Sub-District Level Spatiotemporal Changes of Carbon Storage and Driving Factor Analysis: A Case Study in Beijing
Analyzing the current trends and causes of carbon storage changes and accurately predicting future land use and carbon storage changes under different climate scenarios is crucial for regional land use decision-making and carbon management. This study focuses on Beijing as its study area and introdu...
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| Format: | Article |
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MDPI AG
2025-01-01
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| author | Yirui Zhang Shouhang Du Linye Zhu Tianzhuo Guo Xuesong Zhao Junting Guo |
| author_facet | Yirui Zhang Shouhang Du Linye Zhu Tianzhuo Guo Xuesong Zhao Junting Guo |
| author_sort | Yirui Zhang |
| collection | DOAJ |
| description | Analyzing the current trends and causes of carbon storage changes and accurately predicting future land use and carbon storage changes under different climate scenarios is crucial for regional land use decision-making and carbon management. This study focuses on Beijing as its study area and introduces a framework that combines the Markov model, the Patch-based Land Use Simulation (PLUS) model, and the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model to assess carbon storage at the sub-district level. This framework allows for a systematic analysis of land use and carbon storage spatiotemporal evolution in Beijing from 2000 to 2020, including the influence of driving factors on carbon storage. Moreover, it enables the simulation and prediction of land use and carbon storage changes in Beijing from 2025 to 2040 under various scenarios. The results show the following: (1) From 2000 to 2020, the overall land use change in Beijing showed a trend of “Significant decrease in cropland area; Forest increase gradually; Shrub and grassland area increase first and then decrease; Decrease and then increase in water; Impervious expands in a large scale”. (2) From 2000 to 2020, the carbon storage in Beijing showed a “decrease-increase” fluctuation, with an overall decrease of 1.3 Tg. In future carbon storage prediction, the ecological protection scenario will contribute to achieving the goals of carbon peak and carbon neutrality. (3) Among the various driving factors, slope has the strongest impact on the overall carbon storage in Beijing, followed by Human Activity Intensity (HAI) and Nighttime Light Data (NTL). In the analysis of carbon storage in the built-up areas, it was found that HAI and DEM (Digital Elevation Model) have the strongest effect, followed by NTL and Fractional Vegetation Cover (FVC). The findings from this study offer valuable insights for the sustainable advancement of ecological conservation and urban development in Beijing. |
| format | Article |
| id | doaj-art-8135fb83437e4dcd91415110918d7270 |
| institution | Kabale University |
| issn | 2073-445X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Land |
| spelling | doaj-art-8135fb83437e4dcd91415110918d72702025-01-24T13:38:07ZengMDPI AGLand2073-445X2025-01-0114115110.3390/land14010151Sub-District Level Spatiotemporal Changes of Carbon Storage and Driving Factor Analysis: A Case Study in BeijingYirui Zhang0Shouhang Du1Linye Zhu2Tianzhuo Guo3Xuesong Zhao4Junting Guo5State Key Laboratory of Water Resource Protection and Utilization in Coal Mining, Beijing 102211, ChinaState Key Laboratory of Water Resource Protection and Utilization in Coal Mining, Beijing 102211, ChinaCollege of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing 100083, ChinaCollege of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing 100083, ChinaKey Laboratory of China-ASEAN Satellite Remote Sensing Applications, Ministry of Natural Resources of the People’s Republic of China, Nanning 530022, ChinaState Key Laboratory of Water Resource Protection and Utilization in Coal Mining, Beijing 102211, ChinaAnalyzing the current trends and causes of carbon storage changes and accurately predicting future land use and carbon storage changes under different climate scenarios is crucial for regional land use decision-making and carbon management. This study focuses on Beijing as its study area and introduces a framework that combines the Markov model, the Patch-based Land Use Simulation (PLUS) model, and the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model to assess carbon storage at the sub-district level. This framework allows for a systematic analysis of land use and carbon storage spatiotemporal evolution in Beijing from 2000 to 2020, including the influence of driving factors on carbon storage. Moreover, it enables the simulation and prediction of land use and carbon storage changes in Beijing from 2025 to 2040 under various scenarios. The results show the following: (1) From 2000 to 2020, the overall land use change in Beijing showed a trend of “Significant decrease in cropland area; Forest increase gradually; Shrub and grassland area increase first and then decrease; Decrease and then increase in water; Impervious expands in a large scale”. (2) From 2000 to 2020, the carbon storage in Beijing showed a “decrease-increase” fluctuation, with an overall decrease of 1.3 Tg. In future carbon storage prediction, the ecological protection scenario will contribute to achieving the goals of carbon peak and carbon neutrality. (3) Among the various driving factors, slope has the strongest impact on the overall carbon storage in Beijing, followed by Human Activity Intensity (HAI) and Nighttime Light Data (NTL). In the analysis of carbon storage in the built-up areas, it was found that HAI and DEM (Digital Elevation Model) have the strongest effect, followed by NTL and Fractional Vegetation Cover (FVC). The findings from this study offer valuable insights for the sustainable advancement of ecological conservation and urban development in Beijing.https://www.mdpi.com/2073-445X/14/1/151carbon storageland useMarkov–PLUS modelGTWRSen + MK |
| spellingShingle | Yirui Zhang Shouhang Du Linye Zhu Tianzhuo Guo Xuesong Zhao Junting Guo Sub-District Level Spatiotemporal Changes of Carbon Storage and Driving Factor Analysis: A Case Study in Beijing Land carbon storage land use Markov–PLUS model GTWR Sen + MK |
| title | Sub-District Level Spatiotemporal Changes of Carbon Storage and Driving Factor Analysis: A Case Study in Beijing |
| title_full | Sub-District Level Spatiotemporal Changes of Carbon Storage and Driving Factor Analysis: A Case Study in Beijing |
| title_fullStr | Sub-District Level Spatiotemporal Changes of Carbon Storage and Driving Factor Analysis: A Case Study in Beijing |
| title_full_unstemmed | Sub-District Level Spatiotemporal Changes of Carbon Storage and Driving Factor Analysis: A Case Study in Beijing |
| title_short | Sub-District Level Spatiotemporal Changes of Carbon Storage and Driving Factor Analysis: A Case Study in Beijing |
| title_sort | sub district level spatiotemporal changes of carbon storage and driving factor analysis a case study in beijing |
| topic | carbon storage land use Markov–PLUS model GTWR Sen + MK |
| url | https://www.mdpi.com/2073-445X/14/1/151 |
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