Indigenous communities in remote Australia face dangerous temperature extremes. These extremes are associated with increased risk of mortality and ill health. For many households, temperature extremes increase both their reliance on those services that energy provides, and the risk of those services being disconnected. Poor quality housing, low incomes, poor health and energy insecurity associated with prepayment all exacerbate the risk of temperature-related harm. Here we use daily smart meter data for 3,300 households and regression analysis to assess the relationship between temperature, electricity use and disconnection in 28 remote communities. We find that nearly all households (91%) experienced a disconnection from electricity during the 2018–2019 financial year. Almost three quarters of households (74%) were disconnected more than ten times. Households with high electricity use located in the central climate zones had a one in three chance of a same-day disconnection on very hot or very cold days. A broad suite of interrelated policy responses is required to reduce the frequency, duration and negative effects of disconnection from electricity for remote-living Indigenous residents.
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The data used in this paper are not freely available and were sourced from the data custodians. The electricity data were sourced from Power and Water Corporation (https://www.powerwater.com.au/) and the Australian BOM (http://www.bom.gov.au/). We note that access to data will be a key part of local communities helping to develop appropriate policy responses to the challenges outlined in this paper. The Partnership Agreement on Closing the Gap calls for the greater sharing of, and access to, data and information at a regional level noting that “disaggregated data and information is most useful to Aboriginal and Torres Strait Islander organizations and communities to obtain a comprehensive picture of what is happening in their communities and to support decision-making”60.
The code used to estimate the regressions (in Stata MP 16.1) and create the graphics (in R v.4.1.1) is available on request. The ‘xtpcse’ and ‘xtprobit’ commands in Stata MP 16.1 were used for the regressions. The regression estimates were graphed using the ‘ggplot’ command in R. Statistical tests for normality and autocorrelation were performed in Stata MP 16.1 using the ‘xtsktest’ and ‘xtserial’.
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We acknowledge and thank the Power and Water Corporation for providing the electricity data. We acknowledge the Australian BOM and thank them for providing temperature data and maps. A range of people provided useful advice that helped to shape this paper and they include E. Ings and J. Hulcombe. We thank the Board and staff of Tangentyere Council Aboriginal Corporation in Mparntwe (Alice Springs) and the Board and staff of Julalikari Council Aboriginal Corporation in Tennant Creek. Our research methodology was informed by the principles underpinning ethical Australian Indigenous research outlined in the AIATSIS Code of Ethics for Aboriginal and Torres Strait Islander Research (AIATSIS 2020): Indigenous self-determination, Indigenous leadership, impact and value, sustainability and accountability. We acknowledge and thank colleagues at the Australian National University. T.L., B.R. and L.V.W. thank their colleagues from the ANU Grand Challenge Zero-Carbon Energy for the Asia-Pacific and the Institute for Climate, Energy and Disaster Solutions.
The authors declare no competing interests.
Peer review information Nature Energy thanks Kimberley O’Sullivan, Sangeetha Chandrashekeran and Stefan Bouzarovski for their contribution to the peer review of this work
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