Building resilient manufacturing industrial chains while advancing low-carbon transformation is an important issue for sustainable development. This study examines the relationships among configurations of command-and-control, market-incentive, voluntary, and implicit environmental regulation, green technological innovation, and manufacturing industrial chain resilience across 31 provincial-level administrative regions in China. We combine fuzzy-set qualitative comparative analysis with a complex mediation model. The former identifies environmental regulation configurations associated with high green technological innovation, while the latter examines their relationships with manufacturing industrial chain resilience and the mediating role of green technological innovation. Resilience is measured from the dimensions of entity resilience and structural resilience. The results show that no single type of environmental regulation is necessary for high green technological innovation. Three configurations are identified: the “government–enterprise-driven,” “enterprise–society synergistic,” and “policy–society co-promotion” configurations. Among them, the first two are significantly and positively associated with manufacturing industrial chain resilience, with green technological innovation partially mediating both relationships. When voluntary environmental regulation is a core present condition in both configurations, the mediating role of green technological innovation is more pronounced in the government–enterprise-driven configuration. The findings indicate that coordinating multiple forms of environmental regulation is important for promoting green technological innovation and strengthening manufacturing industrial chain resilience.

With the explosive growth in demand for artificial intelligence and computing power, the energy consumption of data centers has sharply increased, making their green and low-carbon operation an urgent need. Shared energy storage (SES), as a flexible regulation resource, can effectively facilitate renewable energy consumption and reduce system costs. However, existing research mostly regards data center loads as rigid loads, ignoring the elastic scheduling potential of latency-insensitive computing tasks, and lacking a game decision-making model from the perspective of SES operators to provide strategies for data center SES transactions. Therefore, this study constructs a SES trading optimization model that takes into account the flexibility of computing power. The upper-level targets profit maximization for the SES operator by optimizing charge and discharge strategies and service pricing, while the lower level minimizes the total energy cost of each computing center by jointly optimizing computing task scheduling and energy storage utilization plans. Finally, a bi-level Stackelberg game-based optimization model is constructed to solve the SES dispatch and, achieve benefit coordination between SES and computing center. The simulation results indicate that using SES without implementing load shifting cannot optimize the total cost of the computing power center, offering only storage revenue. Only by integrating SES with load shifting can a significant cost reduction be realized. The model presented in this article resulted in an SES revenue of 2605.34 yuan and a 3.14% reduction in computing center costs. This study provides a theoretical basis and decision-making support for shared energy storage participation in the computing power market and contributes to accelerating the coordinated development of computing power and electricity systems.