Disentangling the drivers of green total factor productivity growth: how environmental protection tax acts through pollution reduction, factor allocation, and digital intelligence

Abstract

Within the context of high-quality development imperatives, understanding mechanisms through which environmental regulation elevates corporate green total factor productivity (GTFP) represents a critical research priority. Adopting an endogenous policy shock lens, this study examines Chinese A-share listed firms spanning 2013–2023. Leveraging the 2018 implementation of China’s Environmental Protection Tax Law as a quasi-natural experiment, we employ a difference-in-differences (DID) specification to systematically assess how environmental taxation influences corporate GTFP, while investigating the moderating roles of artificial intelligence (AI) adoption and digital transformation (DIG) processes. The study reveals three main results. First, the environmental protection tax substantially increases GTFP among heavily polluting firms. This improvement mainly comes from better pollution reduction performance and is driven by green technological advancement rather than gains in operational efficiency. Second, the positive impact of the policy is stronger in industries with intense competition and in firms that receive limited attention from financial analysts. Third, the adoption of AI and digital transformation markedly reinforces the beneficial effects of the tax. These results confirm that the tax boosts GTFP primarily by encouraging technological progress through end-of-pipe pollution control measures. The article offers three fresh contributions to the literature. On the theoretical front, it uses a dual decomposition approach, separating input–output changes from shifts in technology and efficiency, to clearly distinguish between pollution-abatement effects and factor-reallocation effects. This uncovers how the environmental tax promotes GTFP via technology upgrades rooted in end-of-pipe solutions, offering a novel analytical framework for examining the Porter hypothesis at the firm level. Next, the study introduces AI and digital transformation as key moderating factors in the relationship between environmental regulation and GTFP. It demonstrates that digital technologies act as powerful amplifiers of policy effectiveness, opening up promising new avenues for research at the intersection of environmental rules and digital innovation. Finally, the analysis examines how the policy’s impact varies depending on the degree of market competition and the level of external information oversight. These insights provide useful guidance for designing more tailored environmental policies. From a practical perspective, the findings can inform the setting of differentiated tax rates, the development of supportive policies that leverage digital tools for green transformation, and the implementation of targeted measures suited to specific industries and firms with different information environments. Overall, the analytical framework and empirical evidence developed here offer a replicable approach and research template for future studies on green innovation, policy coordination, and digital empowerment, thereby creating potential multiplier effects in the academic community.