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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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-12-01
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| Series: | International Journal of Digital Earth |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2025.2457969 |
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| Summary: | 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. |
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| ISSN: | 1753-8947 1753-8955 |