Spatiotemporal analysis of the impact of green finance on carbon dioxide emissions based on panel data of cities in China
Green finance represents a resource-saving and environmentally friendly economic approach. It plays a critical role in achieving economic growth and environmentally sustainable development. To investigate its tangible effects on China since the implementation of green finance, we utilized the nightt...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | International Journal of Digital Earth |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2025.2457969 |
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| author | Qiuyi Xu Li Yao Kaifang Shi Wei Zhou Xuguang Tang |
| author_facet | Qiuyi Xu Li Yao Kaifang Shi Wei Zhou Xuguang Tang |
| author_sort | Qiuyi Xu |
| collection | DOAJ |
| description | Green finance represents a resource-saving and environmentally friendly economic approach. It plays a critical role in achieving economic growth and environmentally sustainable development. To investigate its tangible effects on China since the implementation of green finance, we utilized the nighttime lighting (NTL) data to evaluate urban CO2 emission of Chinese cities from 2010 to 2020. Then, this study utilized the geographically and temporally weighted regression (GTWR) model to examine the correlations between green finance and carbon intensity in different cities across four dimensions including green credit, green insurance, green investment, and government support. The findings revealed significant regional disparities in the influence of green finance development on carbon intensity. Overall, both green credit and green insurance emerge as pivotal factors in mitigating carbon intensity. Notably, green credit exhibits the strongest mitigation of carbon intensity in the eastern region, while green insurance demonstrates the highest impact in the western region. However, government support has a negative effect, while the impact of green investment impact is insignificant. These findings underscore the importance of implementing region-specific strategies to bolster the efficacy of green finance in mitigating carbon emissions. |
| format | Article |
| id | doaj-art-43641f5de0d14c158ea9c285e8f99d00 |
| institution | Kabale University |
| issn | 1753-8947 1753-8955 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj-art-43641f5de0d14c158ea9c285e8f99d002025-01-30T02:04:53ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552025-12-0118110.1080/17538947.2025.2457969Spatiotemporal analysis of the impact of green finance on carbon dioxide emissions based on panel data of cities in ChinaQiuyi Xu0Li Yao1Kaifang Shi2Wei Zhou3Xuguang Tang4International Research Centre of the Grand Canal, Zhejiang International Studies University, Hangzhou, People’s Republic of ChinaInternational Research Centre of the Grand Canal, Zhejiang International Studies University, Hangzhou, People’s Republic of ChinaSchool of Geography and Tourism, Anhui Normal University, Wuhu, People’s Republic of ChinaSchool of Geographical Sciences, Southwest University, Chongqing, People’s Republic of ChinaInstitute of Remote Sensing and Geosciences, Hangzhou Normal University, Hangzhou, People’s Republic of ChinaGreen finance represents a resource-saving and environmentally friendly economic approach. It plays a critical role in achieving economic growth and environmentally sustainable development. To investigate its tangible effects on China since the implementation of green finance, we utilized the nighttime lighting (NTL) data to evaluate urban CO2 emission of Chinese cities from 2010 to 2020. Then, this study utilized the geographically and temporally weighted regression (GTWR) model to examine the correlations between green finance and carbon intensity in different cities across four dimensions including green credit, green insurance, green investment, and government support. The findings revealed significant regional disparities in the influence of green finance development on carbon intensity. Overall, both green credit and green insurance emerge as pivotal factors in mitigating carbon intensity. Notably, green credit exhibits the strongest mitigation of carbon intensity in the eastern region, while green insurance demonstrates the highest impact in the western region. However, government support has a negative effect, while the impact of green investment impact is insignificant. These findings underscore the importance of implementing region-specific strategies to bolster the efficacy of green finance in mitigating carbon emissions.https://www.tandfonline.com/doi/10.1080/17538947.2025.2457969Carbon intensityCO2 emissionsGreen financethe GTWR modelSpatial heterogeneity |
| spellingShingle | Qiuyi Xu Li Yao Kaifang Shi Wei Zhou Xuguang Tang Spatiotemporal analysis of the impact of green finance on carbon dioxide emissions based on panel data of cities in China International Journal of Digital Earth Carbon intensity CO2 emissions Green finance the GTWR model Spatial heterogeneity |
| title | Spatiotemporal analysis of the impact of green finance on carbon dioxide emissions based on panel data of cities in China |
| title_full | Spatiotemporal analysis of the impact of green finance on carbon dioxide emissions based on panel data of cities in China |
| title_fullStr | Spatiotemporal analysis of the impact of green finance on carbon dioxide emissions based on panel data of cities in China |
| title_full_unstemmed | Spatiotemporal analysis of the impact of green finance on carbon dioxide emissions based on panel data of cities in China |
| title_short | Spatiotemporal analysis of the impact of green finance on carbon dioxide emissions based on panel data of cities in China |
| title_sort | spatiotemporal analysis of the impact of green finance on carbon dioxide emissions based on panel data of cities in china |
| topic | Carbon intensity CO2 emissions Green finance the GTWR model Spatial heterogeneity |
| url | https://www.tandfonline.com/doi/10.1080/17538947.2025.2457969 |
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