The long-standing, unrestrained utilization of energy resources by China’s manufacturing sector has created irreversible obstacles to regional sustainable development. Consequently, the Chinese government has implemented a water resource tax policy in certain regions, with the aim of compelling manufacturing enterprises to adopt green and energy-saving innovations. This study used panel data from Chinese manufacturing companies listed on the A-share market from 2009 to 2020 and employed a double machine learning model to explore whether the water resource fee-to-tax reform can compel enterprises to enhance their tripartite green energy-saving innovation drive. These innovations consist of vision-driven and mission-driven green energy-saving technological innovations and green management energy-saving innovations. Following a quasi-natural experiment, our findings revealed the following: (1) The water resource fee-to-tax policy promoted the internal coupling coordination of the triple-driven system. (2) The policy compelled progress in mission-driven green energy-saving technological innovations and green energy-saving management innovations but hindered vision-driven green energy-saving technological innovations. (3) Within the internal systems of manufacturing enterprises, green energy-saving management innovations play a positive mediating role between the water resource fee-to-tax policy and the mission-driven green energy-saving technology innovation subsystem, but they lack a similar positive mediating mechanism for the vision-driven green energy-saving technology innovation subsystem. (4) The counterfactual framework verified that the mechanistic pathway “water resource fee-to-tax → green energy-saving management innovation → mission-driven/vision-driven green energy-saving technological innovation” could be further extended to other manufacturing enterprises not currently under policy compulsion. (5) In the interaction system between manufacturing enterprises and external markets, the development of marketization and financial technology positively regulated the promoting effect of the water resource fee-to-tax policy on mission-driven green energy-saving technological innovations and green energy-saving management innovations, but it did not have a similar effect on vision-driven green energy-saving technological innovations.

The creation of food safety demonstration cities (CFSDC) is among the key measures the Chinese government implemented to address the intensifying food safety problem. While effectively managing food safety issues, whether this measure will have an impact on agricultural development in China remains unknown. In this paper, based on panel data from 277 cities in China from 2011 to 2019, the impact of the construction of food safety demonstration cities on agricultural development is empirically examined using the difference-in-differences (DID) model. The results show that the CFSDC significantly improves the level of agricultural development, which still holds after various robustness tests. Analysis of the underlying mechanism indicates that the CFSDC promotes urban agricultural development by accelerating both technological innovation and industrial restructuring. The results of heterogeneity analysis show that the policy effects of the CFSDC exhibit significant heterogeneous characteristics depending on city size, city location, and city administrative level. The findings contribute to the understanding of the relationship between policy pilot projects and agricultural development and provide empirical support for promoting the governance of food safety issues and agricultural development.

Against the backdrop of artificial intelligence accelerating its penetration into the real economy as a general-purpose technology (GPT) and green, low-carbon transformation becoming a core objective of high-quality development, how to leverage policy guidance to empower corporate green transformation through AI has emerged as a critical issue of shared concern among academia and policymakers. Drawing on data from Chinese A-share listed corporates from 2010 to 2023, this paper employs a Difference-in-Differences (DID) model to systematically evaluate the impact of China's National New-Generation Artificial Intelligence Innovation and Development Pilot Zone Policy (NAIIDPZ) on corporate green transformation. Findings reveal that the pilot policy significantly promotes corporate green transformation, with conclusions validated through parallel trend tests and placebo tests. Mechanism analysis indicates that the pilot policy promotes green transformation through three pathways: enhancing corporate access to credit, improving the efficiency of green innovation R&D and commercialization, and reducing agency costs. Applying the TOE framework, the study further reveals that policy effects are more pronounced under conditions such as executives with digital backgrounds, high proportions of technical personnel, strong ESG performance, high-quality internal controls, stricter environmental regulations, and more competitive industries. Heterogeneity analysis reveals stronger policy effects in non-eastern regions, second-tier cities, non-polluting industries, technology-intensive corporates, and corporates in the decline phase of their life cycle. Findings demonstrate that NAIIDPZ can effectively drive corporate green transformation through multidimensional synergies, providing theoretical support and practical evidence for optimizing policy design and enhancing corporate green development capabilities.