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Convergence characteristics and distribution patterns of residential electricity consumption in China: An urban-rural gap perspective

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  • Wang, Na
  • Fu, Xiaodong
  • Wang, Shaobin
  • Yang, Hao
  • Li, Zhen

Abstract

This paper mainly investigates the distribution patterns and convergence characteristics of per capita residential electricity consumption (PCREC) in urban and rural areas in China at the provincial level from 2000 to 2017. First, the results show significant positive spatial autocorrelation of PCREC in rural areas. In contrast, it presents insignificant spatial autocorrelation in urban areas. In addition, the distribution pattern of rural PCREC has changed dramatically, while the distribution pattern of urban areas changes more slowly than in rural areas. Second, the sigma convergence trends show that the discrete distribution level of PCREC in rural areas is higher than that in urban areas, and its sigma and absolute beta convergence rates are faster than that of urban areas. Further, the conditional variables for beta convergence indicate that population size, population density, and per capita GDP should be considered for the convergence mechanism of PCREC. In sum, our results reveal the reduction of the inequality of PCREC in China during the study period, especially in rural areas. In addition, convergence modeling indicates that PCREC converges to different equilibrium levels in urban and rural areas, which indicates the need for an urban-rural-tailored set of energy policies in China.

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  • Wang, Na & Fu, Xiaodong & Wang, Shaobin & Yang, Hao & Li, Zhen, 2022. "Convergence characteristics and distribution patterns of residential electricity consumption in China: An urban-rural gap perspective," Energy, Elsevier, vol. 254(PB).
    • Handle: RePEc:eee:energy:v:254:y:2022:i:pb:s0360544222011951
    DOI: 10.1016/j.energy.2022.124292
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    4. Du, Mengbing & Ruan, Jianhui & Zhang, Li & Niu, Muchuan & Zhang, Zhe & Xia, Lang & Qian, Shuangyue & Chen, Chuchu, 2024. "China's local-level monthly residential electricity power consumption monitoring," Applied Energy, Elsevier, vol. 359(C).

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