Analysis of Spatiotemporal Predictions and Drivers of Carbon Storage in the Pearl River Delta Urban Agglomeration via the PLUS-InVEST-GeoDetector Model
Land use and land cover change (LUCC) significantly influences the dynamics of carbon storage in thin terrestrial ecosystems. Investigating the interplay between land use alterations and carbon sequestration is crucial for refining regional land use configurations, sustaining the regional carbon bal...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-10-01
|
| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/17/20/5093 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850204633360760832 |
|---|---|
| author | Jinghang Cai Hui Chi Nan Lu Jin Bian Hanqing Chen Junkeng Yu Suqin Yang |
| author_facet | Jinghang Cai Hui Chi Nan Lu Jin Bian Hanqing Chen Junkeng Yu Suqin Yang |
| author_sort | Jinghang Cai |
| collection | DOAJ |
| description | Land use and land cover change (LUCC) significantly influences the dynamics of carbon storage in thin terrestrial ecosystems. Investigating the interplay between land use alterations and carbon sequestration is crucial for refining regional land use configurations, sustaining the regional carbon balance, and augmenting regional carbon storage. Using land use data from the Pearl River Delta Urban Agglomeration (PRDUA) from 2010 to 2020, this study employed PLUS-InVEST models to analyze the spatiotemporal dynamics of land use and carbon storage. Projections for the years 2030, 2040, and 2050 were performed under three distinct developmental scenarios, namely, natural development (ND), city priority development (CPD), and ecological protection development (EPD), to forecast changes in land use and carbon storage. The geographic detector model was leveraged to dissect the determinants of the spatial and temporal variability of carbon storage, offering pertinent recommendations. The results showed that (1) during 2010–2020, the carbon storage in the PRDUA showed a decreasing trend, with a total decrease of 9.52 × 10<sup>6</sup> Mg, and the spatial distribution of carbon density in the urban agglomeration was imbalanced and showed an overall trend in increasing from the center to the periphery. (2) Clear differences in carbon storage were observed among the three development scenarios of the PRDUA between 2030 and 2050. Only the EPD scenario achieved an increase in carbon storage of 1.10 × 10<sup>6</sup> Mg, and it was the scenario with the greatest potential for carbon sequestration. (3) Among the drivers of the evolution of spatial land use patterns, population, the normalized difference vegetation index (NDVI), and distance to the railway had the greatest influence on LUCC. (4) The annual average temperature, annual average rainfall, and GDP exerted a significant influence on the spatiotemporal dynamics of carbon storage in the PRDUA, and the interactions between the 15 drivers and changes in carbon storage predominantly manifested as nonlinear and double-factor enhancements. The results provide a theoretical basis for future spatial planning and achieving carbon neutrality in the PRDUA. |
| format | Article |
| id | doaj-art-b39229f042e3455aa1ffcef607365fd7 |
| institution | OA Journals |
| issn | 1996-1073 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-b39229f042e3455aa1ffcef607365fd72025-08-20T02:11:15ZengMDPI AGEnergies1996-10732024-10-011720509310.3390/en17205093Analysis of Spatiotemporal Predictions and Drivers of Carbon Storage in the Pearl River Delta Urban Agglomeration via the PLUS-InVEST-GeoDetector ModelJinghang Cai0Hui Chi1Nan Lu2Jin Bian3Hanqing Chen4Junkeng Yu5Suqin Yang6Department of Ocean Engineering and Energy, Guangdong Ocean University, Zhanjiang 524088, ChinaDepartment of Ocean Engineering and Energy, Guangdong Ocean University, Zhanjiang 524088, ChinaDepartment of Ocean Engineering and Energy, Guangdong Ocean University, Zhanjiang 524088, ChinaDepartment of Ocean Engineering and Energy, Guangdong Ocean University, Zhanjiang 524088, ChinaDepartment of Ocean Engineering and Energy, Guangdong Ocean University, Zhanjiang 524088, ChinaDepartment of Ocean Engineering and Energy, Guangdong Ocean University, Zhanjiang 524088, ChinaGuangdong Yuehai Water Investment Co., Ltd., Zhanjiang 524088, ChinaLand use and land cover change (LUCC) significantly influences the dynamics of carbon storage in thin terrestrial ecosystems. Investigating the interplay between land use alterations and carbon sequestration is crucial for refining regional land use configurations, sustaining the regional carbon balance, and augmenting regional carbon storage. Using land use data from the Pearl River Delta Urban Agglomeration (PRDUA) from 2010 to 2020, this study employed PLUS-InVEST models to analyze the spatiotemporal dynamics of land use and carbon storage. Projections for the years 2030, 2040, and 2050 were performed under three distinct developmental scenarios, namely, natural development (ND), city priority development (CPD), and ecological protection development (EPD), to forecast changes in land use and carbon storage. The geographic detector model was leveraged to dissect the determinants of the spatial and temporal variability of carbon storage, offering pertinent recommendations. The results showed that (1) during 2010–2020, the carbon storage in the PRDUA showed a decreasing trend, with a total decrease of 9.52 × 10<sup>6</sup> Mg, and the spatial distribution of carbon density in the urban agglomeration was imbalanced and showed an overall trend in increasing from the center to the periphery. (2) Clear differences in carbon storage were observed among the three development scenarios of the PRDUA between 2030 and 2050. Only the EPD scenario achieved an increase in carbon storage of 1.10 × 10<sup>6</sup> Mg, and it was the scenario with the greatest potential for carbon sequestration. (3) Among the drivers of the evolution of spatial land use patterns, population, the normalized difference vegetation index (NDVI), and distance to the railway had the greatest influence on LUCC. (4) The annual average temperature, annual average rainfall, and GDP exerted a significant influence on the spatiotemporal dynamics of carbon storage in the PRDUA, and the interactions between the 15 drivers and changes in carbon storage predominantly manifested as nonlinear and double-factor enhancements. The results provide a theoretical basis for future spatial planning and achieving carbon neutrality in the PRDUA.https://www.mdpi.com/1996-1073/17/20/5093carbon storagePLUS-InVEST modelLUCCgeographic detectormulti-scenario simulation |
| spellingShingle | Jinghang Cai Hui Chi Nan Lu Jin Bian Hanqing Chen Junkeng Yu Suqin Yang Analysis of Spatiotemporal Predictions and Drivers of Carbon Storage in the Pearl River Delta Urban Agglomeration via the PLUS-InVEST-GeoDetector Model Energies carbon storage PLUS-InVEST model LUCC geographic detector multi-scenario simulation |
| title | Analysis of Spatiotemporal Predictions and Drivers of Carbon Storage in the Pearl River Delta Urban Agglomeration via the PLUS-InVEST-GeoDetector Model |
| title_full | Analysis of Spatiotemporal Predictions and Drivers of Carbon Storage in the Pearl River Delta Urban Agglomeration via the PLUS-InVEST-GeoDetector Model |
| title_fullStr | Analysis of Spatiotemporal Predictions and Drivers of Carbon Storage in the Pearl River Delta Urban Agglomeration via the PLUS-InVEST-GeoDetector Model |
| title_full_unstemmed | Analysis of Spatiotemporal Predictions and Drivers of Carbon Storage in the Pearl River Delta Urban Agglomeration via the PLUS-InVEST-GeoDetector Model |
| title_short | Analysis of Spatiotemporal Predictions and Drivers of Carbon Storage in the Pearl River Delta Urban Agglomeration via the PLUS-InVEST-GeoDetector Model |
| title_sort | analysis of spatiotemporal predictions and drivers of carbon storage in the pearl river delta urban agglomeration via the plus invest geodetector model |
| topic | carbon storage PLUS-InVEST model LUCC geographic detector multi-scenario simulation |
| url | https://www.mdpi.com/1996-1073/17/20/5093 |
| work_keys_str_mv | AT jinghangcai analysisofspatiotemporalpredictionsanddriversofcarbonstorageinthepearlriverdeltaurbanagglomerationviatheplusinvestgeodetectormodel AT huichi analysisofspatiotemporalpredictionsanddriversofcarbonstorageinthepearlriverdeltaurbanagglomerationviatheplusinvestgeodetectormodel AT nanlu analysisofspatiotemporalpredictionsanddriversofcarbonstorageinthepearlriverdeltaurbanagglomerationviatheplusinvestgeodetectormodel AT jinbian analysisofspatiotemporalpredictionsanddriversofcarbonstorageinthepearlriverdeltaurbanagglomerationviatheplusinvestgeodetectormodel AT hanqingchen analysisofspatiotemporalpredictionsanddriversofcarbonstorageinthepearlriverdeltaurbanagglomerationviatheplusinvestgeodetectormodel AT junkengyu analysisofspatiotemporalpredictionsanddriversofcarbonstorageinthepearlriverdeltaurbanagglomerationviatheplusinvestgeodetectormodel AT suqinyang analysisofspatiotemporalpredictionsanddriversofcarbonstorageinthepearlriverdeltaurbanagglomerationviatheplusinvestgeodetectormodel |