ORIGINAL RESEARCH
Can Digital Finance Improve Urban Environmental Performance? Evidence From Partially Linear Functional-Coefficient Model
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1
Yantai Institute of Science and Technology, No. 34, West Fairyland Road, Penglai District, Yantai City, Shandong Province, China 265600
 
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School of Management, Wenzhou Business College, Chashan Higher Education Park, Wenzhou, Zhejiang Province, China 325035
 
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Dongbei University Of Finance And Economics, No.217 Jianshan Street, Shahekou District, Dalian City, Liaoning Province, China 116025
 
 
Submission date: 2023-10-18
 
 
Final revision date: 2024-01-05
 
 
Acceptance date: 2024-01-25
 
 
Online publication date: 2024-02-29
 
 
Publication date: 2024-07-12
 
 
Corresponding author
Hongyan Tang   

School of Management, Wenzhou Business College, Chashan Higher Education Park, Wenzhou, Zhejiang Province, China 325035
 
 
Pol. J. Environ. Stud. 2024;33(5):5969-5978
 
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ABSTRACT
The Sustainable Development Goals (SDGs) have made it necessary for us to reevaluate the connection between environmental preservation and economic growth. The article uses Chinese city-level panel data from 2011 to 2019 to analyze the relationship between digital finance (DF) and environmental performance (EP), relaxing the linear relationship in the conventional empirical model and applying the partial linear functional-coefficient (PLFC) model, in order to better understand the factors influencing EP. The findings indicate as follows: (i) DF can greatly improve EP, and the robustness analysis confirms this statement. (ii) Science, technology, and innovation (STI) influence the promotion effect of DF on EP; when the level of STI exceeds a certain threshold, the promotion effect of DF on EP increases, and this effect increases with the level of STI. (iii). There is substantial geographical variation in the moderating influence of STI level. It has a larger role in eastern coastal cities. Finally, policy suggestions are offered to promote DF and increase EP based on the nonparametric link between those two variables.
eISSN:2083-5906
ISSN:1230-1485
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