Spatial–Temporal Evolution and Influential Factors of Eco-Efficiency in Chinese Urban Agglomerations
Against the background of a high-quality development philosophy, the realization of the coordinated development of the economy, environment, and resources is particularly important. This study adopts the super-efficiency slacks-based measure (SBM) model to evaluate the eco-efficiency of 208 cities i...
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Veröffentlicht in: | Sustainability 2023-08, Vol.15 (16), p.12225 |
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description | Against the background of a high-quality development philosophy, the realization of the coordinated development of the economy, environment, and resources is particularly important. This study adopts the super-efficiency slacks-based measure (SBM) model to evaluate the eco-efficiency of 208 cities in 19 urban agglomerations in China from 2006 to 2020, and the kernel density estimation and spatial econometric specifications are combined to reveal the spatial–temporal evolution. Finally, Tobit regression is used to analyze the driving factors of the eco-efficiency of urban agglomerations in China. The main results can be summarized as follows: (1) The eco-efficiency of Chinese urban agglomerations is generally low, and the differences in eco-efficiency between urban agglomerations are obvious, with different trends of change. (2) In terms of the time series, the sample period shows a “steadily rising” trend followed by a “fluctuating downward” trend. From the results of the kernel density estimation, the internal difference in the overall eco-efficiency of urban agglomerations shows the trend of a small decline followed by a gradual increase. (3) From the spatial point of view, the eco-efficiency of urban agglomerations decreased from the coast to the inland areas, and there was a “cluster effect”. The overall eco-efficiency of urban agglomerations shows a trend of spatial aggregation. (4) From the perspective of influencing factors, fiscal expenditure, opening-up level, and population density have a significant negative correlation with the eco-efficiency of urban agglomerations, while science and technology investment, industrial structure, and urbanization level have a significant positive correlation with the eco-efficiency of urban agglomerations. The research in this paper provides guidance for the coordinated development of urban agglomerations and the formulation of environmental policies. |
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This study adopts the super-efficiency slacks-based measure (SBM) model to evaluate the eco-efficiency of 208 cities in 19 urban agglomerations in China from 2006 to 2020, and the kernel density estimation and spatial econometric specifications are combined to reveal the spatial–temporal evolution. Finally, Tobit regression is used to analyze the driving factors of the eco-efficiency of urban agglomerations in China. The main results can be summarized as follows: (1) The eco-efficiency of Chinese urban agglomerations is generally low, and the differences in eco-efficiency between urban agglomerations are obvious, with different trends of change. (2) In terms of the time series, the sample period shows a “steadily rising” trend followed by a “fluctuating downward” trend. From the results of the kernel density estimation, the internal difference in the overall eco-efficiency of urban agglomerations shows the trend of a small decline followed by a gradual increase. (3) From the spatial point of view, the eco-efficiency of urban agglomerations decreased from the coast to the inland areas, and there was a “cluster effect”. The overall eco-efficiency of urban agglomerations shows a trend of spatial aggregation. (4) From the perspective of influencing factors, fiscal expenditure, opening-up level, and population density have a significant negative correlation with the eco-efficiency of urban agglomerations, while science and technology investment, industrial structure, and urbanization level have a significant positive correlation with the eco-efficiency of urban agglomerations. The research in this paper provides guidance for the coordinated development of urban agglomerations and the formulation of environmental policies.</description><identifier>ISSN: 2071-1050</identifier><identifier>EISSN: 2071-1050</identifier><identifier>DOI: 10.3390/su151612225</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Cities ; Consumption ; Data envelopment analysis ; Decision making ; Economic development ; Efficiency ; Environmental impact ; Modernization ; Sustainable development ; Urbanization</subject><ispartof>Sustainability, 2023-08, Vol.15 (16), p.12225</ispartof><rights>COPYRIGHT 2023 MDPI AG</rights><rights>2023 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-c371t-8764b61d1731daa2b69144c7a2996fb9642c88d9520287db10d20feda60e33e23</citedby><cites>FETCH-LOGICAL-c371t-8764b61d1731daa2b69144c7a2996fb9642c88d9520287db10d20feda60e33e23</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids></links><search><creatorcontrib>Zhang, Xiyao</creatorcontrib><creatorcontrib>Wang, Xiaolei</creatorcontrib><creatorcontrib>Liu, Jia</creatorcontrib><title>Spatial–Temporal Evolution and Influential Factors of Eco-Efficiency in Chinese Urban Agglomerations</title><title>Sustainability</title><description>Against the background of a high-quality development philosophy, the realization of the coordinated development of the economy, environment, and resources is particularly important. This study adopts the super-efficiency slacks-based measure (SBM) model to evaluate the eco-efficiency of 208 cities in 19 urban agglomerations in China from 2006 to 2020, and the kernel density estimation and spatial econometric specifications are combined to reveal the spatial–temporal evolution. Finally, Tobit regression is used to analyze the driving factors of the eco-efficiency of urban agglomerations in China. The main results can be summarized as follows: (1) The eco-efficiency of Chinese urban agglomerations is generally low, and the differences in eco-efficiency between urban agglomerations are obvious, with different trends of change. (2) In terms of the time series, the sample period shows a “steadily rising” trend followed by a “fluctuating downward” trend. From the results of the kernel density estimation, the internal difference in the overall eco-efficiency of urban agglomerations shows the trend of a small decline followed by a gradual increase. (3) From the spatial point of view, the eco-efficiency of urban agglomerations decreased from the coast to the inland areas, and there was a “cluster effect”. The overall eco-efficiency of urban agglomerations shows a trend of spatial aggregation. (4) From the perspective of influencing factors, fiscal expenditure, opening-up level, and population density have a significant negative correlation with the eco-efficiency of urban agglomerations, while science and technology investment, industrial structure, and urbanization level have a significant positive correlation with the eco-efficiency of urban agglomerations. The research in this paper provides guidance for the coordinated development of urban agglomerations and the formulation of environmental policies.</description><subject>Cities</subject><subject>Consumption</subject><subject>Data envelopment analysis</subject><subject>Decision making</subject><subject>Economic development</subject><subject>Efficiency</subject><subject>Environmental impact</subject><subject>Modernization</subject><subject>Sustainable development</subject><subject>Urbanization</subject><issn>2071-1050</issn><issn>2071-1050</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNpVkc9Kw0AQxoMoWGpPvsCCJ5HU_ZdNciyl1UJBsO05bDa7cUuyG3c3Ym--g2_ok5hSD3bmMMPw-745fFF0i-CUkBw--h4liCGMcXIRjTBMUYxgAi__7dfRxPs9HIoQlCM2itSm40Hz5ufreyvbzjregMWHbfqgrQHcVGBlVNNLc4TAkotgnQdWgYWw8UIpLbQ04gC0AfM3baSXYOdKbsCsrhvbSsePRv4mulK88XLyN8fRbrnYzp_j9cvTaj5bx4KkKMRZymjJUIVSgirOcclyRKlIOc5zpsqcUSyyrMoTDHGWViWCFYZKVpxBSYjEZBzdnXw7Z9976UOxt70zw8sCZ0lKKc3yZKCmJ6rmjSy0UTY4LoauZKuFNVLp4T5LGU5oRikaBPdngoEJ8jPUvPe-WG1ez9mHEyuc9d5JVXROt9wdCgSLY1DFv6DIL0HPhMU</recordid><startdate>20230801</startdate><enddate>20230801</enddate><creator>Zhang, Xiyao</creator><creator>Wang, Xiaolei</creator><creator>Liu, Jia</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>ISR</scope><scope>4U-</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope></search><sort><creationdate>20230801</creationdate><title>Spatial–Temporal Evolution and Influential Factors of Eco-Efficiency in Chinese Urban Agglomerations</title><author>Zhang, Xiyao ; Wang, Xiaolei ; Liu, Jia</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c371t-8764b61d1731daa2b69144c7a2996fb9642c88d9520287db10d20feda60e33e23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Cities</topic><topic>Consumption</topic><topic>Data envelopment analysis</topic><topic>Decision making</topic><topic>Economic development</topic><topic>Efficiency</topic><topic>Environmental impact</topic><topic>Modernization</topic><topic>Sustainable development</topic><topic>Urbanization</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhang, Xiyao</creatorcontrib><creatorcontrib>Wang, Xiaolei</creatorcontrib><creatorcontrib>Liu, Jia</creatorcontrib><collection>CrossRef</collection><collection>Gale In Context: Science</collection><collection>University Readers</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</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><jtitle>Sustainability</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhang, Xiyao</au><au>Wang, Xiaolei</au><au>Liu, Jia</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Spatial–Temporal Evolution and Influential Factors of Eco-Efficiency in Chinese Urban Agglomerations</atitle><jtitle>Sustainability</jtitle><date>2023-08-01</date><risdate>2023</risdate><volume>15</volume><issue>16</issue><spage>12225</spage><pages>12225-</pages><issn>2071-1050</issn><eissn>2071-1050</eissn><abstract>Against the background of a high-quality development philosophy, the realization of the coordinated development of the economy, environment, and resources is particularly important. This study adopts the super-efficiency slacks-based measure (SBM) model to evaluate the eco-efficiency of 208 cities in 19 urban agglomerations in China from 2006 to 2020, and the kernel density estimation and spatial econometric specifications are combined to reveal the spatial–temporal evolution. Finally, Tobit regression is used to analyze the driving factors of the eco-efficiency of urban agglomerations in China. The main results can be summarized as follows: (1) The eco-efficiency of Chinese urban agglomerations is generally low, and the differences in eco-efficiency between urban agglomerations are obvious, with different trends of change. (2) In terms of the time series, the sample period shows a “steadily rising” trend followed by a “fluctuating downward” trend. From the results of the kernel density estimation, the internal difference in the overall eco-efficiency of urban agglomerations shows the trend of a small decline followed by a gradual increase. 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subjects | Cities Consumption Data envelopment analysis Decision making Economic development Efficiency Environmental impact Modernization Sustainable development Urbanization |
title | Spatial–Temporal Evolution and Influential Factors of Eco-Efficiency in Chinese Urban Agglomerations |
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