Digital Economy Development, Industrial Structure Upgrading and Green Total Factor Productivity: Empirical Evidence from China's Cities.

Yang Liu,Huihui Li,Kaiyang Zhong,Yanlin Yang

doi:10.3390/ijerph19042414

Abstract

The digital economy is an important engine to promote sustainable economic growth. Exploring the mechanism by which the digital economy promotes economic development, industrial upgrading and environmental improvement is an issue worth studying. This paper takes China as an example for study and uses the data of 286 cities from 2011 to 2019. In the empirical analysis, the direction distance function (DDF) and the Global Malmquist-Luenberger (GML) productivity index methods are used to measure the green total factor productivity (GTFP), while Tobit, quantile regression, impulse response function and intermediary effect models are used to study the relationship among digital economy development, industrial structure upgrading and GTFP. The results show that: (1) The digital economy can significantly improve China’s GTFP; however, there are clear regional differences. (2) The higher the GTFP, the greater the promotion effect of the digital economy on the city’s GTFP. (3) From a dynamic long-term perspective, the digital economy has indeed positively promoted China’s GTFP. (4) The upgrading of industrial structures is an intermediary transmission mechanism for the digital economy to promote GTFP. This paper provides a good reference for driving green economic growth and promoting the environment.

Highlights

Initiative” released at the G20 Summit in 2016, the digital economy refers to a series of economic activities with the use of digital knowledge and information as key production factors, modern information network as an important carrier and the effective use of information and communication technology as an important driving force for efficiency improvement and economic structure optimization) has extended the industrial chain, spawned a series of new industries and upgraded traditional industries
The direction distance function (DDF) and Global Malmquist-Luenberger (GML) productivity index methods are used to measure green total factor productivity (GTFP). (2) Tobit, quantile regression, impulse response function and intermediary effect models are used to study the relationship among digital economy development, industrial structure upgrading and GTFP
This study uses quantile regression to study the influence of the digital economy on the overall conditional distribution of GTFP, which can better explore the differential influence of the digital economy on different levels of GTFP

Summary

Introduction

Promoting green development is the most effective way to prevent the outbreak and spread of unknown human infectious diseases from the source [1]. The. COVID-19 pandemic further stimulated the public’s demand for changing the extensive development mode and pushing green development. COVID-19 pandemic further stimulated the public’s demand for changing the extensive development mode and pushing green development In this way, as a booster of the high-quality development of the economy [2], the digital economy Initiative” released at the G20 Summit in 2016, the digital economy refers to a series of economic activities with the use of digital knowledge and information as key production factors, modern information network as an important carrier and the effective use of information and communication technology as an important driving force for efficiency improvement and economic structure optimization) has extended the industrial chain, spawned a series of new industries and upgraded (industrial upgrading is defined as the process that nations, firms and workers, as economic actors, move from low-value to relatively high-value activities in global production networks [3]) traditional industries.

Methods

Results

Conclusion