Regional CO₂ emissions and cross-boundary mitigation potential in China

Abstract

This paper presents a new framework for investigating regional CO₂ emissions from the perspective of the domestic supply chain, with a combination of linkage analysis and structural decomposition analysis (SDA), which allows for a better understanding of spatial emission distributions and cross-boundary potential for CO₂ mitigation. Based on the multi-regional input–output (MRIO) tables of 2007 and 2012, Chinese provinces (cities) are categorised into three groups according to linkage characteristics, among which, Group I is suggested to be given priority in formulating mitigation policies due to their stronger regional CO₂ influence characteristics. Moreover, regions in Group I have been more affected by inter-regional trade than other groups with regard to their local CO₂ emissions. Therefore, turning Group I into a low-carbon production pattern could help construct greener domestic supply chains. The results emphasise that regional analysis on CO2 emissions should go beyond the local factors, and that regional mitigation polices should consider the position and participation degree of different regions in domestic supply chains.

KEYWORDS:

• Regional CO₂ emissions
• MRIO model
• linkage analysis
• SDA
• supply chain

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 Dietzenbacher and Los (1997, 1998) found that the average of all the forms was approximated very well by the average of just two extreme or polar forms, and then this method was generally used to deal with the non-unique problem in SDA.

2 This can be proven by the pre- and post-multiplication of Equation 12 with $(\mathbf{I}-{\mathbf{A}}_1)$ and $(\mathbf{I}-{\mathbf{A}}_0)$, respectively.

3 Nine types of fuel are used in this paper: coal, coke, crude oil, gasoline, kerosene, diesel oil, fuel oil, natural gas and electricity.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [grant numbers 71573169, 71873084].