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


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.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Mean change in mean monthly summer bills, by vulnerability group and rate assignment.

Data availability

The processed or aggregated data that support the plots within this paper and other findings of this study are available from the corresponding author upon reasonable request. Authors signed a non-disclosure agreement with the utility that provided the data analysed in this paper, and under this agreement are unable to make the raw data publicly available. Source data for Fig. 1 are provided with the paper.


  1. 1.

    Electricity Information 2017 (International Energy Agency/OECD, 2017).

  2. 2.

    Residential Rate Reform / R.12-06-013 (California Public Utilities Commission, accessed 12 January 2018);

  3. 3.

    Todd, A., Cappers, P. & Goldman, C. Residential Customer Enrollment in Time-based Rate and Enabling Technology Programs: Smart Grid Investment Grant Consumer Behavior Study Analysis Report LBNL-6247E (Lawrence Berkeley National Laboratory, 2013).

  4. 4.

    Sperling, D. & Eggert, A. California’s climate and energy policy for transportation. Energy Strateg. Rev. 5, 88–94 (2014).

    Article  Google Scholar 

  5. 5.

    Hernández, D. Understanding ‘energy insecurity’ and why it matters to health. Soc. Sci. Med. 167, 1–10 (2016).

    Article  Google Scholar 

  6. 6.

    Anderson, W., White, V. & Finney, A. Coping with low incomes and cold homes. Energy Policy 49, 40–52 (2012).

    Article  Google Scholar 

  7. 7.

    Anderson, W., White, V. & Finney, A. ‘You Just Have to Get By’: Coping with Low Incomes and Cold Homes (Centre for Sustainable Energy, 2010).

  8. 8.

    Middlemiss, L. & Gillard, R. Fuel poverty from the bottom-up: characterising household energy vulnerability through the lived experience of the fuel poor. Energy Res. Soc. Sci. 6, 146–154 (2015).

    Article  Google Scholar 

  9. 9.

    Bouzarovski, S., Petrova, S. & Sarlamanov, R. Energy poverty policies in the EU: a critical perspective. Energy Policy 49, 76–82 (2012).

    Article  Google Scholar 

  10. 10.

    Snell, C., Bevan, M. & Thomson, H. Justice, fuel poverty and disabled people in England. Energy Res. Soc. Sci. 10, 123–132 (2015).

    Article  Google Scholar 

  11. 11.

    Healy, J. D. Excess winter mortality in Europe: a cross country analysis identifying key risk factors. J. Epidemiol. Commun. Health 57, 784–789 (2003).

    Article  Google Scholar 

  12. 12.

    Boardman, B. Fixing Fuel Poverty: Challenges and Solutions (Earthscan, 2010).

  13. 13.

    Colton, R. Measuring the Outcomes of Low-Income Energy Assistance Programs through a Home Energy Insecurity Scale (US Department of Health and Human Services, 2003).

  14. 14.

    Walker, G. & Day, R. Fuel poverty as injustice: integrating distribution, recognition and procedure in the struggle for affordable warmth. Energy Policy 49, 69–75 (2012).

    Article  Google Scholar 

  15. 15.

    Harlan, S. L., Declet-Barreto, J. H., Stefanov, W. L. & Petitti, D. B. Neighborhood effects on heat deaths: social and environmental predictors of vulnerability in Maricopa County, Arizona. Environ. Health Perspect. 121, 197–204 (2013).

    Article  Google Scholar 

  16. 16.

    Sakka, A., Santamouris, M., Livada, I., Nicol, F. & Wilson, M. On the thermal performance of low income housing during heat waves. Energy Build. 49, 69–77 (2012).

    Article  Google Scholar 

  17. 17.

    Michelozzi, P. et al. The impact of the summer 2003 heat waves on mortality in four Italian cities. Eur. Surveillance 10, 11–12 (2005).

    Article  Google Scholar 

  18. 18.

    Poumadère, M., Mays, C., Le Mer, S. & Blong, R. The 2003 heat wave in France: dangerous climate change here and now. Risk Anal. 25, 1483–1494 (2005).

    Article  Google Scholar 

  19. 19.

    Cayla, J.-M., Maizi, N. & Marchand, C. The role of income in energy consumption behaviour: evidence from French households data. Energy Policy 39, 7874–7883 (2011).

    Article  Google Scholar 

  20. 20.

    Gillard, R., Snell, C. & Bevan, M. Advancing an energy justice perspective of fuel poverty: household vulnerability and domestic retrofit policy in the United Kingdom. Energy Res. Soc. Sci. 29, 53–61 (2017).

    Article  Google Scholar 

  21. 21.

    Walker, G. & Day, R. Necessary energy uses and a minimum standard of living in the United Kingdom: energy justice or escalating expectations? Energy Res. Soc. Sci. 18, 129–138 (2016).

    Article  Google Scholar 

  22. 22.

    Ormandy, D. & Ezratty, V. Health and thermal comfort: from WHO guidance to housing strategies. Energy Policy 49, 116–121 (2012).

    Article  Google Scholar 

  23. 23.

    Vandentorren, S. et al. August 2003 heat wave in France: risk factors for death of elderly people living at home. Eur. J. Public Health 16, 583–591 (2006).

    Article  Google Scholar 

  24. 24.

    Day, R., Walker, G. & Simcock, N. Conceptualising energy use and energy poverty using a capabilities framework. Energy Policy 93, 255–264 (2016).

    Article  Google Scholar 

  25. 25.

    Basu, R. High ambient temperature and mortality: a review of epidemiologic studies from 2001 to 2008. Environ. Health 8, 40 (2009).

    Article  Google Scholar 

  26. 26.

    Knowlton, K. et al. The 2006 California heat wave: impacts on hospitalizations and emergency department visits. Environ. Health Perspect. 117, 61–67 (2009).

    Article  Google Scholar 

  27. 27.

    Nord, M. & Kantor, L. S. Seasonal variation in food insecurity is associated with heating and cooling costs among low-income elderly Americans. J. Nutr. 136, 2939–2944 (2006).

    Article  Google Scholar 

  28. 28.

    Cook, J. T. et al. A brief indicator of household energy security: associations with food security, child health, and child development in US infants and toddlers. Pediatrics 122, e867–e875 (2008).

    Article  Google Scholar 

  29. 29.

    Curriero, F. C. et al. Temperature and mortality in 11 cities of the eastern United States. Am. J. Epidemiol. 155, 80–87 (2002).

    Article  Google Scholar 

  30. 30.

    Pager, D. & Shepherd, H. The sociology of discrimination: racial discrimination in employment, housing, credit, and consumer markets. Annu. Rev. Sociol. 34, 181–209 (2008).

    Article  Google Scholar 

  31. 31.

    Sovacool, B. K. & Dworkin, M. H. Energy justice: conceptual insights and practical applications. Appl. Energy 142, 435–444 (2015).

    Article  Google Scholar 

  32. 32.

    Bednar, D. J., Reames, T. G. & Keoleian, G. A. The intersection of energy and justice: modeling the spatial, racial/ethnic and socioeconomic patterns of urban residential heating consumption and efficiency in Detroit, Michigan. Energy Build. 143, 25–34 (2017).

    Article  Google Scholar 

  33. 33.

    Reames, T. G. Targeting energy justice: exploring spatial, racial/ethnic and socioeconomic disparities in urban residential heating energy efficiency. Energy Policy 97, 549–558 (2016).

    Article  Google Scholar 

  34. 34.

    Uejio, C. K. et al. Intra-urban societal vulnerability to extreme heat: the role of heat exposure and the built environment, socioeconomics, and neighborhood stability. Health Place 17, 498–507 (2011).

    Article  Google Scholar 

  35. 35.

    Faruqui, A. & Palmer, J. Dynamic pricing of electricity and its discontents. SSRN Electron. J. (2011).

  36. 36.

    Cappers, P., Spurlock, C. A., Todd, A. & Jin, L. Are vulnerable customers any different than their peers when exposed to critical peak pricing: evidence from the US. Energy Policy 123, 421–432 (2018).

    Article  Google Scholar 

  37. 37.

    Train, K. E., McFadden, D. L. & Goett, A. A. Consumer attitudes and voluntary rate schedules for public utilities. Rev. Econ. Stat. 69, 383 (1987).

    Article  Google Scholar 

  38. 38.

    Harrison, C. & Popke, J. ‘Because you got to have heat’: the networked assemblage of energy poverty in eastern North Carolina. Ann. Assoc. Am. Geogr. 101, 949–961 (2011).

    Article  Google Scholar 

  39. 39.

    Faruqui, A., Sergici, S. & Palmer, J. The Impact of Dynamic Pricing on Low Income Customers (Institute of Electric Efficiency, 2010).

  40. 40.

    Cappers, P., Spurlock, C. A., Todd, A. & Jin, L. Experiences of Vulnerable Residential Customer Subpopulations with Critical Peak Pricing Report LBNL-1006294 (Lawrence Berkeley National Laboratory, 2016).

  41. 41.

    Commission for Energy Regulation. Electricity Smart Metering Customer Behaviour Trials (CBT) Findings Report Information Paper CER11080a (The Commission for Energy Regulation, 2011).

  42. 42.

    Faruqui, A. & George, S. Quantifying customer response to dynamic pricing. Electr. J. 18, 53–63 (2005).

    Article  Google Scholar 

  43. 43.

    Schofield, J. et al. Residential Consumer Responsiveness to Time-varying Pricing Report A2 Low Carbon London LCNF Project (Imperial College London, 2014).

  44. 44.

    Nicholls, L. & Strengers, Y. Peak demand and the ‘family peak’ period in Australia: understanding practice (in)flexibility in households with children. Energy Res. Soc. Sci. 9, 116–124 (2015).

    Article  Google Scholar 

  45. 45.

    Liddell, C. & Morris, C. Fuel poverty and human health: a review of recent evidence. Energy Policy 38, 2987–2997 (2010).

    Article  Google Scholar 

  46. 46.

    Healy, J. D. & Clinch, J. P. Quantifying the severity of fuel poverty, its relationship with poor housing and reasons for non-investment in energy-saving measures in Ireland. Energy Policy 32, 207–220 (2004).

    Article  Google Scholar 

  47. 47.

    O’Sullivan, K. C., Howden-Chapman, P. L. & Fougere, G. M. Fuel poverty, policy, and equity in New Zealand: the promise of prepayment metering. Energy Res. Soc. Sci. 7, 99–107 (2015).

    Article  Google Scholar 

  48. 48.

    Thomson, H. & Snell, C. Quantifying the prevalence of fuel poverty across the European Union. Energy Policy 52, 563–572 (2013).

    Article  Google Scholar 

  49. 49.

    Liddell, C. & Guiney, C. Living in a cold and damp home: frameworks for understanding impacts on mental well-being. Public Health 129, 191–199 (2015).

    Article  Google Scholar 

  50. 50.

    Rowlands, I. H. & Furst, I. M. The cost impacts of a mandatory move to time-of-use pricing on residential customers: an Ontario (Canada) case-study. Energy Effic. 4, 571–585 (2011).

    Article  Google Scholar 

  51. 51.

    Johnson, E. J. et al. Beyond nudges: tools of a choice architecture. Mark. Lett. 23, 487–504 (2012).

    Article  Google Scholar 

  52. 52.

    Peters, E., Hibbard, J., Slovic, P. & Dieckmann, N. Numeracy skill and the communication, comprehension, and use of risk–benefit information. Health Aff. 26, 741–748 (2007).

    Article  Google Scholar 

  53. 53.

    Marghetis, T., Attari, S. Z. & Landy, D. Simple interventions can correct misperceptions of home energy use. Nat. Energy 4, 874–881 (2019).

    Article  Google Scholar 

  54. 54.

    White, L. V. & Sintov, N. D. Inaccurate consumer perceptions of monetary savings in a demand-side response programme predict programme acceptance. Nat. Energy 3, 1101–1108 (2018).

    Article  Google Scholar 

  55. 55.

    Berisha, V. et al. Assessing adaptation strategies for extreme heat: a public health evaluation of cooling centers in Maricopa County, Arizona. Weather. Clim. Soc. 9, 71–80 (2017).

    Article  Google Scholar 

  56. 56.

    Sovacool, B. K. Fuel poverty, affordability, and energy justice in England: policy insights from the Warm Front Program. Energy 93, 361–371 (2015).

    Article  Google Scholar 

  57. 57.

    Hernández, D. & Phillips, D. Benefit or burden? Perceptions of energy efficiency efforts among low-income housing residents in New York City. Energy Res. Soc. Sci. 8, 52–59 (2015).

    Article  Google Scholar 

  58. 58.

    Qiu, Y., Colson, G. & Wetzstein, M. E. Risk preference and adverse selection for participation in time-of-use electricity pricing programs. Resour. Energy Econ. 47, 126–142 (2017).

    Article  Google Scholar 

  59. 59.

    Nicolson, M., Huebner, G. & Shipworth, D. Are consumers willing to switch to smart time of use electricity tariffs? The importance of loss-aversion and electric vehicle ownership. Energy Res. Soc. Sci. 23, 82–96 (2017).

    Article  Google Scholar 

  60. 60.

    Mostafa Baladi, S., Herriges, J. A. & Sweeney, T. J. Residential response to voluntary time-of-use electricity rates. Resour. Energy Econ. 20, 225–244 (1998).

    Article  Google Scholar 

  61. 61.

    Cohen, J. Statistical Power Analysis for the Behavioral Sciences (Lawrence Earlbaum Associates, 1988).

Download references


The authors thank their utility partner for furnishing the data, D. Mazmanian for extensive advice and J. McPartlan for the considerable time invested in data management.

Author information




Both authors conceived the paper and designed the research. L.W. designed the analysis methods, performed the analyses and wrote and revised the paper. N.S. reviewed several drafts and made revisions.

Corresponding author

Correspondence to Lee V. White.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Notes 1 and 2 and Tables 1–12.

Reporting summary

Source data

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

White, L.V., Sintov, N.D. Health and financial impacts of demand-side response measures differ across sociodemographic groups. Nat Energy 5, 50–60 (2020).

Download citation

Further reading