digital finance research paper

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• Published: 11 September 2023

Research on the influence of digital finance on the economic efficiency of energy industry in the background of artificial intelligence

• Qiao He 1 &
• Ying Xue 2  

Scientific Reports volume  13 , Article number:  14984 ( 2023 ) Cite this article

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• Environmental sciences
• Environmental social sciences
• Solid Earth sciences

China's economic growth has reached a new plateau. It is no longer appropriate to use the old economic growth model, which relied on labor, land resources, mineral resources, and other economic considerations. Under the background of artificial intelligence, high-quality economic development is an inevitable trend. A new financial paradigm called "digital finance" integrates financial services with information technologies. Digital financial technology is thought to be a crucial foundation for fostering high-quality and sustainable economic and social development since it may offer more economic entities reduced cost of capital and more realistic financial service skills than in traditional financial models. In the era of artificial intelligence, how to reasonably release the momentum of digital finance for China's sustained economic growth has become a hot topic of discussion at this stage. This paper studies the impact of digital finance on the economic efficiency of the energy industry in the context of artificial intelligence. Relevant metrics were also calculated. The findings revealed that: The benchmark regression result of digital finance on the efficiency of the green economy was 0.4685 before adding the main restrictions; the benchmark regression result of digital finance on the efficiency of the green economy was 0.2243 after adding the main constraints. As a result, data finance had a favorable impact on the effectiveness of the green economy.

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Introduction

The coordinated growth of the real economy and the digital economy takes the conventional "reverse integration" path, with the financial sector serving as the first example of its transformation and development characteristics in the tertiary sector. Digital finance generally refers to the use of electronic information technology by traditional financial institutions and online businesses to carry out new financial services like payment, project investment, and equity financing. The limitations on time and space between product transactions and financial services have been eliminated by the quick development of digital finance. In the era of artificial intelligence, what is the role of the rise and application of digital finance in the critical period of innovation in promoting strategic planning. Whether it can fill the shortcomings of the traditional financial system and improve the efficiency of urban green economic development and the financial market department's strong support and promotion of innovation and manufacturing, and better support the economy of the energy industry, requires further review and debate. Digital financial technology can provide more accurate Market trend forecast and energy price forecast by processing large-scale data and applying advanced data analysis technology. This will help energy companies make more intelligent decisions, optimize resource allocation, reduce production and operating costs, and thus improve Economic efficiency. The application of digital financial technology can improve the Economic efficiency of the energy industry, improve the efficiency of resource utilization, reduce waste and promote the sustainable development of the energy industry.

Literature review

Numerous professionals and academics have always focused their research on strategies to increase the effectiveness of the urban green economy. In order to create a model that could be used for urban green economy planning, Liu T enhanced the conventional algorithm and merged the principle of machine learning algorithm. Efficiency indicators for the green economy were evaluated in terms of input, anticipated output, and unexpected production. Comparison and analysis were done on the green efficiency determined using the relaxation value calculation model. The study's findings demonstrated that the model could be used in the design phase of urban green planning and that it had specific effects 1 . China's green economic efficiency and green total factor productivity were assessed and examined by Gao X. Furthermore, the shortcomings of conventional clustering techniques in high-dimensional data clustering were highlighted by outlining the properties of high-dimensional data. A sampling and residual squared-based density peak clustering technique was put forth. The experimental comparison on the data set revealed that in terms of time complexity and clustering outcomes, the modified algorithm outperformed the delayed procedure call approach 2 . Sarcheshmeh M examined the performance of urban green space in terms of social and economic indices in the Mashhad metropolitan region. 15 social questions and 5 economic questions from the research questionnaire were tested and examined using the SPSS22 program. The findings demonstrated that there was no appreciable impact on the management effectiveness of the urban green space sector in the city of Mashhad. From the perspectives of citizens and managers, several features of the social index were rated as desirable 3 . In order to examine the dynamic changes in the economic effectiveness of urban land use in South Korea at the regional level and to determine whether it would be feasible to implement the green belt policy, Yongrok C used the ecological efficiency measurement model. In order to increase the economic benefits of urban land use and execute sustainable green space management, more performance-oriented policy solutions were advocated 4 . These studies do have some impact on increasing the effectiveness of urban green economy and urban planning, but digital finance has received far too little attention. The market for digital finance is quickly taking over with the pace of the new economic system. The city's long-term development would have an effect on how effective the urban green economy is.

There are more research on the direct or indirect effects of digital finance on economic growth than there are on the effect of digital finance on the effectiveness of urban green economies. Based on the database for the growth of digital financial inclusion and the China Family Panel Studies, Xie W investigated the relationship between coastal rural residents' entrepreneurship and the development of China Family Panel Studies (CFPS). The empirical findings indicated that a crucial factor in encouraging rural entrepreneurship was the thorough development of digital financial inclusion. The monetary capital index and the payment index both significantly boosted rural inhabitants' entrepreneurial activity. The study also discovered that the effects of digital financial inclusion on rural residents' entrepreneurship exhibited signs of geographical variation 5 . In the context of economic digitization and the development direction of contemporary financial technology legal supervision, Barykin S determined the function of digital finance in the financial system. By adding new features of digital assets, the digital financial cube might be expanded to match the level of openness of industrial firms in the future Industry 4.0 technological framework 6 . The long-term causal impacts of digital financial inclusion on economic growth in sub-Saharan Africa were investigated by Thaddeus K J. The study made use of quarterly data from 2011 to 2017 and a sample of 22 sub-Saharan African nations. The findings indicated a long-term causal link between digital financial inclusion and economic growth in sub-Saharan Africa, with the causal relationship running one way from economic growth to inclusion in the latter 7 . Rastogi S set out to investigate how unified payment interface affects financial inclusion, economic development, and financial literacy of the underprivileged in India. He discovered that financial literacy was being impacted. Financial stability and trust both served as partial moderators of the significant associations between digital financial inclusion and economic development as well as the significant link between financial literacy and financial inclusion. This fostered financial inclusion and economic growth for the underprivileged in addition to supporting financial literacy 8 . Lin Boqiang uses the non radial direction distance function to build green Economic efficiency indicators that can evaluate cities at prefecture level and above in China under the super efficiency framework, and further empirically studies the impact of economic agglomeration on green Economic efficiency. To solve the endogenous problem caused by reverse causality between economic agglomeration and green Economic efficiency 9 .

The perfect combination of digital technology and financial services has created a new financial service model. With the help of intelligent digital technology, digital finance can provide lower capital cost and faster service mode for the real economy, provide financial services with "high efficiency, convenience and sustainable commercial services" for the energy industry, and complete the unification of objectivity and precision of financial services. This paper discusses the influence of digital finance on the economic efficiency of the energy industry under the background of artificial intelligence, and aims to provide theoretical guidance for the improvement of the green economic efficiency in the energy industry.

The influence mechanism of digital finance on the economic efficiency of the energy industry

New energy technologies include solar power generation, water energy, wind energy, tidal energy, sea surface temperature difference energy, wave energy, firewood, peat soil, biochemical material energy conversion, geothermal energy, tar sand, etc. At this stage, it is generally recognized that new energy and renewable resources are based on the development trend of new technology application, and gradually change the development and utilization of renewable resources. The traditional fossil energy resources with environmental pollution problems and limited total amount should be replaced by new energy sources that will not be limited by the total amount and the utilization of the recycling system. The key development areas include solar power generation, tidal energy, hydrogen energy and wind energy.

The new energy industry is the exploration, development and utilization of new energy. It uses social methods to achieve effective utilization and popularization, including the whole process of scientific research, industrial utilization, production, manufacturing and operation. It is a high-tech that commercializes solar power generation, wind energy, bioenergy, etc. From the perspective of the characteristics of the industrial chain, the new energy industry is to replace the new industries with strategic status represented by fossil energy, and has extremely important obligations in replacing fossil energy, promoting economic growth, protecting the environment, and building a harmonious society; From the perspective of the whole industry chain, the new energy industry can be divided into energy supply, product research and development, investment and manufacturing, transportation and trading.

The Corona Virus Disease 2019 pandemic has had a major impact on the traditional financial services provided by financial institutions, but it has also accelerated the digital transformation of these services. According to the statistics and analysis of the China Asset Appraisal Association, during the epidemic period, the average service item replacement rate of online banking reached 96%. Despite the epidemic's considerable effects on small and micro businesses and traditional financial "long-tail clients", However, under the background of the intelligent era, the development speed of digital banking is enough to solve the problems of these groups. Through "zero contact" to provide them with low-cost, convenient and fast service projects, especially the contact-free loan has become an important means to help the sustainable development of the energy industry 10 .

The development of digital finance requires a complete institutional system, and the institutional system of digital finance is the financial ecosystem, which is composed of the main body of the ecosystem and the financial ecological environment. The close combination of the two can produce a regular financial ecosystem with internal logic and self-improvement. Judging from the current overall situation of China's financial institution management system, it has basically formed a large digital financial service ecological chain dominated by banking, Internet banking, non-bank finance, and large and medium-sized financial high-tech companies with electronic payment system, integrity management system, legal norms as infrastructure and institutional guarantee, which is dominated by the "one committee, one bank, two committees and one bureau" supervisory agency 11 , 12 . A schematic representation of the structure of the digital financial ecosystem is given in Fig.  1 .

Digital financial ecosystem.

At this point, a significant trend is the close integration of digital technology with finance. In the era of artificial intelligence, digital technology is playing a unique and important role in modern finance. The following points mostly highlight the benefits of digital finance: Firstly, by increasing financing channels, the threshold for financial services has been lowered; secondly, by greatly reducing service prices, comprehensive financial services have achieved sustainable development; thirdly, the personalized financial services can better meet the various requirements of different users; the fourth is to help reduce information asymmetry and provide new risk management methods 13 .

According to different levels of financial functions, digital finance can be divided into three categories: basic functions, leading functions and derivative functions. Figure  2 shows the mechanism of digital finance on the efficiency of urban green development. There are three behavioral paths for the above three functions. The first is digital finance → intermediary services → inclusive utility. Digital finance uses digital information technology to manufacture and expand the role of finance. The network effect of digital technology expands the boundaries of traditional financial services and reduces the service cost of traditional finance. The scale and economic characteristics of digital finance reduce the entry threshold and related costs for innovative enterprises. At the same time, by relying on digital technology, the ability to obtain data and analyze information has been greatly improved and the information asymmetry and the cost of credit intermediary companies have been reduced, and the credit environment has been optimized. After building a three-dimensional credit image based on enterprise big data and cloud technology, sporadic enterprises and start-up companies that are difficult to obtain the support of traditional credit services would obtain a high probability of credit. In order to increase the effectiveness of the urban green economy, the development of digital finance would also help traditional finance change and grow. It would also make full use of the complementary roles that traditional finance and digital finance play in advancing economic growth. Therefore, digital finance will promote the development of traditional finance, and will promote the economic development of the energy industry, and achieve the effect of improving the economic efficiency of the energy industry 14 , 15 .

The impact of digital finance on how well urban green development is carried out.

The second is digital finance → resource allocation service → upgrade utility. Resource allocation service is the core role of finance and an excellent way to correctly guide use value. On the one hand, the birth of digital finance has promoted competition among financial formats and enhanced the charm of folk capital and the financial system, and improved the efficiency and capability of capital allocation. The use of artificial intelligence and electronic information technology can better match the investment needs and financing needs, reduce the financing pressure of the energy industry, and make the capital used more efficiently and quickly for innovation. On the other hand, the circulation of capital factor commodities has been improved. For a long time, in the factor market, the government department has the dominance and dominance of the vast majority of manufacturing factors, and there may be behaviors such as abuse of power. In addition, the popularity of local protectionism and the emergence of administrative systems have resulted in serious market segmentation. The inconsistency and segmentation of the elements of the sales market make some enterprises, especially state-owned enterprises, lose the driving force of "self-innovation". This harms the development of the urban green economy's efficiency. To provide enough financial factors for the supply-side structure's green development, Digital finance enables the energy industry to overcome regional barriers and enhance the environment for the free flow of capital. Therefore, by enhancing and upgrading the efficiency of regional capital element allocation, data finance can achieve the effect of boosting the efficiency of urban green economy 16 .

The third is digital finance → redistribution of finance → inclusive utility. The rapid development of inclusive finance, on the one hand, helps low-income people get rid of poverty and become rich, which improves the level of per capita consumption and promotes economic transformation and upgrading; on the other hand, with the expansion of the number of netizens and network coverage and the rapid rise of e-commerce and Internet consumer finance, the consumption structure of urban residents has also gradually changed. The demand-side consumption capacity and consumption structure have been upgraded, and the energy industry has increased its demand for high-quality products. This has prompted the energy industry to expand the scope of its technology investment and product development efforts, and to encourage the growth of a local green economy. Therefore, digital financing encourages the energy industry to expand technology investment and product research and development, which has the effect of improving the efficiency of urban green economy 17 .

The energy industry is an indispensable part of economic development. Digital finance provides loans to small and medium-sized energy enterprises to meet the financing needs of small and medium-sized energy enterprises, thus stimulating regional economic growth. However, these small and medium-sized energy enterprises are struggling with financial problems and high financing costs. Only a small number of enterprises can apply for loans from financial institutions through official channels, and other enterprises are under pressure of capital loans. The growth of financial inclusion through digital means has reduced borrowing costs and simplified processes. By providing special loans to such enterprises to help them improve their financing and risk management capabilities, it will help improve their profitability and ultimately improve China's economic growth rate 18 , 19 .

If the capital supply cannot keep up, there will be a lock-in effect, and it is imperative to get rid of this inefficient equilibrium state. The basic strategy is to provide specific capital elements for the energy industry, so the assistance of participating banks is essential, and micro loans for small and medium-sized energy industries can help them achieve higher output. Continuous investment in capital and technology will reduce marginal costs, which will have an impact on increasing output and income 20 , 21 . As shown in Fig.  3 , the structure of micro credit's anti lock support effect.

Anti-lock-in support effect structure diagram of microfinance.

This paper discusses the impact of digital finance on the economic efficiency of the energy industry in the context of artificial intelligence. The calculation formula of some indicators related to the measurement of the economic efficiency of the energy industry is as follows:

\(T\) -set of control variables; \({GTFP}_{au}\) -Green economic efficiency of energy industry; \({df}_{au}\) -digital finance; \({df2}_{au}\) -square term of digital finance; \({\omega }_{au}\) -disturbance term; \({\theta }_{a}\) -time fixed effect

\({m}_{au}{^\prime}\) -a collection of independent variables; \({\mathrm{g}}_{\mathrm{au}}\) -threshold variables

\(distrk\) -degree of capital misallocation

\({\mathrm{lngdp}}_{\mathrm{au}}\) -degree of capital distortion

\({MP}_{au}\) -margin of capital

\(\mathrm{d}\) - \(\mathrm{d}\) kinds of inputs; L-L kinds of expected outputs; J-J kinds of undesired outputs; \(\upgamma \) -green total factor productivity efficiency value.

Restrictions:

Let the formulas be:

\({\mathrm{cap}}_{\mathrm{au}}\) -fixed capital stock of the whole society; \({\propto }_{\mathrm{a}}\) -capital depreciation rate

\({\mathrm{cap}}_{\mathrm{a},0}\) -cap initial capital stock; \({\mathrm{o}}_{\mathrm{a}}\) -cap average annual growth rate.

Empirical study on the impact of digital finance on economic efficiency of energy industry

In order to explore the impact of digital finance on the economic efficiency of the energy industry in the context of artificial intelligence, we calculated some indicators of the economic efficiency development level of the energy industry 22 , 23 . Kao (1999) Panel data cointegration test uses the correlation information between individuals to decompose Panel data into inter individual mean and intra individual changes. If the inter individual mean is non-stationary and the residual term is stationary, then the existence of cointegration can be verified. The results are as follows:

As shown in Fig.  4 , the change index of green economic efficiency development of energy industry in some cities of China from 2010 to 2020. We selected 20 cities in China for data analysis. The standard deviation is used to measure the Statistical dispersion of a group of data. The larger the standard deviation, the higher the volatility of the data. The average is the average of the green Economic efficiency development index. From the average and median, the average development level of green economic efficiency of these energy industries has increased from 0.1782 in 2012 to 0.3891 in 2020, and the median has also increased from 0.1342 in 2012 to 0.3247 in 2020. Both are rising year by year. From these two indicators, the green economic efficiency level of the energy industry shows a trend of doubling, this also means that the green economy development level of the energy industry has made a qualitative leap. The coefficient of variation did not change significantly from 2010 to 2020, with a value of 0.5687 in 2010 and 0.5682 in 2020. From the perspective of range and coefficient of variation, the range describes the difference between the highest level and the lowest level. In 2012, the range value of green economic efficiency of the energy industry was 0.4213, while in 2020, the range value of green economic efficiency of the energy industry was 0.8925, which also shows an increasing trend year by year. This means that the gap between the development levels of green economy of the energy industry is increasing year by year, while the difference between the extreme values from 2018 to 2020 shows a trend of slowing growth, this also shows that we are also increasing the level of green economy development in economically backward energy industries. It can be seen from the figure that the coefficient of variation of the green economic efficiency of the energy industry fluctuates, but it does not change much, and even shows a downward trend, which also shows that the green development level of the energy industry does not show a development trend of two-level differentiation.

Change index of green economic efficiency development of energy industry in some cities.

Regression analysis is conducted with or without control variables to examine the robustness of digital finance on the effectiveness of green economy in the energy industry 24 . The regression results of the efficiency standards of green economy and digital finance in the energy industry are shown in Fig.  5 . Where, A represents the result of basic regression without major control factors, and B represents the result of benchmark regression including major control components. It can be seen that before adding the main restrictions, the benchmark regression result of digital finance on the effectiveness of green economy is 0.4685. After the main limiting factors are included, the benchmark regression result of the effectiveness of digital finance on the green economy is 0.2243. Therefore, data finance has a beneficial impact on the effectiveness of the green economy. The green development level of the energy industry does not show a trend of two-stage differentiation, and the benchmark regression results slightly decrease after adding limiting factors. Digital finance will affect the green development level of the energy industry.

Digital finance and green economy efficiency benchmark regression results.

The benchmark regression coefficient results of the influence of pertinent variables on green economic efficiency are shown in Table 1 . It is clear that the benchmark regression coefficients for improving industrial structure, economic development level, and income from both the public sector and higher education are all positive and pass the 5% significance level test. This demonstrates how investing in financial education, upgrading the industrial structure, and the degree of economic development all help the green economy grow and become more efficient. Despite being positive, the benchmark regression coefficient of environmental legislation on green economic efficiency fails the test of significance. The expense of reducing environmental pollution has perhaps increased, which forces businesses to implement relevant technology advancements. The benchmark regression coefficient for openness to the effectiveness of the green economy is negative, and thus failed the significance threshold test. This may be because the entry of foreign high-tech has raised pressure on environmental governance by bringing about not only economic development but also an industrial chain that produces a lot of pollution and uses a lot of energy.

Choosing cross-sectional analysis with fixed effects rather than random effects means that there are fixed differences between individuals, and the impact of these differences on variables is constant. This fixed effects model assumes that individual specific factors have a significant impact on the observed variables, and these factors are fixed during the observation period.

The computation of the conduction effect is shown in Fig.  6 . They are digital finance-green economy development efficiency, digital finance-scientific and technical innovation-green economy efficiency, and digital finance-green economy efficiency as a whole. The conduction line of direct effect is digital finance-green economy efficiency. It can be seen that the computed value of the direct relationship between digital finance and green economic efficiency is 0.1698, indicating that the growth of urban green economic efficiency would be directly impacted by the development of digital finance. The calculated indirect effect value is 0.0413, which suggests that digital finance can boost technological innovation to make cities more environmentally friendly by saving energy and lowering consumption and pollution. The level of green economic growth can be raised while industrial upgrading is encouraged. The total effect of digital finance on the effectiveness of green economy in the energy industry is the sum of its direct effect and indirect effect, of which the intermediary effect accounts for 19.56% of the total effect.

Conduction effect calculation results.

The panel quantile estimation can assess the effect of digital finance on it under each quantile based on the distribution of green economy efficiency levels. The efficiency of the green economy and digital finance are shown in Fig.  7 as the panel quantile regression findings. It is can be seen that for the five quantiles, the estimated coefficient of digital finance climbs as the quantile increases from 0.3042 for the 10% quantile to 0.4276 for the 90% quantile. The increase in the favorable effect is 0.1234, and the significance threshold is 1%. In other words, digital finance has a good effect on the effectiveness of the green economy, and the promotion effect would get stronger as the quantile value rises. This does not help digital finance increase the efficiency of the green economy. However, as the green economy expands and digital infrastructure continues to advance, the beneficial role that digital finance plays in fostering the growth of the green economy would only grow.

Panel quantile regression results of digital finance and green economy efficiency.

In the panel Quantile regression analysis data of digital finance and green Economic efficiency, the estimation coefficient of digital finance is constantly improving, and the significance threshold has always been 1%, so the rise of quantile value will make the promotion of green Economic efficiency stronger.

Conclusions

This paper analyzes the impact of digital finance on the green economic efficiency of energy industry in the context of artificial intelligence, and evaluates the green economic performance of energy industry in some cities from 2010 to 2020. The empirical research results show that the rapid development of digital finance will significantly improve the efficiency of green economy in the energy industry, and show diversity with the change of city size and industrial development level. Digital finance has the synergistic effect of independent innovation and ecological compensation. Through independent innovation and environmental security management, we can jointly improve the efficiency of green economy. Based on this paper, the following suggestions are put forward: encourage financial institutions, insurance and other traditional finance to transform to digital, use data technology to safeguard the traditional financial system, and accelerate the construction of intelligent facilities in various regions; Give full play to the coordinating role of the financial technology service management system in the introduction of innovation policies, patent applications and other aspects. Accelerate the cooperation between the government and the digital financial platform, and give full play to the aggregation effect of financial markets and policies on independent innovation. Make full use of the ecological compensation effect of digital finance on production units, promote financial innovation through joint development of digital finance, and promote the growth of small and medium-sized enterprises in the upstream and downstream of the green industrial chain and supply chain. The government can formulate policies to encourage energy companies to adopt digital financial technologies, such as blockchain, Big data analysis and artificial intelligence, to improve efficiency and reduce costs. For example, the government can provide tax or subsidy incentives to encourage enterprises to invest in the research and application of digital technology. At the same time, it is necessary to prevent losses caused by excessive economic leverage, so that data finance can better provide energy for the urban real economy.

Data availability

Datasets generated and/or analyzed during the current study are available from the corresponding author on request.

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He, Q., Xue, Y. Research on the influence of digital finance on the economic efficiency of energy industry in the background of artificial intelligence. Sci Rep 13 , 14984 (2023). https://doi.org/10.1038/s41598-023-42309-5

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DOI : https://doi.org/10.1038/s41598-023-42309-5

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Original research article, a bibliometric analysis of fintech trends and digital finance.

• Department of Administrative Sciences, Applied College, University of Bisha, Bisha, Saudi Arabia

Digital finance has piqued the curiosity of academics, students, and institutions all around the globe for more than a decade. Innovative financial services companies are offering a wide range of new financial products and new ways of interacting with customers via digital finance (Fintech). Research on finance and information systems has thus examined these shifts as well as the implications of technological advancements on the financial industry. Through presenting a bibliometric analysis, the article summarizes how scientific research has developed on the connections between financial technology developments and digital finance during the previous years. According to the ScienceDirect database, we base this literature review on journals and articles that have been published. We conducted a content analysis of 343 articles based on the discovered clusters, finding research gaps and suggesting actionable areas for further study. The results offer a solid path for future research in this area. We discuss the significance of the aforementioned publications and articles as well as potential areas of future study. The next step is to analyze the citation linkages between the most important articles to identify how they are related to one another. For financial technology research, the study looks at the way they are organized. The research is concerned with the roles of Fintech and the limits of research in digital financing. We point out potential routes for researchers to take to expand on current knowledge while also seeking possibilities for new, interesting, and creative research that adds to the expansion of the topic of research.

Introduction

The financial industry has witnessed continuous development in providing services due to digitization ( Brandl and Hornuf, 2020 ; Kanungo and Gupta, 2021 ). This improvement is distinguished by increased communication and better information processing in the client interface and back-office processes. Recently, the emphasis on digitization has shifted from enhancing the performance of conventional tasks to mainly creating employment possibilities and new business models for financial services firms ( Gomber et al., 2017 ; Legner et al., 2017 ). Digital finance includes many new financial products, financial companies, related financial programs, and new forms of customer interaction ( Anjum et al., 2017 ; Azizi et al., 2021 ) and interaction offered by financial technology companies and innovative financial services providers, for e.g., Barras (1990) , Gomber et al. (2017) , and Ozili (2018) . In light of this, research on finance and information systems has started to examine these shifts as well as the financial sector’s influence on digital development. Financial technology appears in a short time and attracts much attention from practitioners. Why ?

The answer is the ability to switch supply chain networks in almost all business sectors. New business models and technology concepts provide the foundation for innovative financing solutions, knowledge sharing within a firm, and organizational innovation ( Abbas et al., 2019a ) and knowledge management and sustainable organizational innovation ( Abbas et al., 2020 ), including intelligent, easy-to-use, time and time financial services, and lower costs ( Teece, 2010 ; Gomber et al., 2017 ; Varga, 2017 ). Some studies identified a link between the conflicting and creative characteristics of social media and the paths for future research by providing a better understanding of how social networks on the Internet are used ( Abbas et al., 2019b ; Abbas et al., 2019c ; Lebni et al., 2020 ; Liu et al., 2021 ). Also, results indicated that corporate social responsibility presented a positive impact on firms’ sustainable performance. Also, therefore, there is a definite necessity to employ media or communication resources to achieve timely progress ( Su et al., 2021a ; Su et al., 2021b ).

Current financial service providers, such as banks and insurance companies, are being challenged by digital finance. Because of the growing competition from FinTech companies, the latter provides unique prospects for employers to contact their younger and more innovative technological clientele ( Arner et al., 2015 ; Joshi, 2020 ; Wang et al., 2021 ). Against this background, there is an ongoing discussion on traditional financial intermediaries about handling FinTechs and whether competitive approaches acquisitions and alternatively engaging those firms as service providers that are compatible with their business models, for e.g., Lai (2020) ; Suprun et al. (2020) ; Vučinić, 2020 . The new opportunities presented by technology allow them to maintain their competitiveness and provide new and attractive services to their clients.

The study of Abbas et al., 2019d proved that highly innovative firms exhibited a propensity for building a business network to achieve sustainable performance. Furthermore, the findings indicated that firms achieving sustained performance did so by applying effective business networks and flexible capacities. The study’s results suggest that it presented a holistic and systematic approach for achieving sustainable performance through the dynamic capacities of businesses.

This study thus contributes to the actual literature by studying the linkage between digital finance and fintech. The bibliometric study data are represented in the overall research work on Digital Finance and FinTech in the ScienceDirect database. These data covered the period from 2006 to 2020, where the focus was on data of recent studies completed, especially in the last 3 years (2018, 2019, and 2020). The following parts of this article are structured as follows: Section Research Method and Questions discusses the research method and research questions. Section Methods and Materials deals with the methods and materials. Section Results and Discussion lays out our results and discussion. Section Conclusion and Limitation presents the conclusion and suggestions.

Research Method and Questions

Research method.

The bibliometric analysis takes all kinds of illumination as a research goal and uses mathematical and statistical methods to study science and technology’s technological trends and development ( Moed, 2006 ; Zhang et al., 2021 ). Reference measurements have been used extensively to reveal research status and development trends in a field. They have an essential role for researchers to understand a particular research field in depth ( van Oorschot et al., 2018 ; Vatananan-Thesenvitz et al., 2019 ). Additionally, scientists also use bibliometric methods to systematically study in ScienceDirect database publications to uncover their past, present, and future, especially in recent years, there have been many valuable research findings of this kind. A bibliographic measurement approach was used to analyze all publications in the database.

The main goal of a bibliometric analysis was to collect and evaluate the available research relevant to the area of interest and to produce objective results that can be audited and reproduced over and over again. When it comes to research results, a bibliometric analysis is a rigorous methodological assessment with the goal of grouping existing works on the subject and assisting in the development of evidence-based guidelines for professionals working in the study field ( Kitchenham, 2004 ; Prinsen et al., 2018 ). A bibliometric analysis should also identify the state of the art about the research subject ( Levy and Ellis, 2006 ).

Financial technology and the newer “Fintech” topics are gaining further focus as the effect of digitalization on the financial services sector rises ( Nicoletti et al., 2017 ; Leong and Sung, 2018 ). When it comes to financial services, one of these is why there is a lot of dependence on information, and the other is that most procedures, such as trading on an online platform ( Karagiannaki et al., 2017 ). With new financing models made available, broad and significant digitization of the financial service providers and customers’ needs to occur to facilitate the value chain transformation that is taking place. The word “Fintech” is a contraction of “financial technology,” and Citicorp chairman John Reed most likely coined it in the early 1990s in the light of a newly formed “Smart Card Forum” ( Puschmann, 2017 ). In a digital age, fintech applications redefined today’s product-centered thinking to include emerging ecosystems. Individual channels can become redundant when financial service designers focus on hybrid and incompatible modes of interaction-based consumer operations ( Gill et al., 2015 ).

Research Questions

The actual study compares bibliometric analysis to other methodologies (meta-analysis and systematic review), specifically in digital finance and fintech research. The study’s purpose is tied to its motivation. We will identify its scope and research trends, which will help readers learn more about digital finance and fintech in the scientific community, and the justification and significance of this study’s analysis are obtained from two research topics: the future trends and issues in the literature review on digital finance and fintech.

The following two suggested research questions will help the study accomplish its goals, which are to offer academics and practitioners a systematic, categorized perspective of what has been generated in the literature on digital finance and fintech. According to the main problem, the first question is focused on providing an overall quantitative and longitudinal perspective of the works on this topic, and it is worded as follows:

RQ1. What changes have occurred in the literature on digital finance and fintech?

The following sub-questions were generated from the main question:

RQ1.1. What has been the most influential research, such as those published in ScienceDirect?

RQ1.2. Which important references had the most impact on the studies that were identified?

RQ1.3. Which journals are the most widely read on this topic, and how has the number of publications changed over time?

To find the existing literature to build and develop new studies, a categorization of the key topics and research questions was used to classify the digital finance and fintech activities identified in published materials from the sample into several categories. As a result, the following is the formulation of the second question of this work:

RQ2. What are the most important topics and problems discussed in the scholarly literature on digital finance and fintech?

This section explains the procedure that was followed to complete this bibliometric literature evaluation using a technique that was previously established and verified. Furthermore, bibliometric analysis methods were used to determine the scenario state of the scientific literature on digital finance and fintech ( Ikpaahindi, 1985 ).

Methods and Materials

Bibliometric data.

The quantitative method “bibliometrics” ( Fairthorne, 1969 ; Pritchard, 1969 ) is one of the most quantitative measures used in evaluating literature. Bibliometric forms have been prevalent in digital finance, but few studies have considered them—nonetheless, citations connected to the concept of payments, protection, deposit, and retail provisioning. Fintech trends have been overlooked in publications. Looking at fintech-related metadata and the publications they connected to, the metadata gives us various viewpoints on each publication series.

The bibliometric study data are represented in the overall research work (in title, abstract, and author keywords for the article) on Digital Finance and FinTech in the ScienceDirect database. These data covered the period from 2006 to 2020, where the focus was on data of recent studies completed, especially in the last 3 years (2018, 2019, and 2020).

Study Methods and Tools

Many researchers ( Hood and Wilson, 2001 ; Osareh, 1996a ; Osareh, 1996b ; Tsay, 2005 ) have identified three key bibliometric rules. The first and earliest of these, according to Hood and Wilson (2001) , is Lotka’s law ( Lotka, 1926) which provides a relationship between authors and articles. Bradford’s law ( Bradford, 1934) deals with scattering articles on a scientific subject through scientific journals. Zipf’s law ( Zipf, 1949) is interested in the concept of frequency or occurrences.

The bibliometric study data represent the overall research on “Digital Finance and FinTech” in the ScienceDirect database. These data covered the period from 2006–2020. In which, we expected the use of Digital Finance and FinTech because of the closure and quarantine procedures during the epidemic. Therefore, articles from the ScienceDirect database that included fintech keywords in the title, abstract, and author keywords were reviewed and analyzed. Through review articles that were published starting in 2006 and also the literature from 2006 to 2020, the articles were reviewed and analyzed.

According to the processes and approaches used in bibliometric analysis, citation, co-citation, bib. coupling, co-author were analyzed for keywords, for e.g., Zupic and Čater, 2015 . The study relied on the citation indicator to find out the main keywords that studies focused on and prominent authors in Digital Finance and FinTech. To determine the network of research relationships between them, the practical stages of preparing the bibliometric study was carried out (study design, data collection, analysis, presentation, and guide); see Lobato et al. (2021 ).

Following the methodology of preparing the bibliometric study in management and organization, which is explained by Zupic and Čater (2015 ), the bibliometric analysis was carried out by completing the following steps: research design, study questions, and analysis approach selection (co-occurrence, publication, citation, and co-authorship), bibliometric data compilation, selection, and filtration, analysis (choose the appropriate bibliometric software, clean the data, and generate networks), visualization, and interpretation.

Results and Discussion

Descriptive of bibliometric data.

Analysts must provide complete knowledge regarding ongoing investigations in their respective fields and scholars that contribute to the analysis. These data change with time. Every day, fresh pieces of knowledge are introduced to the systems due to the advancement of new technology and new research. The usage of mathematical techniques to analyze articles, books, magazines, and other publications is bibliometric analysis. Geographic research, top writers, affiliations, colleges, documents, year-by-year articles, and citation analysis are all included in this report. The literature in this article was collected using the ScienceDirect database. Several networks have created keyword-based and titles in Digital Finance and FinTech science, sources, and authors.

To select the articles for final review, we used a three-step process. First, we collected and stored research articles (research articles, review articles, book chapters, and others) for the specified search keywords in the ScienceDirect databases, with an open beginning period to include as many publications as practical up to December 31, 2020. A total of 343 titles were retrieved during the first search. The article title, authors’ names and affiliations, abstract, keywords, and references were all included in the search results, which were downloaded in a CSV format.

We retrieved published research via a topic search of Digital Finance and FinTech of the ScienceDirect database on January 01, 2020. The following search terms were used: topic = (“Digital Finance” or “e-Finance” or “FinTech” Or “Fintech”), in title-abs-key from 2006 to 2020, and we got 343 studies (184 research articles and 14 review articles; 111 book chapters, 02 encyclopedias, 02 case reports, and 32 others) distributed over 15 years, as shown in Table 1 . The database used in bibliometric analysis or previous studies in a topic of digital finance and FinTech is described here, using numerical expressions (descriptive statistics).

TABLE 1 . Statistics of previous studies.

Identifying various contributing publications may enable the identification of the most relevant journal outlets in each area. The various publishing ports are shown in Table 1 . According to the number of published articles, research articles contribute the most, while book chapters rank second. Table 1 shows that the topics of digital finance, FinTech, and e-finance constitute a modern knowledge field, especially since most studies have been completed in the last 3 years (2018, 2019, and 2020). Moreover, most of these studies are research articles or book chapters; this explains the abundance of production in this type of research, which can be clearly shown in the following figure.

Figure 1 shows that most of the research has been performed in recent years: 155 in 2018, 88 in 2019, and 71 in 2020. Why is the curve appearing to rise, indicating the novelty of digital finance and financial technology as an area of knowledge in the financial management discipline?

FIGURE 1 . Publications per year.

For this research, the ScienceDirect database with all types of already published and unpublished publications is considered. There are many types of publications such as articles, journals, conference papers, and book chapters in a database. When researching for FinTech regulations, publication types that formed the majority were articles and conference papers and very few research studies were published in notes, conference reviews, and letters. When the results from the query were analyzed, all kinds of publications were articles, magazines, conference reviews, book chapters, etc. From 2006–2020, the trend has been increasing since 2006 and is being researched and explored more and more. Below line graph shows this trend:

Figure 2 shows that most studies used the following keywords: Fintech, blockchain, financial inclusion, bitcoin, banking, big data, etc.; this indicates that the previous studies used focus on the topic of digital finance and fintech.

FIGURE 2 . Publications per keyword.

In the last 3 years, it appears that the most prominent researchers in the area of digital finance and fintech are John Hill and David Lee Kuo Chuen; any researcher should refer to these in this specialty Figure 3.

FIGURE 3 . Publications per author.

Bibliometric Analysis and Networks

Items: 42 / Clusters: 8 / Links: 194 / Total link strength: 636.

It is noted from the Figure 4A,B that there are 8 clusters in the network that the researcher can take in the field of digital finance and FinTech as research thematic namely Fintech and its related clusters, financial inclusion and blockchain, cryptocurrency and bitcoin, financial services, entrepreneurial finance, P2P lending, distributed ledger technology, and trust.

The researcher should delve deeper into fintech, blockchain, financial inclusion, cryptocurrency, and bitcoin, as shown in the density and the following table in all these areas.

FIGURE 4 . (A) Network of keywords. (B) Density of keywords.

The most common terms used in previous studies were fintech, blockchain, financial inclusion, cryptocurrencies, financial services, and bitcoin. These should be concerned with the research and analysis of researchers in digital finance and FinTech. Now, we come to examine the most visible researchers in this field Table 2 .

TABLE 2 . Occurrence of keywords in the network.

It appears in the Figures 5A,B that the primary researcher in the area of digital finance and FinTech to which all other researchers are related is David Lee Kuo Chuen, and this should return all researchers to his research because of its importance in the field which was similar to the following study: Chuen (2015) , Chuen and Deng (2017) , Nian and Chuen (2015) , and Chuen et al. (2017) .

FIGURE 5 . (A) Network of authors. (B) Density of authors.

There too, John Hill’s research has not been widely used by other researchers, so we find that his name did not appear on the network and the density. The results of the following table show this.

The Table 3 results show that the most visible researcher in digital finance and FinTech is John Hill, especially his research ( Hill, 2018 ). Still, his research is not used by other researchers who are more visible in this field. On this basis, we can say that the study of David Lee Kuo Chuen is more influential than the research of John Hill in this field; this does not negate the return to his research but must refer to it because of its great importance in this field.

TABLE 3 . Occurrence of authors in the network.

Conclusion and Limitation

This article aims to explain digital finance and financial technology (Fintech), two distinct trends imposed in these areas. The second part of this article uses bolometric analysis with the ScienceDirect database to determine research progress in the field. This reveals that these superpowers are also driving research on the topic, with the most significant digital finance and financial technology publications, and FinTech is emerging on the market ( Omarova, 2021 ). We highlighted specific aspects that need further discussion based on the bibliometric analyses of the research focused on implementing FinTech and digital finance and its application disciplines. The methodologies and new main study topics are all discussed in the published studies.

Bibliometric analyses are a well-established method of meta-analytical investigation ( Paul and Criado, 2020 ) or so-called “meta-reviewed” of the literary world. Bibliometric analyses reveal key articles and explain critically if and within articles relate to any study subject or analyze how many other articles have been cited by one another. Finally, these analyses will assess the success of individual writers and their publications and their effect. The bibliometric citation analysis thus enables the meta-analytical evaluation of the history of a particular area or discipline and the identification of main strands and theoretical frameworks.

The analysis suggests a fundamental unit of study. It, therefore, goes farther than a single count of publications to cover impact centers and maps of relations between articles in a particular field of science ( Kim and McMillan, 2008 ; McKiernan et al., 2019 ). The meta-analysis of quotations thus represents the utility of the literature in other similar researchers ( Timulak, 2009 ). The bibliometric cycle review approach is an appropriate meta-analytic instrument for improving the three research objectives described previously. It provides insights into the research area of digital finance and the pattern of correlations in the Fintech industry.

This research will assist scholars and financial policymakers who are interested in digital finance in understanding the current state of Fintech needs and identifying trends in the corporate boardroom. It also supports the emerging acknowledgment that Fintech will play a critical element in the world endeavor to achieve digital financial trends, which is outlined in this research. Furthermore, digital finance and Fintech publications develop with the regularity, the multidisciplinary nature of digital finance, and the high-disciplinary individual’s inclusions. The high- or low-quality literature around digital finance is getting better, and individuals are involved.

The findings of this study can help the digital finance and Fintech industries develop policies and processes to enhance the emerging digital finance trends in the future. Financial and non-financial institutions can directly assess the financing process as strategic dimensions and policy makers’ vision.

Limitations of Research

This study has some methodological limitations, which may be addressed in future research. First, this research analyzed one database, ScienceDirect, which limited research in articles; other databases, such as Web of Science or Scopus, may be suggested in future bibliometric analyses. Second, it may envision future research from the source or topic of the publications, which may help develop a more comprehensive perspective on financial technology and digital finance.

Data Availability Statement

The original contributions presented in the study are included in the article/Supplementary Material; further inquiries can be directed to the corresponding author.

Author Contributions

The author confirms being the sole contributor of this work and has approved it for publication.

Conflict of Interest

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors, and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Acknowledgments

The author extends his appreciation to the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia, to fund this research work through the project number UB-56-1442.

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Citation: Brika SKM (2022) A Bibliometric Analysis of Fintech Trends and Digital Finance. Front. Environ. Sci. 9:796495. doi: 10.3389/fenvs.2021.796495

Received: 16 October 2021; Accepted: 14 December 2021; Published: 10 January 2022.

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RETRACTED ARTICLE: Digital finance, corporate financialization and enterprise operating performance: an empirical research based on Chinese A-share non-financial enterprises

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Based on the phenomenon of rapid development of China's digital finance and the increasingly “transform the economy from substantial to fictitious” in real enterprises, it may have a huge impact on the healthy development of the enterprise. Therefore, the paper selects the data of listed companies in the non-financial industry in the A-share market of Chinese Shanghai and Shenzhen stock markets from 2011 to 2018, and examines the relationship among the development of digital finance, the level of corporate financialization, and corporate operating performance. It found that digital finance can prompt the enterprises to reduce the level of financialization, thereby has a positive effect on the business performance of the company; Significant heterogeneity is found to exist in terms of company size, region and digital financial structure, the breadth of coverage and depth of use of large-scale enterprises and digital finance are more prominent. The mechanism test shows that the difference in the degree of development of digital finance and the nature of property rights has strengthened the negative impact of corporate financialization on corporate business performance.

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Liu, Y., Jin, D., Liu, Y. et al. RETRACTED ARTICLE: Digital finance, corporate financialization and enterprise operating performance: an empirical research based on Chinese A-share non-financial enterprises. Electron Commer Res 23 , 231–256 (2023). https://doi.org/10.1007/s10660-022-09606-z

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Digital Finance and Green Development: Characteristics, Mechanisms, and Empirical Evidences

Rulong zhuang.

1 School of Business, Ningbo University, Ningbo 315211, China

2 School of International Trade & Economics, Ningbo University of Finance & Economics, Ningbo 315175, China

Chaoyang Zhang

Associated data.

This paper takes 30 provincial-level units in China as samples; Tibet, Hong Kong, Macao, and Taiwan are excluded due to the lack of data. The green development (energy consumption per unit of GDP) mainly comes from the China Statistical Yearbook, the China Energy Statistical Yearbook, and regional energy balance tables. Digital finance data come from Peking University Internet Finance Research Center. The remaining economic and social statistics mainly come from statistical yearbooks and statistical bulletins of various provinces, autonomous regions, and municipalities. The air pollution data of robustness tests are obtained from the online monitoring and analysis platform of China’s air quality.

As the emergence of digital finance is relatively short, research results on digital finance mainly focus on products, services, coverage, policies, etc. The mechanism and role of digital finance in influencing green development are still lacking attention. In the above context, this paper used spatial analysis methods to describe spatiotemporal characteristics in detail, and empirically tested the mechanism and path of digital finance affecting green development through spatial econometric models and intermediary models. The results showed that: (1) During the study period, digital finance and green development have been improved to varying degrees, but the inter-provincial differences are still obvious. (2) The spatial trends of digital finance and green development are similar, and the overall performance is “high in the east, low in the west, high in the south, and low in the north”. (3) The empirical tests found that digital finance is an effective force to reduce energy consumption per unit of GDP and improve the level of green development. It validates Hypothesis 1. Meanwhile, the Heterogeneity effect is noteworthy due to different regions, types, and levels. (4) The promotion of green development by digital finance is mainly concentrated in the local region and has not yet shown a significant green spillover effect for surrounding areas. It validates Hypothesis 2. (5) Energy structure, industrial upgrading, and technological progress are three paths for digital finance affecting green development. Hypothesis 3 is verified. Finally, the innovation of this paper lies in the design of the research framework, diversity of research methods, and policy implications. The main contribution is to enrich and expand the environmental finance theory and provide detailed empirical evidence. In addition, we put forward effective measures and suggestions including local governments, financial institutions, and enterprises based on the empirical results. Local governments should pay attention to policy implementation and operation effects, financial institutions constantly need to strengthen the supply of advanced digital financial products and services, and enterprises should attach importance to the use of digital financial tools to achieve green and low-carbon development in the future.

1. Introduction

For quite some time, an extensive economic development model with large factor input has caused serious resource and environmental problems, and the concept of green development has become the consensus of the whole society. In 2021, China’s carbon emissions have reached 11.47 billion tons, twice that of the United States (5 billion tons) and four times that of the European Union (2.79 billion tons), and have not yet reached the peak. At the same time, digital finance has developed rapidly. In 2020, China’s mobile payment business and payment finance reached 123.22 billion transactions and 432.16 trillion CNY, with year-on-year growth of 21.48% and 24.50% respectively. The most frequently used mobile payment products are WeChat, Alipay, and UnionPay cloud flash payment. In addition, 150 million users in China have purchased online financial products in 2020. Influenced by the COVID-19 pandemic in 2020, traditional financial services are subject to many restrictions in terms of sales, investment, after-sales, etc., further strengthening the trend of online “contactless” financial services.

Digital finance is a kind of new type of financial service, which is mainly formed by combining the Internet and Information technology with traditional financial services, including mobile payment, online banking, financial service outsourcing, online loans, online insurance, online funds, and other forms. What is important is that digital finance itself is also a kind of green finance. In the process of achieving green development, digital finance will play a non-negligible role in optimizing resource allocation, enhancing technological innovation, and promoting industrial upgrading, and so on. It will become one of the important driving forces to achieve the “dual carbon” goal [ 1 , 2 , 3 , 4 ]. The “dual carbon” goal was first proposed by Chinese President Xi at the 75th United Nations General Assembly in 2020. The main contents are that China will improve its national independent contribution, adopt more powerful policies and measures, strive to reach the peak of carbon dioxide emissions by 2030, and strive to achieve carbon neutrality by 2060. Against this background, we also regard the realization of the “dual carbon” goal as the important research content and direction of this paper.

To better achieve the “dual carbon” goal, promote the green transformation of the industry, and reduce carbon emissions, this paper took digital finance as the research object and systematically depicted the spatiotemporal characteristics. On this basis, an econometric model is constructed to judge the impact of digital finance on green development and its path. Compared with the existing research the contribution of this paper mainly lies in the following three points. Firstly, it brings digital finance and green development into the unified research framework, and carefully analyses the mechanism of digital finance affecting green development. Secondly, it deeply explores the specific path of the impact of digital finance on green development and tries to test the mediation effect from the three dimensions: energy structure, industrial upgrading, and technological progress. Thirdly, considering the possible spatial relationship, this paper discusses the spatial effect of digital finance on green development from the perspective of “Local-foreign”. Finally, based on the above research, this paper puts forward countermeasures and enlightenments for promoting the development of digital finance and realizing the goal of green, low-carbon, and sustainable development.

Figure 1 illustrates the research framework of this paper.

The research framework.

2. Literature Review

2.1. studies on digital finance.

With the vigorous development of artificial intelligence, cloud computing, and big data technology, finance has strengthened the comprehensive integration with emerging technologies. Digital finance is emerging in this context. In addition, with the increasingly important role of digital finance, it has gradually become a research hotspot in the financial field [ 5 , 6 , 7 ]. At present, the academic researches on digital finance mainly focus on two aspects. The first aspect is the discussion of digital finance itself, which originates from a deep understanding of the connotation. These contents include digital financial products, services, industries, supervision, etc. [ 8 , 9 ]. The second aspect is the analysis of the economic and social impact of digital finance through comparison with traditional finance. Those impacts are very extensive, including industrial upgrading, product innovation, technological progress, citizen welfare, poverty alleviation, economic growth, and regional coordinated development, etc. [ 10 , 11 , 12 , 13 , 14 ]. The general conclusion is that digital finance can effectively promote industrial upgrading, technological progress, welfare improvement, and economic development. Some scholars discussed the relationship between government expenditure and intergenerational mobility, which provides a direction for the government to use digital financial tools to play an inclusive role to achieve equal distribution of public services [ 15 ]. At the same time, some scholars have also noticed that digital finance will inevitably be affected by various risks while playing a positive role. Therefore, it is indispensable to strengthen financial supervision to ensure its efficient and healthy development [ 16 ]. In addition, some scholars pay attention to the impact of e-commerce development closely related to digital finance on consumer behaviors and retail space values and analyzed and explained it through store rents [ 17 ]. It is necessary to mention that due to the green nature of digital finance and the proposed carbon emission reduction targets in various countries and regions, the environmental effects of digital finance have also attracted the attention of scholars. These will be explained as follows.

2.2. Studies on Green Development

With the continuous development of urbanization and industrialization, the constraints of resources and the environment have gradually become an important obstacle restricting the sustainable development of the economy and society. In this context, the concept of green development has gradually formed and gained consensus at home and abroad [ 18 , 19 , 20 ]. Green development emphasizes the transformation and optimization of human production and lifestyle, the reduction of resource consumption and environmental damage, and the realization of healthy and sustainable economic and social development. There are many similar concepts to green development, such as sustainable development, green economy, low-carbon economy, green growth, etc. [ 21 , 22 ]. Among them, sustainable development can be seen as the theoretical origin of green development. Through combing the relevant literature at home and abroad, we found that the research on green development mainly includes connotation, concept, level, path, method, etc. Among them, scientific measurement and seeking future development paths have become the focus of many experts and scholars [ 23 ]. As one of the important directions of green development, digital finance has attracted more and more attention [ 24 , 25 ]. With the establishment of the carbon trading mechanism, some scholars also began to study the carbon emission reduction effect of the carbon trading mechanism [ 26 ]. In addition, due to the impact of the COVID-19 epidemic in recent years, the impact of the COVID-19 epidemic on the stock prices of energy enterprises has also attracted attention [ 27 , 28 ]. Finally, the research on the relationship between economic growth and green development cannot be ignored. The most representative topic is the Kuznets curve [ 29 ].

2.3. Studies on the Relationship between Digital Finance and Green Development

Digital finance is the integration of traditional finance and modern science and technology, which still has the basic characteristics of traditional finance, so the research on the impact of traditional finance on environmental pollution can provide references for this paper [ 30 ]. The relationship between finance and the environment has been studied for a long time in the academic circle, but there has always been controversy. The main academic views can be summarized as follows: The first view is that the prosperity of the financial market contributes to economic growth, but economic growth will also lead to an increase in energy demand, which may eventually lead to more pollutant emissions and increase environmental pressure. The second view is that, in addition to increasing energy demand and pollutant emissions, financial development may also improve energy use and resource allocation efficiency through scientific and technological progress and industrial structure upgrading and may also play a positive role in environmental protection [ 31 , 32 , 33 , 34 ]. In addition, the third view holds that there is a nonlinear relationship between financial development and environmental pollution [ 35 ]. Based on existing research, this paper attempts to explore the relationship between digital finance and environmental pollution to enrich and expand its scope and contents. On this subject, there is also some relevant research in the academic circle, mainly involving production efficiency, technological innovation, industrial structure, etc. [ 36 , 37 , 38 ].

Although the above literature provides rich perspectives for our research, there are still some shortcomings. The discussion on digital finance mainly focuses on regulations, services, products, risks, and policies. Regrettably, there is still a lack of enough attention to the internal relationship between digital finance and green development, especially to explore the mechanism of digital finance affecting green development by using reasonable data and scientific methods. In addition, most studies do not consider the possible spatial correlation between digital finance and green development, which will lead to errors in theoretical research and practical analyses.

3. Theoretical Analyses and Research Hypotheses

3.1. digital finance and green development.

Sustainable development theory and environmental finance theory provide useful guidance for the mechanism analysis of digital finance affecting green development [ 39 , 40 , 41 ]. From the perspective of sustainable development theory, digital finance has realized its own green transformation through the combination of information technology, reduced the resource consumption of the financial industry itself, and improved operating efficiency. At the same time, compared with traditional finance, digital finance plays a more significant role in supporting and promoting green industry and green technology, and contributes to the sustainable development of the economy and society. The environmental finance theory believes that the environment is a factor that the financial industry needs to focus on. Finance is also responsible for the health of the environment. Environmental finance theory emphasizes the innovation of financial technology, the upgrading of financial products, and the rationality of financial structure, which provides effective financial support for environmental protection. At the same time, environmental finance theory also involves some deep-seated institutional arrangements.

On the one hand, compared with traditional finance, the digital operation of finance can effectively reduce the consumption of resources, improving the efficiency of resource allocation, which shows an obvious “green effect” on finance and its related industries. On the other hand, digital finance can expand the coverage of financial services, guide and encourage more financial resources to low carbon, environmental protection, technological innovation, and high-tech industries. At the same time, it effectively curbs pollution investment, which not only helps to accelerate the green transformation of China’s economy, but also helps to promote technological progress in environmental protection, new energy, and energy conservation. In addition, digital finance can play the role of lubricant, accelerate the free circulation and effective allocation of capital, information, digital, technology, and other elements, and correct the market failure and financial fragmentation caused by information asymmetry in traditional finance [ 42 , 43 , 44 ]. It is an important hand to realize green development, transformation, and upgrading, and plays a decisive role in supporting green industry and sustainable economic and social development. Based on the above analyses, we propose the first research hypothesis.

Digital finance is conducive to promoting green development.

3.2. The Spatial Effect of Digital Finance on Green Development

According to the first law of geography, digital finance, as an economic phenomenon, inevitably has spatial correlation, which is significantly affected by geographical distance. Furthermore, through the theory of space economy and the theory of factor flow, it can be found that digital finance may release this spillover effect through the flow of various financial elements between regions.

The development of digital finance cannot be separated from traditional finance and the real economy, and the real economy cannot be separated from data, talent, technology, and other production factors. Therefore, the emergence and development of digital finance first appeared in metropolises with a high level of economic development, rich financial elements, and complete mobile Internet facilities. Since then, as “Digital China” has become a national strategy, the degree of financial marketization has been continuously improved, and the cross-regional flow of financial elements has made spatial correlation and interaction dependence increasingly strengthened. Driven by its own development needs and the profit-seeking characteristics of capital, digital finance began to expand and extend to other regions, forming a spatial inclusion. From this perspective, digital finance is not only conducive to local green development but also may have a “green spillover effect” on surrounding areas [ 45 ]. However, China’s financial market is still in the process of integration. Influenced by local governments, markets, and industries, the problem of regional segmentation is still obvious, and a unified financial market has not yet been formed. Although compared with traditional finance, digital finance has a wider coverage and higher liquidity, but in the case of insufficient high-level coordination, rigid constraints of administrative divisions, and local governments’ consideration of maximizing the interests within their jurisdiction, digital finance, as a resource, will also show scarcity among regions. Its development direction is mainly to meet the actual needs of local green development, while for other regions, it may inhibit the green spillover effect and even lead to potential pollution risks in surrounding areas. Based on the above analyses, we propose the second research hypothesis.

There may be a spatial spillover effect of digital finance on green development, but this effect is uncertain.

3.3. The Mechanism of Digital Finance Affecting Green Development

Under the guidance of sustainable development theory, green growth theory, energy economy theory, endogenous economic growth theory, and industrial structure upgrading theory, combined with existing research results, we believe that digital finance may affect green development from three paths: energy structure, technological progress, and industrial upgrading [ 46 , 47 ]. Of course, there should be many paths for digital finance to affect green development, which will be one of the key research contents and directions in the future.

According to energy economics theory, the relationship between energy utilization and environmental pollution has always been the focus of energy economics research. In China, energy utilization is an important source of environmental pollution and has a direct and widespread impact on green development. At the same time, as an advanced form of traditional finance, digital finance will inevitably enter the field of energy production and consumption, which will certainly have an important impact on the energy structure. Therefore, according to the guidance of relevant theories, we believe that digital finance may have an impact on green development by improving the energy structure. In addition, under the guidance of the “dual carbon” goal, green and low-carbon development is a complex project and a long-term task for sustainable economic and social development. Therefore, we must look for the direction and path to promote green development based on the basic national conditions of coal as the main fuel for a long time.

Specifically, digital finance can guide the reduction and replacement of backward production capacity in the energy industry, reduce energy consumption and gradually realize the transformation of energy structure through more green, environmentally friendly, and efficient technical support. At the same time, in the process of formulating and designing policies, products, and services, digital finance pays more attention to the orderly conversion of traditional energy to new energy and takes overall consideration to reducing the proportion of coal power and increasing the proportion of clean energy such as hydropower, wind power and photoelectric. Digital finance can play a further role as a strategy and tool for low-carbon transformation. With the help of structural monetary policy, diversified tools such as credit, bonds, equity investment, and trust can be used to provide financial support for green and low-carbon development. Therefore, digital finance can promote the adjustment of energy structure and gradually transition to green, low-carbon, and clean energy through technical support, capital allocation, and monetary policy for energy and related industries [ 48 , 49 , 50 , 51 ].

From the perspective of endogenous growth theory, combined with Solo models, sustainable economic growth comes from the improvement of total factor productivity, which is usually caused by technological progress [ 52 ]. As an advanced form of traditional financial integration with digital information technology, digital finance itself represents technological progress. At the same time, digital finance will also promote sustainable economic development through technological progress, which implies green development. In addition, from the perspective of industrial structure theory, the upgrading of industrial structure will help to improve productivity, reduce resource consumption, and reduce pollution and damage to the environment [ 53 , 54 ]. Therefore, it will help to achieve green development. Under the guidance of relevant theories, we believe that digital finance may promote green development through industrial upgrading and technological progress. In addition, from the perspective of externality theory, due to its significant positive externalities, digital finance can be actively encouraged by policies as an effective means of internalizing externalities, such as reducing the accuracy of orientation, rediscount, and refinancing, thus promoting green industrialization and industrial greening.

In addition to financial policies, the huge advantages of digital finance can not only provide targeted financial support for enterprises’ low-carbon transformation or green technology development, but also promote the development of the environmental protection industry, effectively overcome financing constraints, information asymmetry, moral hazard, and other problems, and provide financial security for enterprises’ green transformation. At the same time, financial policies can effectively curb the financing scale of environmentally unfriendly enterprises, increase the pressure on enterprise development, and ultimately promote enterprise technology upgrading and green transformation. In addition, compared with traditional finance, digital finance can effectively disperse the investment risk of enterprises, effectively gather and guide the green investment of social funds, and provide risk guarantee for enterprises’ green technology upgrading, green product development, and research and development of major energy conservation and environmental protection projects. Finally, digital finance can also provide the precise impetus for the low-carbon transformation of economic and social development by enabling green finance [ 42 , 43 ]. Based on the above analyses, we propose a third research hypothesis.

Digital finance can boost green development by improving energy structure, promoting industrial upgrading and technological progress through the play of its own advantages.

4. Model Constructions and Variable Selections

4.1. model constructions.

We established a panel econometric model to effectively identify the possible impact of digital finance on green development. Hausman Test found that the fixed effect model is better than that of the random effect model, and hence, we chose the dual fixed effect model which controlled spatiotemporal effects. The model is constructed as follows:

In the above formula, gd represents green development, characterized by energy consumption per unit of GDP; df is the digital financial index; X ′ i t are the control variables; ϕ is the coefficient of each control variable. a i and λ t represent individual and time effects respectively. γ is a constant term, and u i t is the random error term.

Considering that there may be a spatial correlation between variables, the original assumption that samples are independent of each other is challenged, which may lead to biased error in the estimation of the general panel econometric model. Therefore, the spatial econometric model which takes spatial relationships into measurement is further constructed to capture more accurate causality between variables [ 55 ]. Taking the Spatial Durbin Model (SDM) as an example, the construction is as follows:

In the above formula, W represents the spatial weights matrix. Considering the possible law of distance attenuation, we selected two kinds of matrices: spatial contiguity matrix and nearest neighbors matrix. W ln g d i t , W ln d f i t and W X ′ i t are spatial lag terms respectively, and ρ , ξ , ζ are coefficients of them.

Combined with the above Hypothesis 3, the mechanism of digital finance affecting green development is also one of the key research contents of this paper. Therefore, referring to the relevant research progress of Zhonglin Wen et al., we used the causal steps approach to empirically test whether there is a real mediation effect in energy structure, industrial upgrading, and technological progress [ 56 , 57 ]. The model is constructed as follows:

Three econometric models are needed to test the intermediary effect. Mediator variables represent intervening variables, which are energy structure, industrial upgrading, and technological progress. The remaining variables are consistent with those in formula (1). The verification steps are as follows:

Firstly, we should check γ , γ 2 , and η in the model (3)~(5). If the three coefficients are significant, it shows that there is a mediation effect. Secondly, we should check γ 3 in the model (5). If significant, it means that there is a direct effect, that is, “partial mediator”. Otherwise, only the mediation effect can be established. Thirdly, we should compare the signs of γ 3 and γ 2 × η . If the two are the same, it means that a partial mediation effect is established, otherwise, it is the hiding effect. Lastly, by comparison, we found that γ 3 in model (5) is lower than γ in model (3), which further indicates that the mediating variable plays a significant mediating role.

4.2. Variable Selections

(1) Explained variable. According to the energy economy theory and relevant research literature, we finally chose energy consumption per unit of GDP ( gd ) to represent green development, and the unit is metric ton standard coal/ten thousand CNY. Energy consumption per unit GDP can not only directly reflect the dependence of economic development on energy, but can also indirectly reflect the industrial structure and technological innovation level. The higher the energy consumption per unit GDP is, the stronger the dependence of economic development on energy is, which will be detrimental to reducing pollutant emissions and achieving green development. At the same time, high energy consumption per unit GDP also means a backward industrial structure and a low level of technological innovation. The high energy consumption per unit GDP reflects that the industrial structure is relatively backward, and the technological innovation level needs to be improved, which further reflects the low level of green development. Therefore, we agree it is reasonable to choose energy consumption per unit GDP as the indicator of green development. What is worth noting is that the energy consumption is calculated as regional comprehensive terminal energy consumption by four categories of coal, oil, natural gas and electricity, heat, and other energy. The reason why this paper picked terminal energy consumption is that it can truly reflect the actual energy consumption of economic and social development and people’s life.

(2) Core explanatory variable. We picked the digital inclusive finance index ( df ) published by Peking University Internet Finance Research Center over the years as the proxy variable of digital finance. This index is synthesized since financial service data provided by Ant Financial includes three dimensions: breadth of coverage, depth of use, and digitalization, which can systematically and comprehensively measure the development degree of digital finance [ 58 ].

(3) Mediator variables. According to Hypothesis 3, energy structure, industrial upgrading, and technological progress are three mediator variables. Among them, the energy structure is represented by the proportion of coal consumption ( cp ), and the reduction of the proportion of coal consumption will help promote the green development of the region. Industrial upgrading ( is ) referring to Linghui Fu [ 59 ], is represented by the advanced index of industrial structure calculated by the cosine method. Technological progress ( ti ) is represented by the expenditure on science and technology in fiscal expenditure.

(4) Control variables. To effectively avoid the possible estimation bias caused by omitted variables and to control as many factors as possible that affect green development, the following control variables are selected from the aspects of industrial structure, opening to the outside world, environmental regulation, transportation, etc. by referring to relevant theories and references. Industrial structure ( scbz ), measured by the proportion of tertiary industry; population density ( rkmd ), by the ratio of resident population to administrative area; opening to the outside world ( wstz ), by foreign direct investment; ecological environment ( ldmj ), by regional green space area; environmental regulation ( hbzc ), by environmental protection expenditure in fiscal expenditure; transportation ( rjgllc ), measured by road mileage per capita.

4.3. Data Sources

This paper takes 30 provincial-level units in China as samples, the Tibet, Hong Kong, Macao, and Taiwan excluded due to the lack of data. Green development mainly comes from the China Statistical Yearbook ( http://www.stats.gov.cn/tjsj/ndsj/ , accessed on 26 June 2022), the China Energy Statistical Yearbook, and regional energy balance tables ( https://data.cnki.net/yearbook/Single/N2022060061 , accessed on 6 July 2022). The digital financial index comes from Peking University Internet Finance Research Center ( https://idf.pku.edu.cn/ , accessed on 13 August 2022). The remaining economic and social statistics mainly come from statistical yearbooks and statistical bulletins of various provinces, autonomous regions, and municipalities. The air pollution data of robustness test are obtained from the online monitoring and analysis platform of China air quality ( https://www.aqistudy.cn/ , accessed on 26 August 2022). In addition, regarding the research period, taking full account of the accessibility, coherence, and authority of each variable data, the research period is limited to 2013–2020.

5. The Characteristics of Digital Finance and Green Development

5.1. spatial and temporal characteristics.

To have a comprehensive and systematic understanding of the current situation of digital finance and green development, 2013 and 2020 were selected to analyze the evolution of the spatiotemporal pattern with the help of the natural discontinuity point grading method. In the process of data analysis, we particularly emphasize the visual expression of data to obtain comprehensive spatial analysis results. The results are shown in Figure 2 .

The spatiotemporal pattern characteristics of digital finance and green development. ( a – c ) represents digital finance, and ( d – f ) represents green development.

From the perspective of digital finance, the units with high levels in 2013 are mainly concentrated in the eastern region, such as Shanghai, Beijing, Zhejiang, Guangdong, Fujian, etc., while the level of digital finance in the central and western regions is relatively low, such as Qinghai, Guizhou, Gansu, Ningxia, Yunnan, etc. By 2020, the overall spatial pattern has not changed, but the digital finance development level of all provinces, regions, and municipalities has been significantly improved, especially the units in the eastern and central regions, such as Shanghai, Beijing, Jiangsu, Anhui, Henan, Jiangxi, etc. Among them, Shanghai’s digital finance development level is the highest and the improvement is the largest, rising from 222.14 in 2013 to 431.93, while the level of digital finance in Inner Mongolia, Heilongjiang, Xinjiang, Liaoning, Jilin, etc. is low, and the promotion is slow, with a small growth rate. The characteristics of this time-space pattern indicate that the level of economic and social development is the basis for the development of digital finance. A higher level of economic and social development can effectively promote the rapid development of digital finance. In contrast, in the backward areas of economic and social development, digital finance lacks the impetus for development, which inevitably leads to a low level of development.

Green development and digital finance have similar spatial characteristics. As the green development level is represented by energy consumption per unit of GDP, it is a negative indicator. Therefore, it can be considered that the southeast region has low energy consumption per unit of GDP and a high green development level, while the northwest region has high energy consumption per unit of GDP and a low green development level. In 2013, Beijing, Jiangsu, Guangdong, Zhejiang, Fujian, and other provinces and cities led the country in green development, while Ningxia, Qinghai, Xinjiang, Guizhou, Shanxi, etc. obviously lagged. In 2020, the overall pattern did not change, but the energy consumption per unit of GDP of most provinces, autonomous regions, and municipalities decreased significantly, indicating that the level of green development is constantly improving. Among them, Guizhou, Shanxi, Sichuan, Hubei, Qinghai, and other central and western provinces, autonomous regions, and municipalities showed outstanding performance. It is worth noting that some provincial units did not show a good trend in green development. The energy consumption per unit of GDP did not decrease but increased, such as in Inner Mongolia, Ningxia, Liaoning, Heilongjiang, and Tianjin. Therefore, these provinces, autonomous regions, and municipalities still have great space and potential in saving energy and reducing consumption for realizing green transformation. Overall, the southeast of China is an economically developed area with a high level of production technology. Although it has a large energy consumption, compared with GDP, the energy consumption per unit GDP is low. However, the northwest and northeast regions are backward in economic development and low in production technology. Although the energy consumption is small, the unit GDP is still high.

5.2. Spatial Trend Analysis

To deeply explore the spatial change trend of digital finance and green development in the two directions of “east-west” and “north-south”, a trend analysis was conducted by ArcGIS10.6. Figure 3 a represents the spatial change trend of digital finance, while Figure 3 b represents the spatial change trend of green development. The x -axis represents the east-west direction, the y -axis represents the north-south direction, and the z -axis represents the level of digital finance or green development. We used the spatial trend projection line for analysis. Results are shown in Figure 3 .

Analysis of the spatial trend of digital finance and green development.

We can see from Figure 3 a that, in 2013, the east-west projection trend line shows that the development level of digital finance gradually decreases from east to west and has a linear trend. The north-south projection trend line shows that the development level of the south is high and that of the north is low. By 2020, the trend lines in both directions begin to show a downward U-shaped trend, especially in the north-south direction. The change of this trend mainly lies in the obvious improvement in the digital financial level of some provinces, autonomous regions, and municipalities in the east and central China. It directly increases the projection value of the middle position. Looking from Figure 3 b, in 2013, the east-west projected trend line indicates that the western region had high energy consumption per unit of GDP and low green development level, while the opposite is true in the east and middle. From south to north, the energy consumption per unit of GDP in the north is high and the level of green development is low, while the opposite is true in the south. By 2020, the overall trend of east-west and north-south directions does not change significantly, but the location of the projected trend line is closer to the z -axis due to the general reduction of energy consumption per unit of GDP.

6. Empirical Tests

6.1. the benchmark regression results.

Table 1 reports the regression results of the effect of digital finance on green development. Columns (1)~(2) belong to mixed cross-section regression, and columns (3)~(6) belong to dual fixed effect regression. To ensure the robustness of the results, the models without and with control variables are estimated separately.

Benchmark regression results on the impact of digital finance on green development.

* p < 0.1, ** p < 0.05, *** p < 0.01. The significance level in the following table is the same as that in this table.

The results showed that the causal relationship between digital finance and green development maintains a good negative robustness in different estimation models, and digital finance helps to significantly reduce energy consumption per unit of GDP, thus promoting green development. Therefore, digital finance has “green attributes” for economic development, which verifies Hypothesis 1. By gradually adding control variables, it is found that the estimated coefficient of digital finance on green development is stable at about 0.6, that is, if the development level of digital finance increases by 1%, the energy consumption per unit GDP will decrease by 0.6% at least. The reason for this result can be considered that digital finance has effectively reduced the carbon emissions of the financial industry and its related industries, improved the carbon emission efficiency, and has important significance and value for green development.

6.2. Analysis of Heterogeneity

Due to the vast territory of China, there are great differences in digitalization process, industrial structure, energy utilization, and economic volume among different regions. Therefore, digital finance may have heterogeneous effects on green development. Based on this, this paper tried to analyze it from different types, different regions, and the data structure itself. The results are shown in Table 2 .

Heterogeneity test of the impact of digital finance on green development.

** p < 0.05, *** p < 0.01.

(1) Digital finance has multi-dimensional and diversified characteristics. To systematically study the impact of digital finance on green development, it is divided into three dimensions: breadth of coverage ( cb ), depth of use ( ud ), and degree of digitalization ( dl ). Columns (1)~(3) showed that all three dimensions of digital finance can reduce energy consumption per unit of GDP to varying degrees, thus boosting green development. The magnitude of impact from high to low is the degree of digitization, breadth of coverage, and depth of use. The reason for this result is that the degree of digitalization is the basis of digital finance. The level of digitalization directly affects and determines the development level of digital finance. Therefore, the degree of digitalization is an important factor affecting the role of digital finance. In contrast, coverage and depth of use are based on the degree of digitization, so they are less effective than the degree of digitization. Thus, it can be seen that the development of digital finance requires not only the expansion of coverage and the realization of in-depth excavation, but also, more importantly, digital financial infrastructure and the digitalization level of relevant supporting software and hardware.

(2) According to the differences in human geography, sub-regional tests were conducted from the east, central and western regions, as well as the south and the north. Columns (4)~(5) are listed as the subsample estimation results of the eastern and central and western regions, and it is found that the impact of digital finance on green development is slightly stronger in the eastern region than in the central and western regions. The reason may be that digital finance in the eastern region is relatively high and the digital financial service system is relatively complete, which can provide more comprehensive and detailed green financial services for economic development, thus producing a more significant green growth effect. In addition, Columns (6)~(7) listed the estimation results of subsamples in the north and south, and the results showed that the role of digital finance in green development in the north is greater than that in the south. The possible reasons lie in the high proportion of industry, especially heavy industry in the north, where the energy consumption is higher than in the south. Therefore, the development of digital finance plays a more significant role in reducing energy consumption per unit of GDP and improving the level of green development in the north.

(3) To comprehensively describe the conditional distribution trend of digital finance to green development from the data structure itself, with reference to Wei Guo [ 60 ], the quantile regression method is introduced. By using the self-help method, we construct a covariance matrix to estimate models from 0.1 quantile to 0.9 quantile, which includes digital finance and its breadth of coverage, depth of use, and degree of digitization. The results are shown in Figure 4 . Overall, digital finance and its three dimensions have a significant negative impact on energy consumption per unit of GDP, which is consistent with the estimated conclusions above. However, it is worth noting that the quantile regression coefficients of digital finance on green development begin to increase after 0.8 quantile, and the influence of the right end of the conditional distribution is greater than that of the middle and left. It was indicated that in a few areas with a higher level of green development, digital finance does not show a significant effect on reducing energy consumption. That is, digital finance will play a more prominent role in areas with a low level of green development, which fully reflects the universal characteristics of inclusive growth of digital finance. The possible reason for this result is that regions with high levels of digital finance development are often also regions with advanced economic and social development. These regions have advanced production technology and experience, and strict environmental regulations. Therefore, the role of digital finance in reducing consumption in these regions is relatively small. In contrast, regions with low levels of digital finance development are often backward regions, which do not have high production technology and environmental regulation. Therefore, the consumption reduction effect of digital finance is more obvious.

Quantile regression results of the impact of digital finance on green development.

6.3. Analysis of Spatial Effects

Table 3 showed the results of spatial correlation tests and spatial econometric model selections. Firstly, we measured the Global Moran’s I index of digital finance and green development separately. The results showed that both passed the significance test each year, indicating that both have significant positive spatial correlations, which creates conditions for the use of spatial econometric models to estimate the spillover effect of digital finance on green development. Secondly, we selected the spatial econometric models. Lagrange Multiplier Test and Robust Lagrange Multiplier Test showed that the spatial lag model has better adaptability than the spatial error model. The Likelihood-ratio Test shows that the spatial Durbin model cannot be simplified to spatial lag and spatial error model, and the dual fixed effect is better than the time or individual fixed effect. Therefore, the Dubin model with dual fixed effects is chosen. The estimation results were shown in Table 4 . We took the estimation results of the Dubin model under dual fixed effects as an example to illustrate in detail. Meanwhile, the results of the spatial lag and spatial error models were also put into the table to verify the robustness of the results. Each model uses two kinds of matrices, spatial contiguity weights, and nearest neighbors weights.

Spatial correlation and econometric model selection tests.

Estimated results of the spatial econometrics for the digital finance and green development.

* p < 0.1, ** p < 0.05, *** p < 0.01.

The results showed that the estimation results of the spatial econometric models considering spatial relationships were similar to those of the general panel model, and digital finance still shows a positive impact on green development by reducing energy consumption per unit of GDP. Moreover, the spatial lag terms of green development ( W*lngd ) all passed the significance test in both the spatial Dubin and spatial Lag models, indicating that the positive spillover effect of green development between regions is obvious, and the full play of this effect is conducive to the reduction of energy consumption and high-quality economic development for the entire area. However, the spatial lag term of digital finance ( W*lndf ) does not pass the significance test, indicating that the spillover effect of digital finance on green development is not obvious, that is, digital finance can effectively improve the level of green development in the local region, but does not show a significant effect on the green development of surrounding areas, which verifies Hypothesis 2. The possible reason for this result is that digital finance, as an advanced financial service, is also subject to the invisible influence of regional and urban boundaries. Especially in China, administrative divisions and the resulting market segmentation may limit the play of digital financial spillover effects. Therefore, how to effectively eliminate the regional differences in digital finance, accelerate the flow of financial elements, and improve the level of digital financial infrastructure will be the key issues to promote the effective play of digital financial spillovers.

6.4. Robustness and Endogeneity Tests

To improve the credibility of the empirical test results, this paper adopts three methods: replacing the core explanatory variables, replacing the explained variables, and simultaneously replacing the core explanatory variables and the explained variables. The results were shown in Table 5 .

The robustness and endogenous results of digital finance affecting green development.

* p < 0.1, *** p < 0.01.

First, we selected the air quality index ( aqi ) as one of the explained variables. Air quality is closely related to energy consumption, which may produce various air pollutants, thus affecting the level of air quality. At the same time, air quality is also an important basis for evaluating the green and sustainable development of a regional economy. Column (1) showed that digital finance can significantly reduce the air quality index. Since the smaller the air quality index, the better it is, it can be considered that digital finance can help to enhance and improve air quality. Moreover, we also selected the Greenness Index of economic growth ( gi ) from the China Green Development Index Report published by Beijing Normal University as an alternative variable. The index emphasizes the green efficiency of industrial development and carries out a comprehensive evaluation by integrating various indices of the three industries. Column (2) showed that digital finance has a significant positive impact on the greenness of economic growth, indicating that it helps to improve the green efficiency of industrial development.

Firstly, digital economy ( de ) is used as the alternative variable of digital finance. Digital economy and digital finance have a close relationship, and they have a lot in common. Meanwhile, digital finance is an important part of the digital economy. Therefore, with reference to the research results of Tao Zhao [ 61 ], the development of the Internet is taken as the core index, and digital transaction indicators are added to construct a digital economic evaluation index system. Column (3) showed that the results of the digital economy on green development verified the conclusion of benchmark regression. Secondly, referring to relevant research results, we chose “the proportion of computer services and software employees” as the alternative variable of digital finance, which can reflect the development level of digital finance to a certain extent. The estimation results in column (4) were similar to those in column (3), which also indicates a significant impact on green development.

The third is to simultaneously replace the explanatory variable and the explained variable. In column (5), total energy consumption ( nyxf ) is taken as the explained variable, and Internet employees ( ie ) as the explanatory variable. In column (6), the greenness index of economic growth ( gi ) is taken as the explained variable, and Internet users ( iu ) as the explanatory variable. These replacement variables have a high correlation with the original variables, which can approximately reflect the causality. The estimation results all passed the significance test in varying degrees, which once again verified the positive effect of digital finance on green development.

Finally, to alleviate the endogenous problems caused by various reasons, we took the number of Internet users and digital finance, which lagged by one period respectively, as instrumental variables to conduct the two-stage least square method [ 62 ]. The number of Internet users can reflect the popularity of the Internet, which is highly compatible with the extensive coverage and in-depth digitalization of digital finance. Moreover, because the variable with one lag period is correlated with the current period variable, but not with the disturbance term in the predestined current period, the estimation bias caused by endogeneity can be alleviated. The results shown in columns (7) and (8) of Table 5 indicated that the green development effect of digital finance still holds. In addition, the Durbin-Wu-Hausman Test found the original hypothesis was obviously rejected and that all explanatory variables are exogenous, indicating the existence of endogenous explanatory variables. The statistical value is 9.45, and the p value is 0.002. Under-identification test (Kleibergen-Paap rk LM statistic) found the original hypothesis was obviously rejected. Weak identification test (Cragg-Donald Wald F statistic, Kleibergen-Paap Wald rk F statistic) showed that the critical value of statistics was greater than 15% or 10%, indicating that there was no weak correlation within the instrumental variables. Based on the above analysis, we believed that the selection of instrumental variables was effective and reliable.

7. Impact Mechanism Tests

Combined with the above mechanism analysis, digital finance may contribute to green development by improving the energy structure, promoting industrial upgrading, and technological progress. In the following, the mediation effect model was used to identify and judge by stepwise test, and the results were shown in Table 6 .

Test results of the mediation effect of digital finance on green development.

From the perspective of energy consumption structure, column (1) showed that digital finance can significantly reduce the proportion of coal consumption to improve the energy consumption structure. In column (2), both digital finance and energy consumption were significant, and the direct effect was of the same sign as the mediating effect. Therefore, it can be concluded that energy structure is the mediating variable of digital finance affecting green development, and it belongs to the “partial mediation”. Columns (3) and (5) showed that digital finance can significantly promote industrial upgrading and technological progress. Further, columns (4) and (6) showed that industrial upgrading and technological progress can be used as effective mediating variables for digital finance to influence green development. Therefore, the above conclusions can effectively confirm hypothesis 3.

8. Conclusions

Taking 30 provincial-level units in China as samples, this paper systematically discusses the characteristics and mechanism of digital finance and green development by using the research methods of human geography and environmental economics and makes an empirical test. The main conclusions are as follows:

(1) From the perspective of spatial and temporal patterns, digital finance, and green development were promoted to different degrees during the study period, but the inter-provincial differences remained. Digital finance is developing rapidly, especially in eastern and central China. Meanwhile, with the reduction of energy consumption per unit of GDP, the level of green development also improves in general, and the performance of the central and western regions is outstanding. Yet the energy consumption per unit of GDP is rising in some provinces and cities. In addition, from the perspective of spatial trends, digital finance and green development have different trends in the east-west and north-south directions, and the overall performance is “high in the east, low in the west, high in the south, and low in the north”.

The reasons behind this phenomenon are that the eastern region has a high level of economic and social development and advanced industrial structure and production technology, which can not only effectively promote the development of digital finance, but also achieve a high level of green development. However, the economic and social development level in the central and western regions is relatively low, the industrial structure is relatively backward, the green production technology and capacity are limited, and the impetus for green development is insufficient. Yet overall, with the implementation of the “dual carbon” strategy, the upgrading of industrial structure and technological progress, digital finance, and green development will continue to develop.

(2) Through the estimation of the econometric models, we found that digital finance can reduce energy consumption per unit of GDP, showing a significant role in promoting green development, and still obtain robust results by replacing variables. At the same time, results were heterogeneous according to different types, different regions, and different levels. In addition, the green spillover effect of digital finance is not obvious, and its role is mainly concentrated in the local area. Furthermore, through mediation model testing, it is found that digital finance can achieve green development by improving energy structure, promoting industrial upgrading, and technological progress.

Through the above results, we can find that digital finance can indeed promote green development. The possible reasons are that, on the one hand, digital finance can effectively reduce the carbon emissions of the financial industry and its related industries by improving operational efficiency. On the other hand, digital finance can provide more inclusive and green financial services and products, which are conducive to industrial upgrading, technological progress, and green development of enterprises. Finally, the positive role of digital finance in factor flow, resource allocation, and efficiency improvement will also help to achieve green development.

9. Suggestions and Discussion

The conclusions of this paper are helpful to enrich and expand the theory of environmental finance. Generally speaking, the theory of environmental finance focuses on the impact of traditional financial services on environmental protection and advocates the responsibility for environmental quality. This paper extends the environmental finance theory from traditional finance to digital finance and provides theoretical support and empirical evidence for the impact of digital finance on green development. Therefore, this paper has certain theoretical implications. At the same time, this paper will also provide management implications for local government managers in developing digital finance and promoting green development. Therefore, based on the conclusion of the foregoing research and the actual development of our country, the government, financial institutions, and enterprise put forward the following countermeasures and suggestions for the mutual promotion of digital finance and green development:

Firstly, the government should give more attention to the role of encouragement and supervision. Focusing on the goal of “carbon emission peak and carbon neutrality” and the general requirements of green and low-carbon development in the economy and society, the government should strengthen the top-level design of digital finance, establish, and improve the laws, regulations, and policy system to promote green development. Further, we suggest the government enrich the toolbox of green finance support policies, coordinate and introduce more preferential policies to support green and low-carbon development, and guide financial institutions to promote industrial upgrades. Moreover, to comprehensively enhance the ability of digital finance to support green and low-carbon development, focusing on green technology innovation, increasing the allocation of green assets, and strengthening environmental risk management are also essential. In addition, the government should also accelerate the construction of the digital financial regulatory system, improve the laws, detailed rules, and regulations of green finance, and quickly improve the regulatory regulations according to the characteristics of digital finance to avoid the absence of regulation.

Secondly, financial institutions should play the leading role of digital finance in leading green development. On the one hand, we suggest that financial institutions follow the principle of “domestic unification and international integration”, focus on energy consumption, pollution control, energy conservation, emission reduction, green technology, and other fields, constantly improve the digital financial policy and standard system, so as to provide an important guarantee for standardizing digital financial business, ensuring the commercial sustainability of green finance and promoting the green development of economy and society. On the other hand, we suggest that financial institutions coordinate and develop specialized green financial products in different regions, design specific investment plans according to the R&D cycle of green technologies and the feedback effect of enterprises, vigorously promote business innovations such as green credit, green securities, green insurance, green guarantee, and green funds, etc. Moreover, they should also improve multi-level green financial products and a unified digital market system, provide more and better digital financial products and services for green and low-carbon development, and create the foundation and conditions for the green spillover effect.

Thirdly, from the perspective of enterprises, we suggest they could play the main role in realizing green transformation through digital finance. Enterprises are the main body of the application of digital financial products, but also the main position to achieve green transformation and development. Therefore, it is suggested to build a tripartite information-sharing platform and communication and cooperation mechanism of “government-bank-enterprise”, to smooth the interconnection of capital, technology, talents, and markets, and help enterprises better integrate into the capital chain, value chain, and industrial chain of digital finance. On the one hand, enterprises should make full use of digital financial products and platforms to improve production technology and promote green transformation and environmental benefits. On the other hand, enterprises should also pay attention to industry-research cooperation, set up green digital financial construction teams, and train professionals who understand not only financial knowledge but also digital and environmental protection technology, to lay a solid foundation for the high-quality development of green financial business.

Finally, this article basically achieved the set goals and completed tests of hypotheses. under the guidance of the environmental finance theory and related theories, and with reference to the latest research results, we selected provincial units in China as the research samples and obtained credible conclusions by selecting appropriate data and building models. However, we also realized that there are some deficiencies in this paper. The sample selection lacks attention to other administrative levels. For example, regional level, city level, enterprise level, etc. In addition, due to data availability and model design requirements, more possible factors were not considered when selecting intermediate variables. The impact of digital finance on green development is complex and diverse, which needs further analysis and verification. In the future, we think there are three warm tips for future researchers. First, the scope of digital finance can be extended to coverage, depth of use, and other aspects. Second, this paper reflects green development by using energy consumption per unit GDP. In future research, attention can be paid to the water environment, soil environment, air environment, etc. These are important indicators reflecting the level of green development. Third, we suggest that scholars explore how digital finance plays a role in green development at the enterprise level.

Acknowledgments

The authors would like to thank the editors and anonymous reviewers for their thoughtful and constructive comments.

Funding Statement

This paper was supported by the Fundamental Research Funds for the Provincial Universities of Zhejiang (SJWZ2023001).

Author Contributions

Ideas, conceptualization, R.Z.; methodology, software, validation, formal analysis, writing—original draft preparation, R.Z., M.Z., and C.Z.; investigation, resources, data curation, K.M., M.Z., and C.Z. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Data availability statement, conflicts of interest.

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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