Manuscript received July 3, 2024; revised July 25, 2024; accepted August 7, 2024; published August 19, 2024.
Abstract—This paper employs a multi-period double differential model to analyze how digital infrastructure impacts the green transformation of enterprises and contributes to the high-quality development of the digital economy. It utilizes panel data from China’s Shanghai and Shenzhen A-share companies spanning 2008 to 2021. The “smart city” policy serves as a proxy variable for digital infrastructure, while green total factor productivity is used to measure the green transformation of enterprises. The results show that digital infrastructure effectively drives the green transformation of enterprises, and their reliability is confirmed through robustness tests. The study identifies heterogeneous effects at both enterprise and city levels, demonstrating that digital infrastructure notably enhances the green transformation of state-owned enterprises. Furthermore, the urban outcomes in the eastern region exhibit more pronounced effects compared to those in the central and western regions. Moreover, heightened environmental regulation at enterprise locations diminishes the influence of digital infrastructure on their green transformation. Finally, enterprises and governments are urged to consider the impact of the digital economy on sustainable development when promoting digital and green transformation initiatives. This study offers new insights and empirical evidence on the relationship between digital infrastructure and the green development of enterprises, providing policy guidance for advancing China’s green and sustainable economic development.
Keywords—digital infrastructure, green transformation of enterprises, difference-in-difference method
Cite: Gao Yuqi, "Research on the Impact of Digital Infrastructure Construction on Enterprise Green Transformation-Quasi-Natural Experiment Based on the Pilot Policy of 'Smart City'," Journal of Economics, Business and Management, vol. 12, no. 3, pp. 295-301, 2024.
Copyright © 2024 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).