Details

Autor(en) / Beteiligte

• Su, Yongxian
• Wang, Yilong
• Zheng, Bo
• Ciais, Philippe
• Wu, Jianping
• Chen, Xiuzhi
• Wang, Yang
• Wang, Changjian
• Ye, Yuyao
• Li, Qian
• Zhang, Chaoqun
• Zhang, Hongou
• Huang, Guangqing
• Huang, Ningsheng
• Lafortezza, Raffaele

Titel

Retrospect driving forces and forecasting reduction potentials of energy-related industrial carbon emissions from China's manufacturing at city level

Ist Teil von

• Environmental research letters, 2020-07, Vol.15 (7), p.74020

Ort / Verlag

Bristol: IOP Publishing

Erscheinungsjahr

2020

Link zum Volltext

Quelle

Free E-Journal (出版社公開部分のみ）

Beschreibungen/Notizen

• Lack of either spatial or temporal coverage in city-level carbon emissions analysis might curb our understanding of historical drivers and make future forecasting uncertain. To fill these gaps, we analyzed time-series energy-related industrial carbon emissions (EICEs) from manufacturing in over 99 cities nationwide in China during the period 2000-2015. We estimated these cities' EICEs reduction potential up until 2030 by improving scenario design, which imposed constraints separately on different city groups based on historical drivers. Results indicated distinct changes of EICEs around 2013 for the heavy manufacturing [HM], light manufacturing [LM] and high-tech development [HD] city groups and of emissions intensity for the energy production [EP] city group. The slowing economic growth would partly explain these transformations since 2013. Energy efficiency and industrial structure contributed most to these switches for the EP and HD city groups, respectively, while energy mix and energy efficiency were also major contributors for the HM and LM city groups. Given economic growth at a normal speed, EICEs will increase by 59%, 78%, 90% and 95% for the EP, HM, LM and HD city groups, respectively, from 2015-2030. Our scenarios show that energy efficiency improvement and industrial structure optimization will spur the EICEs to peak before 2030 and limit future EICEs increase by 6.4% and 33.4% in 2030 for the EP and HD city groups, respectively. This implies that energy efficiency improvement and industrial structure optimization are key emissions mitigation factors for the EP and HD cities. Equally important, our study found more unclean fuel structure with higher coal share in the HM and LM city groups than in the other groups. It is therefore imperative to improve their energy efficiency and optimize energy and industrial structures in the HM and LM cities. Results highlight the need to impose different constraints in scenario design and provide mitigation strategies at city level.