太阳成8722(中国)有限公司-GREEN NO.1

标题： Critical sectors and paths for climate change mitigation within supply chain networks

作者：Wang, Z (Wang, Zhen); Cui, C (Cui, Can); Peng, S (Peng, Sha)  

来源出版物：JOURNAL OF ENVIRONMENTAL MANAGEMENT  卷：226 页码：30-36  DOI：10.1016/j.jenvman.2018.08.018 出版年：NOV 15 2018  

摘要：Certain sectors and paths along supply chains play a critical role in climate change mitigation. We develope a consumption-based framework, which combines input-output analysis, a power-of-pull approach and structural path analysis, and applied it to supply chain networks derived from 2010 and 2012 Jing-Jin-Ji interregional input-output tables. The aim of this study is to identify (1) the key economic sectors for controlling carbon emissions and their changes, (2) the critical directions from a carbon-pulling sector to the emissions of key economic sectors, and (3) the paths with the largest carbon emissions flux in these critical directions. Our results show that the key sectors are from Hebei and Tianjin, more concentrated in Hebei. Most sectors have the largest pulling power over their own carbon emissions, and within-region connections dominated in the emission network, with a stronger tie between Beijing and the other two regions. Critical paths along carbon-pulling directions are located in tiers 0 and 1. Our framework can provide new insight into the creation of carbon emissions control policies.

入藏号：WOS:000446147000004

文献类型：Article

语种：English

作者关键词：Structural path analysis; Power-of-pull analysis; Supply chain; Carbon emissions; Climate change; Input-output analysis

通讯作者地址：Wang, Z (reprint author), Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Hubei, Peoples R China.
Peng, S (reprint author), Hubei Univ Econ, Sch Low Carbon Econ, Wuhan 430205, Hubei, Peoples R China.

电子邮件地址：sinoo@whu.edu.cn; pengsha@hbue.edu.cn

地址：

[Wang, Zhen; Cui, Can] Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Hubei, Peoples R China.
[Peng, Sha] Hubei Univ Econ, Sch Low Carbon Econ, Wuhan 430205, Hubei, Peoples R China.

影响因子：4.005