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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.

As the world’s largest emerging market country, not only has China faced the contradiction between its huge population size and per capita energy scarcity for a long time, but the rigid constraints brought by energy poverty have also plagued the lives and production of Chinese residents. Based on panel data from 30 provinces (except Tibet) in mainland China from 2009 to 2021, this study employs double machine learning and spatial difference-in-difference for causal inference to explore the impact of a medium- to long-term regional innovation pilot policy in China—the new policy for innovative transformation in regional industrial chains—on energy poverty alleviation. This study also introduces China’s conversion of new and old kinetic energy into this quasi-natural experiment. This study presents the following findings: (1) The new policy for innovative transformation in regional industrial chains and the concept of the conversion of new and old kinetic energy can both significantly promote energy poverty alleviation. (2) The mechanism pathway of “the new policy for innovative transformation in regional industrial chains → the conversion of new and old kinetic energy → the energy poverty alleviation in heating/household electricity/transportation segments” has proved to be an effective practice in China. (3) Based on the spatial double difference model, the spatial direct effect of the new regional industrial chain innovation and change policy on energy poverty alleviation is significantly positive, while the spatial direct effect and spatial spillover effect of the new and old kinetic energy transformation on energy poverty alleviation are both significantly positive. (4) Based on the counterfactual framework analysis, in addition to the causal mediating mechanism of the demand-side conversion of new and old kinetic energy being impeded, both the supply-side and the structural-side conversion of new and old kinetic energy are able to play a significant positive causal mediating role in both the treatment and control groups.

This study used a double machine learning model (based on the random forest algorithm) and spatial Durbin DIDs model to conduct quasi-natural experiments. The results are as follows: (1) innovation and reform policy regarding regional industrial chains as well as their resilience can significantly and positively address the development of China’s impossible triangle coupling of energy; (2) implementing the innovation and reform policy for regional industrial chains in other regions can have a significant positive spatial transmission effect on the impossible triangle coupling coordinated development of energy in the region; (3) regional industrial chain resilience can produce a significant positive mediating effect between the innovation and reform policy of regional industrial chains and the safety, reliability, and economic feasibility of green and clean energy systems; (4) under the counterfactual framework, the mechanism path “innovation and reform policy of the regional industry chain→regional industry chain resilience→coordination degree of impossible triangle coupling of energy” has significantly positive direct and indirect effects in both the treatment group and the control group. However, “innovation and reform policy of the regional industrial chain→regional industrial chain resilience→the energy sector’s impossible triangle coupling coordination degree” and “innovation and reform policy of the regional industrial chain→leading power of the regional industrial chain→the energy sector’s impossible triangle coupling coordination degree” have significantly positive direct and indirect effects in the treatment group, but only the direct effect is significant in the control group.