Studying the Eco-Environmental Quality Variations of Jing-Jin-Ji Urban Agglomeration and Its Driving Factors in Different Ecosystem Service Regions from 2001 to 2015
Exploring the regional eco-environmental quality (EEQ) and its driving factors is of great significance for regional management. Although existing studies have paid much attention to evaluate EEQ, few studies have been performed to investigate the spatiotemporal variations of EEQ and its driving fac...
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| description | Exploring the regional eco-environmental quality (EEQ) and its driving factors is of great significance for regional management. Although existing studies have paid much attention to evaluate EEQ, few studies have been performed to investigate the spatiotemporal variations of EEQ and its driving factors in different ecosystem service regions (ESR) at an urban agglomeration scale. In this study, we selected Jing-Jin-Ji urban agglomeration (JJJ) as the study area to evaluate its EEQ, analyze its spatiotemporal variations, and investigate potential driving factors explanatory power based on the geographical detector methods in different ESR during 2001∼2015. The main conclusions were as follows: (1) The EEQ of JJJ had improved from 2001 to 2015, with the average RSEI increased from 0.43 to 0.46; among them, Bashang Plateau and Western Hebei Ecosystem Service Region (BWHE) had the highest RSEI change rate (±26.19%) and the highest NTEDI value (0.13), while Central Hebei Plain Ecosystem Service Region (CHPE) had the lowest RSEI change rate (-5.41%) and the lowest NTEDI value (-0.02). (2) The EEQ of JJJ had strong spatial agglomeration effects, with the global Moran's I increased from 0.82 to 0.88. Spatially, the LL regions mainly changed into the HH regions in the northwestern part, while in the central and eastern areas, some isolated LL regions displayed an aggregated trend. (3) In terms of the driving factors, soil type and elevation were primary factors in explaining the variations of EEQ. Specifically, natural factors explained the highest variations in BWHE. The interaction of topographical and socio-economic factors had high explanatory power in Yanshan and Taihang Mountain Ecosystem Service Region (YTME) and CHPE; To Bohai and Coastal Ring Ecosystem Service Region (BCRE), the interaction of meteorological and socio-economic factors accounted for the high variations of EEQ. All these findings could provide more valuable advice for relevant policy-makers. |
| doi_str_mv | 10.1109/ACCESS.2020.3018730 |
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Although existing studies have paid much attention to evaluate EEQ, few studies have been performed to investigate the spatiotemporal variations of EEQ and its driving factors in different ecosystem service regions (ESR) at an urban agglomeration scale. In this study, we selected Jing-Jin-Ji urban agglomeration (JJJ) as the study area to evaluate its EEQ, analyze its spatiotemporal variations, and investigate potential driving factors explanatory power based on the geographical detector methods in different ESR during 2001∼2015. The main conclusions were as follows: (1) The EEQ of JJJ had improved from 2001 to 2015, with the average RSEI increased from 0.43 to 0.46; among them, Bashang Plateau and Western Hebei Ecosystem Service Region (BWHE) had the highest RSEI change rate (±26.19%) and the highest NTEDI value (0.13), while Central Hebei Plain Ecosystem Service Region (CHPE) had the lowest RSEI change rate (-5.41%) and the lowest NTEDI value (-0.02). (2) The EEQ of JJJ had strong spatial agglomeration effects, with the global Moran's I increased from 0.82 to 0.88. Spatially, the LL regions mainly changed into the HH regions in the northwestern part, while in the central and eastern areas, some isolated LL regions displayed an aggregated trend. (3) In terms of the driving factors, soil type and elevation were primary factors in explaining the variations of EEQ. Specifically, natural factors explained the highest variations in BWHE. The interaction of topographical and socio-economic factors had high explanatory power in Yanshan and Taihang Mountain Ecosystem Service Region (YTME) and CHPE; To Bohai and Coastal Ring Ecosystem Service Region (BCRE), the interaction of meteorological and socio-economic factors accounted for the high variations of EEQ. All these findings could provide more valuable advice for relevant policy-makers.</description><identifier>ISSN: 2169-3536</identifier><identifier>EISSN: 2169-3536</identifier><identifier>DOI: 10.1109/ACCESS.2020.3018730</identifier><identifier>CODEN: IAECCG</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Eco-environmental quality ; Economic factors ; ecosystem service region ; Ecosystems ; Elevation ; Environmental quality ; Geographical detector ; Jing-Jin-Ji ; Mountains ; Regions ; remote sensing ecological index ; Social factors ; Socioeconomic factors</subject><ispartof>IEEE access, 2020-01, Vol.8, p.1-1</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2020</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c474t-9fdfe4e067a536fdcace1923d933a9b88b16a5773cdcbb761abde2cf1521dc0e3</citedby><cites>FETCH-LOGICAL-c474t-9fdfe4e067a536fdcace1923d933a9b88b16a5773cdcbb761abde2cf1521dc0e3</cites><orcidid>0000-0001-6631-7332 ; 0000-0002-4378-5667 ; 0000-0002-1996-6281 ; 0000-0001-5420-8016 ; 0000-0003-1420-8083</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9174716$$EHTML$$P50$$Gieee$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,860,2096,27610,27901,27902,54908</link.rule.ids></links><search><creatorcontrib>Ji, Jianwan</creatorcontrib><creatorcontrib>Wang, Shixin</creatorcontrib><creatorcontrib>Zhou, Yi</creatorcontrib><creatorcontrib>Liu, Wenliang</creatorcontrib><creatorcontrib>Wang, Litao</creatorcontrib><title>Studying the Eco-Environmental Quality Variations of Jing-Jin-Ji Urban Agglomeration and Its Driving Factors in Different Ecosystem Service Regions from 2001 to 2015</title><title>IEEE access</title><addtitle>Access</addtitle><description>Exploring the regional eco-environmental quality (EEQ) and its driving factors is of great significance for regional management. Although existing studies have paid much attention to evaluate EEQ, few studies have been performed to investigate the spatiotemporal variations of EEQ and its driving factors in different ecosystem service regions (ESR) at an urban agglomeration scale. In this study, we selected Jing-Jin-Ji urban agglomeration (JJJ) as the study area to evaluate its EEQ, analyze its spatiotemporal variations, and investigate potential driving factors explanatory power based on the geographical detector methods in different ESR during 2001∼2015. The main conclusions were as follows: (1) The EEQ of JJJ had improved from 2001 to 2015, with the average RSEI increased from 0.43 to 0.46; among them, Bashang Plateau and Western Hebei Ecosystem Service Region (BWHE) had the highest RSEI change rate (±26.19%) and the highest NTEDI value (0.13), while Central Hebei Plain Ecosystem Service Region (CHPE) had the lowest RSEI change rate (-5.41%) and the lowest NTEDI value (-0.02). (2) The EEQ of JJJ had strong spatial agglomeration effects, with the global Moran's I increased from 0.82 to 0.88. Spatially, the LL regions mainly changed into the HH regions in the northwestern part, while in the central and eastern areas, some isolated LL regions displayed an aggregated trend. (3) In terms of the driving factors, soil type and elevation were primary factors in explaining the variations of EEQ. Specifically, natural factors explained the highest variations in BWHE. The interaction of topographical and socio-economic factors had high explanatory power in Yanshan and Taihang Mountain Ecosystem Service Region (YTME) and CHPE; To Bohai and Coastal Ring Ecosystem Service Region (BCRE), the interaction of meteorological and socio-economic factors accounted for the high variations of EEQ. All these findings could provide more valuable advice for relevant policy-makers.</description><subject>Eco-environmental quality</subject><subject>Economic factors</subject><subject>ecosystem service region</subject><subject>Ecosystems</subject><subject>Elevation</subject><subject>Environmental quality</subject><subject>Geographical detector</subject><subject>Jing-Jin-Ji</subject><subject>Mountains</subject><subject>Regions</subject><subject>remote sensing ecological index</subject><subject>Social factors</subject><subject>Socioeconomic factors</subject><issn>2169-3536</issn><issn>2169-3536</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>DOA</sourceid><recordid>eNpNUd1q2zAUNmODla5P0BvBrp1JlmRZlyFNt4zC6LLuVsjSkacQW52kBPJAe88pcSkVHB1x-H50-KrqluAFIVh-Wa5W6-120eAGLygmnaD4XXXVkFbWlNP2_Zv3x-ompR0upysjLq6qf9t8sCc_DSj_AbQ2oV5PRx_DNMKU9R49HvTe5xP6raPX2YcpoeDQ90Koy1UKPcVeT2g5DPswQrxgkJ4s2uSE7qI_nrXvtckhJuQndOedg1jEz2bplDKMaAvx6A2gnzBcHFwMI2owJiiH0gn_VH1wep_g5qVfV0_361-rb_XDj6-b1fKhNkywXEtnHTDArdBlWWeNNkBkQ62kVMu-63rSai4ENdb0vWiJ7i00xhHeEGsw0OtqM-vaoHfqOfpRx5MK2qvLIMRB6Zi92YPqiO54z7mxIBlvOgngQPaUUSuIk65ofZ61nmP4e4CU1S4c4lS-rxrGWcsYbXhB0RllYkgpgnt1JVid41VzvOocr3qJt7BuZ5YHgFeGJIIJ0tL_j3OihA</recordid><startdate>20200101</startdate><enddate>20200101</enddate><creator>Ji, Jianwan</creator><creator>Wang, Shixin</creator><creator>Zhou, Yi</creator><creator>Liu, Wenliang</creator><creator>Wang, Litao</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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Although existing studies have paid much attention to evaluate EEQ, few studies have been performed to investigate the spatiotemporal variations of EEQ and its driving factors in different ecosystem service regions (ESR) at an urban agglomeration scale. In this study, we selected Jing-Jin-Ji urban agglomeration (JJJ) as the study area to evaluate its EEQ, analyze its spatiotemporal variations, and investigate potential driving factors explanatory power based on the geographical detector methods in different ESR during 2001∼2015. The main conclusions were as follows: (1) The EEQ of JJJ had improved from 2001 to 2015, with the average RSEI increased from 0.43 to 0.46; among them, Bashang Plateau and Western Hebei Ecosystem Service Region (BWHE) had the highest RSEI change rate (±26.19%) and the highest NTEDI value (0.13), while Central Hebei Plain Ecosystem Service Region (CHPE) had the lowest RSEI change rate (-5.41%) and the lowest NTEDI value (-0.02). (2) The EEQ of JJJ had strong spatial agglomeration effects, with the global Moran's I increased from 0.82 to 0.88. Spatially, the LL regions mainly changed into the HH regions in the northwestern part, while in the central and eastern areas, some isolated LL regions displayed an aggregated trend. (3) In terms of the driving factors, soil type and elevation were primary factors in explaining the variations of EEQ. Specifically, natural factors explained the highest variations in BWHE. The interaction of topographical and socio-economic factors had high explanatory power in Yanshan and Taihang Mountain Ecosystem Service Region (YTME) and CHPE; To Bohai and Coastal Ring Ecosystem Service Region (BCRE), the interaction of meteorological and socio-economic factors accounted for the high variations of EEQ. All these findings could provide more valuable advice for relevant policy-makers.</abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/ACCESS.2020.3018730</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0001-6631-7332</orcidid><orcidid>https://orcid.org/0000-0002-4378-5667</orcidid><orcidid>https://orcid.org/0000-0002-1996-6281</orcidid><orcidid>https://orcid.org/0000-0001-5420-8016</orcidid><orcidid>https://orcid.org/0000-0003-1420-8083</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Eco-environmental quality Economic factors ecosystem service region Ecosystems Elevation Environmental quality Geographical detector Jing-Jin-Ji Mountains Regions remote sensing ecological index Social factors Socioeconomic factors |
| title | Studying the Eco-Environmental Quality Variations of Jing-Jin-Ji Urban Agglomeration and Its Driving Factors in Different Ecosystem Service Regions from 2001 to 2015 |
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