Abstract
Due to people’s insufficient anticipation of the negative impact of highly developed industries and the lack of prevention, global environmental pollution has occurred. These pollutants include air pollution, water pollutants, and land pollution, which not only cause direct damage and impact the ecosystem but also endanger the health of urban residents and economic development. Therefore, researching environmental pollution management is necessary to help solve these imminent environmental problems. In addition, digital finance, based on digital technology, can identify bottlenecks in environmental pollution management, formulate more effective governance strategies, and reduce environmental pollution at the source. In this context, this study uses the environmental pollution data of 287 cities in China from 2011 to 2021. It uses the fixed-effects and mediation effect models to analyze digital finance’s role in environmental pollution management. The research shows that digital finance can promote environmental pollution management and play a promoting role through two channels of influence: green technology innovation and government green subsidies. At the same time, the effect of this promotion is more significant in cities in the Midwest and in resource-based cities. The research results propose strategies for government organizations in environmental pollution management, and alleviate current resource and environmental problems, in addition to realizing sustainable urban development.
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Addendum
Due to space limitations, we can only show the results of environmental pollution management calculations for 31 provincial capital cities in the “Appendix” section. The environmental pollution management data of the remaining cities can be obtained from our author.
Funding
This article is funded by the National Social Science Foundation of China (Grant No. 19BTJ056).
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All authors contributed to the conception and design of the research. Zhe Zhang is responsible for material preparation and data collection. The data analysis was done by Zheming Dong, and the first draft was written by Shujun Yao and Zheming Dong. All authors have read and approved the final manuscript.
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Appendix
Appendix
Measurement results of environmental pollution management levels in 31 provincial capital cities in China
2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | |
|---|---|---|---|---|---|---|---|---|---|---|---|
Beijing | 0.8380 | 0.8282 | 0.8271 | 0.8291 | 0.8344 | 0.8486 | 0.8505 | 0.8652 | 0.8587 | 0.8799 | 0.8722 |
Tianjin | 0.5431 | 0.5459 | 0.5342 | 0.5426 | 0.5415 | 0.5733 | 0.5558 | 0.5888 | 0.5889 | 0.6278 | 0.6204 |
Shanghai | 0.5873 | 0.5217 | 0.5563 | 0.5317 | 0.5420 | 0.5227 | 0.6249 | 0.7046 | 0.7001 | 0.7064 | 0.7033 |
Chongqing | 0.1088 | 0.1172 | 0.1089 | 0.1034 | 0.1012 | 0.1007 | 0.1218 | 0.0847 | 0.0867 | 0.0737 | 0.1021 |
Lanzhou | 0.8957 | 0.9090 | 0.9100 | 0.9140 | 0.9183 | 0.9196 | 0.9085 | 0.9126 | 0.9225 | 0.8618 | 0.8635 |
Xining | 0.8421 | 0.8383 | 0.8379 | 0.8412 | 0.8435 | 0.8020 | 0.8032 | 0.8026 | 0.8162 | 0.6863 | 0.6680 |
Xi’an | 0.8454 | 0.8498 | 0.8612 | 0.8700 | 0.8826 | 0.9105 | 0.9045 | 0.9084 | 0.9058 | 0.9158 | 0.9147 |
Zhengzhou | 0.6554 | 0.6601 | 0.6787 | 0.6592 | 0.6399 | 0.7122 | 0.7113 | 0.7098 | 0.7093 | 0.7412 | 0.7338 |
Jinan | 0.8272 | 0.8266 | 0.8288 | 0.8334 | 0.8311 | 0.8333 | 0.8437 | 0.8480 | 0.8446 | 0.8020 | 0.8144 |
Taiyuan | 0.7706 | 0.7860 | 0.8031 | 0.8200 | 0.8389 | 0.8679 | 0.8798 | 0.8843 | 0.8882 | 0.8591 | 0.8668 |
Hefei | 0.9063 | 0.9036 | 0.9038 | 0.8972 | 0.9021 | 0.9136 | 0.9084 | 0.9169 | 0.9166 | 0.9036 | 0.9026 |
Changsha | 0.8702 | 0.8618 | 0.8592 | 0.8577 | 0.8545 | 0.8758 | 0.8760 | 0.8702 | 0.8619 | 0.8788 | 0.8797 |
Wuhan | 0.8703 | 0.8838 | 0.9044 | 0.8941 | 0.9015 | 0.9065 | 0.9027 | 0.9506 | 0.9513 | 0.8973 | 0.9010 |
Nanjing | 0.6901 | 0.6639 | 0.6538 | 0.6584 | 0.6457 | 0.6780 | 0.7074 | 0.7322 | 0.7260 | 0.7150 | 0.6944 |
Chengdu | 0.8090 | 0.7988 | 0.8012 | 0.7995 | 0.7959 | 0.8145 | 0.8142 | 0.8290 | 0.8216 | 0.8275 | 0.8295 |
Guiyang | 0.8861 | 0.8953 | 0.8923 | 0.8903 | 0.8942 | 0.8510 | 0.8067 | 0.8825 | 0.8761 | 0.8172 | 0.8340 |
Kunming | 0.7267 | 0.8897 | 0.8954 | 0.9142 | 0.9062 | 0.7873 | 0.8311 | 0.8371 | 0.8813 | 0.7534 | 0.7953 |
Harbin | 0.8501 | 0.8823 | 0.9099 | 0.9169 | 0.9259 | 0.9074 | 0.9220 | 0.9332 | 0.9400 | 0.8943 | 0.9153 |
Changchun | 0.4536 | 0.4236 | 0.4214 | 0.4263 | 0.4292 | 0.5287 | 0.5265 | 0.4215 | 0.3854 | 0.4004 | 0.4034 |
Shenyang | 0.6277 | 0.6198 | 0.6096 | 0.6160 | 0.6354 | 0.6803 | 0.6890 | 0.6654 | 0.6620 | 0.6825 | 0.6850 |
Shijiazhuang | 0.6687 | 0.6466 | 0.6483 | 0.6597 | 0.6772 | 0.6329 | 0.7241 | 0.7376 | 0.7439 | 0.7442 | 0.7358 |
Hangzhou | 0.6842 | 0.6564 | 0.6542 | 0.6476 | 0.6524 | 0.6541 | 0.6636 | 0.7464 | 0.7585 | 0.7840 | 0.7774 |
Nanchang | 0.9358 | 0.9258 | 0.9279 | 0.9328 | 0.9293 | 0.9153 | 0.9279 | 0.9651 | 0.9654 | 0.9377 | 0.9385 |
Guangzhou | 0.8477 | 0.8288 | 0.8275 | 0.8292 | 0.8313 | 0.8167 | 0.8244 | 0.8975 | 0.8951 | 0.9015 | 0.8940 |
Fuzhou | 0.8916 | 0.8973 | 0.8965 | 0.9011 | 0.8989 | 0.8613 | 0.8511 | 0.8560 | 0.8427 | 0.8241 | 0.8282 |
Haikou | 0.9973 | 0.9961 | 0.9960 | 0.9957 | 0.9957 | 0.9965 | 0.9956 | 0.9983 | 0.9983 | 0.9950 | 0.9963 |
Nanning | 0.8279 | 0.8195 | 0.8236 | 0.8230 | 0.8285 | 0.8638 | 0.8555 | 0.8446 | 0.8391 | 0.8291 | 0.8545 |
Lasa | 0.9268 | 0.9233 | 0.9224 | 0.9229 | 0.9233 | 0.9381 | 0.9341 | 0.9196 | 0.9148 | 0.8997 | 0.9286 |
Yinchuan | 0.9062 | 0.8906 | 0.8988 | 0.9053 | 0.9101 | 0.9007 | 0.9226 | 0.9235 | 0.9290 | 0.8748 | 0.8729 |
Urumqi | 0.7440 | 0.7385 | 0.7501 | 0.7491 | 0.7536 | 0.7397 | 0.7297 | 0.7414 | 0.7341 | 0.7455 | 0.7500 |
Hohhot | 0.8505 | 0.8476 | 0.8453 | 0.8267 | 0.8489 | 0.8035 | 0.8294 | 0.8296 | 0.8329 | 0.7756 | 0.8131 |
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Yao, S., Dong, Z. & Zhang, Z. How digital finance affects environmental pollution management: evidence from China. Environ Sci Pollut Res 30, 105231–105246 (2023). https://doi.org/10.1007/s11356-023-29787-w
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DOI: https://doi.org/10.1007/s11356-023-29787-w