ORIGINAL RESEARCH
Can Digital Finance Improve Urban
Environmental Performance? Evidence From
Partially Linear Functional-Coefficient Model
More details
Hide details
1
Yantai Institute of Science and Technology, No. 34, West Fairyland Road, Penglai District, Yantai City, Shandong
Province, China 265600
2
School of Management, Wenzhou Business College, Chashan Higher Education Park, Wenzhou, Zhejiang Province,
China 325035
3
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
KEYWORDS
TOPICS
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.