Collaborative control path of pollution and carbon reduction in industrial field in Hohhot-Baotou-Ordos region of Inner Mongolia.

The collaborative control of pollution and carbon emission reduction in industrial fields is crucial for improving regional air quality and mitigating climate change. However, to our knowledge, there is limited research regarding the collaborative control paths for reducing industrial pollution and carbon emissions. The present study assesses the potential for reducing emissions of air pollutants and carbon dioxide (CO) in the industries operating in the Hohhot-Baotou-Ordos (HBO) region of Inner Mongolia. The collaborative control cross elasticity analytical method is employed to examine the synergistic effects of controlling air pollutants and CO emissions. The changes in fine particulate matter (PM) concentrations are simulated under various scenarios. Ultimately, a collaborative control path for pollution and carbon emission reduction is proposed. The results indicate that adjusting the industrial structure, controlling energy consumption intensity, and optimizing the energy structure in collaborative control (CC) and enhanced collaborative control (ECC) scenarios effectively facilitate the coordinated reduction of air pollution and CO emissions. The synergistic control effects on the four evaluated air pollutants and CO in the ECC scenarios surpass those in the CC scenarios. The reductions in PM concentrations from 2020 to 2025 in the CC and ECC scenarios correspond to 10.81 % and 25.36 %, respectively, with even greater reductions projected for 2030 and 2035 (for all scenarios). Under CC and ECC scenarios in 2025, CO emissions per unit of industrial added value would be reduced by 20.12 % and 38.36 %, respectively, compared with the 2020 levels. The CC scheme is highlighted as an effective approach for collaborative pollution control and carbon emission reduction because it meets the continuous improvement requirements for air quality in the HBO region and the industrial CO emission reduction targets. Additionally, the results support the recommendation to prioritize the implementation of measures for controlling energy consumption intensity and adjusting industrial structures, followed by deploying measures to optimize energy structures.