Spatio-temporal evolution of resources and environmental carrying capacity and its influencing factors : A case study of Shandong Peninsula urban agglomeration
Copyright © 2023 Elsevier Inc. All rights reserved..
Promoting ecological conservation and high-quality development in the Yellow River basin is an important objective in China's 14th Five-Year Plan. Understanding the spatio-temporal evolution of and factors affecting the resources and environmental carrying capacity (RECC) of the urban agglomerations is critical for boosting high-quality green-oriented development. We first combined the Driver-Pressure-State-Impact-Response (DPSIR) framework and the improved Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) model to evaluate the RECC of Shandong Peninsula urban agglomeration in 2000, 2010 and 2020; we then used trend analysis and spatial autocorrelation analysis to understand the spatio-temporal evolution and distribution pattern of RECC. Furthermore, we employed Geodetector to detect the influencing factors and classified the urban agglomeration into six zones based on the weighted Voronoi diagram of RECC as well as specific conditions of the study area. The results show that the RECC of Shandong Peninsula urban agglomeration increased consistently over time, from 0.3887 in 2000 to 0.4952 in 2010 and 0.6097 in 2020, respectively. Geographically, RECC decreased gradually from the northeast coast to the southwest inland. Globally, only in 2010 the RECC presented a significant spatial positive correlation, and that in the other years were not significant. The high-high cluster was mainly located in Weifang, while the low-low cluster in Jining. Furthermore, our study reveals three key factors-advancement of industrial structure, resident consumption level, and water consumption per ten thousand yuan of industrial added value-that affected the distribution of RECC. Other factors, including the interactions between residents' consumption level and environmental regulation, residents' consumption level and advancement of industrial structure, as well as between the proportion of R&D expenditure in GDP and resident consumption level also played important roles resulting in the variation of RECC among different cities within the urban agglomeration. Accordingly, we proposed suggestions for achieving high-quality development for different zones.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:234 |
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| Enthalten in: |
Environmental research - 234(2023) vom: 01. Okt., Seite 116469 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Fan, Wenping [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Completed 12.09.2023 Date Revised 25.09.2023 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.envres.2023.116469 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 100 | 1 | |a Fan, Wenping |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Spatio-temporal evolution of resources and environmental carrying capacity and its influencing factors |b A case study of Shandong Peninsula urban agglomeration |
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| 500 | |a Date Revised 25.09.2023 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2023 Elsevier Inc. All rights reserved. | ||
| 520 | |a Promoting ecological conservation and high-quality development in the Yellow River basin is an important objective in China's 14th Five-Year Plan. Understanding the spatio-temporal evolution of and factors affecting the resources and environmental carrying capacity (RECC) of the urban agglomerations is critical for boosting high-quality green-oriented development. We first combined the Driver-Pressure-State-Impact-Response (DPSIR) framework and the improved Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) model to evaluate the RECC of Shandong Peninsula urban agglomeration in 2000, 2010 and 2020; we then used trend analysis and spatial autocorrelation analysis to understand the spatio-temporal evolution and distribution pattern of RECC. Furthermore, we employed Geodetector to detect the influencing factors and classified the urban agglomeration into six zones based on the weighted Voronoi diagram of RECC as well as specific conditions of the study area. The results show that the RECC of Shandong Peninsula urban agglomeration increased consistently over time, from 0.3887 in 2000 to 0.4952 in 2010 and 0.6097 in 2020, respectively. Geographically, RECC decreased gradually from the northeast coast to the southwest inland. Globally, only in 2010 the RECC presented a significant spatial positive correlation, and that in the other years were not significant. The high-high cluster was mainly located in Weifang, while the low-low cluster in Jining. Furthermore, our study reveals three key factors-advancement of industrial structure, resident consumption level, and water consumption per ten thousand yuan of industrial added value-that affected the distribution of RECC. Other factors, including the interactions between residents' consumption level and environmental regulation, residents' consumption level and advancement of industrial structure, as well as between the proportion of R&D expenditure in GDP and resident consumption level also played important roles resulting in the variation of RECC among different cities within the urban agglomeration. Accordingly, we proposed suggestions for achieving high-quality development for different zones | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a Driver-pressure-state-impact-response (DPSIR) framework | |
| 650 | 4 | |a Geodetector | |
| 650 | 4 | |a Resources and environmental carrying capacity (RECC) | |
| 650 | 4 | |a Shandong Peninsula urban agglomeration | |
| 650 | 4 | |a The improved technique for order preference by similarity to ideal solution (TOPSIS) model | |
| 700 | 1 | |a Song, Xueyan |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Mengnan |e verfasserin |4 aut | |
| 700 | 1 | |a Shan, Baoyan |e verfasserin |4 aut | |
| 700 | 1 | |a Ma, Mingliang |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Yan |e verfasserin |4 aut | |
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