Low-carbon urban transformation is considered the path to green economic growth in dual-carbon contexts. The low-carbon city pilot policy (LCCP) in China has vast potential to enhance the integrated development of the economy, energy, and environment (3Es). Taking 240 cities in China from 2005 to 2019 as research samples, this paper investigated the impact of LCCP on the integrated development level of the 3Es using the progressive difference-in-differences model and analyzed the mechanisms of influence. In addition, the implementation effects on different cities were analyzed using the triple difference model. The findings show that implementing the LCCP policy significantly improves the 3Es integrated development level in the sample cities, and a variety of robustness tests were used to validate this conclusion. The influence mechanism analysis shows that the primary avenues for pilot programs to contribute and successfully advance the enhancement of the low-carbon city 3Es integrated development level are technological innovation and financial development. Moreover, the heterogeneity analysis of resource endowment and geographical location shows that LCCP is beneficial to the 3Es integrated development in non-resource-based cities and eastern cities. Consequently, policy recommendations include the continuation of low-carbon pilot city expansion, joint promotion of regional 3Es integration, improving the level of technological innovation and financial development, and tailoring of policy to local circumstances. This study provides theoretical support for the evaluation of China’s LCCP and, to a certain extent, proves that the measures taken by China in the process of exploring green economic growth and realizing the dual-carbon goal are correct.

Digital–intelligent technologies and their extensive application across real-world scenarios are profoundly reshaping traditional industries while fostering emerging and future industries, becoming a key engine for industrial intelligence and the development of new quality productive forces. Advancing new quality productive forces in coal enterprises is a core pathway to overcome bottlenecks such as high safety risks and stringent environmental constraints in the traditional coal sector and to achieve high-quality industrial development; digital–intelligent transformation provides critical support for this process. Using panel data from 30 Chinese A-share listed coal companies from 2012 to 2024, this study empirically examines the impact of digital–intelligent transformation on the development of new quality productive forces in coal enterprises, and explores the mediating role of resource allocation efficiency and the moderating role of local governments’ economic performance assessment pressure. The results show that digital–intelligent transformation significantly promotes the development of new quality productive forces in coal enterprises. Resource allocation efficiency plays a mediating role in this relationship, accounting for 14.89% of the total effect. Moreover, local economic performance assessment pressure negatively moderates the effect of digital–intelligent transformation on the development of new quality productive forces, and this negative moderating effect persists along the mediating pathway via resource allocation efficiency. This study aims to uncover the internal mechanism through which digital–intelligent transformation promotes new quality productive forces in coal enterprises, providing a new theoretical basis and research perspective for coal enterprises to cultivate new quality productive forces.

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.