Research on Sustainable Mechanism of Digital Economy Enabling Green Development

Abstract

“Digitalization” and “greening” are key directions for China’s future economic development and important driving forces and goals for achieving high-quality development in the new era. The digital economy, with its unique advantages, helps foster emerging industries, create new business models, and promote the ecologicalization of the economy, thus creating new growth points for China’s sustainable development. The integrated development of “economy + ecology” has gradually attracted high attention from the government and academia. Based on this, this paper explores whether the digital economy can enhance the efficiency of urban green development and its mechanism of action.

This study, based on the previous understanding of the linear causal relationship between the digital economy and green development, delves into the non-linear relationship between them. Using panel data from 257 prefecture-level and above cities in China from 2011 to 2019 and the “proximity” data of over 5,000 key enterprises across the country generated by Word2Vec and TF-IDF machine learning models, including cognitive, organizational, social, institutional, and geographical aspects, a two-way fixed effect model is constructed. Corresponding robustness tests and heterogeneity analyses are conducted to explore the impact of the digital economy’s development level on urban green development efficiency from the perspective of proximity, as well as the threshold effects of the digital economy itself, social and economic development, and the proximity atmosphere on their relationship.

The research findings show that: First, the digital economy has a significant positive effect on urban green development efficiency. This conclusion remains reliable after controlling for macro factors, introducing instrumental variables, and lagging the control variables by one period for robustness tests. Second, green technological innovation plays an important mediating role in the process of the digital economy enhancing green development efficiency. Third, when the development level of the digital economy is used as the threshold variable for threshold model regression, it passes the double threshold test. Within each threshold interval, the regression coefficient remains significantly positive but decreases, indicating that the impact effect on urban green development efficiency gradually weakens after crossing the threshold value. Fourth, when the proximity level of enterprises is used as the threshold variable for threshold model regression, it passes the single threshold test. That is, when the geographical, social, institutional, and cognitive proximity levels reach certain thresholds, the development level of the digital economy better promotes green development efficiency. This study concludes that the sustainability of the digital economy promoting green development stems from the overall critical mass of a region or city, which not only includes the digital economy and technological development itself but also has important connection with economic and social development, as well as the superposition and interweaving of innovation elements.

The marginal contribution of this paper can be summarized in three points: 1) Using Word2Vec and TF-IDF machine learning models to generate five major “proximity” data of cognition, organization, society, institution, and geography of more than 5,000 key enterprises nationwide, and subsequently constructing an indicator system of the proximity development levels for major cities in China, thus reconstructing and upgrading the traditional empirical econometric model to examine the causal relationship between the digital economy and urban green development efficiency; 2) Analyzing the possible non-linear relationship between the development level of digital economy and urban green development efficiency, and assessing the sustainability of the existing relationship; 3) Using the digital economy, economic development level, and various enterprise proximity dimensions as threshold variables to identify the key factors leading to the non-linear relationship between them, and providing empirical reference for proposing targeted sustainable green development policies.