In the context of China’s “Rural revitalization via digital commerce”, the study explored the impact of the digital economy on farmers’ income and the path mechanism of logistics development in it. Based on the panel data of 71 cities in the Eastern Economic Region of China from 2013 to 2021, this paper calculated the level of the digital economy and used the level of logistics development as a moderating variable. Then using the basic transmission mechanism and the heterogeneous transmission mechanism to empirically analyse the impact of the digital economy on the income of farmers, exploring the non-linear link between the digital economy and farmers’ income. The analysis showed that: (1) the development of digital economy has a significant positive impact on farmers’ income. (2) the impact of the digital economy on the advancement of logistics development, showed a delayed phenomenon. (3) the digital economy’s indirect effect on farmers’ income growth also showed a lag. In this way, this study verified the practical significance of “Rural revitalization via digital commerce”, and put forward the corresponding recommendations for promoting the development of the digital economy and rural logistics.
Research Article
Digital Economy, Logistics Development and the Increase of Farmers’ Income: An Empirical Analysis Based on the Eastern Economic Region of China
https://doi.org/10.21203/rs.3.rs-5282554/v1
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Digital economy
Logistics development
Farmers’ income
Eastern economic region
For the fifth consecutive year, the Central Economic Work Conference of China held in December 2023 has proposed “vigorously developing the digital economy”, and local governments have also successively introduced relevant plans and measures for digital reform. Meanwhile, according to a report released by the China Academy of Information and Communications Technology (CAICT) in 2023, the average growth rate of China’s digital economy has exceeded the nominal GDP growth rate for 11 consecutive years, with a total scale reaching 50.2 trillion yuan, an increase of 10.3% from the previous year. The digital economy is comparable to the share of the secondary industry in China’s GDP, reaching 41.5%. As the “accelerator” of the national economy, the digital economy has become a key force in China’s economic development and an essential foundation for constructing a new development model. The Party and government have explicitly put forward the strategy of “Digital China”.Evidently, the digital economy has become a new engine driving high-quality economic development and transformation in China [1].
With the implementation of the “Rural revitalization via digital commerce” initiative, the digital economy and e-commerce have further penetrated the rural economic market, injecting new vitality and opportunities into China’s rural areas. As the critical focus of advancing the great rejuvenation of the Chinese nation in the new era and on the new journey, the “three rural issues” (agriculture, rural areas, and farmers) have gained significant attention across society, especially in light of the achievements made in the battle against poverty. However, the critical challenge lies in promoting steady and sustained growth in farmers’ incomes, which is essential for effectively addressing the “three rural issues”. Therefore, it is crucial to deeply explore the various factors affecting the growth of farmers’ incomes and identify ways to increase them to tackle the “three rural issues”. Document No. 1 of the Central Government in 2024 proposed for the first time to “implement the project of high-quality development of rural e-commerce”, aiming to accelerate the construction of an agricultural powerhouse by integrating rural e-commerce development with the rural revitalization strategy and the digital village initiative. This approach aims to open up further the dual channels of downstream consumer goods and upstream agricultural products, promoting the integration of agriculture and the logistics industry, thereby increasing farmers’ incomes. With the rapid development of the internet and the accelerated transformation of technologies such as the Internet of Things and big data, commercial models are becoming more intelligent, and the development of online retail is facing new opportunities and challenges. Consequently, the development of the logistics industry provides critical support for rural online retail, undoubtedly becoming one of the key constraints on farmers’ income [2].
Based on the existing research, this paper explores how the digital economy promotes farmers’ income growth from the perspective of “Rural revitalization via digital commerce”. Considering the logistics system as a key link connecting production and consumption, the study integrates it into a comprehensive analytical framework of the digital economy and farmers’ income growth to explore its specific impact on rural residents' income. Due to data availability, the study focuses on panel data from 71 eastern coastal provinces and cities in China, including Shandong, Jiangsu, Zhejiang, Fujian, Guangdong, Shanghai, and Tianjin, from 2013 to 2021, to measure the level of digital economy development. The study uses freight volume in each city to assess the development of logistics and rural residents' per capita disposable income to represent farmers’ income. All data are from the corresponding years of the “China Statistical Yearbook” and local statistical yearbooks, with interpolation methods to supplement some missing values.
The potential marginal contributions of this paper are reflected in the following aspects: (1) In terms of theory, most existing studies focus on exploring the relationship between the digital economy and farmers’ income growth or between the digital economy and logistics. This paper, however, incorporates logistics into the theoretical framework of the impact of the digital economy on farmers’ income growth, further investigating the possible logical relationships among these three factors and empirically testing the mechanism by which the digital economy influences farmers’ income through logistics. (2) In the empirical analysis, Some existing literature explores the development of the digital economy and logistics at the macro level, while other scholars analyze the impact of the digital economy on farmers’ income at the micro level. However, this paper uses data from the more detailed prefecture-level, including the digital economy development level, logistics data, and farmers’ income information, to further explore the connections among these three factors. This approach helps overcome sample size limitations and allows the acquisition of more practical information. (3) In terms of research methodology, after examining the direct mechanism by which the digital economy influences farmers’ income growth, the paper further elaborates on and discusses the moderating role of logistics in promoting farmers’ income growth. It then employs the instrumental variable method to address endogeneity and examine the relationship among the digital economy, logistics, and farmers’ income growth. This study includes the overall effect of the digital economy on farmers’ income growth and the indirect impact generated by improving the level of logistics development.
2.1. Direct effects of the digital economy on farmers’ income growth
Many academic research institutions and experts have elaborated on the indicators and measurements of digital economy development levels. Some scholars have conducted in-depth discussions on the relationship between the digital economy and farmers’ income growth, with research primarily focusing on the following two aspects.
One part is the impact of the digital economy on farmers and household incomes. Although most of the current research is empirical, the conclusions are inconsistent. Some scholars argue that the digital economy has no significant impact on farmers’ income [3,4]. In contrast, most Chinese scholars believe that the development of the digital economy has a positive effect on farmers’ income growth. However, some scholars have found that the use of internet information technology shows significant individual heterogeneity in its income-increasing effect depending on farmers different education levels and age stages of [5]. Additionally, information infrastructure investment and the adoption of information technology, as two primary manifestations of informatization, have different impacts: the former has a positive effect on increasing farmers’ income, while the latter has no significant impact, and there is no interactive effect on farmers’ income between the two [6]. Even so, by implementing various policies, such as promoting rural e-commerce, deeply advancing rural e-commerce development, and using digital empowerment to enhance farmers’ efficiency, it is still possible to effectively achieve income growth for farmers [7].
The other part is the impact of the digital economy on the urban-rural income gap. On the one hand, with the widespread application of the internet, the digital economy's development affects society's employment structure, promotes non-agricultural employment, and drives the growth of farmers’ income [8]. On the other hand, considering the existence of the digital divide, although the internet can promote wage increases, it still widens the income gap between urban and rural areas. Thus, the relationship between digital economy development and the urban-rural income gap shows an “inverted U-shape” trend [9]. Even so, the application of internet technology can effectively address the issue of information asymmetry between urban and rural areas. Additionally, through the development of rural inclusive finance and the construction of network infrastructure, the “inverted U-shape” relationship can be significantly adjusted [10].
2.2. Indirect effects of the digital economy on farmers’ income growth- logistics development level
Current research on the relationship between the digital economy and logistics development mainly focuses on two aspects.
One part is a mutually reinforcing relationship between the digital economy and logistics development. Shu studied 21 provinces in China and found that the coupling and coordination of the rural logistics and digital economy subsystems are consistent in direction, with mutual feedback and influence [11]. Nong also believes that the development of rural logistics can significantly drive the expansion of the rural digital economy, showing a significant positive relationship between the two. The orderly development of the rural economy can promote the construction of rural logistics, further stimulating stable and sustained growth in the rural economy [12]. Zhang and Li both proposed that by enhancing the transmission function of rural logistics information, increasing rural employment channels, adjusting the agricultural structure, and reducing agrarian market costs, the issues of farmers’ difficulties in selling and buying can be effectively resolved, promoting agricultural efficiency, farmers’ income growth, and consumption level improvement, thus achieving the goal of rural revitalization [13,14]. Wang suggested that by leveraging digital infrastructure, the various links in the circulation of agricultural products can achieve platform-based connectivity, enhancing circulation convenience [15]. Hou and Li also emphasized that the digital economy's new agricultural product circulation model can broaden agricultural products' circulation field as much as possible [16,17]. Dai, Liu, and Shan also pointed out that the development of the digital economy can significantly reduce the circulation links of agricultural products and improve the circulation efficiency of agricultural products [18-20].
The other part is the moderating role of logistics in the digital economy and other economic phenomena. In the digital economy era, the application of technologies such as social networks and data analysis provides practical support and guidance for the construction of modern circulation systems, driving the transformation of modern logistics distribution systems, improving circulation efficiency, and reducing logistics costs, thereby enhancing the income-increasing effect of the digital economy on rural residents. First, by optimizing logistics and distribution service models, it addresses the “first mile” of agricultural products entering cities and the “last mile” of consumer goods reaching rural areas, strengthening the connection between production and sales, solidifying the foundation for the development of the rural digital economy, further improving the agricultural product market network, enhancing the resilience of the agricultural product supply chain, and providing vital support for ensuring farmers’ income growth [21]. Second, the development of rural logistics helps increase the influence of agricultural products in the digital economy market, promotes industry integration, aids farmers in better integrating into larger markets, effectively addresses the issue of unsold agricultural products, empowers farmers to increase efficiency, boosts farmers’ production enthusiasm, enhances farmers’ income, and unlocks the development potential of rural areas [22]. Finally, through the high-quality development of rural logistics services, the reduction of commodity circulation links, the improvement of logistics efficiency, and the reduction of agricultural product spoilage rate can help to enhance the role of the digital economy in improving product yields, stabilizing the price of agricultural products, and guiding farmers to increase their income. Therefore, promoting the development of logistics can give full play to the digital economy's positive role in promoting rural residents' consumption [23].
Based on the above analysis, hypothesis 1 and 2 are proposed:
Hypothesis 1: The development of the digital economy can positively promote rural residents' income.
Hypothesis 2: Logistics plays a positive moderating role in the digital economy's promotion of farmers’ income growth.
3.1. Model construction
To test Hypothesis 1, this paper empirically analyzes the impact of the digital economy on farmers’ income growth and its mechanisms, then, establishes the baseline econometric model (1):
ln\(\:{Inc}_{i,t}={\alpha\:}_{0}+{\alpha\:}_{1}{Dig}_{i,t}+{\alpha\:}_{2}{Z}_{i,t}+{\gamma\:}_{i}+{\mu\:}_{t}+{\epsilon\:}_{i,t}\) (1)
In Eq. (1), \(\:i\) represents different regions, \(\:t\) denotes the year, \(\:{\alpha\:}_{0}\) is the constant term, \(\:{\alpha\:}_{1}\)and \(\:{\alpha\:}_{2\:}\)are the parameters to be estimated, reflecting the effects of the explanatory variables on the dependent variable. \(\:\gamma\:\), \(\:\mu\:\) correspond to region and year-fixed effects, respectively, while \(\:\epsilon\:\) represents the random disturbance term. \(\:Inc\) is the explained variable, representing rural residents' income, \(\:Dig\) is the core explanatory variable, representing the level of digital economy development, and \(\:Z\) includes the control variables selected for this study, such as crop planting area, rural investment level, total agricultural output, and urbanization level.
To further explore the research mechanism of the digital economy's impact on farmers’ income growth and verify the role of logistics development in this process as stated in Hypothesis 2, the moderating effect model (2) is established:
ln\(\:{Inc}_{i,t}={\beta\:}_{0}+{\beta\:}_{1}{Dig}_{i,t}+{{\beta\:}_{2}Log}_{i,t}+{{\beta\:}_{3}Diglog}_{i,t}+{\beta\:}_{4}{Z}_{i,t}+{\gamma\:}_{i}+{\mu\:}_{t}+{\epsilon\:}_{i,t}\) (2)
In Eq. (2), β are the parameters to be estimated, Log represents the level of logistics development, and Diglog is the interaction term between the digital economy and logistics development. The meanings of the other variables are consistent with those in the model (1).
3.2. Variable setting
3.2.1. Explained variable
This study examines the impact of digital economy development and logistics development level on the income of rural residents. However, around 2013, the National Bureau of Statistics used two different statistical measures, namely rural per capita net income and rural per capita disposable income, which are not entirely identical. Therefore, to ensure consistency in statistical measures, this paper use rural per capita disposable income from local statistical yearbooks after 2013 to measure farmers’ income. Additionally, the data is processed in natural logarithm.
3.2.2. Core explanatory variable
As a comprehensive concept with broad and complex connotations, the digital economy cannot be measured by a single indicator [24]. Currently, there is no unified standard for measuring the digital economy among government agencies and academia. Therefore, based on the latest related documents released by the state and research by experts and scholars, this paper constructs an evaluation index system for the level of digital economy development, excluding parts directly related to residents' income. The final system consists of three primary evaluation indicators (digital infrastructure, digital service labor, and digital financial development), five secondary indicators, and five tertiary indicators. For a detailed evaluation system of the comprehensive development level of the digital economy, please refer to Table 1 [25, 26]. All indicators are derived and calculated from the original “China City Statistical Yearbook” data. In addition, the index for the level of digital financial development in this paper is measured using the China Digital Inclusive Finance Index compiled by Guo [27]. To ensure comparability and avoid discrepancies due to differences in dimensions among the five indicators, the data was standardized and reduced through principal component analysis, resulting in the comprehensive digital economy development index, Dig.
| Primary Indicator | Secondary Indicator | Tertiary Indicator | Definition |
|---|---|---|---|
| Digital Infrastructure | Internet Penetration Rate | Number of broadband users per 100 people | The proportion of households with broadband access, used as a proxy variable for internet penetration and popularity |
| Mobile Internet Users | Number of mobile phone users per 100people | The proportion of mobile phone users with active mobile internet subscriptions, used to measure the size of the mobile internet population | |
| Related Internet Industries | Average number of transactions per capita in retail industries | The average number of transactions per person in retail industries | |
| Digital Service Labor | Internet-related Practitioners | Number of practitioners in computing-related and internet-related fields | The number of practitioners in computing-related and internet-related fields |
| Digital Financial Development | Digital Inclusive Finance Development | China Digital Inclusive Finance lndex | Evaluates the extent and feasibility of digital financial development in China |
3.2.3. Moderating variable
Currently, the measurement indicators for the level of logistics development generally include inputs in logistics human resources, infrastructure construction levels, and transportation conditions. However, several studies have found that these indicators have serious multicollinearity issues. To avoid affecting the accuracy of model estimation, this paper adopts the freight volume of each city as the indicator to measure the level of logistics development in different regions, drawing on existing methods [28].
3.2.4. Control variables
Based on the relevant literatures, this study selects crop planting area, rural investment level, total agricultural output, and urbanization level as control variables to minimize biases caused by omitted variables [29–31]. The crop planting area is measured using the sown area index from statistical yearbooks; the rural investment level is measured by rural fixed asset investment; the total agricultural output is measured by the natural logarithm of the annual increase in agricultural output; and the urbanization level is measured by the proportion of the urban population to the total urban and rural population [32]. The relevant data is sourced from the National Bureau of Statistics website and local government statistical yearbooks. The data collection timeframe is from 2013 to 2021, covering five provinces and two municipalities along the eastern coast of China (Shandong, Jiangsu, Zhejiang, Fujian, Guangdong, Shanghai, and Tianjin). The specific variables are shown in Table 2.
| Variable Type | Variable Symbol | Indicator Name | Measurement Method | Data Source |
|---|---|---|---|---|
| Explained Variable | lnInc | Income of Rural Residents | The natural logarithm of per capita disposable income of rural residents | China Provincial Statistical Yearbooks |
| Core Explanatory Variable | Dig | Digital Economy Development Level | Calculated by principal component analysis | China Urban Statistical Yearbooks, China Digital Inclusive Finance Index |
| Moderating Variable | Log | Logistics Development Level | The natural logarithm of rural freight volume | China Provincial Statistical Yearbooks |
| lnK | Crop Planting Area | The natural logarithm of crop planting area | China Provincial Statistical Yearbooks | |
| lnT | Rural Investment level | The natural logarithm of rural fixed asset investment amount | China Provincial Statistical Yearbooks | |
| Control Variables | lnP | Total Agricultural Output | The natural logarithm of the value of total agricultural output | China Provincial Statistical Yearbooks |
| U | Urbanization Level | The ratio of the urban population to the total population in urban and rural areas | China Local Government Statistical Yearbooks |
3.3. Data sources
Excluding cities with missing data and those with the urbanization rate of 100% (such as Shenzhen), this paper selects data from 71 cities in the eastern coastal region, including cities from the five provinces of Shandong, Jiangsu, Zhejiang, Fujian, and Guangdong, as well as Tianjin and Shanghai, covering the period from 2013 to 2021(Fig. 1). The original data were sourced from the statistical yearbooks of each province, the “China Statistical Yearbook”, the “China Urban Statistical Yearbook”, and various other public sources. After processing, 639 samples from 71 cities over the nine-year period were obtained for this study.
By analyzing data obtained from the “China Urban Statistical Yearbook” and using principal component analysis, this study determines the weights for the following indicators: the number of broadband internet access users per 100 people (Hlw), the number of mobile phone users per 100 people (Mbp), the total per capita telecommunications business volume (Pdx), the proportion of employees in the computer services and software industry (Cpr), and the China Digital Inclusive Finance Index (Szp) [33]. The results show that the KMO value (0.883) and Bartlett's test (significant at the 1% level) both passed the tests, indicating that this method is appropriate. The scores of the principal components are calculated as follows:
In Eq. (3), \(\:{F}_{i}\) and \(\:{F}_{in\:}\)represent the composite index of the i-th city and the i-th city, respectively. \(\:{w}_{ij}\) denotes the weight of the j-th factor for the i-th city, and \(\:{X}_{im}\) indicates the m-th eigenvalue for the i-th city. λ represents the eigenvalue, and p refers to the coefficients for each variable in the component matrix, forming an n×m matrix. α represents the percentage of variance for the t-th principal component as part of the cumulative variance percentage. Then, the overall level of digital economy development (\(\:\text{D}\text{i}\text{g}\)) is calculated for 69 cities and 2 municipalities. Based on the theoretical analysis and model construction described above, the data analysis is conducted using Stata 17.0 software. For specific variable statistical descriptions, please refer to Table 3.
| Variable Symbol | Indicator Name | Observations | Mean | Std. Dev. | Min | Max |
|---|---|---|---|---|---|---|
| lnInc | Rural Per Capita Income | 639 | 9.826 | 0.351 | 9.085 | 10.68 |
| Dig | Digital Economy Development Level | 639 | 1.011 | 0.501 | -0.565 | 4.186 |
| Log | Rural Logistics Development Level | 639 | 9.576 | 0.749 | 7.723 | 12.09 |
| lnL | Sown Area | 639 | 5.575 | 1.106 | 1.585 | 9.988 |
| lnT | Rural Investment Level | 639 | 7.695 | 0.731 | 5.536 | 12.87 |
| U | Urbanization Level | 639 | 0.642 | 0.128 | 0.383 | 0.952 |
| lnInc | Rural Product Value | 639 | 2.268 | 0.358 | 0.861 | 2.912 |
According to Table 3, the logarithmic average income of rural residents in the eastern coastal region is 9.826 with a standard deviation of 0.351, indicating that the income distribution among rural residents in this region is relatively concentrated, with relatively small income disparities. Meanwhile, the average value of digital economy development is 1.011 with a standard deviation of 0.501, suggesting that there are still significant differences in the level of digital economy development across regions in China. The level of rural logistics development is measured by the freight volume of each city, with the data logarithmically transformed. The average and standard deviation of logistics in the eastern coastal region are 9.576 and 0.749, respectively, indicating that the overall logistics level in the region is relatively high but still has considerable room for improvement and development potential. According to the analysis of data in Table 4, each explanatory variable and control variable has a significant positive or negative impact on the income of rural residents. Additionally, the correlations between variables are low, all less than 0.70, and the Variance Inflation Factor (VIF) values are below 2, indicating that there is no severe multicollinearity problem among the variables.
| lnInc | Dig | Log | lnL | lnT | U | lnP | |
|---|---|---|---|---|---|---|---|
| lnInc | 1 | ||||||
| Dig | 0.391*** | 1 | |||||
| Log | 0.423*** | 0.274*** | 1 | ||||
| lnL | -0.183*** | 0.210*** | 0.324*** | 1 | |||
| lnT | 0.440*** | 0.189*** | 0.651*** | 0.369*** | 1 | ||
| U | 0.669*** | 0.280*** | 0.523*** | 0.020 | 0.502*** | 1 | |
| lnP | -0.099** | 0.134*** | 0.219*** | 0.517*** | 0.413*** | -0.289*** | 1 |
| Note: ***, **, and * indicate signifcant at the 1%, 5%, and 10% statistical levels, respectively. | |||||||
4.1. Baseline regression results
This paper designs two different models to verify better the direct effects of the digital economy on farmers’ income and the indirect effects through logistics development. Before conducting the regression analysis, a Hausman test was performed, which significantly rejected the null hypothesis at the 1% level. Therefore, this paper uses a two-way fixed effects model for both time and individuals for the regression analysis. The regression results are shown in Table 5, Models (1)-(5). According to Results (1)-(5), the development of the digital economy (Dig) has a significantly positive effect on farmers’ income at the 1% level, which confirms Hypothesis 1. According to Results (3)-(5), the level of logistics development (Log) has a significant positive impact on farmers’ income, indicating that logistics development promotes an increase in farmers’ income. This is likely because improved logistics development can increase the added value and trading rate of agricultural products while effectively reducing the logistics costs of agricultural products, thereby achieving an increase in farmers’ income.
| (1) | (2) | (3) | (4) | (5) | |
|---|---|---|---|---|---|
| Dig | 0.061*** (11.14) | 0.042*** (8.13) | 0.041*** (7.83) | 0.307*** (7.78) | 0.381*** (8.64) |
| Log | 0.026* (1.70) | 0.034** (2.14) | 0.055*** (3.39) | ||
| Diglog | -0.057*** (-6.66) | -0.082*** (-8.42) | |||
| lag_Diglog | 0.011*** (6.68) | ||||
| lnL | -0.131*** (-12.77) | -0.133*** (-12.76) | -0.120*** (-11.84) | -0.122*** (-12.37) | |
| lnT | 0.103*** (3.96) | 0.091*** (3.47) | 0.075*** (3.06) | 0.045** (2.04) | |
| U | 0.007*** (10.94) | 0.006*** (10.60) | 0.006*** (11.05) | 0.007*** (12.05) | |
| lnP | 0.062*** (2.84) | 0.060*** (2.78) | 0.048** (2.28) | 0.055*** (2.71) | |
| Constant Term | 4.274*** (747.07) | 4.074*** (73.65) | 4.042*** (69.92) | 4.023*** (70.12) | 4.028*** (69.41) |
| Individual Fixed Effects | Yes | Yes | Yes | Yes | Yes |
| Time Fixed Effects | Yes | Yes | Yes | Yes | Yes |
| N | 639 | 639 | 639 | 639 | 568 |
| R2 | 0.153 | 0.615 | 0.617 | 0.643 | 0.655 |
| F | 124.0 | 166.0 | 141.2 | 156.1 | 120.3 |
| Note:The values in parentheses are standard errors; ***, **, and * indicate signifcant at the 1%, 5%, and 10% statistical levels, respectively. Tables5, 6, 7, 8, 9, 10, 11, 12, 13 are the same. | |||||
In addition, in Result (4), the coefficient of the interaction term Diglog is significantly negative. In Result (5), after adding the lagged by one period interaction term lag_Diglog, the coefficient of Diglog remains significantly negative, while the coefficient of lag_Diglog is significantly positive. This result indicates that the impact of the digital economy on the level of logistics development is delayed, which in turn leads to a lagged effect of the digital economy on increasing farmers’ income through the indirect influence of logistics development. In other words, the indirect effect of the digital economy on farmers’ income has a certain degree of lag. Based on the previous analysis, although the development of the digital economy and the improvement of logistics both have a positive effect on farmers’ income when acting independently, the initial coefficient of Diglog is significantly negative. This suggests that the initial investment in digital transformation may be relatively high, leading to significant investment pressure. Moreover, the lack of unified standards and regulations for digital construction makes it difficult to fully leverage the utility of logistics. However, the significantly positive coefficient of lag_Diglog in the lagged by one period suggests that the digital economy begins to have a positive impact on logistics development with a lag, indicating that logistics development can positively moderate the relationship between the digital economy and rural household consumption, thus verifying Hypothesis 2. A possible explanation is that in the early stages of digital economy development, increased investment in digital infrastructure, along with the emergence of new transportation infrastructure and highways, has increased the number of service jobs and wage levels, thereby broadening employment opportunities for rural residents. However, in the short term, some farmers may lack the necessary vocational skills, making it difficult for them to adapt to new business environments, leading to an initial period of “chaos” and preventing the full realization of the digital economy's potential to promote logistics development. As farmers become more skilled, the improved transportation conditions and rural infrastructure resulting from the digital economy gradually exert a positive influence on logistics development, thereby contributing to increased income for farmers.
From the regression results, it can be seen that among the control variables, the area of crop planting (lnL), rural investment level (lnT), urbanization level (U), and total agricultural output (lnP) significantly impact farmers’ income in the 71 provinces and cities of the eastern economic region. Specifically, the coefficient of crop planting area (lnL) is significantly negative, indicating that crop planting area has a negative effect on farmers’ income, which passes the significance test. This may be because farmers’ income has multiple sources; for example, many farmers no longer rely solely on agricultural labor but engage in business activities, work outside, or rent out property, thereby increasing household income. Households engaged solely in agricultural production tend to have lower income, which can even significantly reduce the income level of agricultural workers. The higher the rural investment level (lnT), the higher the farmers’ income; there is a positive relationship between urbanization level (U) and farmers’ income, suggesting that improving the level of urbanization has a positive impact on increasing farmers’ income.
4.2. Robustness test
The above conclusions indicate that the improvement of the digital economy and logistics development levels in the China’s eastern economic provinces and cities can promote the growth of farmers’ income. Therefore, to further verify the robustness and validity of this conclusion, tests will be conducted from the following four aspects.
4.2.1. Sample by administrative level of city
Considering that Tianjin and Shanghai are municipalities directly under the central government, Hangzhou, Ningbo, Xiamen, Qingdao, Jinan, Nanjing, and Guangzhou are all sub-provincial cities, there is a significant difference in size between these 9 cities and other cities. Therefore, the impact of digital economy development on farmers’ income growth may also be difference. Based on this, the paper conducts regressions twice: once using a sample excluding the 2 municipalities, and once excluding the 2 municipalities and the 7 sub-provincial cities (Fig. 2). The results are shown in Table 6.
| (1) Excluding Municipalities | (2) Excluding Municipalities and Sub-Provincial Cities | |||||
|---|---|---|---|---|---|---|
| Dig | 0.047*** (11.19) | 0.047*** (10.92) | 0.220*** (5.29) | 0.046*** (11.44) | 0.046*** (11.60) | 0.051 (0.94) |
| Log | 0.047*** (3.06) | 0.065*** (4.02) | 0.095*** (6.18) | 0.105*** (7.63) | ||
| Diglog | -0.045*** (-4.83) | -0.006 (-0.45) | ||||
| lag_Diglog | 0.009*** (6.67) | 0.007*** (6.25) | ||||
| lnL | -0.141*** (-13.46) | -0.147*** (-13.80) | -0.137*** (-12.96) | -0.129*** (-11.53) | -0.142*** (-12.97) | -0.142*** (-13.37) |
| lnT | 0.078*** (3.52) | 0.053** (2.55) | 0.025 (1.25) | 0.083*** (3.68) | 0.033* (1.70) | 0.012 (0.62) |
| U | 0.007*** (12.73) | 0.007*** (12.21) | 0.007*** (12.77) | 0.008*** (13.02) | 0.008*** (12.91) | 0.008*** (13.23) |
| lnP | 0.087*** (4.11) | 0.086*** (4.15) | 0.079*** (3.87) | 0.067*** (2.92) | 0.070*** (3.19) | 0.069*** (3.27) |
| Constant Term | 4.110*** (74.20) | 4.054*** (68.69) | 4.052*** (63.94) | 4.039*** (72.57) | 3.899*** (68.08) | 3.944*** (68.78) |
| Individual Fixed Effects | Yes | Yes | Yes | Yes | Yes | Yes |
| Time Fixed Effects | Yes | Yes | Yes | Yes | Yes | Yes |
| N | 621 | 621 | 552 | 558 | 558 | 496 |
| R2 | 0.643 | 0.649 | 0.668 | 0.671 | 0.695 | 0.708 |
| F | 215.3 | 180.0 | 129.5 | 250.5 | 228.4 | 164.0 |
It is worth noting that in the three samples (71 cities, 69 cities excluding municipalities, and 62 cities excluding 2 municipalities and 7 sub-provincial cities), the coefficient of lag_Diglog, which lagged by one period, gradually decreases as the city size shrinks. In samples that include larger cities, the indirect impact of the digital economy on rural residents’ income is more significant. In summary, the impact of the digital economy in county areas on rural residents’ income in China exhibits a certain degree of regional heterogeneity. The empirical test results also indicate that farmers’ income is influenced by factors such as crop planting area and urbanization level.
4.2.2. Adding different control variables
As shown in Table 7, the model sequentially includes the highway mileage per 100 people (lnH), the number of internet broadband access users (Hlw), and the proportion of employees in computer services and software industries (Cpr) as control variables. A two-way fixed effects model is used to estimate the impact of digital economy and logistics development levels on rural residents' income in the eastern economic region. The regression coefficients are robust and similar to the benchmark results. According to the results in Table 7, highway mileage (lnH) has a negative effect on farmers’ income and is significant at the 1% level, while the number of internet broadband access users (Hlw) does not significantly affect rural residents' income. Additionally, the proportion of employees in computer services and software industries (Cpr) has a negative effect on farmers’ income.
| (1) | (2) | (3) | (4) | |
|---|---|---|---|---|
| Dig | 0.506*** (9.62) | 0.523*** (9.71) | 0.487*** (7.97) | 0.526*** (9.82) |
| Log | 0.154*** (11.17) | 0.172*** (11.80) | 0.154*** (11.20) | 0.156*** (11.18) |
| Diglog | -0.111*** (-9.51) | -0.115*** (-9.67) | -0.110*** (-9.44) | -0.110*** (-9.47) |
| lag_Diglog | 0.014*** (6.04) | 0.014*** (6.63) | 0.014*** (5.99) | 0.013*** (5.75) |
| lnH | -0.204*** (-10.32) | |||
| Hlw | 0.003 (0.65) | |||
| Cpr | -1.392** (-2.02) | |||
| Constant Term | 3.652*** (63.63) | 4.401*** (47.93) | 3.545*** (20.63) | 3.758*** (48.42) |
| Individual Fixed Effects | Yes | Yes | Yes | Yes |
| Time Fixed Effects | Yes | Yes | Yes | Yes |
| N | 568 | 568 | 568 | 568 |
| R2 | 0.322 | 0.445 | 0.323 | 0.327 |
| F | 80.04 | 83.75 | 63.78 | 62.57 |
4.2.3. Changing the explained variable
This paper replaces the original explained variable with the indicator of rural residents’ per capita consumption expenditure (lnExp) to further verify the robustness of the results. This indicator is commonly used to assess individual spending in rural areas, which is often influenced by personal income. Therefore, the level of farmers’ spending can reflect their income level to some extent. Given this, an OLS model is used to regress the relationship between the digital economy and rural residents' per capita consumption expenditure. The results in Table 8 show that the coefficients of the core explanatory variables are significant and consistent in sign with the basic regression results, indicating that the digital economy in the eastern economic region positively and significantly promotes farmers’ consumption expenditure. Among the variables, crop planting area and agricultural output value do not significantly affect farmers’ consumption expenditure, while rural fixed asset investment level and urbanization level have a positive impact on farmers’ consumption expenditure.
| (1) | (2) | (3) | (4) | (5) | |
|---|---|---|---|---|---|
| Dig | 0.078*** (7.37) | 0.077*** (5.52) | 0.078*** (5.57) | 0.306*** (4.55) | 0.429*** (4.42) |
| Log | -0.012 (-0.57) | -0.004 (-0.21) | 0.013 (0.64) | ||
| Diglog | -0.049*** (-3.23) | -0.087*** (-3.98) | |||
| lag_Diglog | 0.014*** (4.55) | ||||
| lnL | -0.121*** (-10.71) | -0.120*** (-10.24) | -0.109*** (-9.03) | -0.102*** (-8.55) | |
| lnT | 0.086*** (3.39) | 0.092*** (3.54) | 0.078*** (3.31) | 0.067*** (2.67) | |
| U | 0.003*** (2.62) | 0.003*** (2.80) | 0.003*** (2.80) | 0.003** (2.30) | |
| lnP | -0.059* (-1.77) | -0.058* (-1.75) | -0.068** (-2.04) | -0.081** (-2.25) | |
| Constant Term | 4.117*** (594.64) | 4.438*** (42.53) | 4.453*** (38.81) | 4.436*** (38.24) | 4.424*** (35.54) |
| Individual Fixed Effects | Yes | Yes | Yes | Yes | Yes |
| Time Fixed Effects | Yes | Yes | Yes | Yes | Yes |
| N | 639 | 639 | 639 | 639 | 568 |
| \(\:{R}^{2}\) | 0.165 | 0.352 | 0.352 | 0.365 | 0.362 |
| F | 54.28 | 71.37 | 70.02 | 72.88 | 52.89 |
4.2.4. Endogeneity test
The core explanatory variable, the digital economy, is composed of multiple indicators. Although the current model controls for several variables that may affect farmers’ income, such as digital economy, logistics development, and urbanization rate, there are still many potential confounding factors that could lead to endogeneity bias [34–36]. Additionally, the development of the rural economy could promote the rapid development of the digital economy in that region, leading to endogeneity issues due to reverse causality. To address the endogeneity problem caused by omitted variables, this paper selects two instrumental variables: the number of internet broadband access users and the total volume of postal and telecommunication services, and performs IV-2SLS estimation to test for endogeneity issues. The number of internet broadband access users is measured by the number of long-term broadband users per 100 people [37, 38]. Effective instrumental variables must satisfy two conditions: Firstly, relevance—network effects of internet information and communication infrastructure play a crucial role in digital economy development, and broadband access is a fundamental support for this development. Therefore, the selected instrumental variable is highly correlated with the digital economy. Secondly, exogeneity—the number of internet broadband access users does not directly affect farmers’ income, thus meeting the requirement of exclusivity. Table 9, Column (1) reports the 2SLS regression results using the number of internet broadband access users as the instrumental variable. The p-value for the instrument is less than 0.01, indicating that the instrument passes the weak instrument test. Additionally, the F-statistic for the weak identification test is 145, exceeding the threshold of 10, suggesting there is no issue with weak instruments. This indicates that using the number of internet broadband access users as the instrument is appropriate and effective.
This paper references the selection of instrumental variables from Zhao, Liu and others. The total volume of postal and telecommunication services in various regions is used as an instrumental variable [26, 39]. On one hand, generally, higher levels of improvement in traditional communication technology are correlated with higher levels of digital economy development. On the other hand, the total volume of postal and telecommunication services does not have a direct correlation with current farmers’ income, thus satisfying the exogeneity condition. Therefore, the total volume of postal and telecommunication services is considered as an instrumental variable. After taking the logarithm and using 2SLS for regression, the results are shown in Table 9, Column (2). This result indicates that at the 1% confidence level, the null hypothesis is rejected, confirming the adequacy of the instrument. Additionally, the F-value for the weak identification test is 13, which is greater than 10, allowing us to reject the null hypothesis of weak instruments based on empirical rules. This means that using the total volume of postal and telecommunication services as an instrumental variable is reasonable and effective. Moreover, the regression results with the instrumental variable do not show substantial changes compared to the benchmark regression results, indicating the robustness of the benchmark results.
| (1) | (2) | |
|---|---|---|
| Dig | 0.382*** (3.80) | 1.856*** (3.67) |
| Log | 0.055*** (3.86) | 0.105*** (3.63) |
| Diglog | -0.083*** (-3.75) | -0.405*** (-3.66) |
| lag_Diglog | 0.011*** (5.59) | 0.028*** (4.35) |
| lnL | -0.122*** (-12.07) | -0.067*** (-2.72) |
| lnT | 0.045** (2.25) | 0.033 (0.96) |
| U | 0.007*** (13.10) | 0.005*** (5.82) |
| lnP | 0.055*** (3.13) | -0.029 (-0.73) |
| Constant Term | 4.028*** (72.09) | 3.856*** (35.26) |
| Individual Fixed Effects | Yes | Yes |
| Time Fixed Effects | Yes | Yes |
| N | 568 | 568 |
| R2 | 0.650 | 0.019 |
| F | 145 | 13 |
In the context of “Rural revitalization via digital commerce”, enabling rural economic development through the digital economy has become an important task. This paper explores the impact of digital economy development on farmers’ income in the eastern economic region of China from three perspectives: basic transmission mechanisms, nonlinear transmission mechanisms, and heterogeneous transmission mechanisms. Based on panel data from 71 counties in the eastern economic region from 2013 to 2021, the study constructs a digital economy index to measure the level of digital economy development. It uses a two-way fixed effects model and moderation effects for empirical testing. The research findings are as follows: (1) In the eastern economic region of China, the development of the digital economy significantly promotes the increase in rural residents’ income levels and has become a key driver of its sustained growth, the results remain robust even after considering robustness and endogeneity tests. (2) When adding the level of logistics development as a moderating variable for the impact of the digital economy on farmers’ income, the lagged by one period digital economy shows a positive effect on logistics development, which in turn drives the growth of farmers’ income. (3) The impact of the digital economy on farmers’ income in the eastern economic region of China exhibits significant regional heterogeneity, with more pronounced and robust effects in larger city clusters. Based on these conclusions and considering the current empirical data on the digital economy, logistics development, and farmers’ income, the paper proposes the following recommendations:
Firstly, the construction of rural infrastructure will be strengthened to promote coordinated development of the regional digital economy. The contribution of the digital economy to farmers’ income in the eastern region is significant but still has substantial room for growth. To enhance the role of the digital economy in increasing farmers’ income, the following measures are recommended: (1) Strengthen the construction of information infrastructure in rural areas to provide a good service environment for farmers to use digital technology. Taking “Broadband China” as an opportunity, upgrade traditional infrastructure, popularize intelligent mobile terminals, advance regional information infrastructure, and build new infrastructure such as 5G, IPv6, and big data centers. Achieving full coverage of rural networks will help drive further development of the digital economy in rural areas. (2) Promoting the penetration and diffusion of digital technology in rural areas and implementing the “Rural revitalization via digital commerce” project. Relying on the driving role of the digital economy, improve relevant facilities in rural areas, strengthen the agricultural product supply chain, enhance the agricultural product value chain, and empower the agricultural product ecological chain. Vigorously support the construction of logistics infrastructure for the “first mile” in rural areas to facilitate the movement of agricultural products from villages to cities. This will better leverage the “digital dividend” to promote farmers’ income growth and enhance the vitality of the rural economy.
Secondly, promoting the integrated development of the digital economy and logistics to create a new engine for driving farmers’ income growth. The development of logistics and the digital economy complement each other; through the moderating role of logistics development, the impact of the digital economy on farmers’ income growth becomes more significant. Therefore, encouraging the orderly development of logistics helps enhance the income-increasing effect of the digital economy. By accelerating the digitization of assets, strengthening the backbone network and cold chain logistics for agricultural products, standardizing circulation technology and system equipment, and improving product traceability and comprehensive informatization, an integrated and modern regional layout can be promoted. This will reduce logistics costs, increase circulation efficiency, create job opportunities, and open new channels for farmers’ income growth.
Thirdly, implementing differentiated policies for the digital economy in different regions to narrow the “digital divide”. The impact of the digital economy on farmers’ income in the eastern economic region of China shows significant variability. Therefore, when formulating policies for the development of the digital economy, adjustments should be made based on specific conditions and actual needs. For the more developed eastern economic regions, fully leverage the role of “digital empowerment”. In regions with well-developed logistics and information infrastructure, focus on enhancing digital skills, improving farmers’ digital literacy, strengthening data security, raising awareness of data protection among farmers, and cultivating modern “new farmers” to establish digital “benchmark” areas. Conversely, for regions that are relatively late in developing digital infrastructure, they can learn from the successful experiences and practices of regions that have achieved significant progress in digital economy development. By combining their own strengths and weaknesses, and utilizing digital technology, these regions should explore and develop distinctive industries, promote the development of the digital economy tailored to local conditions, and build digital empowerment platforms for industries to create new engines for farmers’ income growth.
• Author Contributions: Conceptualization, X.M. and Z.L.; methodology, M.N.; software, X.M.; validation, X.M., Z.L. and A.X.; formal analysis, X.M.; investigation, X.M. and A.X.; resources, X.M.; data curation, X.M.; writing—original draft preparation, X.M.; writing—review and editing, X.M. and M.N.; visualization, X.M.; supervision, Z.L.; project administration, Z.L.; funding acquisition, A.X. All authors have read and agreed to the published version of the manuscript.
• Funding: The China National Social Science Foundation (21BGL148), Preliminary Research Projects for Fujian Province's Tenth Five-Year Plan.
• Availability of data and materials: The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.
Acknowledgement
I am grateful to my supervisors for their guidance, to my colleagues for their contributions, and to[The China National Social Science Foundation and Fujian Development and Reform Commission] for their support. I also appreciate the reviewers' feedback.
- Su,K.Q.; Wang,R.X.; Han,Z.; Chen,J.C. &Deng,X.Z. Examining the path of urban–rural industry convergence and its impacts on farmers’ income growth: evidence from Xinjiang Uyghur Autonomous Region, China. Annals of Operations Research.1,5762. https://doi.org/10.1007/s10479-023-05762-z(2023).
- Nanak, K. The Relative Deprivation Curve and Its Applications. Journal of Business & Economic Statistics. 2(4),384-394(1984).
- Bauer, J.M. The Internet and income inequality: Socioeconomic challenges in a hyperconnected society. Telecommunications Policy. 42(4),333-343. https://doi.org/10.1016/j.telpol.2017.05.009(2018).
- Lechman, E &Popowska, M. Harnessing digital technologies for poverty reduction. Evidence for low-income and lower-middle income countries. Telecommunications Policy. 46(6),102313. https://doi.org/10.1016/j.telpol.2022.102313(2022).
- Jiang,Q.; Li,J.Z.; Si,H.Y. &Su,Y.Y. The Impact of the Digital Economy on Agricultural Green Development: Evidence from China. Agriculture-Basel. 12(8),12081107. https://doi.org/10.3390/agriculture12081107(2022).
- Li, H. &Jiang, H.M. Effect of the Digital Economy on Farmers Household Income: County-Level Panel Data for Jilin Province, China. Sustainability. 15(5),4450. https://doi.org/10.3390/su15054450(2023).
- Yang,C.L.;Zhou,D.J.;Zou,M.L.;Yang,X.L.;Lai,Q.F. &Liu, F.X. The impact of social capital on rural residents income and its mechanism analysis-Based on the intermediary effect test of nonagricultural employment. Heliyon. 10(14), e34228. https://doi.org/10.1016/j.heliyon.2024.e34228(2023).
- Ding,C.H.; Liu,C.; Zheng,C.Y. & Li,F. Digital Economy, Technological Innovation and High-Quality Economic Development: Based on Spatial Effect and Mediation Effect. Sustainability. 14(1),216. https://doi.org/10.3390/su14010216(2021).
- Wang, S. J. &Xue, Z.C. Has the development of the digital economy improved green total factor productivity in China? -A study based on data at the provincial level in China. Frontiers in Environmental Science. 11,1073997. https:// doi.org/10.3389/fenvs.2023.1073997(2023).
- Jiang, Y.Y.;Huang, L.;Liu, Y. &Wang, S. Impact of Digital Development and Technology Innovation on the Marine Fishery Economy Quality. Fishes. 9(7),266. https://doi.org/10.3390/fishes9070266(2024).
- Shu,H.;Zhan,L.Z.;Lin, X.W. &Zhou, X.D. Coordination measure for coupling system of digital economy and rural logistics: An evidence from China. Plos One. 18(4), e0281271. https://doi.org/10.1371/journal.pone.0281271(2023).
- Nong,W.X.;Wen, J. &He,J.Y. Spatial-Temporal Variations and Driving Factors of the Coupling and Coordination Level of the Digital Economy and Sustainable Rural Development: A Case Study of China. Agriculture. 14(6), 849. https://doi.org/10.3390/agriculture14060849(2024).
- Zhang, H.F.; Ding, X.J. & Liu, Y. The Impact of Low-Carbon Pilot Cities on the Development of Digital Economy: Empirical Evidence from 284 Cities in China. Sustainability. 15(13), 10392. https://doi.org/10.3390/su151310392(2023).
- Li,J. &Wang, Q.M. Impact of the Digital Economy on the Carbon Emissions of China’s Logistics Industry. Sustainability. 14(14),8641. https://doi.org/10.3390/su14148641(2022).
- Wang,X.M.; Huang,Y.J.; Zhao,Y.Y. &Feng, J.X. Digital Revolution and Employment Choice of Rural Labor Force: Evidence from the Perspective of Digital Skills. Agriculture-Basel. 13(6),1260. https://doi.org/10.3390/agriculture13061260(2023).
- Hou, L.C.;Tian, C.S.;Xiang, R.B.;Wang, C.C. &Gai, M. Research on the Impact Mechanism and Spatial Spillover Effect of Digital Economy on Rural Revitalization: An Empirical Study Based on China’s Provinces. Sustainability. 15(15),11607. https://doi.org/10.3390/su151511607(2023).
- Li,Y.L.; Wang,M.X.; Liao,G.K. &Wang,J.X. Spatial Spillover Effect and Threshold Effect of Digital Financial Inclusion on Farmers’ Income Growth-Based on Provincial Data of China. Sustainability. 14(3),1838. https://doi.org/10.3390/su14031838(2022).
- Dai,D.B.;Fan,Y.D. &Wang, G.Y. Digital Economy, R. &D Investment, and Regional Green Innovation-Analysis Based on Provincial Panel Data in China. Sustainability. 14(11),6508. https://doi.org/10.3390/su14116508(2022).
- Liu,Q.W.;Wu,S.M.;Lei,Y.L.;Li,S.T. &Li, L. Exploring spatial characteristics of city-level CO2 emissions in China and their influencing factors from global and local perspectives, Science of The Total Environment. Sustainable cities and society. 754,142206. https://doi.org/10.1016/j.scitotenv.2020.142206(2020).
- Shan, S. & Liu, C. Z. Research on the Impact of Financial Deepening on Digital Economy Development: An Empirical Analysis from China. Sustainability. 15(14),11358. https://doi.org/10.3390/su151411358(2023).
- Liu, W.H. The digital economy and environmental pollution: New evidence based on the support of logistics development. Journal of Cleaner Production.,427,139210. https://doi.org/10.1016/j.jclepro.2023.139210(2023).
- Zhang, K. &Pang, J. Does digital finance promote entrepreneurship? Evidence from city-level and household-level data in China. Applied Economics Letters. 11,2278596. https://doi.org/10.1016/j.pacfin.2024.102316(2023).
- Zhao, Y.X. & Feng, Y. J. Research on the Development and Influence on the Real Economy of Digital Finance: The Case of China. Sustainability. 14,8227. https://doi.org/10.3390/su14148227(2022).
- Zhao,S.Q.; Peng,D.Y.; Wen,H.W. &Song,H.L.Does the Digital Economy Promote Upgrading the Industrial Structure of Chinese Cities? Sustainability. 14(16),10235. https://doi.org/10.3390/su141610235(2022).
- Cai,Y.; Liu,S.Q. &Zhang,X. The digital economy and job quality: Facilitator or inhibitor? -Evidence from micro-individuals. Heliyon. 10(4), e26536. https://doi.org/10.1016/j.heliyon.2024.e26536(2024).
- Zhao,T.; Zhang,M.Z.; Wang,C.L. &Sun, Q. Model-Free Predictive Current Control of Three-Level Grid-Connected Inverters with LCL Filters Based on Kalman Filter. IEEE Access. 11,21631-21640. https://doi.org/10.1109/ACCESS.2023.3251410(2023).
- Guo,F.; Zhang,X.F. &Lee,P.L. Editorial: Digital information for patient education, Frontiers in Public Health,11,1211285. https://doi.org/10.3389/fpubh.2023.1211285(2023).
- Li,C.L.; Wang,G.Y.; Shen,Y.Z. &Beauclair,A.A.N. The Effect of Hog Futures in Stabilizing Hog Production, Agriculture.14(3),335. https://doi.org/10.3390/agriculture14030335(2024).
- Zhong,M.; Wang,Z.T. &Ge,X. Does Cross-Border E-Commerce Promote Economic Growth? Empirical Research on China’s Pilot Zones, Sustainability. 14(17),11032. https://doi.org/10.3390/su141711032(2022).
- Tapscott D. The Digital Economy: Promise and Peril in the Age of Networked Intelligence; New York: McGraw-Hill.199; pp. 69–75．
- Luo,Y.X.; Xiong,T.R.; Meng,D.F.; Gao,A.R. &Chen,Y. Does the Integrated Development of Agriculture and Tourism Promote Farmers’ Income Growth? Evidence from Southwestern China, Agriculture-Basel. 13(9),817. https://doi.org/10.3390/agriculture13091817 (2023).
- Xu K. Digital finance, social security expenditures, and rural-urban household income poverty. Evidence based on an area and household level analysis, Finance Research Letters. 60,104845. https://doi.org/10.1016/j.frl.2023.104845(2024).
- Liu,K;Meng,C.Y.; Tan,J. &Zhang,G.L. Do smart cities promote a green economy? Evidence from a quasi-experiment of 253 cities in China, Environmental Impact Assessment Review. 99,107009. https://doi.org/10.1016/j.eiar.2022.107009 (2023).
- Zheng, P.J.; Li, Y. &Li, X.The impact of the digital economy on land transfer-out decisions among Chinese farmers: evidence from CHFS micro-data. Scientific Reports.14(1), 19684. https://doi.org/10.1038/s41598-024-70605-1 (2024).
- Chen,G.F.; Han,J. &Yuan,H.W. Urban digital economy development, enterprise innovation, and ESG performance in China, Frontiers in Environmental Science. 10,955055. https://doi.org/10.3389/fenvs.2022.955055(2022).
- Luo,D.;Liang,L.W.;Wang,Z.B.;Chen, L.K. &Zhang,F.M. Exploration of coupling effects in the Economy-Society-Environment system in urban areas: Case study of the Yangtze River Delta Urban Agglomeration.Ecological Indicators.128,107858. https://doi.org/10.1016/j.ecolind.2021.107858(2021).
- Chu,H.; Ning,Y.C. &Qie,X.T. Measurement and Spatial Correlation Analysis of the Development Level of the Digital Economy in the Yangtze River Delta Urban Agglomeration, Sustainability. 15(18),13329. https://doi.org/10.3390/su151813329(2023).
- Yang, S.M. &Jia,J.L. Digital Economy, Technological Innovation, and Environmental Quality Improvement, Sustainability.14(22),15289. https://doi.org/10.3390/su142215289(2022).
- Liu,Y;Dong,J.Y.;Ying, Y. &Jiao, H. Status and digital innovation: A middle-status conformity perspective. Technological Forecasting and Social Change.168,120781. https://doi.org/10.1016/j.techfore.2021.120781(2021).
No competing interests reported.