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Faced with the dual challenges of environmental pollution and climate change, it is of great significance to study the impact of relevant environmental regulations on the synergistic effect of pollution reduction and carbon emission reduction and their influence mechanisms. Based on a theoretical analysis using the panel data of 30 provinces in China, a spatial econometric model and an intermediary effect model are used to investigate the impact of environmental regulations on the synergistic effect of pollution reduction and carbon reduction and the transmission mechanisms potentially responsible for these effects. The empirical results show the following: (1) The three kinds of environmental regulation effectively facilitate the synergistic effect of pollution reduction and carbon reduction, taking the following order when ranked according to the intensity of their effects: command-type environmental regulation (ER1) > market-based environmental regulation (ER2) > voluntary environmental regulation (ER3). (2) Environmental regulation effectively promotes the synergistic effect of pollution and carbon reduction through the three transmission mechanisms of stimulating technological innovation (TI), industrial structure upgrading (ISU), and restricting foreign direct investment (FDI), which take the following effectiveness order: TI > FDI > ISU. Based on the results of the study, policy suggestions to facilitate pollution reduction and carbon synergies are proposed to help China's green and low-carbon development. [ABSTRACT FROM AUTHOR]

The energy and power industry is an important field for CO2 emission reduction. The CO2 emitted by thermal power enterprises is a major cause of global climate change, and also a key challenge for China to achieve the goals of "carbon peaking and carbon neutrality." Therefore, it is essential to scientifically and accurately predict the CO2 emissions of key thermal power enterprises in the region. This will guide carbon reduction strategies and policy recommendations for leaders, and also provide a valuable reference for similar regions globally. This study utilizes the factor analysis method to extract the common factors influencing CO2 emissions based on the carbon verification data of 17 thermal power enterprises in Gansu Province. Additionally, the DISO (distance between indices of simulation and observation) index is employed to comprehensively evaluate three prediction models, namely multiple linear regression, support vector regression, and GA-BP neural network. Ultimately, this study provides a reasonable prediction of CO2 emissions for the aforementioned enterprises in Gansu Province. The results show that the three common factors obtained by factor analysis, namely energy consumption and output factor, energy quality factor, and energy efficiency factor, can effectively predict the CO2 emissions from thermal power enterprises. In the three prediction models, GA-BP neural network has the best overall performance with DISO value of 0.95, RMSE value of 11848.236, and MAE value of 7880.543. Over the period 2022–2030, CO2 emissions from 17 thermal power enterprises in Gansu Province are predicted to increase. Under the low-carbon, scenario baseline, and high-carbon scenarios, the CO2 emissions will reach 71.58 Mt, 79.25 Mt, and 87.97 Mt, respectively, by 2030. [ABSTRACT FROM AUTHOR]