Teleconnections detection of land use carbon emissions beyond geographical proximity promotes the construction of cross-city networks
Considering the spatial association network of land use carbon emissions (LUCE) between cities is essential for supporting the sustainable development of urban agglomerations and achieving carbon neutrality. The Yangtze River Delta urban agglomeration (YRDUA), as one of China's most developed r...
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Elsevier
2025-01-01
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| Series: | Ecological Indicators |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1470160X25000214 |
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| author | Lei Shen Yinghong Jiang Duanqiang Zhai Peng Wang |
| author_facet | Lei Shen Yinghong Jiang Duanqiang Zhai Peng Wang |
| author_sort | Lei Shen |
| collection | DOAJ |
| description | Considering the spatial association network of land use carbon emissions (LUCE) between cities is essential for supporting the sustainable development of urban agglomerations and achieving carbon neutrality. The Yangtze River Delta urban agglomeration (YRDUA), as one of China's most developed regions, has seen rapid growth in all aspects of the cities due to rapid urbanization. However, urban interactions in some areas can no longer meet the goal of regional integration for the YRDUA. Therefore, this study first identifies LUCE at the pixel scale from both direct and indirect perspectives. Then a complex network model is used to construct the spatial correlation strength, network structure, and network clustering among cities to identify spatial associations between cities within the YRDUA. Finally, the CatBoost machine learning regression model is used to analyze the factors influencing LUCE. The results show that: (1) The levels of LUCE in the YRDUA have significantly changed, showing a spatial distribution characterized by local concentration and overall fragmentation. The total LUCE from land use in the YRDUA increased by 3.96 times, while the growth rate decreased from 100.62% to 10.38%. (2) The intensity of the associations between cities is continuously rising, with large cities exerting an increasing influence on smaller cities. Shanghai, Suzhou, Wuxi, and Nanjing are situated at the core of the network structure, exerting the most significant influence on the cities within the region. (3) The interactions between different sectors have become more frequent, and the spillover effect is more evident. However, cities tend to collaborate with others within the same block. (4) GDP is the primary factor affecting LUCE, with its contribution amounting to 27.6% in 2000 and 32.5% in 2020. Moreover, the impact of climate change on LUCE is becoming increasingly complex. |
| format | Article |
| id | doaj-art-3cbfa7a07f7b446e962d01f70d11dbb7 |
| institution | Kabale University |
| issn | 1470-160X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Indicators |
| spelling | doaj-art-3cbfa7a07f7b446e962d01f70d11dbb72025-01-31T05:10:50ZengElsevierEcological Indicators1470-160X2025-01-01170113092Teleconnections detection of land use carbon emissions beyond geographical proximity promotes the construction of cross-city networksLei Shen0Yinghong Jiang1Duanqiang Zhai2Peng Wang3ZJU-STEC Urban Development and Planning Innovation Joint Research Center, Zhejiang University, Hangzhou 310058 China; China Eco-city Academy Co., Ltd., Tianjin 300467 ChinaZJU-STEC Urban Development and Planning Innovation Joint Research Center, Zhejiang University, Hangzhou 310058 China; Shanghai Urban Construction Design and Research Institute (Group) Co., Ltd., Shanghai 200000 ChinaCollege of Architecture and Urban Planning, Tongji University, Shanghai 200092 China; Key Laboratory of Spatial Intelligent Planning Technology, Ministry of Natural Resources of the People's Republic of China, Shanghai 200092 China; Corresponding author.China Eco-city Academy Co., Ltd., Tianjin 300467 China; Institute of Soil and Water Conservation, Northwest A&F University, Yangling 712100 Shaanxi, ChinaConsidering the spatial association network of land use carbon emissions (LUCE) between cities is essential for supporting the sustainable development of urban agglomerations and achieving carbon neutrality. The Yangtze River Delta urban agglomeration (YRDUA), as one of China's most developed regions, has seen rapid growth in all aspects of the cities due to rapid urbanization. However, urban interactions in some areas can no longer meet the goal of regional integration for the YRDUA. Therefore, this study first identifies LUCE at the pixel scale from both direct and indirect perspectives. Then a complex network model is used to construct the spatial correlation strength, network structure, and network clustering among cities to identify spatial associations between cities within the YRDUA. Finally, the CatBoost machine learning regression model is used to analyze the factors influencing LUCE. The results show that: (1) The levels of LUCE in the YRDUA have significantly changed, showing a spatial distribution characterized by local concentration and overall fragmentation. The total LUCE from land use in the YRDUA increased by 3.96 times, while the growth rate decreased from 100.62% to 10.38%. (2) The intensity of the associations between cities is continuously rising, with large cities exerting an increasing influence on smaller cities. Shanghai, Suzhou, Wuxi, and Nanjing are situated at the core of the network structure, exerting the most significant influence on the cities within the region. (3) The interactions between different sectors have become more frequent, and the spillover effect is more evident. However, cities tend to collaborate with others within the same block. (4) GDP is the primary factor affecting LUCE, with its contribution amounting to 27.6% in 2000 and 32.5% in 2020. Moreover, the impact of climate change on LUCE is becoming increasingly complex.http://www.sciencedirect.com/science/article/pii/S1470160X25000214Carbon emissionsLand useComplex networkMachine learningUrban agglomeration |
| spellingShingle | Lei Shen Yinghong Jiang Duanqiang Zhai Peng Wang Teleconnections detection of land use carbon emissions beyond geographical proximity promotes the construction of cross-city networks Ecological Indicators Carbon emissions Land use Complex network Machine learning Urban agglomeration |
| title | Teleconnections detection of land use carbon emissions beyond geographical proximity promotes the construction of cross-city networks |
| title_full | Teleconnections detection of land use carbon emissions beyond geographical proximity promotes the construction of cross-city networks |
| title_fullStr | Teleconnections detection of land use carbon emissions beyond geographical proximity promotes the construction of cross-city networks |
| title_full_unstemmed | Teleconnections detection of land use carbon emissions beyond geographical proximity promotes the construction of cross-city networks |
| title_short | Teleconnections detection of land use carbon emissions beyond geographical proximity promotes the construction of cross-city networks |
| title_sort | teleconnections detection of land use carbon emissions beyond geographical proximity promotes the construction of cross city networks |
| topic | Carbon emissions Land use Complex network Machine learning Urban agglomeration |
| url | http://www.sciencedirect.com/science/article/pii/S1470160X25000214 |
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