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Coupling Coordination Analysis of Regional IEE System: A Data-Driven Multimodel Decision Approach
Coordinating regional innovation–economy–ecology (IEE) systems is an important prerequisite for overall continuous regional development. To fully understand the coordination relationship among the three, this study builds a data-driven multimodel decision approach to calculate, assess, diagnose, and...
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Published in: | Processes 2022-11, Vol.10 (11), p.2268 |
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creator | Yang, Yaliu Hu, Fagang Ding, Ling Wu, Xue |
description | Coordinating regional innovation–economy–ecology (IEE) systems is an important prerequisite for overall continuous regional development. To fully understand the coordination relationship among the three, this study builds a data-driven multimodel decision approach to calculate, assess, diagnose, and improve the regional IEE system. First, the assessment indicator system of the regional IEE system is established. Secondly, the range method, entropy weight method, and weighted summation method are employed to calculate the synthetic developmental level. Thirdly, a multimodel decision approach including the coupling degree model, the coordination degree model, and the obstacle degree model is constructed to assess the spatiotemporal evolution characteristics of the regional IEE system coupling coordination and diagnose the main obstacles hindering its development. Finally, the approach is tested using Anhui Province as a case study. The results show that the coupling coordination degree of the Anhui IEE system presents a stable growth trend, but the coupling degree is always higher than the coordination degree. The main obstacle affecting its development has changed from the original innovation subsystem to the current ecology subsystem. Based on this, some countermeasures are put forward. This study, therefore, offers decision support methods to aid in evaluating and improving the regional IEE system. |
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To fully understand the coordination relationship among the three, this study builds a data-driven multimodel decision approach to calculate, assess, diagnose, and improve the regional IEE system. First, the assessment indicator system of the regional IEE system is established. Secondly, the range method, entropy weight method, and weighted summation method are employed to calculate the synthetic developmental level. Thirdly, a multimodel decision approach including the coupling degree model, the coordination degree model, and the obstacle degree model is constructed to assess the spatiotemporal evolution characteristics of the regional IEE system coupling coordination and diagnose the main obstacles hindering its development. Finally, the approach is tested using Anhui Province as a case study. The results show that the coupling coordination degree of the Anhui IEE system presents a stable growth trend, but the coupling degree is always higher than the coordination degree. The main obstacle affecting its development has changed from the original innovation subsystem to the current ecology subsystem. Based on this, some countermeasures are put forward. This study, therefore, offers decision support methods to aid in evaluating and improving the regional IEE system.</description><identifier>ISSN: 2227-9717</identifier><identifier>EISSN: 2227-9717</identifier><identifier>DOI: 10.3390/pr10112268</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Analysis ; Barriers ; Case studies ; China ; Civilization ; Climate change ; Coordination ; Correlation analysis ; Coupling ; Decision analysis ; Decision making ; Ecology ; Economic development ; Economic growth ; Efficiency ; Emissions ; Entropy ; Entropy (statistics) ; Innovations ; Measurement techniques ; Methods ; Regional development ; Subsystems ; Sustainable development ; Weighting methods</subject><ispartof>Processes, 2022-11, Vol.10 (11), p.2268</ispartof><rights>COPYRIGHT 2022 MDPI AG</rights><rights>2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c264t-f1d776c64c60af5eab2a371a0baeaeb985f940388db6bf83483610a3532235ea3</citedby><cites>FETCH-LOGICAL-c264t-f1d776c64c60af5eab2a371a0baeaeb985f940388db6bf83483610a3532235ea3</cites><orcidid>0000-0001-5861-2630</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2734709609/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2734709609?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,25753,27924,27925,37012,44590,75126</link.rule.ids></links><search><creatorcontrib>Yang, Yaliu</creatorcontrib><creatorcontrib>Hu, Fagang</creatorcontrib><creatorcontrib>Ding, Ling</creatorcontrib><creatorcontrib>Wu, Xue</creatorcontrib><title>Coupling Coordination Analysis of Regional IEE System: A Data-Driven Multimodel Decision Approach</title><title>Processes</title><description>Coordinating regional innovation–economy–ecology (IEE) systems is an important prerequisite for overall continuous regional development. To fully understand the coordination relationship among the three, this study builds a data-driven multimodel decision approach to calculate, assess, diagnose, and improve the regional IEE system. First, the assessment indicator system of the regional IEE system is established. Secondly, the range method, entropy weight method, and weighted summation method are employed to calculate the synthetic developmental level. Thirdly, a multimodel decision approach including the coupling degree model, the coordination degree model, and the obstacle degree model is constructed to assess the spatiotemporal evolution characteristics of the regional IEE system coupling coordination and diagnose the main obstacles hindering its development. Finally, the approach is tested using Anhui Province as a case study. The results show that the coupling coordination degree of the Anhui IEE system presents a stable growth trend, but the coupling degree is always higher than the coordination degree. The main obstacle affecting its development has changed from the original innovation subsystem to the current ecology subsystem. Based on this, some countermeasures are put forward. 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Hu, Fagang ; Ding, Ling ; Wu, Xue</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c264t-f1d776c64c60af5eab2a371a0baeaeb985f940388db6bf83483610a3532235ea3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Analysis</topic><topic>Barriers</topic><topic>Case studies</topic><topic>China</topic><topic>Civilization</topic><topic>Climate change</topic><topic>Coordination</topic><topic>Correlation analysis</topic><topic>Coupling</topic><topic>Decision analysis</topic><topic>Decision making</topic><topic>Ecology</topic><topic>Economic development</topic><topic>Economic growth</topic><topic>Efficiency</topic><topic>Emissions</topic><topic>Entropy</topic><topic>Entropy (statistics)</topic><topic>Innovations</topic><topic>Measurement techniques</topic><topic>Methods</topic><topic>Regional development</topic><topic>Subsystems</topic><topic>Sustainable development</topic><topic>Weighting methods</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yang, Yaliu</creatorcontrib><creatorcontrib>Hu, Fagang</creatorcontrib><creatorcontrib>Ding, Ling</creatorcontrib><creatorcontrib>Wu, Xue</creatorcontrib><collection>CrossRef</collection><collection>Engineered Materials Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection (Proquest) (PQ_SDU_P3)</collection><collection>Materials Research Database</collection><collection>Materials Science Database</collection><collection>ProQuest Biological Science Collection</collection><collection>ProQuest Biological Science Journals</collection><collection>Materials science collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><jtitle>Processes</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yang, Yaliu</au><au>Hu, Fagang</au><au>Ding, Ling</au><au>Wu, Xue</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Coupling Coordination Analysis of Regional IEE System: A Data-Driven Multimodel Decision Approach</atitle><jtitle>Processes</jtitle><date>2022-11-01</date><risdate>2022</risdate><volume>10</volume><issue>11</issue><spage>2268</spage><pages>2268-</pages><issn>2227-9717</issn><eissn>2227-9717</eissn><abstract>Coordinating regional innovation–economy–ecology (IEE) systems is an important prerequisite for overall continuous regional development. To fully understand the coordination relationship among the three, this study builds a data-driven multimodel decision approach to calculate, assess, diagnose, and improve the regional IEE system. First, the assessment indicator system of the regional IEE system is established. Secondly, the range method, entropy weight method, and weighted summation method are employed to calculate the synthetic developmental level. Thirdly, a multimodel decision approach including the coupling degree model, the coordination degree model, and the obstacle degree model is constructed to assess the spatiotemporal evolution characteristics of the regional IEE system coupling coordination and diagnose the main obstacles hindering its development. Finally, the approach is tested using Anhui Province as a case study. The results show that the coupling coordination degree of the Anhui IEE system presents a stable growth trend, but the coupling degree is always higher than the coordination degree. The main obstacle affecting its development has changed from the original innovation subsystem to the current ecology subsystem. Based on this, some countermeasures are put forward. This study, therefore, offers decision support methods to aid in evaluating and improving the regional IEE system.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/pr10112268</doi><orcidid>https://orcid.org/0000-0001-5861-2630</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Analysis Barriers Case studies China Civilization Climate change Coordination Correlation analysis Coupling Decision analysis Decision making Ecology Economic development Economic growth Efficiency Emissions Entropy Entropy (statistics) Innovations Measurement techniques Methods Regional development Subsystems Sustainable development Weighting methods |
title | Coupling Coordination Analysis of Regional IEE System: A Data-Driven Multimodel Decision Approach |
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