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ORIGINAL RESEARCH article

Front. Energy Res.
Sec. Energy Efficiency
Volume 12 - 2024 | doi: 10.3389/fenrg.2024.1375361

Energy Efficiency in Chinese Cities in the Era of Digital Economy--Empirical Study and Improvement Mechanisms Provisionally Accepted

 Wenshuai Wu1* Jiangtian Wang1 Dongfeng Zhang1
  • 1Institute of Statistics and Applied Mathematics, Anhui University of Finance and Economics, China

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There is an urgent need to improve energy efficiency in the face of the twin challenges of a fragile energy supply and "dual-carbon" targets. New production methods driven by digital technologies have attracted much attention because of their positive impacts on green and low carbon development, raising questions about whether the digital economy can improve energy efficiency through factor reorganization and information flow upgrading. In this study, 285 cities in China from 2010 to 2019 are studied, and the non expected output SBM model combined with the common frontier Malmquist index is used to measure energy efficiency, and the relationship between the digital economy and energy efficiency is explored. The findings show that the development of the digital economy can effectively improve energy efficiency, and this conclusion holds even when endogeneity is controlled using the two stage least squares (2SLS) method of selecting instrumental variables. There is heterogeneity in the digital economy on energy efficiency, mainly in terms of resource endowment, city size and regional differences. In addition, the digital economy has spatial relevance on energy efficiency, affecting the energy efficiency of neighboring regions through spatial spillover effects. Overall, this study provides a useful reference for digital economy empowerment to improve energy technology efficiency, highlighting the potential role of digital economy in promoting sustainable energy development.

Keywords: digital economy, energy efficiency, Digital empowerment, Spatial spillovers, Malmquist index

Received: 23 Jan 2024; Accepted: 15 Apr 2024.

Copyright: © 2024 Wu, Wang and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Mr. Wenshuai Wu, Institute of Statistics and Applied Mathematics, Anhui University of Finance and Economics, Bengbu, Anhui Province, China