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Health and financial impacts of demand-side response measures differ across sociodemographic groups

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  • Lee V. White

    (The Ohio State University School of Environment and Natural Resources
    University of Southern California Sol Price School of Public Policy
    Australian National University School of Regulation and Global Governance)

  • Nicole D. Sintov

    (The Ohio State University School of Environment and Natural Resources)

Abstract

Demand-side response (DSR) measures, which facilitate the integration of high shares of intermittent renewable generation into electric grids, are gaining prominence. DSR measures, such as time-of-use (TOU) rates, charge higher rates during high-demand ‘on-peak’ times. These rates may disproportionately impact the energy bills and health of vulnerable households, defined as those who face greater energy needs combined with greater social and financial pressures. Here we examine 7,487 households that took part in a randomized control TOU pilot in the southwestern United States. We found that assignment to TOU rather than control disproportionately increases bills for households with elderly and disabled occupants, and predicts worse health outcomes for households with disabled and ethnic minority occupants than those for non-vulnerable counterparts. These results suggest that vulnerable groups should be considered separately in DSR rate design, and future pilots should seek to determine which designs most effectively avoid exacerbating existing energy injustices or creating new ones.

Suggested Citation

  • Lee V. White & Nicole D. Sintov, 2020. "Health and financial impacts of demand-side response measures differ across sociodemographic groups," Nature Energy, Nature, vol. 5(1), pages 50-60, January.
  • Handle: RePEc:nat:natene:v:5:y:2020:i:1:d:10.1038_s41560-019-0507-y
    DOI: 10.1038/s41560-019-0507-y
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    Cited by:

    1. Calver, Philippa & Simcock, Neil, 2021. "Demand response and energy justice: A critical overview of ethical risks and opportunities within digital, decentralised, and decarbonised futures," Energy Policy, Elsevier, vol. 151(C).
    2. Ribó-Pérez, David & Heleno, Miguel & Álvarez-Bel, Carlos, 2021. "The flexibility gap: Socioeconomic and geographical factors driving residential flexibility," Energy Policy, Elsevier, vol. 153(C).
    3. Bo wang & Nana Deng & Wenhui Zhao & Zhaohua Wang, 2022. "Residential power demand side management optimization based on fine-grained mixed frequency data," Annals of Operations Research, Springer, vol. 316(1), pages 603-622, September.
    4. Barjaková, Martina & Belton, Cameron & Purcell, Karl & Lunn, Pete, 2021. "Efficient ways of communicating time-of-use electricity tariffs in Ireland: Plain and simple," Papers WP704, Economic and Social Research Institute (ESRI).
    5. Hammerle, Mara & Burke, Paul J., 2022. "From natural gas to electric appliances: Energy use and emissions implications in Australian homes," Energy Economics, Elsevier, vol. 110(C).
    6. Chad Zanocco & Tao Sun & Gregory Stelmach & June Flora & Ram Rajagopal & Hilary Boudet, 2022. "Assessing Californians’ awareness of their daily electricity use patterns," Nature Energy, Nature, vol. 7(12), pages 1191-1199, December.
    7. Gianluca Trotta & Kirsten Gram-Hanssen & Pernille Lykke Jørgensen, 2020. "Heterogeneity of Electricity Consumption Patterns in Vulnerable Households," Energies, MDPI, vol. 13(18), pages 1-17, September.
    8. Baik, Sosung & Hines, Jeffrey F. & Sim, Jaeung, 2023. "Racial disparities in the energy burden beyond socio-economic inequality," Energy Economics, Elsevier, vol. 127(PA).
    9. Cappers, Peter A. & Todd-Blick, Annika, 2021. "Heterogeneity in own-price residential customer demand elasticities for electricity under time-of-use rates: Evidence from a randomized-control trial in the United States," Utilities Policy, Elsevier, vol. 73(C).

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