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https://doi.org/10.21203/rs.3....
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Environmental Science and Pollution Research
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Can digital finance reduce carbon emission intensity? A perspective based on factor allocation distortions: evidence from Chinese cities

Authors: Gangqiang Yang; Ziyu Ding; Mao Wu; Mingzhe Gao; Ziyang Yue; Haisen Wang;

Can digital finance reduce carbon emission intensity? A perspective based on factor allocation distortions: evidence from Chinese cities

Abstract

Abstract The world is facing the challenges of climate change and energy structure adjustments. The role of digital finance, a new branch of business that combines digital technology and traditional financial products, in reducing global carbon emissions needs to be studied. This paper uses panel data on 280 cities in China from 2011 to 2019 to empirically examine the efficacy of digital finance for governing carbon emission reductions and the mechanism by which it does so. The results show that: 1. digital finance can facilitate carbon emission reductions and help reduce carbon emission intensity within regions.2. Digital finance helps promote the rational allocation of resources and alleviates factor distortions by encouraging firms to rationally use their own factor endowments so as to reduce carbon emission intensity, which holds robustly after considering the endogenous issues such as possibly omitting variables, collinearity and so on. 3.Differences in geographical location, the vitality of regional innovation and entrepreneurship, regional willingness to protect the environment, and environmental protection levels lead to heterogeneity in the effect of digital finance on carbon emission intensity. Therefore, it is necessary to vigorously develop digital finance as a long-term tool for carbon governance.

Related Organizations
Keywords

China, Climate Change, Carbon, Economic Development, Cities

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
28
Top 10%
Average
Top 10%
hybrid