FinTech and corporate green innovation: An external attention perspective

Li, Bo; Du, Jianguo; Yao, Tianhang; Wang, Quanding

doi:10.1016/j.frl.2023.104661

Cited by 11 publications

(2 citation statements)

References 24 publications

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“…In the equation above, w 1 iθ represents the economic spatial weight matrix, which can be used to represent the relationship and economic distance between regions; w 2 iθ and w 3 iθ denote adjacency and geographic distance spatial weight matrices, respectively, which indicate whether geographic units are adjacent and the proximity between regions. Meanwhile, the economic-geographic nested spatial weight matrix W 1 (2,3,4) comprehensively illustrates the coupling of economic and geographic aspects between regions. Here, d iθ denotes the central geographic distance between regions i and θ, y i , and y θ represent the average actual GDP of a city from 2009 to 2021 (a span of 13 years), while y and y N represent the national average actual GDP between 2009 and 2021 and the average actual GDP of the city NN.…”

Section: Control Variablesmentioning

confidence: 99%

“…As China accelerates the pace of technological innovation, the role of financial technology (fintech) has become increasingly prominent, exerting a significant impact on regional green technological innovation [2]. Fintech refers to a new business format that utilizes advanced technology to enhance the efficiency and quality of the financial industry.…”

Section: Introductionmentioning

confidence: 99%

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The Impact of Green Finance and Financial Technology on Regional Green Energy Technological Innovation Based on the Dual Machine Learning and Spatial Econometric Models

Xie,

Zhao,

2024

Energies

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Regional green energy technological innovation is an important means to alleviate economic–environmental contradictions. The purpose of this study was to explore the mechanisms of green finance, financial technology, and regional green energy technological innovation. In this study, we constructed dual machine learning models, spatial econometric models, and panel threshold effect models to investigate the effects of green finance and financial technology on regional green energy technological innovation, using panel data from 266 cities nationwide from 2009 to 2021. The research findings are as follows: (1) Both green finance and financial technology significantly promote regional green energy technological innovation. (2) Based on a spatial weight matrix embedded in economic geography, both green finance and financial technology generate positive spatial spillover effects on regional green energy technological innovation. (3) The interaction between green finance and financial technology significantly contributes to regional green energy technological innovation. Financial technology can strengthen the positive local and neighboring effects of green finance on regional green energy technological innovation. (4) Based on the threshold effect of financial technology, green finance cannot significantly promote regional green energy technological innovation when financial technology is in an underdeveloped stage. With the advancement of financial technology, green finance continues to have a positive impact on regional green energy technological innovation. Based on this analysis and our conclusions, we propose practical policy recommendations that can provide a more sustainable approach to green energy technology innovation.

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Section: Control Variablesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%