Digital transformation and corporate green total factor productivity: Based on double/debiased machine learning robustness estimation

Abstract

With the rapid development of the digital economy, China's digital technology and industry continue to integrate deeply into all areas of society. Digital transformation(DT) has become an important engine and key pedestal for economic and social transformations and upgrades. This paper takes 3525 listed companies from 2013 to 2021 as a sample to measure the green total factor productivity(GTFP) of listed enterprises based on the super-efficiency slacks-based measure with the Global Malmquist-Luenberger Index (SBM-GML) and the super-efficiency epsilon-based measure with the Global Malmquist-Luenberger Index (EBM-GML). It draws on the two existing DT indicators to explore the impact and mechanism of enterprise DT on GTFP. The results show that DT can significantly enhance GTFP, and this conclusion still holds after double/debiased machine learning and other robustness tests; the heterogeneity analysis shows that DT of high-tech enterprises, manufacturing industries and low-financialisation enterprises has a more obvious effect on the enhancement of GTFP; and the four-stage mediated impact mechanism suggests that the effect of DT on GTFP can be achieved by improving internal control ability and technological innovation ability. This paper will provide relevant policy insights on how to better drive enterprise DT and green low-carbon development under the “dual-carbon” goal.