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Direct Rebound Effect for Electricity Consumption of Urban Residents in China Based on the Spatial Spillover Effect

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  • Ying Han

    (School of Business Administration, Northeastern University, Shenyang 110169, China)

  • Jianhua Shi

    (School of Business Administration, Northeastern University, Shenyang 110169, China)

  • Yuanfan Yang

    (School of Business Administration, Northeastern University, Shenyang 110169, China)

  • Yaxin Wang

    (School of Business Administration, Northeastern University, Shenyang 110169, China)

Abstract

Based on methods of price decomposition and spatial econometrics, this paper improves the model for calculating the direct energy rebound effect employing the panel data of China’s urban residents’ electricity consumption for an empirical analysis. Results show that the global spatial correlation of urban residents’ electricity consumption has a significant positive value. The direct rebound effect and its spillover effects are 37% and 13%, respectively. Due to the spatial spillover effects, the realization of energy-saving targets in the local region depends on the implementation effect of energy efficiency policies in the surrounding areas. However, the spatial spillover effect is low, and the direct rebound effect induced by the local region is still the dominant factor affecting the implementation of energy efficiency. The direct rebound effect for urban residents’ electricity consumption eliminating the spatial spillover effect does not show a significant downward trend. The main reason is that the rapid urbanization process at the current stage has caused a rigid residents’ electricity demand and large-scale marginal consumer groups, which offsets the inhibition effect of income growth on the direct rebound effect.

Suggested Citation

  • Ying Han & Jianhua Shi & Yuanfan Yang & Yaxin Wang, 2019. "Direct Rebound Effect for Electricity Consumption of Urban Residents in China Based on the Spatial Spillover Effect," Energies, MDPI, vol. 12(11), pages 1-16, May.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:11:p:2069-:d:235695
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    5. Rishan Adha & Cheng-Yih Hong, 2021. "How Large the Direct Rebound Effect for Residential Electricity Consumption When the Artificial Neural Network Takes on the Role? A Taiwan Case Study of Household Electricity Consumption," International Journal of Energy Economics and Policy, Econjournals, vol. 11(3), pages 354-364.
    6. Halvorsen, Bente & Larsen, Bodil Merethe, 2021. "Identifying drivers for the direct rebound when energy efficiency is unknown. The importance of substitution and scale effects," Energy, Elsevier, vol. 222(C).

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