Analyzing carbon emission transfer network structure among provinces in China: new evidence from social network analysis
Domestic trade plays a key role in China’s rapid economic progress. However, the increased domestic trade causes significant variations in carbon emission transfer among provinces. This study adopted the multi-region input-output (MRIO) model and social network analysis (SNA) to estimate the carbon...
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| Veröffentlicht in: | Environmental science and pollution research international 2020-06, Vol.27 (18), p.23281-23300 |
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| creator | Sun, Licheng Qin, Lin Taghizadeh-Hesary, Farhad Zhang, Jijian Mohsin, Muhammad Chaudhry, Imran Sharif |
| description | Domestic trade plays a key role in China’s rapid economic progress. However, the increased domestic trade causes significant variations in carbon emission transfer among provinces. This study adopted the multi-region input-output (MRIO) model and social network analysis (SNA) to estimate the carbon emission transfer. Furthermore, the carbon emission transfer network characteristics among 30 provinces and 27 sectors were analyzed by using interprovincial input-output tables for 2007, 2010, and 2012. The results showed that (1) Large differences exist in carbon emission transfer flow and its network characteristics between provinces. (2) The three industrial sectors of metal smelting and pressing sector, power, heat production, and supply sector, petroleum processing, coking, and nuclear fuel processing sector have high carbon emission transfer and pose a strong influence on the carbon emission transfer network. (3) Provinces of the eastern region have a “bidirectional spillover” role, while those of the western region have a mediating role as an “agent.” Provinces of the central region have a “main inflow” role. Finally, useful policy implications and suggestions of this study are summarized. |
| doi_str_mv | 10.1007/s11356-020-08911-0 |
| format | Article |
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However, the increased domestic trade causes significant variations in carbon emission transfer among provinces. This study adopted the multi-region input-output (MRIO) model and social network analysis (SNA) to estimate the carbon emission transfer. Furthermore, the carbon emission transfer network characteristics among 30 provinces and 27 sectors were analyzed by using interprovincial input-output tables for 2007, 2010, and 2012. The results showed that (1) Large differences exist in carbon emission transfer flow and its network characteristics between provinces. (2) The three industrial sectors of metal smelting and pressing sector, power, heat production, and supply sector, petroleum processing, coking, and nuclear fuel processing sector have high carbon emission transfer and pose a strong influence on the carbon emission transfer network. (3) Provinces of the eastern region have a “bidirectional spillover” role, while those of the western region have a mediating role as an “agent.” Provinces of the central region have a “main inflow” role. Finally, useful policy implications and suggestions of this study are summarized.</description><identifier>ISSN: 0944-1344</identifier><identifier>EISSN: 1614-7499</identifier><identifier>DOI: 10.1007/s11356-020-08911-0</identifier><identifier>PMID: 32337669</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Aquatic Pollution ; Atmospheric Protection/Air Quality Control/Air Pollution ; Carbon ; China ; Coking ; domestic trade ; Earth and Environmental Science ; Ecotoxicology ; Emission analysis ; Emissions ; Energy management ; Environment ; Environmental Chemistry ; Environmental Health ; Environmental science ; heat production ; issues and policy ; Network analysis ; Nuclear fuels ; petroleum ; Research Article ; Smelting ; Social network analysis ; Social networks ; Social organization ; Waste Water Technology ; Water Management ; Water Pollution Control</subject><ispartof>Environmental science and pollution research international, 2020-06, Vol.27 (18), p.23281-23300</ispartof><rights>Springer-Verlag GmbH Germany, part of Springer Nature 2020</rights><rights>Springer-Verlag GmbH Germany, part of Springer Nature 2020.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c527t-21283cf3784f1550615f37388c77458d1002bd0bd08a4f1e8433293c2f9c6e553</citedby><cites>FETCH-LOGICAL-c527t-21283cf3784f1550615f37388c77458d1002bd0bd08a4f1e8433293c2f9c6e553</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11356-020-08911-0$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11356-020-08911-0$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32337669$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Sun, Licheng</creatorcontrib><creatorcontrib>Qin, Lin</creatorcontrib><creatorcontrib>Taghizadeh-Hesary, Farhad</creatorcontrib><creatorcontrib>Zhang, Jijian</creatorcontrib><creatorcontrib>Mohsin, Muhammad</creatorcontrib><creatorcontrib>Chaudhry, Imran Sharif</creatorcontrib><title>Analyzing carbon emission transfer network structure among provinces in China: new evidence from social network analysis</title><title>Environmental science and pollution research international</title><addtitle>Environ Sci Pollut Res</addtitle><addtitle>Environ Sci Pollut Res Int</addtitle><description>Domestic trade plays a key role in China’s rapid economic progress. However, the increased domestic trade causes significant variations in carbon emission transfer among provinces. This study adopted the multi-region input-output (MRIO) model and social network analysis (SNA) to estimate the carbon emission transfer. Furthermore, the carbon emission transfer network characteristics among 30 provinces and 27 sectors were analyzed by using interprovincial input-output tables for 2007, 2010, and 2012. The results showed that (1) Large differences exist in carbon emission transfer flow and its network characteristics between provinces. (2) The three industrial sectors of metal smelting and pressing sector, power, heat production, and supply sector, petroleum processing, coking, and nuclear fuel processing sector have high carbon emission transfer and pose a strong influence on the carbon emission transfer network. (3) Provinces of the eastern region have a “bidirectional spillover” role, while those of the western region have a mediating role as an “agent.” Provinces of the central region have a “main inflow” role. Finally, useful policy implications and suggestions of this study are summarized.</description><subject>Aquatic Pollution</subject><subject>Atmospheric Protection/Air Quality Control/Air Pollution</subject><subject>Carbon</subject><subject>China</subject><subject>Coking</subject><subject>domestic trade</subject><subject>Earth and Environmental Science</subject><subject>Ecotoxicology</subject><subject>Emission analysis</subject><subject>Emissions</subject><subject>Energy management</subject><subject>Environment</subject><subject>Environmental Chemistry</subject><subject>Environmental Health</subject><subject>Environmental science</subject><subject>heat production</subject><subject>issues and policy</subject><subject>Network analysis</subject><subject>Nuclear fuels</subject><subject>petroleum</subject><subject>Research Article</subject><subject>Smelting</subject><subject>Social network analysis</subject><subject>Social networks</subject><subject>Social 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Int</addtitle><date>2020-06-01</date><risdate>2020</risdate><volume>27</volume><issue>18</issue><spage>23281</spage><epage>23300</epage><pages>23281-23300</pages><issn>0944-1344</issn><eissn>1614-7499</eissn><abstract>Domestic trade plays a key role in China’s rapid economic progress. However, the increased domestic trade causes significant variations in carbon emission transfer among provinces. This study adopted the multi-region input-output (MRIO) model and social network analysis (SNA) to estimate the carbon emission transfer. Furthermore, the carbon emission transfer network characteristics among 30 provinces and 27 sectors were analyzed by using interprovincial input-output tables for 2007, 2010, and 2012. The results showed that (1) Large differences exist in carbon emission transfer flow and its network characteristics between provinces. (2) The three industrial sectors of metal smelting and pressing sector, power, heat production, and supply sector, petroleum processing, coking, and nuclear fuel processing sector have high carbon emission transfer and pose a strong influence on the carbon emission transfer network. (3) Provinces of the eastern region have a “bidirectional spillover” role, while those of the western region have a mediating role as an “agent.” Provinces of the central region have a “main inflow” role. Finally, useful policy implications and suggestions of this study are summarized.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>32337669</pmid><doi>10.1007/s11356-020-08911-0</doi><tpages>20</tpages></addata></record> |
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| subjects | Aquatic Pollution Atmospheric Protection/Air Quality Control/Air Pollution Carbon China Coking domestic trade Earth and Environmental Science Ecotoxicology Emission analysis Emissions Energy management Environment Environmental Chemistry Environmental Health Environmental science heat production issues and policy Network analysis Nuclear fuels petroleum Research Article Smelting Social network analysis Social networks Social organization Waste Water Technology Water Management Water Pollution Control |
| title | Analyzing carbon emission transfer network structure among provinces in China: new evidence from social network analysis |
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