The impact of digital transformation on corporate carbon information disclosure: evidence from China's resource-based listed companies

Abstract

As major energy consumers and carbon emitters, resource-based enterprises play a critical role in supporting low-carbon social development. The rapid advancement of digital transformation has created new opportunities for enhancing carbon information disclosure within this sector. Drawing on empirical data from 2010 to 2023 for listed resource-based firms on the Shanghai and Shenzhen stock exchanges in China, this study employs a combination of an OLS model and a causal forest model to systematically examine the impact of digital transformation on carbon information disclosure. The findings suggest that digital transformation has a significant impact on enhancing the quality of carbon information disclosure among resource-based enterprises. A multidimensional examination of digital transformation effects indicates that artificial intelligence and big data technologies serve as key drivers for advancing carbon information disclosure in resource-based enterprises, whereas blockchain currently lacks evidence demonstrating such effective functionality. Mechanism testing reveals that this effect operates primarily through three pathways: improving information transparency, alleviating financing constraint, and upgrading the talent structure. We estimated the conditional average treatment effect for each observation using the causal forest model and conducted a heterogeneity analysis based on this estimate. Heterogeneity analysis reveals that the treatment effects of firm asset size and R&D intensity on digital transformation exhibit a roughly nonlinear U-shaped relationship, while firm age shows an inverted U-shaped relationship with treatment effects. This study provides valuable insights for policymakers and practitioners seeking to promote digital transformation and enhance the carbon disclosure practices of resource-based enterprises.