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Urbanization impact on residential energy consumption in China: the roles of income, urbanization level, and urban density

  • Qiaoran Wang
  • Xianming Yang
Research Article
  • 17 Downloads

Abstract

This paper investigated the impact of urbanization on residential energy consumption (REC) in China by taking cognizance of the levels of income, urbanization and urban density. Threshold analyses were employed to investigate the nonlinear relationships based on the STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) framework using a balanced panel dataset of 29 provinces of China over the period of 1998–2014. The common correlated effects mean group estimator (CCEMG) was used to address time-series cross-section (TSCS) issues. The results confirmed the existence of the nonlinear relationship between urbanization and REC in China. The impact of urbanization on REC varied at different economic development levels and urbanization levels. Specifically, urbanization decreased REC at the stage that per capita disposable income of urban residents (PDI) less than 2615 USD, while it increased REC at the stage that PDI higher than 2615 USD. Similarly, urbanization decreased REC at the stage that urbanization rate lower than 55.31% and increased REC after urbanization rate exceeded 55.31%. This study did not find evidence to support the urban environmental transition theory, indicating there was still no region in China had stepped into the win-win stage of urbanization and energy consumption. Furthermore, the nonlinear impact of urban density on REC was estimated and the results indicated that urban density exerted a positive effect on REC when urban density was lower than 808 inhabitants per square kilometer, while it was no longer relevant to REC after that threshold point. Based on these results, the corresponding countermeasures and suggestions to achieve low-carbon urbanization were put forward.

Keywords

Residential energy consumption Household income Urbanization Urban density STIRPAT Threshold regression CCEMG 

Notes

Acknowledgments

Thanks to Professor Henrik Lund from Aalborg University for his valuable advice. Comments and suggestions received from anonymous reviewers are insightful and useful for improvements to the paper.

Funding information

This study was funded by the National Natural Science Foundation of China [grant number 71362026] and the Ministry of Education of Yunnan [grant number 2017YJS098]. The authors would like to thank the China Scholarship Council for providing the fellowship [student number 201707030012].

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Development StudiesYunnan UniversityKunmingChina
  2. 2.Department of PlanningAalborg UniversityAalborgDenmark

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