The enhancement of green total factor productivity of resource-based enterprises has a crucial impact on building an environmentally friendly society, promoting a circular economy, and guaranteeing the security of national resource supplies. This study takes the green transformation of resource-based enterprises as the main line, and then constructs the research framework. A random forest algorithm is used to discover the effects of different characteristics of green transformation on green total factor productivity. Further, using panel data-based qualitative comparative analysis and necessary condition analysis, it clarifies what kind of green transformation group path promotes green total factor productivity from the group perspective. This study shows that (1) environmental regulations, green cognition, and green investment are important factors in condition configuration and there exists a complex nonlinear relationship among them, indicating the need for further research from a configurational perspective; (2) green technological innovation, green investment, green development strategies, green governance level, green cognition, environmental subsidies, and environmental regulation are not necessary conditions for a high level of green total factor productivity; (3) there are three grouping paths for green total factor productivity improvement in resource-based enterprises; and (4) there are time and case differences in the explanatory strength of the three grouping paths. Finally, based on the results of this study, and in conjunction with China’s green transformation practice, countermeasures are proposed from both the enterprise and government perspectives.
Guo et al. (Tue,) studied this question.
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