Driving factors of carbon emissions in China’s municipalities: a LMDI approach
China, as the world’s largest carbon dioxide emitter, is bound to assume the important responsibility of energy conservation and emission reduction. To this end, each city, led by representative municipalities, must enhance efforts in carbon emission reduction to jointly realize China’s low-carbon t...
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Veröffentlicht in: | Environmental science and pollution research international 2022-03, Vol.29 (15), p.21789-21802 |
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description | China, as the world’s largest carbon dioxide emitter, is bound to assume the important responsibility of energy conservation and emission reduction. To this end, each city, led by representative municipalities, must enhance efforts in carbon emission reduction to jointly realize China’s low-carbon transition. Taking four representative municipalities, namely, Beijing, Tianjin, Shanghai, and Chongqing as the case cities, this paper establishes a decomposition analysis for the driving factors of carbon emissions by applying the LMDI method covering data from 2007 to 2017. Kaya identity is used to decompose the effects into eight driving factors: GDP effect, industrial structure effect, energy intensity effect, overall energy structure effect, population effect, urbanization effect, per capita energy consumption effect, urban and rural energy structure effect. The results show that at the municipality level, the driving factors that contribute to carbon emissions are the GDP growth effect and the population effect, with the former still being the most important factor in the municipalities with faster economic growth; and industrial structure effect is the most important factor that inhibits carbon emissions, followed by energy structure effect. This paper considers the driving factors of both the production side and the residential consumption side from the city level. The research reveals the main driving factors that effect the carbon emissions of megacities in developing countries, and highlights the leading role of megacities in terms of carbon emission reduction in China and even the world. The paper thereby puts forward policy implications for China’s economic policies. |
doi_str_mv | 10.1007/s11356-021-17277-w |
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To this end, each city, led by representative municipalities, must enhance efforts in carbon emission reduction to jointly realize China’s low-carbon transition. Taking four representative municipalities, namely, Beijing, Tianjin, Shanghai, and Chongqing as the case cities, this paper establishes a decomposition analysis for the driving factors of carbon emissions by applying the LMDI method covering data from 2007 to 2017. Kaya identity is used to decompose the effects into eight driving factors: GDP effect, industrial structure effect, energy intensity effect, overall energy structure effect, population effect, urbanization effect, per capita energy consumption effect, urban and rural energy structure effect. The results show that at the municipality level, the driving factors that contribute to carbon emissions are the GDP growth effect and the population effect, with the former still being the most important factor in the municipalities with faster economic growth; and industrial structure effect is the most important factor that inhibits carbon emissions, followed by energy structure effect. This paper considers the driving factors of both the production side and the residential consumption side from the city level. The research reveals the main driving factors that effect the carbon emissions of megacities in developing countries, and highlights the leading role of megacities in terms of carbon emission reduction in China and even the world. The paper thereby puts forward policy implications for China’s economic policies.</description><identifier>ISSN: 0944-1344</identifier><identifier>ISSN: 1614-7499</identifier><identifier>EISSN: 1614-7499</identifier><identifier>DOI: 10.1007/s11356-021-17277-w</identifier><identifier>PMID: 34767167</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Aquatic Pollution ; Atmospheric Protection/Air Quality Control/Air Pollution ; Carbon ; Carbon dioxide ; Carbon Dioxide - analysis ; China ; Cities ; Decomposition ; Developing countries ; Earth and Environmental Science ; Economic Development ; Economic growth ; Economic policy ; Economics ; Ecotoxicology ; Emissions ; Emissions control ; Emitters ; Energy ; Energy conservation ; Energy consumption ; Energy utilization ; Environment ; Environmental Chemistry ; Environmental Health ; Environmental science ; GDP ; Gross Domestic Product ; issues and policy ; LDCs ; Megacities ; Municipalities ; Research Article ; Urbanization ; Waste Water Technology ; Water Management ; Water Pollution Control</subject><ispartof>Environmental science and pollution research international, 2022-03, Vol.29 (15), p.21789-21802</ispartof><rights>The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021</rights><rights>2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.</rights><rights>The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c507t-5b9bc9e4fbe6b75f61405948ed227368e83f38da7d6cce940093174ec90b39c83</citedby><cites>FETCH-LOGICAL-c507t-5b9bc9e4fbe6b75f61405948ed227368e83f38da7d6cce940093174ec90b39c83</cites><orcidid>0000-0002-1150-7750</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11356-021-17277-w$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11356-021-17277-w$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>230,314,776,780,881,27901,27902,41464,42533,51294</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34767167$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Liu, Yuanxin</creatorcontrib><creatorcontrib>Jiang, Yajing</creatorcontrib><creatorcontrib>Liu, Hui</creatorcontrib><creatorcontrib>Li, Bo</creatorcontrib><creatorcontrib>Yuan, Jiahai</creatorcontrib><title>Driving factors of carbon emissions in China’s municipalities: a LMDI approach</title><title>Environmental science and pollution research international</title><addtitle>Environ Sci Pollut Res</addtitle><addtitle>Environ Sci Pollut Res Int</addtitle><description>China, as the world’s largest carbon dioxide emitter, is bound to assume the important responsibility of energy conservation and emission reduction. To this end, each city, led by representative municipalities, must enhance efforts in carbon emission reduction to jointly realize China’s low-carbon transition. Taking four representative municipalities, namely, Beijing, Tianjin, Shanghai, and Chongqing as the case cities, this paper establishes a decomposition analysis for the driving factors of carbon emissions by applying the LMDI method covering data from 2007 to 2017. Kaya identity is used to decompose the effects into eight driving factors: GDP effect, industrial structure effect, energy intensity effect, overall energy structure effect, population effect, urbanization effect, per capita energy consumption effect, urban and rural energy structure effect. The results show that at the municipality level, the driving factors that contribute to carbon emissions are the GDP growth effect and the population effect, with the former still being the most important factor in the municipalities with faster economic growth; and industrial structure effect is the most important factor that inhibits carbon emissions, followed by energy structure effect. This paper considers the driving factors of both the production side and the residential consumption side from the city level. The research reveals the main driving factors that effect the carbon emissions of megacities in developing countries, and highlights the leading role of megacities in terms of carbon emission reduction in China and even the world. The paper thereby puts forward policy implications for China’s economic policies.</description><subject>Aquatic Pollution</subject><subject>Atmospheric Protection/Air Quality Control/Air Pollution</subject><subject>Carbon</subject><subject>Carbon dioxide</subject><subject>Carbon Dioxide - analysis</subject><subject>China</subject><subject>Cities</subject><subject>Decomposition</subject><subject>Developing countries</subject><subject>Earth and Environmental Science</subject><subject>Economic Development</subject><subject>Economic growth</subject><subject>Economic policy</subject><subject>Economics</subject><subject>Ecotoxicology</subject><subject>Emissions</subject><subject>Emissions control</subject><subject>Emitters</subject><subject>Energy</subject><subject>Energy conservation</subject><subject>Energy consumption</subject><subject>Energy utilization</subject><subject>Environment</subject><subject>Environmental Chemistry</subject><subject>Environmental Health</subject><subject>Environmental science</subject><subject>GDP</subject><subject>Gross Domestic Product</subject><subject>issues and policy</subject><subject>LDCs</subject><subject>Megacities</subject><subject>Municipalities</subject><subject>Research Article</subject><subject>Urbanization</subject><subject>Waste Water Technology</subject><subject>Water Management</subject><subject>Water Pollution Control</subject><issn>0944-1344</issn><issn>1614-7499</issn><issn>1614-7499</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><recordid>eNqFkbtuFDEUhi1ERJaQF0iBLNGkGeL7hQIp2nCJtFEoSG15vJ5dR7P2YM8kSsdr8Ho8CQ6bGxRQuTjf-XzO-QE4wOgtRkgeFYwpFw0iuMGSSNlcPwMzLDBrJNP6OZghzViDKWO74GUplwgRpIl8AXYpk0JiIWfgy0kOVyGuYGfdmHKBqYPO5jZF6DehlJBigSHC-TpE-_P7jwI3UwwuDLYPY_DlHbRwcXZyCu0w5GTd-hXY6Wxf_P7duwcuPn74Ov_cLM4_nc6PF43jSI4Nb3XrtGdd60UreVenRlwz5ZeESCqUV7SjamnlUjjnNUNIUyyZdxq1VDtF98D7rXeY2o1fOh_HbHsz5LCx-cYkG8yflRjWZpWujOJKCKyr4PBOkNO3yZfR1H2d73sbfZqKIYIpxSVH5P8o15IpISmv6Ju_0Ms05VgvcStEXFQWV4psKZdTKdl3D3NjZG6zNdtsTc3W_M7WXNem1083fmi5D7MCdAuUWoornx___of2F79ksHE</recordid><startdate>20220301</startdate><enddate>20220301</enddate><creator>Liu, Yuanxin</creator><creator>Jiang, Yajing</creator><creator>Liu, Hui</creator><creator>Li, Bo</creator><creator>Yuan, Jiahai</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QL</scope><scope>7SN</scope><scope>7T7</scope><scope>7TV</scope><scope>7U7</scope><scope>7WY</scope><scope>7WZ</scope><scope>7X7</scope><scope>7XB</scope><scope>87Z</scope><scope>88E</scope><scope>88I</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8FL</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FRNLG</scope><scope>FYUFA</scope><scope>F~G</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K60</scope><scope>K6~</scope><scope>K9.</scope><scope>L.-</scope><scope>M0C</scope><scope>M0S</scope><scope>M1P</scope><scope>M2P</scope><scope>M7N</scope><scope>P64</scope><scope>PATMY</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PYCSY</scope><scope>Q9U</scope><scope>7X8</scope><scope>7S9</scope><scope>L.6</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-1150-7750</orcidid></search><sort><creationdate>20220301</creationdate><title>Driving factors of carbon emissions in China’s municipalities: a LMDI approach</title><author>Liu, Yuanxin ; Jiang, Yajing ; Liu, Hui ; Li, Bo ; Yuan, Jiahai</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c507t-5b9bc9e4fbe6b75f61405948ed227368e83f38da7d6cce940093174ec90b39c83</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Aquatic Pollution</topic><topic>Atmospheric Protection/Air Quality Control/Air Pollution</topic><topic>Carbon</topic><topic>Carbon dioxide</topic><topic>Carbon Dioxide - 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Academic</collection><collection>AGRICOLA</collection><collection>AGRICOLA - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Environmental science and pollution research international</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Liu, Yuanxin</au><au>Jiang, Yajing</au><au>Liu, Hui</au><au>Li, Bo</au><au>Yuan, Jiahai</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Driving factors of carbon emissions in China’s municipalities: a LMDI approach</atitle><jtitle>Environmental science and pollution research international</jtitle><stitle>Environ Sci Pollut Res</stitle><addtitle>Environ Sci Pollut Res Int</addtitle><date>2022-03-01</date><risdate>2022</risdate><volume>29</volume><issue>15</issue><spage>21789</spage><epage>21802</epage><pages>21789-21802</pages><issn>0944-1344</issn><issn>1614-7499</issn><eissn>1614-7499</eissn><abstract>China, as the world’s largest carbon dioxide emitter, is bound to assume the important responsibility of energy conservation and emission reduction. To this end, each city, led by representative municipalities, must enhance efforts in carbon emission reduction to jointly realize China’s low-carbon transition. Taking four representative municipalities, namely, Beijing, Tianjin, Shanghai, and Chongqing as the case cities, this paper establishes a decomposition analysis for the driving factors of carbon emissions by applying the LMDI method covering data from 2007 to 2017. Kaya identity is used to decompose the effects into eight driving factors: GDP effect, industrial structure effect, energy intensity effect, overall energy structure effect, population effect, urbanization effect, per capita energy consumption effect, urban and rural energy structure effect. The results show that at the municipality level, the driving factors that contribute to carbon emissions are the GDP growth effect and the population effect, with the former still being the most important factor in the municipalities with faster economic growth; and industrial structure effect is the most important factor that inhibits carbon emissions, followed by energy structure effect. This paper considers the driving factors of both the production side and the residential consumption side from the city level. The research reveals the main driving factors that effect the carbon emissions of megacities in developing countries, and highlights the leading role of megacities in terms of carbon emission reduction in China and even the world. 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subjects | Aquatic Pollution Atmospheric Protection/Air Quality Control/Air Pollution Carbon Carbon dioxide Carbon Dioxide - analysis China Cities Decomposition Developing countries Earth and Environmental Science Economic Development Economic growth Economic policy Economics Ecotoxicology Emissions Emissions control Emitters Energy Energy conservation Energy consumption Energy utilization Environment Environmental Chemistry Environmental Health Environmental science GDP Gross Domestic Product issues and policy LDCs Megacities Municipalities Research Article Urbanization Waste Water Technology Water Management Water Pollution Control |
title | Driving factors of carbon emissions in China’s municipalities: a LMDI approach |
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