College of Management and Economics, Chongqing University, Shanzheng Street 174, Chongqing, 400044, China.
College of Hongshen, Chongqing University, Shanzheng Street 174, Chongqing, 400044, China.
Environ Sci Pollut Res Int. 2023 Feb;30(7):18643-18659. doi: 10.1007/s11356-022-23442-6. Epub 2022 Oct 11.
In order to support the emissions reduction options in manufacturing industry effectively, it is necessary to quantify the final demand embedded manufacturing consumption (DEMC) emissions which can be estimated by converting intermediate manufacturing consumption into all final demand categories. Here, we quantify the DEMC emissions in China's 30 provinces during 2007-2017 using a multi-regional input-output (MRIO) model and the modified hypothetical extraction method (HEM). Then, we analyze impacts of four factors (including emissions multipliers, consumption structure, investment efficiency, and investment scale) on the DEMC emissions. Finally, considering a large driving effect of investment scale on manufacturing emissions, we conduct four scenarios to quantify the mitigation potential of DEMC emissions during 2020-2035. We find that from 2007 to 2012, the DMEC emissions increased doubled, while during 2012-2017, it decreased from 1217 to 634 Mt. The capital-intensive manufacturing and the labor-intensive manufacturing industries were main sources of intra- and inter-sectoral emissions, respectively. Investment scale was the main driver of the growth in DEMC emissions during 2007-2015, while it led to a reduction of DEMC emissions during 2015-2017. Emission multipliers had the largest positive impact on the reduction of DEMC emissions during the whole period. Consumption structure increased DEMC emissions during 2007-2012, while with the consumption structure shift towards knowledge-intensive manufacturing industry, it induced a reduction of DEMC emissions during 2012-2017. Moreover, implementing an integrated mitigation measures (including reducing emissions multipliers, decreasing investment efficiency, and adjusting consumption structure) could help China to realize the emissions peaking target. However, there are still 8 provinces whose DEMC emissions are unlikely to peak before 2030.
为了有效支持制造业的减排选项,有必要量化嵌入制造业消费的最终需求排放(DEMC),这可以通过将中间制造业消费转化为所有最终需求类别来估算。在这里,我们使用多区域投入产出(MRIO)模型和修正的假设提取方法(HEM),对中国 30 个省份在 2007-2017 年期间的 DEMC 排放进行了量化。然后,我们分析了四个因素(包括排放乘数、消费结构、投资效率和投资规模)对 DEMC 排放的影响。最后,考虑到投资规模对制造业排放的巨大驱动作用,我们进行了四个情景模拟,以量化 2020-2035 年期间 DEMC 排放的减排潜力。我们发现,从 2007 年到 2012 年,DEMC 排放量增加了一倍,而在 2012-2017 年期间,排放量从 1217 减少到 634 Mt。资本密集型制造业和劳动密集型制造业分别是行业内和行业间排放的主要来源。投资规模是 2007-2015 年 DEMC 排放量增长的主要驱动因素,而在 2015-2017 年期间,它导致了 DEMC 排放量的减少。排放乘数对整个时期 DEMC 减排的影响最大。消费结构在 2007-2012 年期间增加了 DEMC 排放量,而随着消费结构向知识密集型制造业的转变,在 2012-2017 年期间,它导致了 DEMC 排放量的减少。此外,实施综合减排措施(包括降低排放乘数、降低投资效率和调整消费结构)可以帮助中国实现排放峰值目标。然而,仍有 8 个省份的 DEMC 排放量不太可能在 2030 年前达到峰值。