Abstract
Rising temperatures are likely to boost residential demand for electricity in warm locations for reasons including increased use of air conditioners, fans, and refrigeration. Yet precise effects may vary by geographical area and with socio-economic conditions. Knowledge on these effects in developing countries is limited due to data availability and reliability issues. Using a high-quality provincial-level monthly dataset for China and fixed-effect panel methods, we find a U-shaped and asymmetrical relationship between ambient temperature and monthly residential electricity use. An additional day with a maximum temperature exceeding 34 °C is on average associated with a 1.6% increase in that month’s per capita residential electricity use relative to if that day’s maximum temperature had been in the 22–26 °C range. The effect of an additional cold day is smaller. There are differences in effects for the south versus the north of China and for urban versus rural areas. Under a high global carbon dioxide emission trajectory, we estimate that expected temperature increases would lead to more than a 25% increase in residential electricity use in July in some provinces by the end of the century, holding other factors constant.
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Data availability
The supporting data are not publicly available due to its proprietary nature. Further information about the data is available upon a reasonable request.
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Notes
China implemented a step tariff for electricity since 2012, with three pricing steps established. The first step, covering around 80% of residents, has modest differences between provinces. The tariff schedule has varied slightly over time.
We set the variable to 1 if Spring Festival is in a given month and 0 otherwise. If the festival overlaps months, we set the variable to 1 for the month covering the majority of the vacation.
The north includes Beijing, Tianjin, Hebei, Shanxi, Inner Mongolia, Liaoning, Jilin, Heilongjiang, Shandong, Henan, Shaanxi, Gansu, Qinghai, Ningxia, and Xinjiang. The south includes Shanghai, Jiangsu, Zhejiang, Anhui, Fujian, Jiangxi, Hubei, Hunan, Guangdong, Guangxi, Hainan, Chongqing, Sichuan, Guizhou, and Yunnan.
The household survey organized by NBS was reformed in 2012 (Gustafsson et al. 2014), with the measurement of residential air conditioners subsequently changing. To avoid this issue, our air conditioner heterogeneity analysis is for 2015–2017. Data are from the NBS (2019b), and the sample size reduces to 1080 in this specification. Air conditioner numbers are as measured in November of each year.
The LSDV method can be used to identify whether a pooled OLS regression or a fixed-effect model is more appropriate (Greene 2020). We used the Sargan-Hansen test of overidentifying restrictions using the artificial regression approach described by Arellano (1993) and Wooldridge (2002) to decide whether a fixed-effect model or random-effect model is preferred.
The average number of days for which the maximum temperature exceeds 34 °C in southern provinces is around 22 per annum. This is about five times larger than the average for Northern provinces.
Annual residential electricity consumption per capita in the south was around 27% higher than the north in 2017. According to the NBS (2019b) and a calculation by the authors, per-household ownership of air conditioners in 2017 was about two-thirds higher in the south than the north.
The Economist Intelligence Unit (EIU) estimates that disposable income per capita in China is likely to be around 70,000 yuan in 2050 (in year-2008 terms). See https://www.eiu.com.
The NorESM2-LM model provides a global gridded daily maximum temperature dataset with 1.9 \(^\circ \times\) 2.5 \(^\circ\) resolution. We resampled the gridded data to around 1 km \(\times\) 1 km and then took the average of the maximum temperature for the grids in each province.
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This work was supported by funding from the National Science Fund for Distinguished Young Scholars (No. 71925008).
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All the authors contributed to the study conception and design. Meixuan Teng performed the data analysis and wrote the first draft of the manuscript. Hua Liao and Paul J. Burke contributed to the writing. All the authors discussed the results and approved the final manuscript. The funding was obtained by Hua Liao.
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Teng, M., Liao, H., Burke, P.J. et al. Adaptive responses: the effects of temperature levels on residential electricity use in China. Climatic Change 172, 32 (2022). https://doi.org/10.1007/s10584-022-03374-3
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DOI: https://doi.org/10.1007/s10584-022-03374-3