Made in China 2025: Artificial intelligence intervention and urban green economy development.

Journal: Journal of environmental management
Published Date:

Abstract

Against the backdrop of the profound adjustment of the global industrial structure and the rise of the Fourth Industrial Revolution, the contradiction between rapid economic growth and ecological environmental protection has become increasingly prominent. Promoting green development has become a key path to achieving sustainable urban development. In 2015, China launched the "Made in China (2025)″ policy, aiming to promote industrial transformation and upgrading through intelligent manufacturing and green production. This study, based on panel data from 268 cities from 2007 to 2022, uses the difference-in-differences (DID) method to analyze the impact of policy implementation on urban green economy (GE) and introduces artificial intelligence (AI) related variables to explore the moderating effect of AI intervention. The findings indicate that the policy significantly improves the green economic efficiency of pilot cities, with their GE increasing by 6 %. Specifically, the policy effect is more pronounced in non-western regions, cities with population inflows, cities with a stronger economic foundation, and cities with a better industrial structure. Innovative cities have particularly outstanding green transformation effects due to the synergy of policy and innovation-driven forces. Further research indicates that the intervention of AI has a positive moderating effect on policy outcomes, which can empower urban green sustainable development through paths such as accelerating technology transformation. This study reveals the intrinsic mechanism of the "Made in China (2025)″ policy and AI in promoting urban green development, providing theoretical basis and empirical support for formulating differentiated industrial upgrading strategies and deepening the integration of AI and the GE.

Authors

  • Malin Song
    School of Statistics & Applied Mathematics, Anhui University of Finance and Economics, Bengbu, 233030, China. Electronic address: songmartin@163.com.
  • Ming Yu
    College of Computer and Control Engineering, Northeast Forestry University, Harbin, Heilongjiang Province, China.
  • Xue-Li Chen
    Institute of Economics and Rural Development, Lithuanian Centre for Social Sciences, A. Vivulskio Str. 4a-13, Vilnius, 03220, Lithuania. Electronic address: xueli.chen@ekvi.lt.
  • Oana-Ramona Lobonț
    Department of Finance, Business Information Systems and Modelling, Faculty of Economics and Business Administration, West University of Timisoara, Timisoara, Romania. Electronic address: oana.lobont@e-uvt.ro.
  • Juntao Du
    School of Statistics & Applied Mathematics, Anhui University of Finance and Economics, Bengbu, 233030, China. Electronic address: dujuntaohope@163.com.

Keywords

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