Unraveling the impact of digital transformation on green innovation through microdata and machine learning.

How to use digitalization to support the green transformation of organizations has drawn much attention based on the rapid development of digitalization. However, digital transformation (DT) may be hindered by the "IT productivity paradox." Exploring the influence of DT on green innovation, we analyze panel data encompassing A-share listed companies in Shanghai and Shenzhen spanning the period from 2010 to 2018. It tests the DT's non-linear impact, employing a random-forest and mediation effect models. The results reveal that (i) DT can promote green innovation; (ii) regarding heterogeneity, the promotion effect is mainly manifested in enterprises in non-state-owned and highly competitive industries; (iii) based on mechanism testing, DT relies on two routes to encourage green innovation: improving environmental information disclosure and reducing environmental uncertainty; and (iv) random-forest analysis shows that DT exhibits an inverted U-shaped non-linear effect on green innovation, including the "IT productivity paradox." This study enhances the existing discourse on DT and green innovation by furnishing empirical substantiation for the non-linear influence exerted by DT on green innovation. Furthermore, it imparts insights into the mechanisms and contextual limitations governing this association.

How do environmental regulation and foreign investment behavior affect green productivity growth in the industrial sector? An empirical test based on Chinese provincial panel data.

This study investigates the impact of environmental regulations (ERs) and foreign direct investment (FDI) on the green total factor productivity (GTFP) of the industrial sectors in 30 provinces in China by controlling human capital, technological innovation, energy structure, degree of opening up, and ownership structure for the period of 2004-2017. This not only helps to explain the influence path of ERs and FDI on green economic growth, but also effectively measures the moderating effect of ERs on technology spillover from FDI. The purpose of this paper is to explore the relationship between ERs, FDI and industrial GTFP from the perspective of regional heterogeneity, focusing on studying how ERs regulate the impact of FDI on GTFP. By constructing an influence mechanism of ERs and FDI on industrial GTFP, this study employs the feasible generalized least squares (FGLS) model and dynamic generalized method of moments (GMM) model to analyze the effects of ERs, FDI and their cross-terms on GTFP. The empirical results show that (1) the relationship between ERs and GTFP is not linear, but "U"-shaped and China is still in the left half of the "U"-shaped curve; (2) FDI flowing into China has a "pollution heaven" effect on the GTFP in the eastern and central regions while a "pollution halo" effect on the GTFP in the western region; (3) the strengthening of ERs weakens the negative effect of FDI on GTFP and plays a role in "screening" foreign investment; and (4) the spatial heterogeneity could affect the synergistic effect between ERs and FDI. Therefore, it is necessary for China to consider a series of environmental policies to "screen" inward FDI to ensure its move to a green economy benefits its own sustainable development by contributing to the increase in GTFP.