The Spatial Network Structure of China’s Regional Carbon Emissions and Its Network Effect

Feng Wang, Mengnan Gao, Juan Liu and Wenna Fan
Additional contact information
Feng Wang: School of Management, China University of Mining and Technology, No. 1, College Rd., Tongshan Dist., Xuzhou 221116, Jiangsu, China
Mengnan Gao: School of Management, China University of Mining and Technology, No. 1, College Rd., Tongshan Dist., Xuzhou 221116, Jiangsu, China
Juan Liu: School of Management, China University of Mining and Technology, No. 1, College Rd., Tongshan Dist., Xuzhou 221116, Jiangsu, China
Wenna Fan: School of Management, China University of Mining and Technology, No. 1, College Rd., Tongshan Dist., Xuzhou 221116, Jiangsu, China

Energies, 2018, vol. 11, issue 10, 1-14

Abstract: Under the “new normal”, China is facing more severe carbon emissions reduction targets. This paper estimates the carbon emission data of various provinces in China from 2008 to 2014, constructs a revised gravity model, and analyzes the network structure and effects of carbon emissions in various provinces by using social network analysis (SNA) and quadratic assignment procedure (QAP) analysis methods. The conclusions show that there are obvious spatial correlations between China’s provinces and regions in terms of carbon emissions: Tianjin, Shanghai, Zhejiang, Jiangsu and Guangdong are in the center of the carbon emission network, and play the role of “bridges”. Carbon emissions can be divided into four blocks: “bidirectional spillover block”, “net beneficial block”, “net spillover block” and “broker block”. The differences in the energy consumption, economic level and geographical location of the provinces have a significant impact on the spatial correlation relationship of carbon emissions. Finally, the improvement of the robustness of the overall network structure and the promotion of individual network centrality can significantly reduce the intensity of carbon emissions.

Keywords: carbon emission; spatial correlation; social network analysis (SNA); quadratic assignment procedure (QAP) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (14)

Downloads: (external link)
https://www.mdpi.com/1996-1073/11/10/2706/pdf (application/pdf)
https://www.mdpi.com/1996-1073/11/10/2706/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:11:y:2018:i:10:p:2706-:d:174814

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().