Frontiers in Business, Economics and Management ISSN: 2766-824X | Vol. 13, No. 3, 2024 48 On the Path of Increasing Farmer Income in the Yangtze River Delta Region under the Background of Digital Economy Chuanqi Gu, Weiyang Yu, Junfen Hu*, Zejiong Zhou School of Economics, Anhui University of Finance and Economics, Bengbu, China * Corresponding author: 2732397420@qq.com Abstract: The Yangtze River Delta is one of the most active economic development areas in China, accounting for only 2.1% of China's regional area, concentrating 1/4 of China's total economic output and more than 1/4 of industrial added value, and is regarded as an important engine of China's economic development. The analysis and discussion of the influencing factors of farmers' growth in the Yangtze River Delta region under the background of digital economy has great reference significance for the income growth of farmers in other regions. This paper first analyzes the current situation of farmers' income in Anhui Province, Jiangsu Province, Shanghai Municipality and Zhejiang Province in the Yangtze River Delta region, and then analyzes the impact of the industrial index, integration index and infrastructure index on the income of farmers in the digital economy index through regression model, and tests and corrects them. The results show that the integration index and infrastructure index have a significant impact on the income of farmers in Anhui Province, the spillover index has a significant impact on the income of farmers in Jiangsu Province, the industrial index has a significant impact on the income of farmers in Shanghai, and the integration index and infrastructure index have a significant impact on the income of farmers in Zhejiang Province. Based on this, corresponding suggestions are put forward in order to effectively increase the income of farmers in the four regions and provide corresponding reference for other regions. Keywords: Digital economy; Yangtze River Delta; Increasing farmers' incomes; Regression analysis; Model testing and correction. 1. Introduction The issue of agriculture, rural areas, and farmers has always been a key focus of attention for the Party and the state. The research shows that the improvement of farmers' income level is the driving force for expanding domestic demand in China. At the same time, the rapid development of the Internet has changed the rural economic and social reality, and integrated more science and technology into the original agricultural model. According to the National Agricultural Sustainable Development Plan (2015-2030), the three provinces and one city in the Yangtze River Delta (Shanghai, Jiangsu, Zhejiang, and Anhui) are all part of the optimized development zone, with Jiangsu and Anhui provinces being important agricultural provinces in China. The Yangtze River Delta region is one of the earliest rural areas in China to undergo reforms, with the most complete agricultural industry system, the strongest innovative technological capabilities in agriculture, and the fastest urban-rural integration process. It holds an important strategic position in the overall construction of national agricultural and rural modernization. The development of rural agriculture in our country has always faced the bottleneck of more people and less land. Whether it is to achieve large-scale production in rural industries or agricultural production, or to achieve economic reform in rural industrial ecology, there are physical spatial limitations. The digital economy, on the other hand, breaks free from physical space limitations and empowers the digitalization of "farming, breeding, and processing". It brings "inventory adjustment and sales" to the cloud, providing opportunities for farmers to overcome bottlenecks and overtake in the curve. According to data, in 2016, the Yangtze River Delta region achieved an agricultural growth value of approximately 872.167 billion yuan, and in 2020, the region achieved an agricultural growth value of approximately 999.397 billion yuan. The proportion of agricultural growth value in the Yangtze River Delta region to the total national agricultural economy exceeds 12%. It has played an important role in the development of China's agricultural economy. It is expected that the total agricultural added value of the three provinces and one city in the Yangtze River Delta will exceed one trillion yuan in 2021. In recent years, the gross domestic product (GDP) of grain production in the Yangtze River Delta region has shown a long-term upward trend. In 2022, Jiangsu Academy of Agricultural Sciences led multiple universities to establish the Yangtze River Delta Smart Agriculture Technology Key Laboratory under the Ministry of Agriculture and Rural Affairs. The aim is to provide information technology support for the digital transformation and innovative development of modern agriculture in the Yangtze River Delta, focusing on the industrial needs and key core technology issues of the development of new agriculture in the region. At the same time, the digital economy can effectively improve agricultural productivity and increase farmers' income. However, in some aspects, there are still some issues that need to be addressed, such as the urgent need to improve the digital literacy of agricultural production and the lagging development of the agricultural industry chain compared to the iteration speed of digital technology. In order to promote the increase of farmers' income in the Yangtze River Delta region under the background of digital economy, this article will delve into the pulse of promoting agricultural income through quasi digital economy. The impact of the four indicators of digital 49 economy on the income of farmers in Anhui Province, Jiangsu Province, Shanghai City, and Zhejiang Province will be explored in depth. 2. Overview of Related Research In the mechanism of the impact of the digital economy on increasing farmers' income, Hu Lun et al. (2019) found through research that the digital economy has a significant impact on increasing farmers' income, but there are also differences for individual farmers with differences. Sun Wenting (2022) empirically analyzed the basic and heterogeneous transmission mechanisms of the impact of the digital economy on farmers' income growth, and examined the nonlinear relationship between the digital economy and farmers' income growth using urbanization level as a threshold variable. Cao Bingxue et al. (2019) divided informatization into information infrastructure investment and information technology adoption, and found that information infrastructure investment has a beneficial effect on increasing income, while information technology adoption has no effect on increasing income. Tang Yueheng et al. (2020) believe that in recent years, the government has taken a series of political measures to promote the development of e- commerce, thereby increasing the income of farmers who have a certain foundation and technology in the digital economy. He Yaping et al. (2019) believed that the improvement of Internet level is conducive to increasing people's income, including the income of farmers. Zuo Pengfei (2020) believes that under the general trend of digital economy, the development of the Internet can promote the rural industrial structure, and the upgrading and optimization of the industrial structure will undoubtedly increase farmers' income. Li Xue (2021) proposed that the digital economy is a new economic form, and its development also has a tendency of marginal increase, which indirectly promotes the growth of farmers' income. To sum up, in the context of the digital economy, there are many factors that affect farmers' income, and more and more scholars participate in the exploration of issues related to factors that affect farmers' income in the context of the digital economy, but most of them take a single province or the whole country as the research object, mostly focusing on high-tech, Internet level There is relatively little research on the impact of e-commerce on farmers' income, particularly on the four indicators of digital economy development. Therefore, this article takes Anhui Province, Jiangsu Province, Shanghai City, and Zhejiang Province as the research objects, selects data on farmer income, industry index, spillover index, integration index, and infrastructure index from 2014 to 2020, conducts multicollinearity and autocorrelation tests, empirically explores the factors affecting farmer income in the four regions of the Yangtze River Delta, and proposes suggestions for the growth of farmer income from the four levels of digital economy index. 3. Current Income Status of Farmers in Anhui Province, Jiangsu Province, Shanghai City, and Zhejiang Province in the Yangtze River Delta region From 2014 to 2020, the disposable income of farmers in Anhui Province, Jiangsu Province, Shanghai City, and Zhejiang Province in the Yangtze River Delta region showed an upward trend. The disposable income of farmers was increasing year by year, but the gap in disposable income among farmers in the four regions was gradually increasing. The disposable income of farmers in Anhui Province increased from 9916.4 yuan in 2014 to 16620.2 yuan in 2020; The disposable income of farmers in Jiangsu region increased from 14958.4 yuan in 2014 to 24198.5 yuan in 2020; The disposable income of farmers in Shanghai increased from 21191.6 yuan in 2014 to 34911.3 yuan in 2020; The disposable income of farmers in Zhejiang region increased from 19373.3 yuan in 2014 to 31930.5 yuan in 2020. It is not difficult to find that from 2014 to 2020, the income of the four regions basically increased to between 1.6 and 1.7 times. Among them, the disposable income of farmers in Anhui region shows a straight upward trend. In 2020, the disposable income of farmers was 1.67 times that of 2014, with the largest growth rate among the four regions of the Yangtze River Delta and a relatively stable growth rate. However, at the same time, it is evident from the graph that the disposable income of farmers in Anhui region is the lowest among the four regions, with Shanghai region having the highest disposable income among them. However, the disposable income of farmers in Anhui region cannot yet reach half of that of farmers in Shanghai region. Figure 1. Disposable income of farmers in three provinces and one city from 2014 to 2020 4. Model Construction 4.1. Select variables Due to the fact that the income level of farmers in the four regions of the Yangtze River Delta is influenced by multiple factors of the digital economy, and there is a significant difference in the order of magnitude between the four indicators and farmers' income, this article uses a multiple linear regression logarithmic model for empirical analysis. 4.2. Variable selection The dependent variables are: disposable income of farmers in Anhui Province lny1, disposable income of farmers in Jiangsu Province lny2, disposable income of farmers in Shanghai lny3, and disposable income of farmers in Zhejiang Province lny4. 50 Explanatory variable 1: lnx1- Industry index; Explanatory variable two: lnx2- overflow index; Explanatory variable three: lnx3- fusion index; Explanatory variable four: lnx4- Infrastructure index. Using ln y1 (disposable income of farmers in Anhui Province), ln y2 (disposable income of farmers in Jiangsu Province), ln y3 (disposable income of farmers in Shanghai), and ln y4 (disposable income of farmers in Zhejiang Province) as dependent variables, and lnx1 "industry index", lnx2 "spillover index", lnx3 "integration index", and lnx4 "infrastructure index" as explanatory variables, a multiple logarithmic linear regression equation model is constructed: lny β β lnx1 β lnx2 β lnx3 β lnx4 μ (1) Among them, 𝛽 (i=1, 2, 3, 4) are the partial regression coefficients of the explanatory variables: industry index, spillover index, integration index, and infrastructure index, μ For the random error term, which includes interference from other influencing factors besides the four explanatory variables mentioned above, it is generally assumed that it is not related to the explanatory variable and represents the uncertainty explained by it. 4.3. Data clarification 4.3.1. Dependent variable The disposable income of farmers refers to the income obtained by rural households through initial distribution and redistribution. In order to ensure the efficiency and authority of empirical analysis, this article selected the relevant data of farmers' disposable income from 2014 to 2020 in the National Statistical Yearbook, as shown in Table 1. Table 1. Disposable income of farmers in three provinces and one city from 2014 to 2020 Year Anhui Jiangsu Shanghai Zhejiang 2014 9916.4 14958.4 19373.3 21191.6 2015 10820.7 16256.7 21125.0 23205.2 2016 11720.5 17605.6 22866.1 25520.4 2017 12758.2 19158 24955.8 27825 2018 13996 20845.1 27302.4 30374.7 2019 15416 22675.4 29875.8 33195.2 2020 16620.2 24198.5 31930.5 34911.3 4.3.2. Explanatory variables The industry index, integration index, and spillover index are important driving forces for the overall development of the digital economy. This article selects industry, spillover, integration, and infrastructure index data from Anhui Province, Jiangsu Province, Shanghai City, and Zhejiang Province from the 2021 Yangtze River Delta Digital Economy Development Index Report, which is authoritative. The specific data is shown in Table 2. Table 2. Digital Economy Index of Three Provinces and One City from 2014 to 2020 Area Anhui Jiangsu Shanghai Zhejiang 2014 Industry 1.1 2.6 2.1 2 Spillover 5.35 9.4 4.05 6.8 Fusion 49.3 45 41.8 51.1 infrastructure 0.71 0.76 0.89 0.82 2015 Industry 1.2 2.8 2.2 2.3 Spillover 5.5 9.6 4.1 7 Fusion 48.2 45.5 42.3 51.6 infrastructure 0.75 0.83 0.91 0.85 2016 Industry 0.14 2.9 2.4 2.4 Spillover 5.6 9.7 4.2 7.15 Fusion 48.5 47 42.6 51.8 infrastructure 0.79 0.96 0.94 0.91 2017 Industry 1.5 3 2.5 2.7 Spillover 5.75 9.9 4.35 7.2 Fusion 48.9 48.9 43.5 52.1 infrastructure 0.83 0.89 0.95 0.93 2018 Industry 1.7 3.1 2.7 2.9 Spillover 6 10.2 4.55 7.3 Fusion 49.7 52 46 52.9 infrastructure 0.85 0.92 0.98 0.97 2019 Industry 1.8 3.3 3 3 Spillover 6.1 10.3 4.7 7.35 Fusion 50.2 52.3 46.5 54.3 infrastructure 0.9 0.95 1 0.98 2020 Industry 2 3.5 3.1 3.1 Spillover 6.4 10.6 4.8 7.5 Fusion 51.4 54.6 48 55.4 infrastructure 0.9 1 1 0.99 51 5. Empirical Analysis 5.1. OLS parameter estimation 5.1.1. Anhui Province Use Eviews10 software to perform least squares estimation on the above data, and the results are shown in the table below. Table 3. Least Squares Estimation Results in Anhui Province Variable Coefficient Std.Error t-Statistic Prob. C 4.967944 0.815255 6.093730 0.0259 LNX1 -0.081137 0.118109 -0.686968 0.0431 LNX2 1.534036 0.321159 4.776565 0.2511 LNX3 0.527130 0.281723 1.871094 0.0322 LNX4 1.123730 0.182324 6.163355 0.0253 Note: R-squared=0.9997, adjusted R-squared=0.9990, F=1435.899, DW=2.2324. According to the data, the estimated result of the logarithmic model of multiple linear regression in Anhui Province is: lny 4.9679  0.0811lnx 1.534lnx  0.5271lnx 1.1237lnx (2) From the results, it can be seen that the coefficient of determination is 0.9997, and the corrected coefficient of determination is 0.9990. The overall fit of the model to the sample is good; When significance level α= At 0.05, the F- statistic of the model is 1435.899, which is greater than the critical value of 3.11, and the regression equation is significant, that is, the Anhui Province Industry Index, Spillover Index, Fusion Index, and Infrastructure Index have a significant impact on the income of farmers in Anhui Province; But the t-statistics of the industry index and integration index are both small, when the significance level is α= At 0.05, the t-statistic of both explanatory variables is less than the critical value of 2.145, indicating that the t-test does not pass; In addition, the parameters of Anhui Province's industrial index are negative, and the economic significance test does not pass. In summary, there may be issues with multicollinearity in the regression model of Anhui Province, which require model correction. 5.1.2. Jiangsu Province Use Eviews10 software to perform least squares estimation on the above data, and the results are shown in the table below. Table 4. Least Squares Estimation Results in Jiangsu Province Variable Coefficient Std.Error t-Statistic Prob. C 8.011755 2.675583 2.994396 0.0958 LNX1 1.792846 0.726084 2.469201 0.0322 LNX2 -4.178387 3.034898 -1.376780 0.3024 LNX3 2.428634 1.019958 2.381112 0.0402 LNX4 -0.056842 0.172524 -0.329473 0.0531 Note: R-squared=0.9991, Adjusted R-squared=0.9974, F=571.3415, DW=2.9341. According to the data, the estimated result of the logarithmic model of multiple linear regression in Jiangsu Province is: lny 8.0118  1.7928lnx  4.1784lnx  2.4286lnx  0.0568lnx (3) According to the results, the coefficient of determination is 0.9991, and the corrected coefficient of determination is 0.9974. The overall fit of the model to the sample is good; When significance level α= At 0.05, the F-statistic of the model is 571.3415, which is greater than the critical value of 3.11, and the regression equation is significant, that is, the Jiangsu Province Industry Index, Spillover Index, Fusion Index, and Infrastructure Index have a significant impact on the income of farmers in Jiangsu Province; But the parameters of spillover and infrastructure index in Jiangsu Province are negative, and the economic significance test does not pass. In summary, there may be issues with multicollinearity in the regression model of Jiangsu Province, which require model correction. 5.1.3. Shanghai City Use Eviews10 software to perform least squares estimation on the above data, and the results are shown in the table below. Table 5. Least Squares Estimation Results in Shanghai Variable Coefficient Std.Error t-Statistic Prob. C 9.907985 3.169712 3.125831 0.0889 LNX1 0.284391 0.715619 0.397405 0.0295 LNX2 1.218232 2.182502 0.558181 0.6329 LNX3 -0.436944 1.448779 -0.301594 0.7914 LNX4 1.852973 1.329323 1.393922 0.2980 Note: R-squared=0.9944, adjusted R-squared=0.9831, F=88.0072, DW=2.5629. 52 According to the data, the estimated result of the logarithmic model of multiple linear regression in Shanghai is: lny 9.908  0.2844lnx 1.2182lnx  0.4369lnx 1.853lnx (4) According to the results, the coefficient of determination is 0.9944, and the corrected coefficient of determination is 0.9831. The overall fit of the model to the sample is good; When significance level α= At 0.05, the F-statistic of the model is 88.0072, which is greater than the critical value of 3.11, and the regression equation is significant, that is, the Shanghai Industry Index, Spillover Index, Fusion Index, and Infrastructure Index have a significant impact on the income of farmers in Shanghai; However, the t-statistics of industry index, spillover index, integration index, and infrastructure index are all small when the significance level is low α= At 0.05, the t-statistic of all four explanatory variables is less than the critical value of 2.145, indicating that the t-test does not pass; In addition, the parameters of the Shanghai Integration Index are negative, and the economic significance test did not pass. In summary, the regression model in Shanghai may have issues such as multicollinearity, which require model correction. 5.1.4. Zhejiang Province Use Eviews10 software to perform least squares estimation on the above data, and the results are shown in the table below. Table 6. Least Squares Estimation Results in Zhejiang Variable Coefficient Std.Error t-Statistic Prob. C 1.360507 1.143205 1.190081 0.3561 LNX1 0.413332 0.111459 3.708383 0.0356 LNX2 -0.739631 0.568657 -1.300663 0.3231 LNX3 2.498381 0.247534 10.09309 0.0097 LNX4 0.951917 0.249679 3.812565 0.0124 Note: R-squared=0.9994, adjusted R-squared=0.9982, F=841.4379, DW=2.4513. According to the data, the estimated result of the multiple linear regression logarithmic model in Zhejiang Province is: lny 1.3605  0.4133lnx  0.7396lnx 2.4984lx 0.9519lnx (5) The coefficient of determination is 0.9994, and the corrected coefficient of determination is 0.9982. The overall fit of the model to the sample is good; When significance level α= At 0.05, the F-statistic of the model is 841.4379, which is greater than the critical value of 3.11, and the regression equation is significant, that is, the Zhejiang Province Industry Index, Spillover Index, Fusion Index, and Infrastructure Index have a significant impact on the income of farmers in Zhejiang Province; However, the t-statistics of industry index, spillover index, integration index, and infrastructure index are all small when the significance level is low α= At 0.05, the t- statistic of all four explanatory variables is less than the critical value of 2.145, and the t-test fails. In summary, there may be issues with multicollinearity in the regression model of Zhejiang Province, which require model correction. 5.2. Model Updating 5.2.1. Multicollinearity test Correlation coefficient test. Calculate the correlation coefficient matrices of explanatory variables for Anhui Province, Jiangsu Province, Shanghai City, and Zhejiang Province in Eviews software, and analyze the relationships between each variable through the correlation coefficients, as shown in the following table. Table 7. Correlation coefficients Anhui Y1 Xl X2 X3 X4 Jiangsu Y2 X1 X2 X3 X4 Y3 1.00 0.99 0.99 0.85 0.98 Y 1.00 0.99 0.99 0.99 0.84 X1 0.99 1.00 0.99 0.83 0.98 X 0.99 1.00 0.96 0.96 0.87 X2 0.99 0.99 1.00 0.86 0.95 X 0.99 0.99 0.99 0.99 0.82 X3 0.85 0.83 0.86 1.00 0.73 X 0.99 0.96 1.00 1.00 0.80 X4 0.98 0.98 0.95 0.73 1.00 X 0.84 0.87 0.80 0.80 1.00 Shanghai Y3 Xl X2 X3 X4 Zhejiang Y4 Xl X2 X3 X4 Y3 1.00 0.99 0.99 0.97 0.99 Y4 1.00 0.98 0.97 0.97 0.97 Xl 0.99 1.00 0.99 0.97 0.98 Xl 0.98 1.00 0.97 0.90 0.98 X2 0.99 0.99 1.00 0.99 0.97 X2 0.97 0.97 1.00 0.90 0.98 X3 0.97 0.97 0.99 1.00 0.94 X3 0.97 0.90 0.90 1.00 0.88 X4 0.99 0.98 0.97 0.94 1.00 X4 0.97 0.98 0.98 0.88 1.00 According to the correlation coefficient matrix, the majority of the correlation coefficients between the explanatory variables in the four regions are greater than 0.8, indicating severe multicollinearity. (2) Stepwise regression. According to the correlation coefficient matrix, it can be seen that Anhui Province, Shanghai City, and Zhejiang Province have the highest correlation coefficient between the explanatory variable industry index and the dependent variable, while Jiangsu Province has the highest correlation coefficient between the explanatory variable spillover index and the dependent variable. According to theoretical analysis, Anhui Province, Shanghai City, and Zhejiang Province's industry index are the main influencing factors of farmers' income, while Jiangsu Province's spillover index is the main influencing factor of farmers' income. Therefore, using the corresponding regression equation as the basic model, the remaining variables were introduced one by one, and Eviews software 53 was used for regression analysis. The results are shown in the table below: Table 8. Stepwise Regression Results Anhui Province Jiangsu Province Variable Coefficient Std.Error t-Statistic Prob. Variable Coefficient Std.Eror t-Statistic Prob. C 5.1191 0.7126 7.1841 0.0056 C 9.6882 0.3339 29.0166 0.0000 LNX2 1.3817 0.2109 6.5524 0.0072 LNX1 0.6740 0.3054 2.2071 0.0319 LNX3 0.5438 0.2548 2.1344 0.1225 LNX4 1.8671 0.9934 1.8794 0.1344 LNX4 1.0308 0.1110 9.2899 0.0026 Shanghai Municipality Zhejiang Province Variable Coefficient Std.Error t-Statistic Prob. Variable Coefficient Std.Eror t-Statistic Prob. C 0.5048 0.5366 0.9409 0.0400 C 0.4305 0.9895 0.4351 0.0429 LNX2 4.0715 0.2335 7.4367 0.0000 LNX1 0.3627 0.1159 3.1306 0.0520 LNX3 2.3740 0.2533 9.3731 0.0026 LNX4 0.7896 0.2399 3.2914 0.0460 5.2.2. Heteroscedasticity test Using Eviews software to conduct Park tests on the models, the results showed that when the significance level α= At 0.05, the Prob values for Anhui Province, Jiangsu Province, Shanghai City, and Zhejiang Province were all greater than 0.05, with n values of 6.0013, 6.0185, 6.1091, and 6.1235, respectively, all less than (0.05)=11.0705, indicating acceptance of the null hypothesis and no heteroscedasticity in the model. 5.2.3. Autocorrelation test Using Eviews software, BG tests were conducted on the models, and the results showed that when the significance level was reached α= At 0.05, the Prob values of Anhui Province, Jiangsu Province, Shanghai City, and Zhejiang Province were all greater than 0.05, with n values of 5.9321, 5.8785, 6.0065, and 6.0178, respectively, all less than (0.05)=11.0705, indicating that the auxiliary regression model is not significant. Accepting the null hypothesis, the model does not have autocorrelation. 6. Conclusions and Suggestions 6.1. Conclusions Through model correction and testing, it was found that the spillover index and infrastructure index have a significant promoting effect on the income of farmers in Anhui Province, the industrial index has a significant promoting effect on the income of farmers in Jiangsu Province, the spillover index has a significant promoting effect on the income of farmers in Shanghai, and the integration index and infrastructure index have a significant promoting effect on the income of farmers in Zhejiang Province. 6.2. Suggestions Jiangsu Province should increase overall coordination and resource integration efforts, improve the industrial chain, and promote the growth of the industrial index. To promote the digital transformation of agriculture, integrate agricultural industry resources, digitize and empower various industrial chains of agricultural products and agricultural by-products, improve the efficiency of agricultural resource utilization and the degree of digital resource empowerment, and enable technology to better empower agricultural development. Deepen the reform of the mechanism and system for scientific and technological innovation, and enhance the overall efficiency of the innovation system. Promote agricultural technological innovation in the Yangtze River Delta region from the perspective of mechanism and system, strengthen investment in agricultural production technology and agricultural machinery equipment innovation, improve agricultural labor productivity, reduce agricultural production costs for farmers, and increase their income. Anhui Province and Shanghai City should fully utilize the "digital spillover" effect, and the government should focus on expanding network connectivity, providing users with a high- speed network connectivity experience, and enhancing their digital awareness and skills. Fully realizing the spillover effects of the digital economy requires significant investment from all stakeholders (including the government). Despite the enormous economic potential of digital technology, it is still not possible to fully unleash this potential. The government needs to establish supportive infrastructure and institutions, encourage the public and businesses to use the internet, and incentivize digital entrepreneurship. To this end, the government must work together with various stakeholders, such as citizens, technology companies, education practitioners, infrastructure providers, and enterprises, to create a good digital development environment. Zhejiang Province should strengthen digital empowerment and promote the deep integration of digital technology and the real economy. The main focus is to seize the window period of digital reform in the province, focus on ensuring the elements of key enterprises and projects, and increase efforts to continuously promote the digitalization process of traditional industries in our county. Focusing on the deep integration of "two modernizations", we will cultivate and construct digital workshops and intelligent factories, promote intelligent manufacturing production models, actively cultivate smart warehousing and logistics IoT demonstration projects, and empower the transformation and upgrading of traditional industries. Anhui and Zhejiang provinces should establish necessary hardware and software infrastructure and increase investment in hardware infrastructure, such as high-quality telecommunications networks, supporting facilities, transportation, and urban infrastructure. The government needs to help a small number of people to excel in advanced technical positions, improve digital skills, and participate together in the digital economy. The government must develop lifelong learning plans for non-technical employees to help them enhance their digital skills and meet the needs of future digital economy development. Acknowledgment This work is supported by Anhui University of Finance and Economics National College Student Innovation and 54 Entrepreneurship Training Program, "Study On the Path of Increasing Farmer Income in the Yangtze River Delta Region under the Background of Digital Economy" (Grant No: 202210378029). References [1] Qu Xunyu. From "independent growth" to "common prosperity": the logical idea and paradigm shift of digital economy empowering the integrated development of rural industries[J].Shanxi Agricultural Economics,2023(06):29-31. [2] Luo Qianfeng, Zhao Qifeng, Qiu Hailan. 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