Economic Implications of Digital Transformation on Pollution Reduction: A BART Analysis of Firm-Level Data
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Abstract
Prior research has primarily examined the average economic effects of digital transformation, with limited attention to its heterogeneous treatment effects. This study applies the Bayesian Additive Regression Tree (BART) approach to analyze how digital transformation influences firms’ pollution emissions and its broader economic implications. Using a dataset of 32,340 firm-year observations from Chinese A-share listed companies (2007–2022), we find that the impact of digital transformation on pollution emissions varies significantly across firms. The key economic mechanisms driving this effect include increased green innovation, more efficient factor allocation, and enhanced firm positioning within social networks. These findings offer new insights into the role of digital transformation in corporate environmental strategies, firm productivity, and economic sustainability, highlighting its differential effects across firms.
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