Investigating the driving factors of regional CO2 emissions in China using the IDA-PDA-MMI method

•We developed a new comprehensive decomposition method, named IDA-PDA-MMI method.•Our framework was employed to Chinese provincial data from 2000 to 2016.•Technology gap has great potential for CO2 emission reduction.•Energy technology progress and output technology progress decreased regional CO2 e...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Energy economics 2019-10, Vol.84, p.104521, Article 104521
Hauptverfasser: Zha, Donglan, Yang, Guanglei, Wang, Qunwei
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue
container_start_page 104521
container_title Energy economics
container_volume 84
creator Zha, Donglan
Yang, Guanglei
Wang, Qunwei
description •We developed a new comprehensive decomposition method, named IDA-PDA-MMI method.•Our framework was employed to Chinese provincial data from 2000 to 2016.•Technology gap has great potential for CO2 emission reduction.•Energy technology progress and output technology progress decreased regional CO2 emissions. In this paper, we systematically summarized existing research on the driving factors of CO2 emissions and found that changes in technology gap may be one of the key driving factors of CO2 emissions. Technology efficiency, technology progress, and technology gap were decomposed by using the Meta-frontier Malmquist index (MMI), which was then combined it with the Index Decomposition Analysis (IDA) and the Production-theoretical Decomposition Analysis (PDA). Our framework was applied to Chinese provincial data from 2000 to 2016. We identified nine factors to explain changes of regional CO2 emissions. Results demonstrate that economic scale, energy technology efficiency, and output technology efficiency increased CO2 emissions in Eastern, Central, and Western regions of China, with the economic scale being the largest contributor. Energy structure, energy intensity, energy technology progress, and output technology progress decreased regional CO2 emissions, with the energy technology progress playing the strongest role. Energy technology gap and output technology gap led to an increase in CO2 emissions in Central China and, to a lesser extent, in Western China. The effects of each driving factor on CO2 emissions varied across provinces. Finally, policy implications are suggested to reduce CO2 emissions in China.
doi_str_mv 10.1016/j.eneco.2019.104521
format Article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2354808825</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S014098831930310X</els_id><sourcerecordid>2354808825</sourcerecordid><originalsourceid>FETCH-LOGICAL-c396t-f7c0e3440ae56406f884dcd6e8d60eb292478b7f2564dc385fe0d1d5a8ae94013</originalsourceid><addsrcrecordid>eNp9UMtOwzAQtBBIlMIXcLHEOcVObNc5cKjCK1KrcoCz5dqb1lEbFzutxN_jErhyWO1rZrUzCN1SMqGEivt2Ah0YP8kJLdOE8ZyeoRGV0yITVNJzNCKUkayUsrhEVzG2hBAuuBwhXXdHiL1b6951a9xvANvgjqe60ab3IWLf4ABr5zu9xdUyx7BzMaY2YtfhauM6jQ_xj1w_zrK3FItFjXfQb7y9RheN3ka4-c1j9PH89F69ZvPlS13N5pkpStFnzdQQKBgjGrhgRDRSMmusAGkFgVVe5mwqV9MmT1trCskbIJZarqWGkhFajNHdcHcf_OchaVKtP4T0dFR5wZkkUuY8oYoBZYKPMUCj9sHtdPhSlKiTl6pVP16qk5dq8DKxHgYWJAFHB0FF46AzYF0A0yvr3b_8bw7qfJM</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2354808825</pqid></control><display><type>article</type><title>Investigating the driving factors of regional CO2 emissions in China using the IDA-PDA-MMI method</title><source>PAIS Index</source><source>Elsevier ScienceDirect Journals</source><creator>Zha, Donglan ; Yang, Guanglei ; Wang, Qunwei</creator><creatorcontrib>Zha, Donglan ; Yang, Guanglei ; Wang, Qunwei</creatorcontrib><description>•We developed a new comprehensive decomposition method, named IDA-PDA-MMI method.•Our framework was employed to Chinese provincial data from 2000 to 2016.•Technology gap has great potential for CO2 emission reduction.•Energy technology progress and output technology progress decreased regional CO2 emissions. In this paper, we systematically summarized existing research on the driving factors of CO2 emissions and found that changes in technology gap may be one of the key driving factors of CO2 emissions. Technology efficiency, technology progress, and technology gap were decomposed by using the Meta-frontier Malmquist index (MMI), which was then combined it with the Index Decomposition Analysis (IDA) and the Production-theoretical Decomposition Analysis (PDA). Our framework was applied to Chinese provincial data from 2000 to 2016. We identified nine factors to explain changes of regional CO2 emissions. Results demonstrate that economic scale, energy technology efficiency, and output technology efficiency increased CO2 emissions in Eastern, Central, and Western regions of China, with the economic scale being the largest contributor. Energy structure, energy intensity, energy technology progress, and output technology progress decreased regional CO2 emissions, with the energy technology progress playing the strongest role. Energy technology gap and output technology gap led to an increase in CO2 emissions in Central China and, to a lesser extent, in Western China. The effects of each driving factor on CO2 emissions varied across provinces. Finally, policy implications are suggested to reduce CO2 emissions in China.</description><identifier>ISSN: 0140-9883</identifier><identifier>EISSN: 1873-6181</identifier><identifier>DOI: 10.1016/j.eneco.2019.104521</identifier><language>eng</language><publisher>Kidlington: Elsevier B.V</publisher><subject>Carbon dioxide ; Carbon dioxide emissions ; Change agents ; China ; Decomposition ; Driving ; Driving factors ; Economics ; Efficiency ; Emissions ; Energy ; Energy economics ; Energy technology ; Handheld computers ; IDA ; Indexes ; Meta-frontier Malmquist index ; PDA ; Power efficiency ; Provinces ; Regional development ; Technology</subject><ispartof>Energy economics, 2019-10, Vol.84, p.104521, Article 104521</ispartof><rights>2019 Elsevier B.V.</rights><rights>Copyright Elsevier Science Ltd. Oct 2019</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c396t-f7c0e3440ae56406f884dcd6e8d60eb292478b7f2564dc385fe0d1d5a8ae94013</citedby><cites>FETCH-LOGICAL-c396t-f7c0e3440ae56406f884dcd6e8d60eb292478b7f2564dc385fe0d1d5a8ae94013</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S014098831930310X$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3537,27843,27901,27902,65306</link.rule.ids></links><search><creatorcontrib>Zha, Donglan</creatorcontrib><creatorcontrib>Yang, Guanglei</creatorcontrib><creatorcontrib>Wang, Qunwei</creatorcontrib><title>Investigating the driving factors of regional CO2 emissions in China using the IDA-PDA-MMI method</title><title>Energy economics</title><description>•We developed a new comprehensive decomposition method, named IDA-PDA-MMI method.•Our framework was employed to Chinese provincial data from 2000 to 2016.•Technology gap has great potential for CO2 emission reduction.•Energy technology progress and output technology progress decreased regional CO2 emissions. In this paper, we systematically summarized existing research on the driving factors of CO2 emissions and found that changes in technology gap may be one of the key driving factors of CO2 emissions. Technology efficiency, technology progress, and technology gap were decomposed by using the Meta-frontier Malmquist index (MMI), which was then combined it with the Index Decomposition Analysis (IDA) and the Production-theoretical Decomposition Analysis (PDA). Our framework was applied to Chinese provincial data from 2000 to 2016. We identified nine factors to explain changes of regional CO2 emissions. Results demonstrate that economic scale, energy technology efficiency, and output technology efficiency increased CO2 emissions in Eastern, Central, and Western regions of China, with the economic scale being the largest contributor. Energy structure, energy intensity, energy technology progress, and output technology progress decreased regional CO2 emissions, with the energy technology progress playing the strongest role. Energy technology gap and output technology gap led to an increase in CO2 emissions in Central China and, to a lesser extent, in Western China. The effects of each driving factor on CO2 emissions varied across provinces. Finally, policy implications are suggested to reduce CO2 emissions in China.</description><subject>Carbon dioxide</subject><subject>Carbon dioxide emissions</subject><subject>Change agents</subject><subject>China</subject><subject>Decomposition</subject><subject>Driving</subject><subject>Driving factors</subject><subject>Economics</subject><subject>Efficiency</subject><subject>Emissions</subject><subject>Energy</subject><subject>Energy economics</subject><subject>Energy technology</subject><subject>Handheld computers</subject><subject>IDA</subject><subject>Indexes</subject><subject>Meta-frontier Malmquist index</subject><subject>PDA</subject><subject>Power efficiency</subject><subject>Provinces</subject><subject>Regional development</subject><subject>Technology</subject><issn>0140-9883</issn><issn>1873-6181</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>7TQ</sourceid><recordid>eNp9UMtOwzAQtBBIlMIXcLHEOcVObNc5cKjCK1KrcoCz5dqb1lEbFzutxN_jErhyWO1rZrUzCN1SMqGEivt2Ah0YP8kJLdOE8ZyeoRGV0yITVNJzNCKUkayUsrhEVzG2hBAuuBwhXXdHiL1b6951a9xvANvgjqe60ab3IWLf4ABr5zu9xdUyx7BzMaY2YtfhauM6jQ_xj1w_zrK3FItFjXfQb7y9RheN3ka4-c1j9PH89F69ZvPlS13N5pkpStFnzdQQKBgjGrhgRDRSMmusAGkFgVVe5mwqV9MmT1trCskbIJZarqWGkhFajNHdcHcf_OchaVKtP4T0dFR5wZkkUuY8oYoBZYKPMUCj9sHtdPhSlKiTl6pVP16qk5dq8DKxHgYWJAFHB0FF46AzYF0A0yvr3b_8bw7qfJM</recordid><startdate>20191001</startdate><enddate>20191001</enddate><creator>Zha, Donglan</creator><creator>Yang, Guanglei</creator><creator>Wang, Qunwei</creator><general>Elsevier B.V</general><general>Elsevier Science Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7ST</scope><scope>7TA</scope><scope>7TQ</scope><scope>8BJ</scope><scope>8FD</scope><scope>C1K</scope><scope>DHY</scope><scope>DON</scope><scope>FQK</scope><scope>JBE</scope><scope>JG9</scope><scope>SOI</scope></search><sort><creationdate>20191001</creationdate><title>Investigating the driving factors of regional CO2 emissions in China using the IDA-PDA-MMI method</title><author>Zha, Donglan ; Yang, Guanglei ; Wang, Qunwei</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c396t-f7c0e3440ae56406f884dcd6e8d60eb292478b7f2564dc385fe0d1d5a8ae94013</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Carbon dioxide</topic><topic>Carbon dioxide emissions</topic><topic>Change agents</topic><topic>China</topic><topic>Decomposition</topic><topic>Driving</topic><topic>Driving factors</topic><topic>Economics</topic><topic>Efficiency</topic><topic>Emissions</topic><topic>Energy</topic><topic>Energy economics</topic><topic>Energy technology</topic><topic>Handheld computers</topic><topic>IDA</topic><topic>Indexes</topic><topic>Meta-frontier Malmquist index</topic><topic>PDA</topic><topic>Power efficiency</topic><topic>Provinces</topic><topic>Regional development</topic><topic>Technology</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zha, Donglan</creatorcontrib><creatorcontrib>Yang, Guanglei</creatorcontrib><creatorcontrib>Wang, Qunwei</creatorcontrib><collection>CrossRef</collection><collection>Environment Abstracts</collection><collection>Materials Business File</collection><collection>PAIS Index</collection><collection>International Bibliography of the Social Sciences (IBSS)</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>PAIS International</collection><collection>PAIS International (Ovid)</collection><collection>International Bibliography of the Social Sciences</collection><collection>International Bibliography of the Social Sciences</collection><collection>Materials Research Database</collection><collection>Environment Abstracts</collection><jtitle>Energy economics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zha, Donglan</au><au>Yang, Guanglei</au><au>Wang, Qunwei</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Investigating the driving factors of regional CO2 emissions in China using the IDA-PDA-MMI method</atitle><jtitle>Energy economics</jtitle><date>2019-10-01</date><risdate>2019</risdate><volume>84</volume><spage>104521</spage><pages>104521-</pages><artnum>104521</artnum><issn>0140-9883</issn><eissn>1873-6181</eissn><abstract>•We developed a new comprehensive decomposition method, named IDA-PDA-MMI method.•Our framework was employed to Chinese provincial data from 2000 to 2016.•Technology gap has great potential for CO2 emission reduction.•Energy technology progress and output technology progress decreased regional CO2 emissions. In this paper, we systematically summarized existing research on the driving factors of CO2 emissions and found that changes in technology gap may be one of the key driving factors of CO2 emissions. Technology efficiency, technology progress, and technology gap were decomposed by using the Meta-frontier Malmquist index (MMI), which was then combined it with the Index Decomposition Analysis (IDA) and the Production-theoretical Decomposition Analysis (PDA). Our framework was applied to Chinese provincial data from 2000 to 2016. We identified nine factors to explain changes of regional CO2 emissions. Results demonstrate that economic scale, energy technology efficiency, and output technology efficiency increased CO2 emissions in Eastern, Central, and Western regions of China, with the economic scale being the largest contributor. Energy structure, energy intensity, energy technology progress, and output technology progress decreased regional CO2 emissions, with the energy technology progress playing the strongest role. Energy technology gap and output technology gap led to an increase in CO2 emissions in Central China and, to a lesser extent, in Western China. The effects of each driving factor on CO2 emissions varied across provinces. Finally, policy implications are suggested to reduce CO2 emissions in China.</abstract><cop>Kidlington</cop><pub>Elsevier B.V</pub><doi>10.1016/j.eneco.2019.104521</doi></addata></record>
fulltext fulltext
identifier ISSN: 0140-9883
ispartof Energy economics, 2019-10, Vol.84, p.104521, Article 104521
issn 0140-9883
1873-6181
language eng
recordid cdi_proquest_journals_2354808825
source PAIS Index; Elsevier ScienceDirect Journals
subjects Carbon dioxide
Carbon dioxide emissions
Change agents
China
Decomposition
Driving
Driving factors
Economics
Efficiency
Emissions
Energy
Energy economics
Energy technology
Handheld computers
IDA
Indexes
Meta-frontier Malmquist index
PDA
Power efficiency
Provinces
Regional development
Technology
title Investigating the driving factors of regional CO2 emissions in China using the IDA-PDA-MMI method
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-02T14%3A16%3A40IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Investigating%20the%20driving%20factors%20of%20regional%20CO2%20emissions%20in%20China%20using%20the%20IDA-PDA-MMI%20method&rft.jtitle=Energy%20economics&rft.au=Zha,%20Donglan&rft.date=2019-10-01&rft.volume=84&rft.spage=104521&rft.pages=104521-&rft.artnum=104521&rft.issn=0140-9883&rft.eissn=1873-6181&rft_id=info:doi/10.1016/j.eneco.2019.104521&rft_dat=%3Cproquest_cross%3E2354808825%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2354808825&rft_id=info:pmid/&rft_els_id=S014098831930310X&rfr_iscdi=true