Innovative Application of Financial Technology in Promoting Green Supply Chain Finance and Carbon Emission Management

Fintech refers to the technical applications, business models and product services that realize the innovation of financial services through the full use of cutting-edge scientific and technological means such as cloud computing, big data and artificial intelligence [19]. The core of financial technology is science and technology, which is the full use of scientific and technological means in the financial field. With the vigorous development of modern information technology, financial technology makes the financial sector has undergone a certain “genetic mutation”, the full application of financial technology not only realizes the innovation of financial products and services, and promotes the rise of customer experience satisfaction, but also can curb the problem of information asymmetry, and then effectively control the increase in costs. At the same time, the effective application of financial technology in the field of finance can also innovate and optimize the traditional financial business, promote the enhancement of the efficiency of the financial business and the effective prevention of risk, which is of great significance for the sustained and sound development of the financial industry.

Supply chain is a systematic and perfect functional network chain structure based on the connection of business [20]. This structure has systematic characteristics, is the core enterprise as the center of the network chain structure, all kinds of upstream and downstream enterprises through effective control of capital flow, information flow and logistics to achieve the procurement of raw materials, intermediate products and final product manufacturing process, the use of sales network to further realize the output of finished products from the supply chain to consumers. Supply chain finance is a new financial model derived from the development process of supply chain, established in the supply chain enterprise trade authenticity background, the core enterprise can provide credit for other upstream and downstream small and medium-sized enterprises in the supply chain, the bank in the supply chain enterprises to provide financial services in the process of financial services can be included in the supply chain of various types of enterprises in the comprehensive financial services model. Compared with the traditional credit model, this new financial model of supply chain finance realizes the interconnection between core enterprises and SMEs in the supply chain as well as the transformation of the risk nature of SMEs in the supply chain from uncontrollable risk to controllable risk of the whole supply chain.

Carbon emissions are the process by which greenhouse gases, such as carbon dioxide, produced by human activities enter the atmosphere [21]. These gases can be produced from many different sources, including power sources such as automobiles, electricity production, industrial production and processes such as construction, agriculture and deforestation. Infrared-active gases, mainly water vapor, carbon dioxide, and ozone, occur naturally in the Earth's atmosphere and absorb thermal infrared radiation emitted from the Earth's surface and atmosphere. The atmosphere is heated by this mechanism, which in turn emits infrared radiation, and a large portion of this energy is used to heat the surface and lower atmosphere. As a result, the average surface air temperature of the Earth is about 30 degrees Celsius higher than it would be in the absence of atmospheric absorption and re-radiation of infrared energy. This phenomenon is often referred to as the “greenhouse effect”, and the infrared-active gases responsible for this effect are also known as greenhouse gases. The rapid increase in greenhouse gas concentrations since the beginning of the industrial revolution to the present day has raised concerns about potential climate change.

Greenhouse gases prevent heat from the Earth's surface from radiating into space, which leads to higher temperatures in the atmosphere. As the trend of global warming becomes more pronounced, many countries and organizations are taking steps to reduce carbon emissions or otherwise combat the effects of climate change. Among them, carbon dioxide emissions account for nearly 80% of greenhouse gas emissions and are the main source of greenhouse gas emissions. For the convenience of research, carbon dioxide, which has the greatest impact among greenhouse gases, is selected as the research object in this paper, and the carbon emissions described below refer to carbon dioxide emissions.

FinTech plays a crucial role in supporting investments in renewable energy and clean technologies. Renewable energy and cleantech have important environmental, social and economic contributions, but their low profitability and high initial costs often limit the motivation and willingness of investors and entrepreneurs. This is where FinTech can provide an innovative way to address these issues and thus promote the development of renewable energy and cleantech.

First, fintech can reduce investment risk through intelligent credit assessment models. Such models can evaluate borrowing companies based on factors such as their financial statements, historical transaction records and technology patents, and extract key data from them to predict their future performance. These models can not only increase investor trust in renewable energy and cleantech companies, but also reduce concerns and potential risks associated with innovative technologies.

Second, fintech can improve the efficiency of transaction and financing management through blockchain technology. The decentralized nature of blockchain technology and the self-executing function of smart contracts can reduce the intermediary links of traditional financial offices, shorten the actual time and cost of transactions, and build a global financing network to better serve renewable energy and clean green supply chain financiers. Blockchain technology can also enhance the credibility of fintech-related businesses by guaranteeing the security and transparency of transactions through traceability and safeguarding the interests of each participant in the transaction.

Finally, fintech can provide diversified financing instruments and incentives to encourage innovation in renewable energy and clean technology. For example, it can provide multiple investment routes and risk-reward ratios for companies and investors through customized equity and bond financing schemes, renewable energy securities and carbon emissions trading markets. In this way, not only can the high cost requirements of renewable energy and clean technology be met, but also more investors can be attracted to participate in and promote this development. At the same time, fintech can also formulate corresponding preferential policies, such as tax breaks and financing guarantees, to encourage more enterprises and investors to enter this market.

With the increasing global awareness of environmental protection and the severe challenges posed by climate change, the carbon market is increasingly becoming a powerful tool for solving environmental problems. Fintech, as a combination of technology and finance, has the ability to break the game with disruptive innovation and can help the development of the carbon market.

First of all, FinTech can improve the efficiency of the carbon market. The traditional carbon market has high operating costs, complex processes and long settlement cycles, thus limiting the development of the market. With the help of automated algorithms and intelligent analysis, fintech can help the carbon market realize information sharing and efficient matching transactions, improve market liquidity and price fairness, and reduce market operating costs and transaction risks.

Secondly, FinTech can enhance the presentation and promotion of the carbon market. Fintech integrates data science, blockchain, cloud computing and other technologies, which can improve the data and information management of the carbon market, improve the regulation and transparency of carbon trading, and increase the stability and reliability of the market. In addition, by utilizing the online channels of FinTech, the carbon market can interact and communicate with all sectors of society more quickly, promoting the expansion and popularization of the market.

Finally, FinTech can bring innovation and diversification to the carbon market. The traditional carbon market mainly focuses on carbon quota trading and lacks diversified products and services. Fintech, however, can stimulate the imagination and innovation of the carbon market, extend the business scope and related fields of the carbon market through the development of new products, such as carbon letters of credit and carbon asset securitization, etc., to meet the needs of different market players and investors.

Traditional supply chain finance risk management usually relies on cumbersome manual operations and paper document flow, which can easily lead to information asymmetry and processing delays. Fintech, on the other hand, has significantly improved the efficiency of supply chain finance risk management by introducing digital technology and automated processes. For example, a supply chain finance platform based on blockchain technology can realize real-time sharing and verification of transaction information, eliminate “information silos” and fraud, and enhance the transparency and reliability of the entire supply chain finance business. In addition, the application of artificial intelligence and big data analysis technology enables supply chain financial institutions to more quickly identify potential risk factors and take corresponding measures for prevention and control, thus effectively reducing the cost and time of risk management.

Traditional supply chain finance risk assessment mainly relies on financial data and corporate credit ratings, and is unable to comprehensively consider the risk factors of other links in the supply chain. Fintech, however, can achieve a more comprehensive and in-depth risk assessment by integrating multi-dimensional data sources, including suppliers' transaction records, logistics information, market trends and so on. In addition, by utilizing machine learning and data mining algorithms, FinTech is also able to identify potential risk patterns and correlations hidden in massive data, providing supply chain financial institutions with more accurate risk warning and decision-making support. In this way, supply chain financial institutions are able to better assess the repayment ability of borrowers and the performance ability of suppliers, reducing the risk of bad debts and defaults.

The explanatory variable is the carbon emission intensity of each province, which indicates the carbon emissions generated per unit of output value while the economy grows, and is used to assess the relationship between economic growth and carbon emission growth. When studying the impact of financial technology on carbon emission reduction, carbon emission intensity is chosen as an indicator to more accurately reflect and assess the role and effect of financial technology. This paper adopts the estimation method provided in the 2006 Guidelines for National Greenhouse Gas Inventories formulated by the IPCC to obtain the carbon emissions, and then compares it with the gross regional product to obtain the carbon emission intensity.

The core explanatory variable is Fintech, which is a green supply chain finance through the application of big data, mobile payment, cloud computing, artificial intelligence, 5G technology, Internet of Things, etc., to realize the empowerment of the traditional financial industry, aiming to improve efficiency and effectively cut operating costs. The data comes from the number of results of Baidu News advanced search for related keywords, obtained by crawler.

The STIRPAT model evolved from the IPAT model, which is a model used to quantitatively describe the impact of human activities on the environment [22].

It has been shown that when the impact of human activities on the environment is jointly composed of population growth, economic development and scientific and technological progress, it will be better to portray it with the IPAT model, so the pressure of economic growth on the environment can be measured by examining the population size, economic status and scientific and technological level.

Through an in-depth analysis of the causes of environmental quality deterioration, the pressure of human activities on the environment is expressed as the product of population size, affluence and technological progress: 10I=P×A×T

Although the IPAT model has been influential in the field of environmental science and sustainable development, it has also been subject to some criticism. Some argue that the model is too simplified and does not take into account the differences in different regions and societies, or the substitutability of different resources, while the IPAT model can only reflect the impact of changes in the influencing factors as a whole on the environmental load, so in order to explore the impact of the changes in one of the factors on the environmental load while the other factors remain unchanged, the STIRPAT model was created, and its use in carbon emission The STIRPAT model is commonly used in carbon emission related studies. 11I=αPb×Ac×Tde α is the coefficient, b, c and d are the indices of each influencing factor and e is the error. Taking the logarithm on both sides, we get: 12lnI=lnα+blnP+clnA+dlnT+lne

Based on this, combined with the problems studied in this paper, the model is improved, the main research content of this paper is to study the impact of financial science and technology on the intensity of carbon emissions, in which other control variables affecting the intensity of carbon emissions are added to carry out a systematic analysis, and the following model is obtained: 13lnCEit=α0+α1Fintechit+α2Xi+ui+vt+εit where CE is the carbon emission intensity, Fintech is the level of fintech development, X_ii is the control variable, u_i denotes the time unobserved effect, v_i denotes the regional unobserved effect, i.e., the region-specific unobserved factors that are not taken into account or not explicitly controlled for in the model, i is the province (city, district), t is the time, and ε_i is the random error term.

A spatial weight matrix is a tool commonly used in spatial analysis and geostatistics to measure geospatial proximity or connectedness. It is mainly used to describe the spatial relationship between a region and its surrounding regions, and by specifying the weights between regions, it is possible to quantify the degree of interaction between them. The spatial weight matrix is used to express the interrelationships between different observations in geospatial space, and it is critical to the accuracy and reliability of the model. The following are the formulas for the spatial adjacency matrix, the inverse distance matrix and the economic distance matrix: 14Wij={ 1, region i is adjacent to region j0, region i is not adjacent to region j 15Wj={ 1dij2,i≠j0,i=j 16Wj=={ 11n| ∑20112021xi−∑20112021xj |,i≠j0,i=j Where W_ij is an element of the matrix, d_j denotes the geographic distance between region i and region j, x_j denotes the GDP per capita of region i, and n is the number of periods of the year in which the calculation is done, the matrix is formed from the above elements and normalized.

The spatial correlation test is a statistical method used to check whether there is a spatial autocorrelation or spatial cross-correlation between observations in a data set. Spatial autocorrelation refers to the correlation between observations of the same variable at different spatial locations, while spatial cross-correlation refers to the correlation between observations of two different variables at different spatial locations.

In spatial correlation test, Moran's index is the most commonly used spatial autocorrelation test statistic to measure spatial autocorrelation [23]. It assesses whether similar values are clustered together or dispersed in geographic space. Taking values between -1 and 1, if the Moran index is close to 1, it indicates the presence of positive spatial autocorrelation, i.e., similar values are clustered together. Its formula is: 17I=n∑i=1n∑j=1nwijzijzij∑i=1n∑j=1nzj2 where Z_j is xi−X¯ , denoting the deviation of the observed value of the variable x from the mean for region i, n is the total number of regions, and w_ij is the spatial weight matrix between regions i and j.

This paper uses Gaussian kernel density estimation to analyze the evolutionary trend of the synergistic development of fintech and green supply chain finance, represented by the Yangtze River Economic Belt. Figure 1 shows the dynamic distribution evolution of the coupling coordination degree of green supply chain finance and fintech in the Yangtze River Economic Belt. From the peak of the distribution curve, the width of the main peak of the coupling coordination degree distribution curve becomes narrower and the height becomes higher, which indicates that the spatial difference in the coupling coordination degree of the Yangtze River Economic Belt shows a widening trend, and the spacing of the two peaks gradually expands, due to the fact that the downstream region of the Yangtze River Economic Belt has a higher degree of coupling coordination and the gap between it and the middle and upstream regions grows year by year. Starting from 2016, the coupling coordination degree has a double peak, and the position of the side peak of the coupling coordination degree distribution curve is shifted to the left, and the height does not change much, remaining between 4.6 and 5.5. It indicates that in the process of increasing the degree of coordinated development of the Yangtze River Economic Belt, the coupling coordination degree of a few cities did not improve, pulling away from the average level of cities in the Yangtze River Economic Belt. And the main peak of the distribution curve of the coupling coordination degree keeps extending to the right, indicating that the coupling coordination degree of the Yangtze River Economic Belt as a whole is improving. On both sides of the main peak of the coupling coordination degree, there is a wave peak on each side, with a relatively low height, indicating that the phenomenon of polarization of the coupling coordination degree of the urban agglomeration of the Yangtze River Economic Belt is not only embodied in the cities with a high degree of coupling coordination, but also in those with a low degree of coupling coordination, with the coordination degree of Chongqing and Wuhan rising at a faster speed and at a higher level, whereas most of the cities of Yunan Province have a low degree of coupling coordination and are slow in their development.

Figure 1

Dynamic distribution of coupled coordination

According to the first law of geography, the degree of coordinated development of green finance and fintech may have spatial correlation among neighboring geographical areas. Meanwhile, this paper further applies the Moran index scatterplot to deeply explore the local spatial autocorrelation of the degree of coordinated development in the Yangtze River Economic Belt. This not only provides an overall understanding of the spatial distribution of the degree of coupled coordination in the Yangtze River Economic Belt, but also helps to understand the interactions and influencing mechanisms between different regions, providing useful references for future policy making and regional development.

Global spatial autocorrelation analysis. Figure 2 shows the Moran´s I index of the Yangtze River Economic Belt and its P-value from 2010 to 2021. It can be seen that under the spatial weight matrix, the Moran´s index always exhibits significant positive spatial correlation and continuously improves from 0.4086 in 2010 to 0.6145 in 2021, indicating that the degree of synergistic development of green supply chain finance and fintech is characterized by strong spatial correlation, significant spatial spillover effects, and a closer relationship among regions, which provides the Yangtze River Economic Belt cities with a better chance of Promoting the development of the coordinated degree of green finance and fintech provides insights.

Figure 2.

The Changjiang economy brings the Moran’s I index and its p-values in 2010-2021

Table 5 shows the results of the endogeneity test. Although this paper tries to control the factors affecting the development of urban decarbonization as well as the non-observable factors affected by time change, there are still non-measurable factors that will have an impact on the empirical results. In addition, the lower the level of decarbonization, the more cities tend to obtain green funds for pollution control and green low-carbon development, and there may be reverse causality between the two. In order to overcome the endogeneity problem caused by omitted variables and reverse causality, this paper adopts the two-stage least squares method, respectively selects three instrumental variables of the interaction term between the number of fixed-line telephones per 100 people and the volume of telecommunication business over time in 1984 (IV1), the number of commercial banks in each city (IV2), and the coupling and coordination index with one period of lag (IV3) in each city to form an instrumental variables set for the endogeneity test, and the results are are shown in Table 5. The analysis shows that the three instrumental variables are significantly correlated with the coupling coordination degree. After considering the endogeneity problem, the impacts of FinTech and green finance synergy on urban decarbonization are all significantly positive, respectively 0.0138, 0.0245, 0.8545. It indicates that FinTech and green supply chain finance synergy has a positive effect on carbon emissions.