School of Management Science and Engineering, Beijing Information Science and Technology University, Beijing, 102206, China; Beijing Key Laboratory of Big Data Decision Making for Green Development, Beijing, 100192, China.
School of Management Science and Engineering, Beijing Information Science and Technology University, Beijing, 102206, China.
Environ Res. 2024 Dec 15;263(Pt 1):120018. doi: 10.1016/j.envres.2024.120018. Epub 2024 Sep 16.
Realizing a synergistic reduction of air pollutant and CO emissions (APCE) is an important approach to promote a green socio-economic transformation in China, and it can provide a solid foundation for the achievement of clean energy production and climate action under a sustainable development goal framework. The objective of this study is to explore the quantitative relationship and evolution of synergies between APCE in industrial sectors driven by different socio-economic effects from 2007 to 2020 in China. The results indicated that the main sectors of pollutant emissions had consistency, however, large differences in the reduction efficiency of emissions exist among pollutants. The efficiency in reducing CO emissions was about 48% lower when compared with reductions of SO (95%), NO (86%), and smoke and dust (83%) emissions from 2007 to 2020. The effects of improved technology were the main contributor to a reduction in pollutant emissions, but the synergies between APCE driving by it were not achieved. While the synergies between APCE driven by structure and final demand effects were significant. The synergies between NO and CO emissions were stronger driven by final demand structure and type effects, with correlation coefficients of 1.06 and 1.13, respectively. Besides, the degree of synergistic reduction between APCE in most industrial sectors was around zero. Therefore, the efficiency of synergistic pollution reduction should be improved with the development of a synergistic governance system for industrial sectors. The structural decomposition analysis based on input-output model combined with the cross-elasticity analysis method to quantitively synergies between APCE from the consumption (demand) perspective, considering the connections between industrial sectors with socio-economic developing, which would contribute to the industrial synergistic reduction and green transformation as the consumption driven gradually increasing.
实现污染物与二氧化碳协同减排（APCE）是推动中国绿色社会经济转型的重要途径，可为在可持续发展目标框架下实现清洁能源生产和气候行动奠定坚实基础。本研究旨在探讨 2007 年至 2020 年期间，不同社会经济效应驱动的工业部门中 APCE 的协同关系及其演变。结果表明，主要污染物排放部门具有一致性，但不同污染物的减排效率存在较大差异。与 2007 年至 2020 年期间 SO（95%）、NO（86%）和烟尘（83%）减排相比，CO 减排效率降低约 48%。提高技术的效果是污染物减排的主要贡献者，但驱动的 APCE 协同效应并未实现。而结构和最终需求效应驱动的 APCE 协同效应显著。最终需求结构和类型效应对 NO 和 CO 排放协同的驱动作用较强，相关系数分别为 1.06 和 1.13。此外，大多数工业部门的 APCE 协同减排程度约为零。因此，应通过发展工业部门协同治理体系来提高协同减排效率。基于投入产出模型的结构分解分析结合交叉弹性分析方法，从消费（需求）角度定量分析 APCE 之间的协同关系，考虑工业部门与社会经济发展的联系，这将有助于消费驱动逐渐增加时的工业协同减排和绿色转型。
