Frontiers in Computing and Intelligent Systems ISSN: 2832-6024 | Vol. 1, No. 2, 2022 5 Research on the Influence of Digital Economy on Rural Governance Nan Xia1, Jingwen Xie1, Yucheng Yuan2, Qifan Cao1, Deyu Li1, * 1 School of Economics, Anhui University of Finance and Economics, Bengbu, Anhui, China 2 School of Accountancy, Anhui University of Finance and Economics, Bengbu, Anhui, China * Corresponding author: Deyu Li (Email: ldy0048@163.com) Abstract: Based on the panel data of various provinces and cities in China, this paper uses the entropy method to measure the development index of digital economy and rural governance in China during 2016-2020, and then empirically analyzes the driving effect of digital economy on rural governance. The research results show that the indicator development of the digital economy plays a fundamental role in the operation of some mechanisms of rural governance, and is further promoting the transformation of rural governance structure through positive external effects. Keywords: Digital economy; Rural governance; Driving effect. 1. Introduction Since the 18th National Congress of the Communist Party of China, the Party Central Committee and the State Council have attached great importance to agricultural and rural informatization. In the COVID-19 epidemic, many rural areas took advantage of the digital economy to effectively govern, which more fully demonstrated the practical value of the digital economy for rural governance. In the process of continuous development, digital economy has become an important engine of China's economic development, and has accelerated its extensive penetration into agriculture and rural areas. Digital economy has brought new blood to rural governance and brought digital transformation to rural development. However, the impact of digital economy on rural governance needs to be discussed in depth. Based on the above, this study uses entropy method to measure China's digital economy development index and rural governance development index during 2016-2020 based on the panel data of various provinces and cities in China from 2016 to 2020, and then empirically analyzes the driving effect of digital economy on rural governance. The research finds that the digital economy has a positive role in promoting rural governance in China. Therefore, in the process of giving full play to the driving role of the digital economy in rural governance, we should pay attention to its promoting role and create a new pattern of rural digital governance. 2. Research Design 2.1. Variable measurement and data source 2.1.1. Independent variable: Digital Economy As a new economic form, digital economy has rich connotation, which cannot be measured by a single index. Most of the existing measurement methods measure the development level of the digital economy by establishing a multi-dimensional digital index measurement system. Therefore, this paper chooses to establish a multi-dimensional measurement system according to the experience and methods of the predecessors. Driven by the digital economy, human production and life style has gradually changed into information processing, and the development of information technology has increasingly become the basis of the development of the digital economy. The rapid development of the Internet has greatly increased the amount of information processing, improved the level of information processing, and changed people's living conditions to a certain extent. The Internet has gradually become an important engine and carrier of the digital economy. With the support of information processing technology and Internet platform, the digitalization of modern trade is increasingly strengthened. Digital consumption and trade have become an important support of social economy, reshaping people's consumption behavior and exerting a subtle impact on people's lives. To sum up, through combing and summarizing the existing literature, this paper believes that the digital economy is a new economic form that relies on big data, takes digital information as the core, provides products and services through digital technology, enables producers and consumers to trade, and realizes the optimal allocation and regeneration of resources. 2.1.2. Dependent variable: Rural Governance Rural governance is one of the important contents of Rural Revitalization and an important aspect of national governance system and governance capacity building. In the report of the 19th National Congress of the Communist Party of China, the general requirements of "flourishing industry, ecological livability, civilized rural style, effective governance and ecological prosperity" were put forward for the implementation of the Rural Revitalization Strategy. Among them, "effective governance", as a guarantee factor to promote rural revitalization, plays a pivotal role in many factors. 2.1.3. Selection of evaluation indicators In building digital economy indicators, this paper draws on the measurement method of Zhao Lifang et al. (2021), and builds a digital economy evaluation index system including three first-class indicators of information development, digital transaction development and Internet development, two second-class indicators of information foundation and information impact, and 14 third class indicators of mobile phone base station density. Based on the index system, relying on the panel data of 30 provinces and cities in China from 2016 to 2020 except Tibet, the digital economy development 6 index of each province and city is obtained by entropy method. Among them, the basic data of the measurement indicators of the digital economy development index are all from the China Statistical Yearbook from 2017 to 2021. Table 1. Description of digital economy development indicators Primary index Secondary index Measure index Index attribute Informatization development index Basic information Optical cable density + Mobile phone base station density + Proportion of informatization employees + Informatization influence Total telecom business + Software business income + Internet development indicators Fixed internet foundation Internet access port density + Mobile Internet Foundation Mobile phone penetration rate + Fixed-end internet influence Share of broadband users + Mobile internet influence Proportion of mobile internet users + Digital transaction development index Fundamentals of digital transactions Enterprise website proportion + Proportion of computers used in enterprises + E-commerce proportion + Digital transaction impact Electronic business sales + Online retail sales + Based on the general requirements of rural revitalization, this paper will combine the actual situation of China's villages, follow the principles of scientificity, systematization and operability, and learn from the measurement method of Yan Yuman et al. (2021), to build a rural governance capacity index system that includes four secondary indicators such as ecological governance, industrial governance, industrial governance, civilized governance and life governance, and 17 tertiary indicators such as per capita park green area. The ecological governance dimension selects four indicators: per capita park area, sanitary toilet penetration rate, solar water heater and fertilizer use intensity. The former two reflect the intensity of building beautiful villages in China, and the latter two reflect the efficiency of energy utilization in China. The industrial governance dimension selects four indicators: grain yield per unit area, comprehensive utilization rate of breeding waste, agricultural mechanization rate, and contribution rate of agricultural scientific and technological progress. The former two reflect the efficiency of agricultural production, and the latter two reflect the level of agricultural modernization in China's rural areas. The civilized governance dimension selects four indicators: the rate of high school education, the number of cases accepted per 10000, the number of village level cultural stations, and the consumption rate of education, culture and entertainment. The first reflects the comprehensive level of education and culture in various provinces and cities in China, and the latter three reflect the active level of rural culture in various provinces and cities. The life governance dimension selects five indicators: Rural Residents' Engel coefficient, urban-rural residents' income ratio, per capita disposable income, rural tap water penetration rate, and life informatization degree. Among them, the first three indicators reflect the income and consumption of rural residents in China, and the latter two indicators reflect the living standards of rural residents in China. Similarly, the method adopted by the rural governance system is the same as that of the digital economy development index. Its data source is China Statistical Yearbook and China Rural Statistical Yearbook from 2017 to 2022. Table 2. Development indicators of rural governance Primary index Measure index Index attribute Ecological management Per capita park green space area + Sanitary toilet penetration rate + Solar water heater + Fertilizer application intensity - Industrial governance Grain yield per unit area + Comprehensive utilization rate of aquaculture waste + Agricultural mechanization rate + Contribution rate of agricultural scientific and technological progress + Civilized governance Ratio of high school education or above + Number of cases accepted per 10,000 people - Number of village cultural stations + Education, culture and entertainment consumption ratio + Life governance Engel's coefficient of rural residents - Income ratio of urban and rural residents - Rural tap water penetration rate + per capita disposable income + Information degree of life + 2.2. Model construction 2.2.1. Model setting In order to test the impact of digital economy on rural revitalization, this paper builds the following model: ruralm,t = θ0 + θ1digem,t + θ2X + γm,t (1) Where, rurali,t refers to the comprehensive index of rural governance in the mth province and the t-th year; digem,t refers to the digital economy composite index of the mth province and the t-th year, which is the core explanatory variable of this paper; X represents a series of control variables affecting rural governance (for example, infrastructure construction level, rural cultural development level, system construction level); θ0 represents the intercept term, θ1 denotes the regression coefficient of the explanatory variable, θ2 represents the regression coefficient of the control variable; γm,t denotes a random perturbation term. 7 2.2.2. Determination of digital economy index and rural governance index Objectively speaking, entropy method can deeply reflect the differentiation ability of indicators, determine better weights, make the weights more independent, have certain theoretical basis, and have more credibility than other index calculation methods. The specific steps are as follows: The first step is to determine the positive direction of negative indicators and preprocess the indicators. The formula is as follows: 𝑋𝑚 ′ = 𝑚𝑎𝑥𝑋𝑚−𝑋𝑛 𝑚𝑎𝑥𝑋𝑚−𝑋0 𝑋0 ≤ 𝑋𝑛 ≤ 𝑚𝑎𝑥𝑋𝑚 (2) In the forward processing of indicators, there is still a problem that each indicator unit is not unified. For the convenience of research, this paper further unifies each indicator into a dimensionless unit. The formula is as follows: 𝑋𝑚𝑛 ′ = 𝑋𝑚𝑛−𝑋𝑛̅̅ ̅̅ 𝑆𝑛 (3) In the above formula, Xj̅indicates the average value of n indicators in the mth Province, Sn represents the standard deviation of the nth index. The second step is to use the entropy method to analyze the index system. (1) Calculate the proportion 𝑃𝑚𝑛 of the nth index under the mth Province. 𝑃𝑚𝑛 = 𝑋𝑚𝑛 ∑ 𝑋𝑚𝑛 𝑘 𝑚=1 (4) (2) Calculate the entropy ej of the nth index. 𝑒𝑗 = −𝑎∑ 𝑝𝑚𝑛 ln 𝑝𝑚𝑛 𝑘 𝑚=1 (5) Assuming that pmn=1/b，k= 1/lnb. (3) Calculate the difference coefficient gi of the nth index. The larger the difference coefficient is, the betterit is, which means that the index plays a greater role in the research object and the more accurate the index representation. 𝑔𝑖 = 1 − 𝑒𝑗 (6) (4) Empower indicators and define weights. 𝛼𝑛 = 𝑔𝑖 ∑ 𝑔𝑖 𝑛 𝑖=1 (7) (5) Calculate the sample evaluation value through the weight. The evaluation value of index J under the ith province is: 𝐹𝑚𝑛 = ∑ 𝐹𝑚𝑛 𝑘 𝑗=1 (8) According to the above steps, the digital economy evaluation index and rural governance evaluation index for 2016-2020 can be calculated (see Table 3). Table 3. Evaluation index Province 2016 2017 2018 2019 2020 Dig Gov Dig Gov Dig Gov Dig Gov Dig Gov Beijing 0.4159 0.3494 0.4765 0.4004 0.5310 0.4431 0.5996 0.5116 0.635 0.6005 Tianjin 0.1785 0.1901 0.1700 0.2138 0.2172 0.2187 0.2256 0.2446 0.2984 0.2815 Hebei 0.0980 0.3135 0.1047 0.3337 0.1537 0.3255 0.1368 0.3371 0.1649 0.3445 Shanxi 0.0694 0.1823 0.0746 0.1710 0.9214 0.180 0.9219 0.1923 0.1081 0.1693 Inner Mongolia 0.0724 0.2078 0.0750 0.2394 0.0758 0.2596 0.0839 0.2700 0.9560 0.2725 Liaoning 0.1284 0.1855 0.1358 0.1937 0.1545 0.1997 0.1593 0.1936 0.1758 0.2115 Jilin 0.0931 0.1998 0.0935 0.2107 0.0968 0.2258 0.0941 0.2364 0.1061 0.2434 Heilongjiang 0.0813 0.2633 0.0836 0.2942 0.0934 0.2973 0.0932 0.3194 0.1115 0.3265 Shanghai 0.2874 0.2384 0.3026 0.2469 0.3387 0.3078 0.3939 0.3040 0.4519 0.3287 Jiangsu 0.2742 0.3700 0.3070 0.3856 0.3597 0.4432 0.3934 0.4284 0.4488 0.4369 Zhejiang 0.2569 0.2924 0.2907 0.3004 0.3481 0.3178 0.3939 0.3179 0.4441 0.3200 Anhui 0.1052 0.2875 0.1003 0.3008 0.1377 0.3028 0.1548 0.3191 0.1764 0.3258 Fujian 0.1448 0.1768 0.1533 0.1842 0.1758 0.2034 0.1944 0.2097 0.2102 0.2030 Jiangxi 0.0831 0.1926 0.0807 0.2041 0.0856 0.2010 0.0936 0.2235 0.1198 0.2300 Shandong 0.2509 0.4310 0.2966 0.4518 0.3667 0.4610 0.3675 0.4749 0.4162 0.4848 Henan 0.1244 0.3581 0.1303 0.3827 0.1538 0.3940 0.1695 0.4192 0.1923 0.4183 Hubei 0.1373 0.2331 0.1473 0.2445 0.1711 0.2526 0.1825 0.2663 0.2051 0.2693 Hunan 0.1046 0.2475 0.1117 0.2618 0.1332 0.2572 0.1390 0.2796 0.1741 0.2080 Guangdong 0.3841 0.2077 0.4463 0.2153 0.4500 0.2595 0.6077 0.2511 0.6908 0.2576 Guangxi 0.0749 0.1643 0.0990 0.1751 0.9416 0.1708 0.0989 0.1948 0.1335 0.1915 Hainan 0.1205 0.1540 0.1159 0.1538 0.1248 0.1591 0.1223 0.1776 0.1329 0.1550 Chongqing 0.1230 0.1559 0.1290 0.1705 0.1568 0.1775 0.1599 0.1877 0.1932 0.1950 Sichuan 0.1270 0.2611 0.1460 0.2749 0.1789 0.2298 0.1902 0.2996 0.2328 0.2971 Guizhou 0.0903 0.1414 0.0897 0.1528 0.1015 0.1515 0.1101 0.1688 0.1257 0.1741 Yunnan 0.0824 0.1969 0.0765 0.2192 0.0917 0.2137 0.0991 0.2257 0.1239 0.2326 Shaanxi 0.1128 0.1914 0.1149 0.2210 0.1394 0.2170 0.1402 0.2349 0.1703 0.2448 Gansu 0.0637 0.1369 0.0527 0.1547 0.0579 0.1459 0.0618 0.1697 0.0728 0.1764 Qinghai 0.0745 0.1080 0.0695 0.1130 0.0734 0.1164 0.0745 0.1343 0.0791 0.1294 Ningxia 0.0832 0.1408 0.0843 0.1534 0.0855 0.1624 0.0856 0.1690 0.0883 0.1801 Xinjiang 0.0592 0.1707 0.0687 0.1731 0.0740 0.1699 0.0749 0.1755 0.0818 0.1790 3. Empirical Analysis 3.1. Descriptive statistical analysis Through the data collection of selected variables from 2016 to 2020, and combined with 150 cases extracted from them as the benchmark, the corresponding descriptive statistical analysis table is obtained (see Table 4). It can be seen from table 4 that in 2016-2020, the average value of the comprehensive rural governance index of all provinces and cities in China was 0.2499, and its standard deviation was 0.0920. This shows that the gap in the development level of rural governance in all parts of China has been reduced. Compared with other provinces, the 8 development level and governance capacity of villages in the province and even provinces have been steadily improved, and the difference in regional development structure has been further broken. But from the digital economy index of the same period, it shows some specificity. The average value and standard deviation of the digital economy comprehensive index of various provinces and cities in China are 0.1782 and 0.1334, which indicates that there are obvious differences in the development level of digital economy among various provinces and cities in China, and limited by the economic level and geographical environment of various regions, the digital economy still shows temporal differences and spatial imbalance in development in the short term. Table 4. Descriptive statistical analysis Average value SD Number of cases Digital economy 0.1782 0.1334 150 Rural governance 0.2499 0.0920 150 3.2. Correlation analysis To further explore the internal relationship between digital economy and rural governance, we used Pearson correlation analysis to study the correlation between the two variables. According to the analysis results (see Table 5), there is a relatively significant correlation between the two core variables. The Pearson coefficient of digital economy and rural governance is 0.645, and the corresponding significance level is lower than 0.01. This means that the samples surveyed in all provinces and cities in China show a relatively obvious positive correlation between digital economy and rural governance index, which preliminarily verifies that with the development of digital economy and the maturity of economic structure, China's rural governance capacity and development level will also enter a high-quality development stage. This speculation also proves the theoretical hypothesis put forward in this paper again. Table 5. Correlation Analysis Digital economy Correlation analysis Rural governance Pearson correlation 0.645 Saliency (two tails) 0.00 3.3. Regression analysis According to the obtained indicators of digital economy and rural governance, we use SPSS to carry out regression analysis and obtain the relevant regression equation model (see Table 6). Digital economy is set as a as the prediction variable, and rural governance is brought into the model as the dependent variable. The initial r value is 0.645, and the adjusted R² is 0.7115, and the variance test f is 105.205, which shows that the relationship between the digital economy and rural governance is obvious, the model has a good goodness of fit, and the regression model is more effective. It also shows that the development of indicators of the digital economy plays a fundamental role in the operation of some mechanisms of rural governance, and the transformation of rural governance structure is further promoted through positive external effects. At the same time, we conducted one-way ANOVA on the above obtained model and made the following settings. (9) ANOVA table (see Table 7) is obtained by analyzing whether there is significant difference in the mean value of dependent variable (Rural Governance) when a single index of digital economy takes different levels. From the table, we can know that the sum of squares of regression is 0.5246, the mean square is 0.5250, the probability of F value (105.205) is about 0.01, the probability is relatively small, and the sum of squares of residuals is 0.7380, and the set mean square is 0.0049. Therefore, we reject the original hypothesis. From the analysis, we can know that there is a relatively significant connection between the digital economy and the indicators of rural governance. Table 6. Regression equation model Mod el R Adjuste d R² Error of standard estimati on R2 varianc e F Sig . 0.645 0 0.7115 0.0706 0.415 105.20 5 0.0 0 a. Predicted variables: (constant), Digital economy b. Dependent variable：Rural governance Table 7. ANOVA table Model Sum of squares Degrees of freedom Mean square F Sig. Coefficie nt 0.5246 1 0.525 105.205 0.01 Residual 0.7380 148 0.0049 Total 1.2627 149 a. Dependent variable: Rural Governance b. Predictor: Constant, Digital economy See Table 8 for the results of regression coefficient analysis. From table 8, we can get that the constant term coefficient of digital economy in the linear equation is 0.1706, the regression coefficient is 0.4447, the standard error is 0.0433, and the standard coefficient is 0.6445. The probability of P of t test is 0.01, less than 0.05. Therefore, the regression coefficient has significant significance. In the analysis of multicollinearity, the VIF (variance expansion factor) of its coefficient value is shown as 1, indicating that the collinearity diagnosis of the model itself is good, the model does not have multicollinearity, and the promotion mechanism of digital mechanism enabled rural governance has certain theoretical correctness. Table 8. Results of regression coefficient analysis. Coefficient (a) Model Non-standardized coefficient Standardize d coefficient t Sig. B SD Beta Consta nt 0.170 6 0.0096 17.702 7 0.0 0 Digital economy 0.444 7 0.043 3 0.6445 10.256 9 0.0 1 a. Dependent variable: Rural Governance See Table 9 for the results of residual statistical analysis. In the statistical analysis of residuals, SPSS research was conducted from 150 selected cases. The minimum value of the prediction value of the model was 0.1941, the average value was 0.2499, and the standard error was 0.0593. The 9 maximum value of the residual error is 0.1713, the average value is 0.0841, and the standard error is 0.0703. The standard error of the corrected standard prediction value is 1 and the error of the standard residual is 0.9966. It is further proved that the model is reasonable. Table 9. Results of residual statistical analysis Residuals Statistics(a) Model Min Max Average value SD Sampl e numbe r Estimate 0.1941 1 0.479 0 0.2499 0.0593 150 Residual - 0.2203 0.173 1 0.084 1 0.070 3 150 Standard predicted value - 0.9408 3.861 7 0.4144 1 150 Standardize d residual - 3.1197 2.451 6 0.1050 0.9966 150 a. Dependent variable: Rural Governance 4. Conclusions and Suggestions The digital economy has played a significant positive role in promoting rural governance in China. Digital economy brings fresh blood to rural governance and digital transformation to rural development. Through empirical analysis, it is found that the development of indicators of digital economy plays a fundamental role in the operation of some mechanisms of rural governance, and is further promoting the transformation of rural governance structure through positive external effects. After the above data analysis and further research, it is not difficult to find that the correlation between the digital economy and the rural governance index has certain regional characteristics. Because of the relatively broad development prospects and great potential in the west, the digital economy plays an important role in promoting the rural development in the west, while the Eastern and central regions have entered a certain period of relaxation due to their relatively good foundation, But it is constantly deepening high-quality development. Based on this, in order to better promote the further improvement of the digital economy architecture in rural areas and help rural industrial revitalization and economic reform, we hereby propose some policy suggestions: (1) Deeply explore the promotion mechanism of digital mechanism enabling rural revitalization, optimize the top- level design, improve the digital economy architecture, and promote the in-depth development of the digital economy system. We will further build the infrastructure of digital economy in the western region, improve the digital level of rural development from both agricultural production and farmers' lives, and meet the material and cultural needs of the people. On the other hand, we should further link the endogenous mechanism of emerging information technologies in the East and central regions, make use of emerging information technologies such as big data, cloud computing and the Internet of things, increase the application scenarios of agricultural digitalization, catalyze the basic research and promotion of rural industries, promote the innovation and transformation of core technologies, further extend the industrial chain of digital countryside, open up the upstream, middle and downstream, promote the transformation and upgrading of agricultural production, and cultivate and expand high-efficiency, high-yield Modern agriculture with high technology and high quality. The social positive external effects of the digital economy are fed back to rural development through industrialization and clustering, and the old rural development structure is transformed, so that the increasingly popular network and pan linked information nodes coordinate with traditional production factors, fully integrate with the real economy, and improve the quality and efficiency of Rural Revitalization. At the same time, we still need to seize policy opportunities, adhere to policy guidance such as the outline of digital village development strategy and the key points of digital village development in 2022, improve China's digital village construction policy system at the specific implementation level, and inject new impetus into sustainable, high-quality, wide-ranging and three- dimensional rural revitalization. (2) Improve the online education and promotion platform of digital technology, and build a relatively complete data think tank in the local area. With the continuous development of the digital economy, some regions in the central and western regions will increasingly face the problems of insufficient talent reserves, small product supply and low quality. Therefore, it is very important to establish a relatively complete training and service platform. We can train more agricultural talents, agricultural product marketing talents and digital agricultural development talents through policy means or professional education, so as to reserve sufficient human capital for Rural Revitalization. Information technology can also be used to establish a platform. For example, in some rural areas of Fujian, a comprehensive digital operation platform with a certain scale has been established, constantly expanding the business scope and achieving effective product supply, further extending the customer group, and transmitting digital products and services to the vast countryside through a comprehensive network. On the one hand, a diversified digital platform can enable farmers to obtain more cultural and entertainment resources, It helps stimulate the creative vitality of rural culture, empower the progress of rural cultural industry, cultivate good rural customs and build "beautiful China". On the other hand, the establishment of think tanks on the basis of combining the field and using platforms such as big data has provided farmers with a channel to cultivate digital literacy, and is also expected to cultivate more modern digital farmers with culture, good management, new skills and a good life. (3) Make full use of the unique data structure elements and information platform of the digital economy, stimulate the endogenous power of rural digital economy development, focus on breaking the digital barriers between cities and villages, improve the Internet coverage in rural areas, and improve the phenomenon of information poverty in some areas. At the same time, we should build a relatively complete data flow mechanism as soon as possible, classify data resources with wide sources and large scale by using diversified information platforms, and clarify the nature, ownership and tradable scope of data. And a unified information transaction standard is formulated by multiple subjects in coordination, so as to realize the free and orderly flow of data, promote the process of valuing data elements, effectively give play to the important role of digital structural elements, big data, cloud platforms, etc. in promoting the digital transformation of rural governance, and realize the 10 empowerment of digital economy for Rural Revitalization. At the same time, we can use digital financing platforms to adopt diversified investment models, improve the level of infrastructure construction in rural areas in central and Western China, and improve the efficiency of digital resource allocation. Acknowledgments This work is supported by 2022 Anhui University of Finance and Economics Undergraduate Scientific Research Innovation Fund Project, "Research on the impact of digital economy on rural governance" (Grant No.: XSKY22071), Anhui University of Finance and Economics National College Student Innovation and Entrepreneurship Training Program, "Efficiency evaluation of digitalization on rural governance" (Grant No.: 202210378028). References [1] Gao Peiyong, Fan liming, Hong Yinxing. Deeply study and implement the spirit of the general secretary's important speech and accelerate the construction of an economic system with Chinese characteristics [J]. Management world, 2022, 38 (06): 1-56. [2] Shang Peijiang. Research on the development of new foreign trade formats in China under the background of digital economy [D]. Hebei University of economics and trade, 2022. [3] Gao Qiang. Practical exploration and path selection of improving modern rural governance system [J]. 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