ORIGINAL RESEARCH article
Front. Environ. Sci.
Sec. Environmental Economics and Management
Volume 13 - 2025 | doi: 10.3389/fenvs.2025.1520715
This article is part of the Research TopicClimate Risk and Green and Low-Carbon Transformation: Economic Impact and Policy ResponseView all 24 articles
Can the establishment of the National Big Data Comprehensive Experimental Zone promote green low-carbon development? Evidence from China
Provisionally accepted
- 1PingDingShan Vocational and Technical College, Pingdingshan, Henan Province, China
- 2Pingdingshan University, Pingdingshan, China
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The rapid development of digital economy provides necessary technical support for green and low-carbon transformation, and it is of great significance to explore the impact of digital economy on green and low-carbon development for the sustainable development of economy and society. Balanced panel data of 30 provinces and cities from 2005 to 2021 are used to construct a dual machine learning model for empirical analysis. It is found that digital economic development can reduce regional carbon emission intensity and promote green and low-carbon development. The mechanism test shows that digital economic development significantly promotes regional green low-carbon development through the knowledge spillover dimension. The heterogeneity analysis shows that the level of green low-carbon development in the central region is significant at the 1% level, while the eastern and western regions are not significant. Government with less fiscal pressure has a more significant effect on green low-carbon development.
Keywords: digital economy, Green low-carbon, dual machine learning, Knowledge spillover, big data
Received: 31 Oct 2024; Accepted: 09 May 2025.
Copyright: © 2025 Yao, Wang and Cao. 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: Zhe Wang, Pingdingshan University, Pingdingshan, China
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