Spatiotemporal evolution of agricultural carbon emissions intensity in China and analysis of influencing factors
Abstract This study aims to investigate the spatiotemporal evolution of China’s agricultural carbon emissions intensity and their influencing factors from 2001 to 2022. It employs analytical methods such as kernel density estimation, Gini coefficient, Moran’s index, spatial Markov analysis, and spat...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-06-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-04973-7 |
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| author | Xue Zhu Xiwu Shao |
| author_facet | Xue Zhu Xiwu Shao |
| author_sort | Xue Zhu |
| collection | DOAJ |
| description | Abstract This study aims to investigate the spatiotemporal evolution of China’s agricultural carbon emissions intensity and their influencing factors from 2001 to 2022. It employs analytical methods such as kernel density estimation, Gini coefficient, Moran’s index, spatial Markov analysis, and spatiotemporal geographically weighted regression (GWTR) to visualize the data and analyze the results. The study ultimately draws the following. Conclusions (1) China’s agricultural carbon emissions intensity shows a decreasing trend, a noticeable decrease in the number of provinces below the medium level. Gini coefficient analysis indicates a significant disparity between eastern and western China, with agricultural carbon emissions intensity exhibiting the spatial pattern: east > central > west. (2) China’s agricultural carbon emissions intensity exhibits clear spatial clustering characteristics and a stable transfer trend. High-high agglomeration areas are primarily located in the Northwest and Northeast, while low-low agglomeration areas are mainly found in Guangdong and Fujian provinces. Furthermore, the transfer of agricultural carbon emission levels is stable. (3) Financial support and the development level of technology markets positively impact China’s agricultural carbon emissions, while research and development intensity, openness to the outside world, human capital level, and urbanization negatively affect these emissions. |
| format | Article |
| id | doaj-art-42b6aa402dd84333aba036c76d3436ff |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-42b6aa402dd84333aba036c76d3436ff2025-08-20T03:16:34ZengNature PortfolioScientific Reports2045-23222025-06-0115111910.1038/s41598-025-04973-7Spatiotemporal evolution of agricultural carbon emissions intensity in China and analysis of influencing factorsXue Zhu0Xiwu Shao1College of Economics and Management, Jilin Agricultural UniversityCollege of Economics and Management, Jilin Agricultural UniversityAbstract This study aims to investigate the spatiotemporal evolution of China’s agricultural carbon emissions intensity and their influencing factors from 2001 to 2022. It employs analytical methods such as kernel density estimation, Gini coefficient, Moran’s index, spatial Markov analysis, and spatiotemporal geographically weighted regression (GWTR) to visualize the data and analyze the results. The study ultimately draws the following. Conclusions (1) China’s agricultural carbon emissions intensity shows a decreasing trend, a noticeable decrease in the number of provinces below the medium level. Gini coefficient analysis indicates a significant disparity between eastern and western China, with agricultural carbon emissions intensity exhibiting the spatial pattern: east > central > west. (2) China’s agricultural carbon emissions intensity exhibits clear spatial clustering characteristics and a stable transfer trend. High-high agglomeration areas are primarily located in the Northwest and Northeast, while low-low agglomeration areas are mainly found in Guangdong and Fujian provinces. Furthermore, the transfer of agricultural carbon emission levels is stable. (3) Financial support and the development level of technology markets positively impact China’s agricultural carbon emissions, while research and development intensity, openness to the outside world, human capital level, and urbanization negatively affect these emissions.https://doi.org/10.1038/s41598-025-04973-7Agricultural carbon emissionsSpatiotemporal evolutionGini coefficientSpatial Markov |
| spellingShingle | Xue Zhu Xiwu Shao Spatiotemporal evolution of agricultural carbon emissions intensity in China and analysis of influencing factors Scientific Reports Agricultural carbon emissions Spatiotemporal evolution Gini coefficient Spatial Markov |
| title | Spatiotemporal evolution of agricultural carbon emissions intensity in China and analysis of influencing factors |
| title_full | Spatiotemporal evolution of agricultural carbon emissions intensity in China and analysis of influencing factors |
| title_fullStr | Spatiotemporal evolution of agricultural carbon emissions intensity in China and analysis of influencing factors |
| title_full_unstemmed | Spatiotemporal evolution of agricultural carbon emissions intensity in China and analysis of influencing factors |
| title_short | Spatiotemporal evolution of agricultural carbon emissions intensity in China and analysis of influencing factors |
| title_sort | spatiotemporal evolution of agricultural carbon emissions intensity in china and analysis of influencing factors |
| topic | Agricultural carbon emissions Spatiotemporal evolution Gini coefficient Spatial Markov |
| url | https://doi.org/10.1038/s41598-025-04973-7 |
| work_keys_str_mv | AT xuezhu spatiotemporalevolutionofagriculturalcarbonemissionsintensityinchinaandanalysisofinfluencingfactors AT xiwushao spatiotemporalevolutionofagriculturalcarbonemissionsintensityinchinaandanalysisofinfluencingfactors |