Jingxin Xue

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.

It is still necessary to regularly investigate the breakdown of socio-economic elements as a starting point for analyzing the effects of diverse human production activities on PM_2.5 intensity from industrial and regional viewpoints. In this paper, the emission factor model was adopted to measure PM_2.5 emissions in the Beijing–Tianjin–Hebei (BTH) region at the regional and industrial levels. The logarithmic mean Divisia index (LMDI) decomposition model was employed to analyze the factors affecting PM_2.5 emissions related to energy consumption in the BTH region at the regional and sectoral levels. Building on this foundation, a system dynamics (SD) model was established to make a prediction regarding PM_2.5 pollution in the BTH region in 2025. This study found that secondary industry was a major source of PM_2.5 emissions in the BTH region. Coal remained the main form of energy consumption in the BTH region. Secondly, the effect size of the factors affecting PM_2.5 intensity ranked in the order of energy intensity, energy structure, and industrial structure. Thirdly, in 2025, PM_2.5 emissions in the BTH region will decline appreciably, but there is still a certain gap in terms of meeting the targets of “the 14th Five-Year Plan” between the three provinces and cities. These results indicate that the BTH region should achieve the effective management of PM_2.5 pollution at the source through the following initiatives: it is necessary to carry out the continuous adjustment of energy structures to gradually increase the proportion of clean energy; we must steadily promote the decline in energy intensity reduction, and gradually strengthen scientific and technological innovation; and we must continue to promote the optimization of the industrial structure and increase the proportion of tertiary industry every year.