Research on the Digital Transformation Path of the Rural Financial System Assisted by the Internet of Things Based on the Innovation Mode of Supply Chain Finance
Pubblicato online: 27 feb 2025
Ricevuto: 25 set 2024
Accettato: 14 gen 2025
DOI: https://doi.org/10.2478/amns-2025-0123
Parole chiave
© 2025 Xiaohong Wang et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.
After more than 40 years of reform and opening up, the relationship between cities and agriculture in China has been greatly changed, while the rural areas have always been a vulnerable part of the long-term development process, especially in the process of industrialization and urbanization, and the rural areas began to shrink. Therefore, according to the strategic plan of rural cultural revival clearly proposed by the 19th National Congress of the Communist Party of China, to change the development status of rural areas and agricultural industries. The transformation of the major social contradictions mentioned in the report of the 19th National Congress of the Communist Party of China is also relatively concentrated in the field of "agriculture, countryside and farmers," and the major contradictions at this stage have even become the main factor restricting the development of the field of "agriculture, countryside and farmers." In the new era of socialist development, we must focus on making up for the shortcomings in the development of rural areas. In the development and growth process of the agricultural industry in the whole rural area, the structure of the rural production market main body has also changed greatly - the emerging rural production market main bodies such as farmers' federations, leading companies, specialized large households and homesteads have become the market main force of rural production [1, 2]. The management methods and structural forms of these rural economies are completely different from those of the small and medium-sized rural economies in the past. The level of intensification, professionalism, institutionalization and sociality in rural areas has been greatly improved, and the development of financial services for the rural economy and agricultural industry has also presented a complex and clustered trend for the financial service activities and business carried out by Finance for the rural economy and agricultural industry, it is bound to make corresponding changes to adapt to the new demand characteristics. At the same time, the financing difficulties of these new business entities are still prominent [3-5].
With the development and transformation of the mode of production, the new agricultural business entities mentioned above, which are different from the small-scale peasant economy, are mostly in a systematic supply chain. At the same time, the form of competition has gradually extended to the competition between the entire supply chain. In the context of the highly developed credit economy, credit sales have gradually replaced the past way of payment and become the mainstream way of transaction. However, the credit level of small-scale agricultural operators is usually lower than that of downstream enterprises in the supply chain, and it is difficult to obtain financing support. Therefore, the supply chain finance series financing model has begun to receive attention in China [6,7]. Supply chain finance has been a concern for more than ten years and has formed a unique financing mode in the process of combining with the agricultural industry in line with the trend of financial innovation. In today's information age, agricultural supply chain finance supported by the digital economy, relying on the high popularity of the network, has constantly broken the past time and space constraints and gradually occupied a certain position in the financial supply of rural and agricultural industries. Exploration in practice and innovation in models have also produced certain results and benefits. In the development and practice of the strategic planning for rural revitalization, financial institutions have begun to pay attention to the special market environment in the field of "agriculture, rural areas and farmers," adopt the concept of the digital economy and supply chain mode, and actively explore the supply chain financial mode based on the digital economy [8-10].
The completion of rural revitalization is inseparable from the further improvement of rural efficiency, and the way to further improve rural efficiency is through the integration of three industries. The main methods of integration of the three industries are as follows: first, use modern agricultural machinery to further improve the mechanization, automation and industrial production technology of rural agricultural products, and then use rural information equipment to further improve the intelligent and digital production technology of rural goods; Second, use the Internet + agriculture to realize a new consumer-centered rural management model, so as to change the situation that the current level of rural development in China does not meet the needs of the country's economic and social needs; Third, use the combination of traditional agricultural product processing and tourism, sports and traditional culture creation to export experience farming, leisure farming, rural culture and other brands, so as to enhance the added value of China's rural products [11,12]. In recent years, the secret for China's economy to maintain rapid development in the global environment is to achieve the integration of the manufacturing economy and service industry and use information technology to carry out the supply-side reform of manufacturing economic operation. At the same time, the improvement of manufacturing economic operation quality has promoted the growth of the service industry in a new way. In order to revitalize the countryside and revive the rural economy, the rural economic work must summarize the successful experience of the past industrial economy and service industry management and development and combine the development of agriculture, industrial management and service industry so as to carry out the transformation and development of the rural economy, thus improving the comprehensive economic benefits of the countryside [13,14]. Enterprise financial services refer to the financial services centered on the core enterprise, based on the capital flow, trade flow and logistics of upstream and downstream companies of the enterprise, and taking the accounts receivable and prepayments, inventory and even fixed assets of the enterprise as the object. Enterprise financing can improve the financing situation of enterprises, especially the financing situation of vulnerable groups of enterprises. The integration of three industries is a reconstruction process in the rural economy. On the one hand, this process promotes the connection between the rural economy and other industries, thus forming an industrial chain and a supply chain centered on rural management. On the other hand, the reform of the rural economic system requires huge investment, but the traditional rural economy is fragile and insufficient to attract investment [15,16].
Based on these studies, we can find out that with the combination of digitized economic techniques and supply chain financial systems, this model has been a major way to raise funds for agriculture. Apart from the conventional funding, it also provides value-added room for all actors in the supply chain, including goods and information. Lastly, it can increase the effectiveness of resource distribution and provision of funds [17-19]. However, there are few studies at home and abroad about how to guide and improve digital Finance in rural areas. Although it is an ideal mode in theory, there is little research on it. So far, it has been widely used in the development of SCM and its integration with the new digital economy.
This paper uses mathematical deduction and scientific derivation to carry out an in-depth analysis of the theoretical basis of digital Finance promoting rural economic revival, carries out empirical analysis and testing of the mechanism of digital Finance promoting rural economic revival through quantitative methods, discusses the thinking and path design of rural financial digital development path selection, and attempts to promote rural economic transformation, Empower the formulation of rural rejuvenation strategies.
Rural supply chain finance has not yet developed enough. Compared with the advanced city financial system, the country's financial development level is relatively low. From the angle of policy and market, the agriculture finance business can offer essential financial support to agriculture production in the countryside and propose the essential sharing system for its operation risk. Thus, the relationship between the agriculture finance business and the countryside is, in essence, the relation of supply and demand. In fact, because of its complexity compared with that of city agriculture, it is confronted with a lot of questions during its fast development.
Compared with the rapid growth of the urban economy, the rural economy has been maintained at a low growth level. The difficulties faced by agricultural economic growth are mainly as follows: first, the rural operation efficiency is poor, the agricultural scale and industrialization are low, and the role of large-scale operation is basically not realized. Second, the supply and demand of rural agriculture do not match. On the one hand, a large number of agricultural products cannot be sold, and on the other hand, a large number of crops from other places are imported. Third, with the development of agriculture and the dual development of the the urban economy, rural agricultural management has not shared the dividend of the rapid growth of the urban economy, and the integration of the three industries is also insufficient. Fourth, the risk in rural areas is relatively high. Most agricultural economic activities depend on the weather, and the risk level is relatively low. Fifth, due to the low level of rural science and technology and intelligence, agricultural development is still in the labor-intensive field, and due to the increase of rural labor production costs, the profitability of agriculture is also poor, so the enthusiasm of farmers for participating in agricultural operation and management is not high. The relative backwardness of agricultural development also affects the development of rural Finance. A small number of farmers in rural development areas can also join the agricultural supply chain bank. However, traditional financial products are still the majority of such products. Studies of agricultural banks in some places also show that the proportion of data on financial products is relatively small. Compared with traditional credit products, the proportion of data credit is only less than 10%. In the environment of vigorously promoting the development of the digital economy of agricultural products in China, rural farmers, homestead farms, agricultural enterprises and other production and operation market entities gradually increase their consumption of digital devices such as mobile phones and laptops, while agricultural production institutions gradually reduce their supply of marketable digital financial services, making it difficult for rural operation market entities to obtain capital loans, The rural diversified financial model is shown in Figure 1 below.
Rural diversified financial model
The traditional agricultural economy is mainly self-sufficient. The main trade activities of commercial entities occur within agriculture, while there are few direct trade activities between rural economic entities and agricultural-related industries. Therefore, the main trade objects of farmers are often accidental. As a result, on the one hand, few farmers can enter the rural supplier system, which makes it difficult to intervene in agricultural supply chain financial institutions. In addition, there is little contact between farmers and agricultural-related companies, and the interests of both sides are not close, which cannot bring sufficient resources for specific enterprise systems. At the same time, opportunism has also emerged within the trade subjects, which has resulted in a higher risk of non-compliance by agricultural supply chain financial institutions. Agricultural Digital financial institutions are prone to information islands due to the lack of development of big data centers with rural characteristics, inconsistent data specifications and fragmented trade data. The main trade information systems of traditional agricultural financial institutions have not been integrated efficiently. There is no efficient agricultural digital credit evaluation model. The risk prevention and control mechanism of digital Finance also needs to be established urgently. Since most rural financial institutions are still in the initial stage of establishing data warehouses, and some of the data analysis content still needs to be obtained manually, the value transformation effect of data analysis also needs to be further improved. This series of problems also makes the efficiency and popularity of rural digital finance relatively low and leads to the relatively slow development of rural inclusive financial services. As a result, the strategy of rural economic revitalization cannot be effectively assisted. The degree of development of rural financial digitalization from 2013 to 2022 is shown in Figure 2.
Development of rural financial Digitalization from 2013 to 2022
The Digitalization of rural Finance is generally low, but the digital divide still exists. The level of information used in the process of agricultural production and operation is still not high, which leads to the scattering of rural production and operation data and the difficulty of obtaining a large number of rural operation data. Rural transactions are mostly offline. Because the agricultural market is mostly an acquaintance economic circle and e-commerce logistics has not been fully extended to rural areas, most of the farmers' business information is published online. The issue of obtaining agricultural business information and trading will also affect the development of agricultural enterprise finance.
In order to comprehensively analyze the digitalization degree of rural Finance, this study uses the cross-sectional data of 1235 county-level administrative units and the relevant indexes calculated above, comprehensively uses a variety of cross-sectional data modeling techniques, and empirically tests the four hypotheses proposed in Chapter 2 that digital inclusive Finance promotes the revitalization of rural industries. The order of impact effect analysis, impact heterogeneity analysis and impact mechanism test are expanded.
After deducting the samples with insufficient quantity and abnormal values, this study uses cross-sectional data from 1235 administrative units at or above the county level in cities across the country that meet the survey conditions. The digital inclusive financial service indicators used in this study mainly come from the administrative units of provinces, cities and counties. Coverage Bread. There are three three-level indicators: Usage Depth and Digitization Level. Comprehensively consider the factors that may affect the development of rural industry and the availability of data, select the control variables, build a benchmark regression model, and add the control variables to the benchmark regression model. In order to empirically test the relevant theories of rural financial Digitalization proposed in this study, the following benchmark regression model is constructed:
The following control variables are selected for analysis and calculation in regression model analysis:
1) Social security level (SOCIAL): A good social security level can play a role in smoothing economic fluctuations and improving the fairness of development. Therefore, the level of social security is one of the potential factors affecting the development of rural industries. Based on the "number of beds in social welfare institutions" in each district and county, the evaluation system of social security level is constructed. Specifically, "the number of beds in social welfare institutions per thousand people" is used to indicate the availability of social security services for residents, and "the number of beds in social welfare institutions per square kilometer" is used to indicate the convenience of residents to obtain social security services. Finally, the entropy weight TOPSIS method described above is used to comprehensively evaluate the availability and convenience of social security services; the social security level index of each district and county is obtained to measure the social security level of each district and county. 2) Education level (Edu): education is an important way to output human capital, and a good education level can lay a foundation for the sustainable development of rural areas in the future. Therefore, the level of education is one of the potential factors affecting the development of rural industries. In view of the availability of data, the regional education level is expressed by the proportion of the number of students in ordinary primary and secondary schools in the total population. 3) Government governance level (Gov): "Effective governance," as one of the five dimensions of rural revitalization, is an important basis for achieving the goals of other dimensions. Good government governance can create a good development environment for rural industries. Therefore, the level of government governance is one of the potential factors affecting the development of rural industries. On the basis of "the number of grass-roots autonomous organizations" and "general public budget expenditure" in each district and county, an evaluation system of government governance level should be built. 4) Informatization level (Info): Due to the rapid development of informatization in China, the economic quality has been greatly improved, the economic connection between regions has become closer, various new service industries are emerging, and the digital development of China's economy is accelerating. Therefore, the level of informatization is one of the potential factors affecting the level of rural industry development. In the available data at the district and county level, there is only the indicator of "number of fixed telephone users," but with the popularization of mobile communication equipment in recent years, this indicator can no longer accurately reflect the regional informatization level. The informatization level at the municipal level can be regarded as the average of the informatization level of all districts and counties under its jurisdiction, which is representative. 5) Health: In the context of fighting against the new epidemic, the impact of health level on the sustainability of economic activities is particularly important. Even if there is no public health crisis, a good medical and health level is also of great significance to the smooth operation of the economy. Therefore, the level of medical and health care is one of the potential factors affecting the level of rural industrial development. Based on the "number of beds in medical and health institutions" in each district and county, an evaluation system of medical and health levels was established.
In order to overcome the potential heteroscedasticity, this method requires that each variable be a natural logarithm. Since there is zero data in e-commerce, add one to the original data and then take the natural logarithm. The descriptive statistical structure of the main variable is shown in Table 1.
Descriptive Statistics of Main Variables
| variable | Number of samples | mean value | Standard error | minimum value | Maximum |
|---|---|---|---|---|---|
| Info | 1235 | 0.725 | 1.235 | 3.256 | 1.356 |
| health | 1235 | 1.325 | 0.356 | 2.456 | 1.232 |
| social | 1235 | 2.356 | 1.231 | 8.365 | 0.235 |
| gov | 1235 | 2.389 | 0.375 | 3.698 | 0.345 |
| edu | 1235 | 2.369 | 0.235 | 3.654 | 5.362 |
In order to eliminate the possible heteroscedasticity problem in the county cross-section data selected in this study, this study uses the "least squares method (OLS)+stable benchmark error" to overcome the heteroscedasticity problem, making the results more stable than other methods. The model calculation will be based on the "least squares method (OLS)+stable benchmark error." At the same time, the estimation results of the benchmark model using the generalized least squares (GLS), weighted least squares (WLS) and feasible weighted least squares (FWLS) are reported as a comparison. Figure 3 shows the baseline model estimates for each variable.
Analysis Results of Benchmark Models for Each Variable
From Graph 3, we can see that the increase in information technology, safety in society, governmental administration, and educational level has a remarkable effect on village industrial growth, but there is no obvious adverse effect on health. According to GLS, WLS and FWLS estimates, all the parameters are in agreement with those obtained by OLS, which shows that the OLS + Robust Standard Error Approach is effective in eliminating the heteroscedasticity.
Based on the regression analysis, it is concluded that Digitized Inclusive Finance has positive effect on the development of Rural Industrial. To research the function of Digital Inclusive Finance in Rural Industrial Economy, which is how Digitized Inclusive Finance can be utilized in Rural Industrial Economy, this section intends to make a comprehensive research on the impact of Digital Inclusive Finance on all levels of Rural Enterprise. Finally, the regression analysis of CPI has been carried out for all the indexes of the numerical finance products at various levels.
It can be seen from Table 2 that the breadth of coverage (DCB), the depth of use (DUD), and the degree of Digitalization (DSS) all have a significant positive impact on the revitalization of rural industry. The coverage also reflects the supply and reach of digital inclusive services. The supply of traditional financial services and traditional inclusive financial services depends on the outlets of financial institutions and their employees, which makes the financial needs of the vast rural areas unable to be met and restrains the development of rural industries. With the help of mobile devices and electronic accounts, digital inclusive Finance provides homogeneous financial services for areas that cannot be covered by financial institutions and more convenient and diversified financial services for areas that can be covered by financial institutions, thus filling the gap in the supply of financial services during the development of rural industries. The model estimation results show that every 1% increase in the coverage of digital inclusive Finance will increase the rural industry index by 4.823%, which has the most obvious effect in the three secondary indicators. Through innovative digital information technology, digital inclusive Finance has shortened the loan review process, improved risk control strength, improved post-loan services, and effectively lowered the threshold for rural industries to obtain credit. At the same time, digital inclusive finance also provides diversified services such as payment and security for vast rural areas and all-around financial security for the revitalization and development of rural industries. The model estimation results show that the rural industry index will increase by 1.882% for every 1% increase in the use depth of digital Inclusive Finance. The level of digitization is a reflection of fair and equitable distribution of finance services. Digital Inclusive Finance implements Inclusive Finance in rural areas through digitized techniques. Through its mobile, convenient, inclusive and trustworthy operating model, it has pushed the Finance sinking into the countryside, which has not only helped the village's SMEs develop but also penetrated into the real living environment of the countryside. Based on this model, it is predicted that with each increment of the digital number of 1 percent, the rural industrial index will grow by 1.704 percent.
Index regression results of village industry index on different dimensions of digital finance
| variable | A | B | C | D | E | F |
|---|---|---|---|---|---|---|
| Info | 0.231 | 0.103 | 0.155 | 0.132 | 0.098 | 0.212 |
| health | 0.214 | 0.023 | 0.056 | 0.086 | 0.096 | 0.035 |
| social | 0.236 | 0.253 | 0.312 | 0.325 | 0.236 | 0.248 |
| gov | 0.154 | 0.171 | 0.169 | 0.186 | 0.156 | 0.184 |
| edu | 0.123 | 0.213 | 0.156 | 0.245 | 0.365 | 0.452 |
Nowadays, the effect of the digital inclusive finance service on Chinese rural industrial development lies in the scope of its coverage, namely, more rural areas and communities. Thus, it is obvious that the application of digital inclusive finance techniques in the countryside has a great effect on the economy, with credit operations being the main one. With each increment of one percent, the country's industrial index would rise by 1.810 percent, and the payment service would come in second. For each 1 percent rise in the index, the country's industrial index will rise by 0.988 percent, while the insurance sector comes at the bottom. With each increment of one percent, the country's industrial index will rise 0.985 percent. This not only reflects the different functions and effects of various digital inclusive Internet financial business types in serving the revitalization of rural industries but also reflects the current structure of financial requirements in the development of China's rural and regional industries. Digital banking has profoundly improved the competition pattern between the supply side and the demand side of financial services and promoted the integration and upgrading of the first, second and third industries. Agricultural financial services should use digital information technology to achieve business process reengineering and service upgrading and create lower cost and more efficient financial services for farmers' production and operation. At the same time, with the development of rural industries, financial demand has shown diversified characteristics, which also requires diversified financial supply to match it. Digital financial technology can help break through the "ceiling" of food by improving the use efficiency of agricultural factors, thus ensuring food security. In addition, digital Finance has also brought new financial impetus to the development of the field of agricultural production integration. At the front end of financial services, China's rural production mode is undergoing a profound transformation. It is gradually moving from the original small-farmer production mode to a refined, large-scale and highly intelligent production process. At the same time, digital technology has also opened up an online channel for the direct marketing of agricultural products, which has created opportunities for agricultural sectors to cross-border use of agricultural product advantages and extend the value chain in the process of industrialization of agricultural production, this stage also puts forward new requirements for Finance, creates a good production environment for the development of digital Finance, enhances the financial responsibility of the majority of rural residents and enterprises, and thus constitutes a major trend of China's rural production development and the upward spiral of digital Finance.
Based on the indexes of rural agriculture, industry and service industry calculated above, the promotion effect of digital Inclusive Finance on different rural industries is now analyzed. Figure 4 shows the estimated results of digital inclusive Finance on rural agriculture, industry and service industry, respectively.
Estimation results of digital inclusive Finance on rural agriculture, industry and service industry index.
As shown in Graph 4, when considered comprehensively, Digital Inclusive Finance has made a significant contribution to all sectors of the country, among them the greatest influence on the manufacturing sector. With each increment of DI of 1 percent, the CPI of manufacturing will go up by 1.884 percent, and agriculture will come in second. With each increment of CPI of 1 percent, agriculture will rise 8. 995 percent, and the service sector will be a little bit weaker. With each increment of DI, the DI will rise by 0.601 percent, which shows that Digital Inclusive Finance has a positive role in revitalizing the countryside and promoting inter-industrial integration. As far as agriculture is concerned, it is becoming more and more mechanized over the past few years, which creates a lot of long-term capital requirements. However, because of the dispersed need for this kind of loan, the risk is too big, and the credit enhancement system is not perfect, so the margin cost is still very high. Digital Inclusive Finance has effectively alleviated the dispersion of customer information through online application services. Through big data analysis technology, the moral hazard problem of reverse decision-making can be avoided, and the level of risk control can be effectively improved. In terms of industry, most industrial companies operating in rural areas are labor-intensive and capital-intensive. They rely on cheap production factors and a relatively loose regulatory environment in rural areas. Most of their products are intermediate goods, daily necessities, etc., which are easy to store and transport and can be sold to the national market. Digital inclusive Finance promotes convenient, economical and fully functional inclusive financial services to vast rural areas through digital technology, providing rural industrial enterprises with different types of financial support in line with their size for capital turnover, product upgrading, scale expansion, business expansion, etc., and providing a strong financial guarantee for their development and growth.
From the perspective of the service industry, the service industry in rural areas is mainly oriented to the local market, and the consumption capacity of local residents determines the upper limit of its development. Therefore, the demand for financial services by the service industry is slightly weaker than that of agriculture and industry. The promotion effect of digital supply chain finance on the rural service industry is more reflected in enhancing the consumption ability and willingness of potential consumers to improve their sales performance. It can be seen that the development status and development needs of different rural industries are different. We should further tap the potential of rural supply chain digital Finance, enhance its flexibility and adaptability, and better serve the revitalization of rural industries.
In order to more comprehensively evaluate the heterogeneous impact of rural financial Digitalization on rural industry revitalization, this part will analyze the heterogeneity of samples based on different perspectives. To evaluate the impact of digital inclusive Finance on rural areas at different stages of rural industry development.
On the basis of the benchmark regression model, the following section threshold model is established:
This study obtains the results shown in Figure 5 through quantile regression of the rural industry index and gives the benchmark regression results of the whole sample as a comparison.
Quantile regression of rural industry index
In general, for every 1% increase in the digital inclusive financial index, the 1/4 quantile of the rural industry index will increase by 3.345%, the 1/2 quantile will increase by 3.397%, and the 3/4 quantile will increase by 3.560%. The regression results show that the condition distribution of digital inclusive Finance in rural industries is generally in a three-tier ladder shape, with the first and third steps shorter and the second step longer. That is, digital inclusive Finance plays a smaller role in pulling the development of rural production in areas with low levels of development but a larger role in pulling the development of rural production in areas with high levels of development, while the role in pulling the development of rural production in areas with a medium level of economic development is between the two, and this interval includes about 50% of the sample areas. In addition, it has a more general effect on boosting rural economic growth, particularly in the middle regions of the countryside. Moreover, it can raise the level of rural industry by applying it. Moreover, the effect of Digital Inclusive Finance on the economic recovery of the countryside has something to do with our country's industrial development. Developing the countryside industry to a higher degree will make it possible to turn finance into the motive for future development. Therefore, to enhance the effect of DFI on the development of the countryside, it is necessary to coordinate the Finance and industry.
In order to test the impact mechanism hypothesis proposed by this paper based on theoretical analysis, this part will establish measurement models from different perspectives for empirical testing. The above calculation results show that different rural industries may face different financial resource constraints. This study further studies the coordinated development of different rural industries and rural traditional Finance by decomposing the digital inclusive financial index. The calculation results show that each sub-index of digital Inclusive Finance has a significant positive impact on the coordinated development of rural industry and traditional Finance. In the secondary index, every 1% increase in the coverage index will increase the rural industry finance coupling coordination index by 2.501%, with the most prominent impact effect. The coverage is mainly reflected by the number of electronic accounts. The use of electronic accounts lowers the threshold for the use of financial services, expands the penetration scope of financial services, provides more choices for rural business entities to use financial services, and forms an effective complement to the traditional financial institutions' mode of reaching users through outlets and service personnel, Jointly promote financial resources to better support the development of rural industries. It can be seen from this that, at the current stage, the accessibility of financial resources is still an important factor for the promotion of rural industry in depth. From the perspective of a specific business, this paper examines the index further decomposed by using the depth index. The regression results show that every 1% increase in the credit service index will increase the rural industry finance coupling coordination index by 0.774%, which has the greatest effect on the coordinated development of rural industry and Finance, while the impact of insurance services and payment services is small. This shows that the financing dilemma is an important constraint for the coordinated development of rural financial digital transformation.
Due to the rapid development of informatization, the network facilities in the agricultural field are constantly improved, and the number of agricultural mobile phones and laptops is also increasing, which provides a strong foundation for the effective development of agricultural digital financial services in rural areas. This project will rely on the need for financial services under the promotion of China's rural revitalization strategy and actively explore new ways to improve the digital transformation of agricultural financial institutions. Two ways are provided for reference: first, the Internet digitalization of agricultural financial services and rural products is deeply integrated, and modern Internet financial information is introduced, including new information such as network, blockchain, artificial intelligence, Internet of things, cloud computing, 5g, etc., to create a big data center of the rural financial system. The second is to digitize the credit evaluation of rural business entities, refer to the successful experience of famous Internet financial institutions at home and abroad, build a digital credit evaluation model, and provide more convenient credit financial services for rural business entities. Credit evaluation is an indispensable part of financing business. In view of the imperfect credit system currently applied to the agricultural industry, financial institutions and Internet enterprises should improve the construction of the agricultural industry credit system as soon as possible, establish a digital credit information management mechanism, design a perfect evaluation system, and make the credit evaluation of small and medium-sized agricultural enterprises more accurate and transparent. In the construction of the credit system of agricultural enterprises, we should adopt the idea of market leading, government guidance and supervision, and scientific and technological innovation support, and build a unified credit evaluation platform through multi-party cooperation. The upload, processing, and rating of credit information are completed through the platform, and credit information is interconnected between government institutions and financial enterprises. At the same time, the relevant government departments can take advantage of the development trend of the rural economy to simultaneously promote the construction of credit reporting systems in rural areas and agricultural enterprises, improve the design of credit systems, and promote the development of financial risk prevention systems. The path of analysis of digital financial inclusion in the rural economy is shown in Figure 6 below.
Analysis of the path of digital financial inclusion in the rural economy
It is an inevitable trend to strengthen the integration of computer technology and the modern agricultural industry to make up for the "short board" of traditional agricultural digital financial services. According to the above investigation and analysis, the following ways for the digital transformation of agricultural financial institutions are extracted.
Through a large amount of statistical data and data analysis, it is shown that the best way for China's financial institutions to integrate deeply with the agricultural economy is to establish a data center for the agricultural financial system. The data center project of agricultural financial institutions adopts the mode of "rural finance + data farming ecosystem" and introduces new financial science and technology, including the Internet, blockchain, artificial intelligence, Internet of Things, virtual reality, 5G and other high-tech. Such as the introduction of blockchain technology and the establishment of the big information center of China's financial system. Agricultural-related financial institutions are important operating entities in the whole rural industrial chain, including rural core companies, agricultural product innovation projects, rural agricultural product professional cooperatives, family farms, rural leisure agricultural product operating entities, agricultural product grain growers, livestock and aquaculture farmers, farmers, agricultural product marketing self-employed households, agricultural product logistics and transportation companies, agricultural product processing companies, agricultural product marketing companies, etc., It can be selected as an important node application of the alliance blockchain. Due to the high-security requirements, access to docking points and authorization systems is required. For example, the government has graded the authorized objects, which generally include Category I, II and III, with Category I being the most authorized, mainly authorized to agricultural college students' financial institutions, while Category II is more authorized, mainly authorized to farmers' companies, farm farms, planting and breeding professionals, etc. with a certain business scale; The three-level management mode is general, which is mainly authorized to farmers and self-employed farmers who produce and process agricultural products. Financial institutions have screened the investment, debt, product, operation, production, consumption and other numbers of customers on the chain, authorized meaningful information into the chain, standardized the information, and implemented the signing of transaction information on the chain using blockchain encryption computing, distributed information database and other methods. A typical rural economic digital industry model is shown in Figure 7.
Typical digital industry model of rural economy
The credit evaluation of agricultural business entities is digitalized. Through the method of building "Internet + big data center of rural financial institutions," the credit data of customers accumulated in the traditional agricultural financial field is online, integrated with the big data center of agricultural financial institutions, and the most valuable data for credit evaluation is screened to develop the investment credit evaluation platform. With the help of the successful experience of famous online Finance at home and abroad, such as Sesame Credit, through the development or sharing of a big data analysis platform, it has obtained a large number of customers' business data, built a quantitative agricultural credit evaluation model, accurately portrayed customers, provided financial institutions with more real credit data, and provided agricultural operators with more convenient credit and financial services. According to the research, 100% of the farmers' families and new rural enterprises in China now have banks, and bank accounts, credit cards or passbooks are used for direct food subsidies, subsidies for improved agricultural varieties, subsidies for agricultural materials, agricultural land transaction funds, income and expenditure tables for farmers' families' farms, and student study fees for rural enterprises, The user data and cash transactions on these accounts have become the information source of digital credit scoring of financial institutions. The information accumulated by consumption and trading platforms such as Tmall, Alipay, WeChat, Taobao, Pinduoduo and JD has been comprehensively screened, and credit grading has been achieved by scoring this information. At the same time, with reference to the sesame credit method, users have been quantitatively evaluated for online comprehensive factor scoring credit. By using virtual reality, machine learning and other technical means, using logistic regression, random forest, decision trees and other methods to model and calculate, comprehensive processing and evaluation are realized. The comprehensive data covers the scale of arable land, breeding industry, agricultural product facilities, agricultural product sales, honest operation history, contract performance level, behavior preference, interpersonal relationship, role character, income level, and consumption level. An objective, comprehensive evaluation of data at 12 levels, including capital accumulation. In addition, the healthy development of digital financial services in rural areas in China can not be separated from the protection of the regulatory system. Digital financial supervision should also attach importance to the combination of offline and online. First of all, through innovative digital supervision models, regulatory technologies and other means, functional supervision and category supervision are carried out at the same time to achieve dynamic supervision of financial institutions, and offline supervision is combined to improve supervision efficiency. Secondly, form a provincial-level coordination and monitoring system, especially the cross-regional risk of data finance, which can effectively and uniformly control the registration place and operation place of data finance, reduce coordination work time, and jointly prevent cross-regional risk spillover and dispute of data finance. The multi-dimensional supervision model of rural Finance is shown in Figure 8 below.
Multi-dimensional supervision mode of rural Finance
The Internet of Things is the third wave of information technology after computers, the Internet and mobile communication networks. It is a huge network of real-time collection of any object or process that needs to be monitored, connected, and interactive, and it collects its various needed information combined with the Internet. Generally speaking, the role of the Internet of things is to sensor the goods, realize all the goods with the network connection, realize the enterprise information transparency through the computer terminal in the integrated network, can let the staff convenient, fine, dynamic identification, monitoring, management and control, which is the wisdom of management. The Internet of Things has three important features,
Overall perception, reliable transmission, and high function, respectively.
The main technologies of the Internet of Things include radio frequency identification technology (RFID), sensor technology, intelligent embedding technology (functional chip), nanotechnology and, wireless transmission networks of telecom operators etc. RFID is the core technology of the Internet of Things, and wireless transmission network is the carrier of the application of the Internet of Things technology. It is generally believed that the Internet of Things consists of the three-level architecture of the perception layer, the network layer and the application layer 14. The bottom layer is the perception layer, including electronic tags, RFID antenna, RFID reader, etc., and the function is sensing data; the middle layer is the network layer, including gateway, access network technology and middleware technology; the function is for data transmission; the highest layer is the application layer, including intelligent server, customer data database and terminal equipment, for customer decision use.
The basic application mode of the Internet of Things: the intelligent labeling of objects, the tracking of objects, the monitoring of the environment of the object and the intelligent control of objects. The specific role of these three modes in the production and circulation enterprises is reflected in the supply chain management, which realizes the visual tracking of the products in the production, storage, transportation and sales links of the enterprises. For the financial services industry, it provides an opportunity to achieve financial innovation based on this visual tracking product.
Building an IO basic infrastructure in countryside, which consists of sensors, data-collecting devices and communication net, is designed for agricultural, livestock, and fishing industries.
Data Gathering and Analyzing: The Internet of Things can gather real-time information about the process of agriculture, such as weather, land and plant development.
Smart agriculture service: Make use of the IOT technique to develop a smart agriculture service platform, which can help the peasants to cultivate, cultivate, irrigate and apply fertilizer, so as to increase the productivity and quality of agriculture.
Risk Management & Insurance Services: Monitor and alert crop growth and climate change with IoT techniques, assist financial organizations in setting up farm insurance products, and mitigate farm production risks.
Innovative finance services: Push forward the development of IW technique in the countryside, create a new financing mode that integrates on-line and off-line services, and offer one time credit, clearing and Finance administration to satisfy the peasants' diverse financing requirements.
Below is a case study of the use of IoT in farm sales
The problem of selling in agriculture is always limiting the economic development of the Chinese country, which is a major obstacle to building a new socialist country. Thus, we should make active efforts to establish a sales network for agricultural products, which will help to overcome the bottleneck of developing the countryside economy.
In China, the main distribution channels for farm produce are peasants' markets and wholesale markets, while the number of retail outlets and supermarkets remains low. What's more, the development of chain shops and supermarts is slow. Even a lot of farm produce is still being sold at roadside stands and markets. Most of the sales in the chain shops and supermarkets are made from farm produce. In other words, there are a few methods, such as online transactions and auction transactions. Because of the weakness of basic facilities and the backward transaction pattern in the Chinese farm and wholesale market, farm goods are restricted to some degree. So, we must positively probe into the new farm produce sales net system.
It is a tendency for the peasants to cooperate with the agriculture cooperative organization and the peasants and the enterprise. Based on the RF recognition equipment, the sensor equipment will be used as an online administration system for agriculture information entry or collaboration, and then the data will be posted on the Logistic Information Platform. Customers will be able to inquire about RFID in accordance with the unique characteristics of the Logistic Information Platform. Supermarkets, farm fairs, chain shops, and wholesale markets will be able to send farm produce data to the logistic information platform.
This article describes Anhui Province by means of fourteen indexes in five dimensions, including the common factor analysis, the entropy approach, and the main component approach. The main application of Factor Analysis and Principal Component Approach is to concentrate the data. Using the ANOVA method, we get the weight of the object data, which requires a lot of data, because of the new idea of "Digital Inclusive Financial" and "Developing Countryside," the data from 2011 - 2020 are not enough to support Factor Analysis and Principal Component Approach. Entropy is used to estimate the weight based on the variance of indicators. The more the index is different, the more entropy is, and the more important it is, the less it is, and the more important it is, the more important it is. Based on the features of the Anhui countryside development index and its convenient and reliable computation, this article uses the entropy method to calculate the weight. Since the value of these indexes is active, it means that the bigger the target is, the more prosperous the village is, and the less the positive step is.
First of all, the standardization of the index treatment, that is, the index of the unless dimension, the purpose is to unify each index unit to facilitate the subsequent treatment research.
Then, the K-value was calculated
Where n is the sample size.
The entropy value of the j th index is then calculated:
Then calculate the difference coefficient; the larger the entropy value, the smaller the difference coefficient; the treatment is as follows:
The weights were then determined based on the difference coefficient:
Finally, the rural revitalization index is determined according to the weight:
According to the above entropy value method, the descriptive statistics of rural revitalization in 16 prefecture-level cities in Anhui Province from 2014 to 2023 are determined as shown in Figure 9.
Descriptive statistics of rural Revitalization Index in Anhui Province
Core explanatory variable: Digital Financial Inclusion Index (DIF). Digital Pratt & Whitney financial index is more authoritative, and most scholars use the digital financial research center of Beijing university digital pratt & Whitney financial index; the data was released four years ago, and the latest data was released in the first half of 2021; the Beijing university digital pratt & whitney financial index (2011-2020), this paper selects the 2011-2020 in Anhui province 16 digital pratt & whitney financial index, from the breadth (DIF 1), use depth (DIF 2), digital degree (DIF 3) three dimensions of Anhui digital pratt & whitney financial situation in Anhui province. At the same time, in order to facilitate the subsequent calculation and the expression of the formula, the digital financial inclusion index is reduced by a thousand times before taking the logarithmic processing.
In Anhui province as the research area, the research years 2014-202316 cities in Anhui province digital financial development in the influence of rural revitalization, the empirical analysis of the paper data from 2014-2023 yearbook in Anhui province and its prefecture city, at the same time using other related literature data processing method, descriptive analysis results as shown in figure 10:
For variable descriptive statistical results
Based on the above study, we choose 6 indicators of Anhui Province's 16 towns during the period 2014-2023. Since n = 16, t = 10, which is a short board, and t is small, it usually requires 15 years or more to be meaningful. Co-integration tests are carried out on the panel data. Based on the general Gao Test of Co-integrated Test, we can determine if there is a cointegrated relation between the two models. The test results are illustrated in Figure 11.
Results of the co-integration test
Correlation analysis is mainly used to measure the closeness of variables, analyze the degree of correlation of two or more variable elements, and judge the feasibility of regression. This paper plans to establish a static regression model to analyze the impact of digital inclusive Finance on rural revitalization in Anhui Province and then discuss how digital inclusive Finance affects rural revitalization from three dimensions. The relevant analysis results are shown in Figure 12.
Variable correlation analysis
By combining quantitative analysis, theoretical deduction and empirical test, this paper deeply analyzes the theoretical mechanism of digital Finance promoting rural industry revitalization through mathematical deduction and theoretical deduction, establishes quantitative calculation to empirically test the mechanism of digital Finance promoting rural industry revitalization, and studies and proposes the ideas and path planning of rural Finance digital transformation path. The research conclusions are as follows:
1) For every 1% increase in the coverage of digital Inclusive Finance, the rural industry index will increase by 4.823%, with the most obvious effect among the three secondary indicators. For every 1% increase in the use depth of digital Inclusive Finance, the rural industry index will increase by 1.882%. The degree of digitalization reflects the fairness and sharing of digitally inclusive financial services. For every 1% increase in Digitalization, the rural industry index will increase by 1.704%. 2) Credit businesses have the most obvious promotion effect on rural industries. Every 1% increase in the index value will increase the rural industry index by 1.810%; The payment business takes second place. The rural industry index will increase by 0.988% for every 1% increase in the index value; The insurance business ranked last. For every 1% increase in the index value, the rural industry index will increase by 0.985%. This not only reflects the different effects of different digitally inclusive financial business types in serving the revitalization of rural industries but also reflects the actual demand structure for financial services from the current industrial development in rural areas. 3) The digital inclusive financial system has exerted an important influence on various sectors of the countryside, and it has the greatest influence on the industrial sector. With each increment of the Digital Inclusive Financial Index of 1 percent, the Industry Index will rise by 1.884 percent. With each increment of NDI, agriculture will rise 8.995 percent, and the service sector will be a little bit weaker. With each increment of the DI, the DI will rise by 0.601 percent, which indicates that DTI has an inclusive impact on the development of the country. 4) With each increment of the DI, a quarter of the Rural Industrial Index would rise by 3,345%, the 1/2 quantitative would be 3,397%, and the third/fourth quantitative would be 3,560%. Moreover, it has little impact on regions where there is a low rate of economic growth, but it has a great impact on regions where there is a lot of growth in the countryside, while it has a moderate impact on the regions where there is a moderate degree of economic growth. The use of data-inclusive finance services to promote the countryside economy is more widespread in the areas where the countryside is developing. 5) Speed up the merger between agriculture finance and modern countryside, and bring in new kinds of finance services, such as Internet, block, AI, IW, Cloud Computing, and 5. Therefore, it is necessary to establish a quantitative assessment mode, which will offer more convenience to the farmers. It is necessary to take the concept of market-leading, governmental guiding and supervising, as well as science and technology innovation in building up our country's credit system. Based on the developing tendency of the country's economic development, the related authorities can push forward the building of the credit reporting system in the countryside and agriculture enterprises and perfect the credit system. 6) Building an IOL infrastructure in the countryside, which consists of sensors, data-collecting devices and a telecommunication net, is designed for agricultural, livestock, and fishing industries.