fikru, Mahelet G., 2019. "Estimated electricity bill savings for residential solar photovoltaic system owners: Are they accurate enough?," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    3. Brown, David P. & Muehlenbachs, Lucija, 2023. "The Value of Electricity Reliability: Evidence from Battery Adoption," Working Papers 2023-5, University of Alberta, Department of Economics.
    4. Brown, David P., 2022. "Socioeconomic and demographic disparities in residential battery storage adoption: Evidence from California," Energy Policy, Elsevier, vol. 164(C).
    5. Fikru, Mahelet G. & Gelles, Gregory & Ichim, Ana-Maria & Kimball, Jonathan W. & Smith, Joseph D. & Zawodniok, Maciej Jan, 2018. "An economic model for residential energy consumption, generation, storage and reliance on cleaner energy," Renewable Energy, Elsevier, vol. 119(C), pages 429-438.
    6. Wiggins, Seth, 2016. "It’s All Local? How Sub-State Policies Affect Western US Residential Solar Adoption," 2016 Annual Meeting, July 31-August 2, Boston, Massachusetts 235667, Agricultural and Applied Economics Association.
    7. Małgorzata Sztorc, 2022. "The Implementation of the European Green Deal Strategy as a Challenge for Energy Management in the Face of the COVID-19 Pandemic," Energies, MDPI, vol. 15(7), pages 1-21, April.
    8. Fikru, Mahelet G. & Gautier, Luis, 2023. "Consumption and production of cleaner energy by prosumers," Energy Economics, Elsevier, vol. 124(C).
    9. Abajian, Alexander & Pretnar, Nick, 2021. "An Aggregate Perspective on the Geo-spatial Distribution of Residential Solar Panels," MPRA Paper 105481, University Library of Munich, Germany.
    10. Ahmed S. Alahmed & Lang Tong, 2022. "Integrating Distributed Energy Resources: Optimal Prosumer Decisions and Impacts of Net Metering Tariffs," Papers 2204.06115, arXiv.org, revised May 2022.
    11. Fabian Feger & Nicola Pavanini & Doina Radulescu, 2022. "Welfare and Redistribution in Residential Electricity Markets with Solar Power [Residential Consumption of Gas and Electricity in the US: The Role of Prices and Income]," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 89(6), pages 3267-3302.
    12. Mustain Billah & Adnan Anwar & Ziaur Rahman & Syed Md. Galib, 2021. "Bi-Level Poisoning Attack Model and Countermeasure for Appliance Consumption Data of Smart Homes," Energies, MDPI, vol. 14(13), pages 1-17, June.
    13. Chen, Chien-fei & Xu, Xiaojing & Adua, Lazarus & Briggs, Morgan & Nelson, Hannah, 2022. "Exploring the factors that influence energy use intensity across low-, middle-, and high-income households in the United States," Energy Policy, Elsevier, vol. 168(C).
    14. Nemet, Gregory F. & Lu, Jiaqi & Rai, Varun & Rao, Rohan, 2020. "Knowledge spillovers between PV installers can reduce the cost of installing solar PV," Energy Policy, Elsevier, vol. 144(C).
    15. Fei Wang & Yili Yu & Xinkang Wang & Hui Ren & Miadreza Shafie-Khah & João P. S. Catalão, 2018. "Residential Electricity Consumption Level Impact Factor Analysis Based on Wrapper Feature Selection and Multinomial Logistic Regression," Energies, MDPI, vol. 11(5), pages 1-26, May.
    16. Wallis, Hannah & Nachreiner, Malte & Matthies, Ellen, 2016. "Adolescents and electricity consumption; Investigating sociodemographic, economic, and behavioural influences on electricity consumption in households," Energy Policy, Elsevier, vol. 94(C), pages 224-234.
    17. Carattini, Stefano & Gillingham, Kenneth T. & Meng, Xiangyu & Yoeli, Erez, 2022. "Peer-to-peer solar and social rewards: evidence from a field experiment," LSE Research Online Documents on Economics 117361, London School of Economics and Political Science, LSE Library.
    18. Satre-Meloy, Aven, 2019. "Investigating structural and occupant drivers of annual residential electricity consumption using regularization in regression models," Energy, Elsevier, vol. 174(C), pages 148-168.
    19. Dimitra Kotsila & Persefoni Polychronidou, 2021. "Determinants of household electricity consumption in Greece: a statistical analysis," Journal of Innovation and Entrepreneurship, Springer, vol. 10(1), pages 1-20, December.
    20. Guo, Peiyang & Lam, Jacqueline C.K. & Li, Victor O.K., 2019. "Drivers of domestic electricity users’ price responsiveness: A novel machine learning approach," Applied Energy, Elsevier, vol. 235(C), pages 900-913.

    More about this item

    Keywords

    Photovoltaics; Renewable energy; Electricity price; Multilevel model; Net metering; Zip code;
    All these keywords.

    JEL classification:

    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:enepol:v:139:y:2020:i:c:s0301421520301087. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/enpol .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.