Detection of driving factors and critical thresholds for carbon sequestration capacity in urban agglomerations using a combined causal inference and machine learning approach
The carbon sequestration capacity in urban agglomeration ecosystems is crucial for enhancing scientific understanding of the carbon cycle and promoting sustainable development to mitigate climate change. However, existing studies on driving factors, particularly regarding determining causal mechanis...
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Taylor & Francis Group
2025-12-01
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| Series: | GIScience & Remote Sensing |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/15481603.2025.2483492 |
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| author | Yin Zhang Weibo Ma Nan Wang Lijun Zhao Qingwu Hu Shaogang Lei Haidong Li |
| author_facet | Yin Zhang Weibo Ma Nan Wang Lijun Zhao Qingwu Hu Shaogang Lei Haidong Li |
| author_sort | Yin Zhang |
| collection | DOAJ |
| description | The carbon sequestration capacity in urban agglomeration ecosystems is crucial for enhancing scientific understanding of the carbon cycle and promoting sustainable development to mitigate climate change. However, existing studies on driving factors, particularly regarding determining causal mechanisms and critical thresholds for carbon sequestration in urban agglomeration ecosystems remain unclear. To address this knowledge gap, we propose a CMSC framework which integrates causal inference and machine learning methods to reveal the underlying mechanisms and determine the thresholds of drivers affecting carbon sequestration in the Yangtze River Delta urban agglomeration (YRDUA). The underlying driving mechanisms of carbon sequestration were heterogeneous between municipal county and non-municipal county in YRDUA. The thresholds of nighttime light, surface solar radiation downwards, air temperature, total precipitation and population density that impacted carbon sequestration in non-municipal (municipal) counties of YRDUA were 0.04 (0.4) nW·cm−2·sr−1·yr−1, −6.1 × 104 (−5.46 × 104) J·m−2·yr−1, 0.013 (0.017) K·yr−1, 3.64 × 10−5 (2.51 × 10−5) m·yr−1 and −0.04 people·km−2·yr−1, respectively. Furthermore, the long-term (from 2021 to 2100) carbon sequestration dataset with county-level scale in the YRDUA was generated using the causal inference-based machine learning model. In the context of carbon neutrality, we found that the optimal emission scenario for low-carbon sustainable development of YRDUA is SSP3, under which the average carbon sequestration in most counties will exceed 1 × 107 t. Our study provides a constructive basis for a science-based carbon cycle and ecological management in the urban agglomeration of China. |
| format | Article |
| id | doaj-art-898a5ecfe813402d97477336cbe6064e |
| institution | OA Journals |
| issn | 1548-1603 1943-7226 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | GIScience & Remote Sensing |
| spelling | doaj-art-898a5ecfe813402d97477336cbe6064e2025-08-20T01:48:19ZengTaylor & Francis GroupGIScience & Remote Sensing1548-16031943-72262025-12-0162110.1080/15481603.2025.2483492Detection of driving factors and critical thresholds for carbon sequestration capacity in urban agglomerations using a combined causal inference and machine learning approachYin Zhang0Weibo Ma1Nan Wang2Lijun Zhao3Qingwu Hu4Shaogang Lei5Haidong Li6Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment, Nanjing, ChinaNanjing Institute of Environmental Sciences, Ministry of Ecology and Environment, Nanjing, ChinaNanjing Institute of Environmental Sciences, Ministry of Ecology and Environment, Nanjing, ChinaNanjing Institute of Environmental Sciences, Ministry of Ecology and Environment, Nanjing, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan, ChinaSchool of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou, ChinaNanjing Institute of Environmental Sciences, Ministry of Ecology and Environment, Nanjing, ChinaThe carbon sequestration capacity in urban agglomeration ecosystems is crucial for enhancing scientific understanding of the carbon cycle and promoting sustainable development to mitigate climate change. However, existing studies on driving factors, particularly regarding determining causal mechanisms and critical thresholds for carbon sequestration in urban agglomeration ecosystems remain unclear. To address this knowledge gap, we propose a CMSC framework which integrates causal inference and machine learning methods to reveal the underlying mechanisms and determine the thresholds of drivers affecting carbon sequestration in the Yangtze River Delta urban agglomeration (YRDUA). The underlying driving mechanisms of carbon sequestration were heterogeneous between municipal county and non-municipal county in YRDUA. The thresholds of nighttime light, surface solar radiation downwards, air temperature, total precipitation and population density that impacted carbon sequestration in non-municipal (municipal) counties of YRDUA were 0.04 (0.4) nW·cm−2·sr−1·yr−1, −6.1 × 104 (−5.46 × 104) J·m−2·yr−1, 0.013 (0.017) K·yr−1, 3.64 × 10−5 (2.51 × 10−5) m·yr−1 and −0.04 people·km−2·yr−1, respectively. Furthermore, the long-term (from 2021 to 2100) carbon sequestration dataset with county-level scale in the YRDUA was generated using the causal inference-based machine learning model. In the context of carbon neutrality, we found that the optimal emission scenario for low-carbon sustainable development of YRDUA is SSP3, under which the average carbon sequestration in most counties will exceed 1 × 107 t. Our study provides a constructive basis for a science-based carbon cycle and ecological management in the urban agglomeration of China.https://www.tandfonline.com/doi/10.1080/15481603.2025.2483492Carbon sequestrationcausal inferencemachine learningdriving factorsthreshold detection |
| spellingShingle | Yin Zhang Weibo Ma Nan Wang Lijun Zhao Qingwu Hu Shaogang Lei Haidong Li Detection of driving factors and critical thresholds for carbon sequestration capacity in urban agglomerations using a combined causal inference and machine learning approach GIScience & Remote Sensing Carbon sequestration causal inference machine learning driving factors threshold detection |
| title | Detection of driving factors and critical thresholds for carbon sequestration capacity in urban agglomerations using a combined causal inference and machine learning approach |
| title_full | Detection of driving factors and critical thresholds for carbon sequestration capacity in urban agglomerations using a combined causal inference and machine learning approach |
| title_fullStr | Detection of driving factors and critical thresholds for carbon sequestration capacity in urban agglomerations using a combined causal inference and machine learning approach |
| title_full_unstemmed | Detection of driving factors and critical thresholds for carbon sequestration capacity in urban agglomerations using a combined causal inference and machine learning approach |
| title_short | Detection of driving factors and critical thresholds for carbon sequestration capacity in urban agglomerations using a combined causal inference and machine learning approach |
| title_sort | detection of driving factors and critical thresholds for carbon sequestration capacity in urban agglomerations using a combined causal inference and machine learning approach |
| topic | Carbon sequestration causal inference machine learning driving factors threshold detection |
| url | https://www.tandfonline.com/doi/10.1080/15481603.2025.2483492 |
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