The role of the digital economy on the coordinated development of green agriculture and food security: evidence from China

Promoting the coordinated development of green agriculture and food security is important for global sustainable development, and digital economy is a potential path to realizing this goal. Using panel data of 30 provinces (municipalities directly under the central government and autonomous regions) in China from 2014 to 2021, this study applies the entropy weight method and the coupling coordination degree model to measure the development of the digital economy and the coordination of green agriculture and food security. Moreover, the study constructs fixed-effects and spatial spillover effect models to determine the effect of the digital economy on the coordination of green agriculture and food security. We find a positive development trend between digital economy and coordination development in China, with no evident polarization phenomenon. The digital economy can effectively promote the coordinated development of green agriculture and food security, and has positive spatial spillover effects. Our findings expand the research related to the digital economy, and contribute to the promotion of sustainable agricultural development and food security.

1 Introduction

Global economic development is facing challenges such as climate change and ecological deterioration (Mikhaylov et al., 2020; Zhang et al., 2022; Xin et al., 2023). In response, green industrial innovation to address these challenges is rapidly developing worldwide (Wang B. et al., 2023). Many countries are implementing measures to accelerate green technological innovation and promote green, green, and sustainable development. For the European Union, promoting green transformation is one of the core elements of its economic recovery plan.¹ Singapore has implemented a green development blueprint to promote sustainable living and green economic development.² Saudi Arabia launched the Green Saudi Initiative to enhance environmental protection.³ China advocates the development concept of “green water and green mountains are golden silver mountains” to promote green economic development.⁴ Agriculture is a major contributor to economic development in most developing countries, and green agricultural development is crucial for advancing the sustainable development of the global economy. Green agriculture refers to the promotion of agricultural economic development, considering resource conservation and ecological environmental protection to advance sustainable agricultural development. In addition, achieving global food security is a growing challenge at the international policy level (Campi et al., 2021). In 2009, the United Nations Food and Agriculture Organization (FAO) asserted that food security in its multiple meanings includes adequate food supply, access, stability, and use (FAO, 2009). In this context, accurately assessing and addressing the relationship between agricultural green and green development and food security is essential for advancing national economic sustainable development and food security.

With the continuous development and application of block chain, cloud computing, big data, and other contemporary science and technology (Su et al., 2020), the digital economy is an important engine for promoting high-quality economic growth (Zhang et al., 2022; Wang B. et al., 2023; Wang Q. et al., 2023; Wang Y. et al., 2023). The digital economy refers to a series of economic activities to improve production efficiency and optimize the economic structure that are specifically manifested in digital information and knowledge as the factors of production, advanced networking as an important carrier, and efficient use of information and communications technology as the core driving force (Wang B. et al., 2023; Wang Q. et al., 2023; Wang Y. et al., 2023). In recent years, the digital economy has rapidly developed around the world. The Global Digital Economy Development Index reveals that the average global digital economy value increased from 45.33 in 2013 to 57.01 in 2021.⁵ The rapidly developing digital economy promotes regional economic growth and green, sustainable development. As the largest developing country and largest agricultural producer in the world, what is the current status of the digital economy and green agricultural development in coordination with food security in China? Furthermore, what is the impact of the rapidly expanding digital economy on the coordination development of green agricultural development and food security? Are spatial spillover effects evident? Addressing these questions will help to advance the role of China's digital economy in promoting the coordination development of green agricultural development and food security and serve as a reference for other developing countries.

The contributions of this paper are as follows. First, the coordination development of green agriculture and food security are combined in the same framework. The study uses the entropy weight method and the coupling coordination degree model to measure the current status of coordination development of green agriculture and food security. Second, in the context of the rapid development of the digital economy, the study constructs a fixed-effects model to empirically analyze the impact of the digital economy on the coordinated development of green agriculture and food security, in addition to examining regional heterogeneity. Third, considering potential spatial interaction, we construct a spatial Durbin model to explore the spatial spillover effects of the digital economy on the coordinated development of green agriculture and food security in China. These approaches expand the research to provide more comprehensive insights.

The remainder of this paper is structured as follows. Section 2 reviews the related literature on the concerned issue and theoretical analysis. Section 3 introduces the estimation models variables and data sources. Section 4 presents and analyzes the baseline results. Section 5 discusses the results, and Section 6 summarizes the main conclusions and proposes related policy recommendations.

2 Literature review and theoretical analysis

2.1 Literature review

Achieving carbon emissions reduction and green transformation in agriculture is crucial for advancing global sustainable development (Liu and Ren, 2023). Previous research related to green agricultural development has primarily focused on carbon emissions (Guo and Zhang, 2023; Raihan, 2023; Rong et al., 2023), agricultural green efficiency (Deng et al., 2023; Sun et al., 2023; Shi et al., 2024), and agricultural carbon footprint (Liu Z. et al., 2023). The issue of food security is at the top of the world's agenda. Existing studies on food security have focused on quantifying food security and its influencing factors. Most scholars have quantified food security in terms of availability, accessibility, utilization, and stability (Adem et al., 2023; O'Connell et al., 2023). Regarding the influencing factors of food security, macro level studies have primarily included climate change (Hadley et al., 2023; Lee et al., 2024), agricultural trade (Aragie et al., 2023), and agricultural entrepreneurship (Kazungu and Kumburu, 2023), and micro level studies have primarily included household resources (Karnik and Peterson, 2023; Olumba et al., 2023), food price shocks (Yovo and Gnedeka, 2023), and related concerns. Previous research has established the foundation for coordinating the development of green agriculture and food security.

The digital economy is an important driver of green economic development (Li Y. et al., 2023; Wang Q. et al., 2023). Existing research on the digital economy has focused on three aspects. First, defining the concept of digital economy, which was initially defined as activities within entrepreneurial clusters (Papaioannou et al., 2009; Chijindu Iheanacho Okpalaoka, 2023). Over time, the digital economy has been given a new connotation as an innovation ecosystem (Nambisan and Baron, 2013; Chijindu Iheanacho Okpalaoka, 2023). Advancing the digital economy is a business model for digital products and services and a new form of economic development (Chijindu Iheanacho Okpalaoka, 2023; Uddin, 2023; Wang B. et al., 2023; Wang Q. et al., 2023; Wang Y. et al., 2023). Second, quantifying digital economy development. In existing research, scholars have predominantly measured the digital economy by constructing indicator evaluation systems and adopting the entropy weighting methods. Zhang and Li (2023) constructed a set of digital economy development measurement systems from the perspectives of digital industrialization and industrial digitization based on input–output data. Lyu et al. (2023) measured digital economy development in China from four dimensions, including the digital economy development carrier, digital industrialization, industrial digitization, and the digital economy development environment. Third, the economic effects of the digital economy. At the macro level, digital economy development can alleviate energy poverty (Lyu et al., 2023; Wang B. et al., 2023; Wang Y. et al., 2023), promote green economic development (Li S. et al., 2023; Wang Q. et al., 2023; Li Y. et al., 2023), and increase green total factor productivity (Deng et al., 2022; Chen et al., 2023). At the micro level, digital economy development can promote enterprises' breakthrough innovation (Liu J. et al., 2023; Wu et al., 2023) and enhance consumption (He et al., 2022).

Additionally, scholars have explored the effects of the digital economy on green agricultural development and its implications for food security. First, existing studies on the impact of the digital economy on green agricultural development primarily focus on three key aspects: (1) Reducing the intensity of agricultural carbon emissions. Jin et al. (2024) found that while the development of the digital economy can effectively lower agricultural carbon emissions, and the effect is nonlinear. Similarly, Zhao et al. (2023) discovered that agricultural digitization in China can reduce the intensity of agricultural carbon emissions by enhancing agricultural technology inputs, human capital, and the rate of urbanization. (2) Encourage the adoption of ecological agricultural technology among farmers. Yang et al. (2024) found that the digital economy can effectively encourage farmers to adopt agroecological technologies through digital production, digital marketing, and digital finance. (3) Enhancing green total factor productivity in agriculture. Lu et al. (2024) finds that rural digitization can effectively boost total factor productivity in agriculture, with the effect growing as the level of rural digitization increases. Meanwhile, Jiang et al. (2024) finds that digital finance can enhance agricultural green total factor productivity through digital rural development. Second, existing research on the digital economy on food security focuses on the following three aspects: (1) Efficiency in food production. Based on data from 600 wheat growers in rural Pakistan, Ahmad et al. (2024) utilized the stochastic frontier approach and propensity-matched score method to investigate the impact of Internet technology adoption on the technical efficiency of food production, finding that Internet use positively affects technical efficiency. Similarly, Chandio et al. (2023) found that Internet use significantly improved rice production in China. (2) Reducing food waste. Annosi et al. (2021) found that applying digital technology in the food supply chain can significantly reduce food waste. (3) Food security. Ferguson et al. (2023) found that digital agricultural technologies can ensure food security for smallholder farmers and their communities in Orissa, India, especially in the context of the COVID-19 pandemic. Meanwhile, Lee et al. (2023) found that the development of the digital economy can effectively ensure food security in China.

In summary, previous research has conducted meaningful inquiries into green agricultural development, food security, and the digital economy; however, room remains for further exploration. First, while promoting green sustainable development in agricultural production, it is equally important to guarantee national food security. Weighing the relationship between the green agricultural development and food security is worthy of further exploration. The digital economy is an important engine for contemporary green economic development, and its impact on the coordinated development of green agriculture and food security has not yet been investigated. Second, regarding spatial interaction, examining whether the impact of the digital economy on the coordinated development of green agriculture and food security has spatial spillover effects is an important pursuit.

2.2 Theoretical analysis

The digital economy is a new aspect of the economy that uses digital information and knowledge as factors of production, which can drive the coordinated development of green agriculture and food security by optimizing factor allocation, improving agricultural production methods and promoting agricultural technological innovation.

First, the digital economy can foster the coordinated development of green agriculture and food security by optimizing factor allocation. By integrating data with labor, land, and capital, the digital economy transforms the internal structure of traditional elements and enhances the integration of rural industries with the digital economy (Wu et al., 2024; Zhang and Qu, 2023). This not only boosts the contribution of traditional production factors like land and labor to agricultural output but also enhances agricultural total factor productivity and drives the transformation and upgrading of the agricultural industrial structure (Shen and Wang, 2024). Additionally, the use of big data, blockchain, and cloud computing in agricultural production can effectively address information asymmetry among factors (Liu Y. et al., 2023). This helps reduce the misallocation of agricultural resources, minimize unnecessary waste, achieve more efficient use of agricultural energy, and lower the intensity of agricultural carbon emissions.

Second, the digital economy can enable smart and precise agricultural production by enhancing production methods, thereby promoting the coordinated development of green agriculture and food security. On one hand, the advent of digital technologies like satellite remote sensing and smart agricultural machinery has made it possible to modernize agricultural production (George et al., 2024; Zhang et al., 2024). This, in turn, helps agricultural producers optimize production management (George et al., 2024) and enhance food production efficiency. On the other hand, as digital technology increasingly integrates into production, operation, management, and services in agriculture, the precision of agricultural practices continues to improve (Wang and Li, 2024). This reduces the environmental impact of fertilizers and pesticides and promotes the growth of green agriculture.

Third, the growth of the digital economy will drive technological innovation in agriculture (Lv and Chen, 2024), encourage the use of green high-tech solutions in the field (Li and Gao, 2024), and thus foster the balanced development of green agriculture and food security. On one hand, the development of the digital economy provides a network platform for disseminating agricultural green technology, breaks down technical barriers (Hao et al., 2023), fosters the exchange and collaboration of green agricultural technologies, enhances production efficiency, and supports sustainable agricultural development. On the other hand, the development of the digital economy removes time and space barriers, lowers the relative cost of technical factors (Rotz et al., 2019), broadens and deepens the diffusion of green technologies, significantly boosts the R&D and application of these technologies (Chen et al., 2024), and promotes the integrated advancement of green agriculture and food security.

Concerning spatial interaction, production factors such as capital, technology, and labor will flow between regions and a strong correlation exists between geographically similar regions. According to Tobler's (1970) first law of geography, the digital economy and coordinated development in each region are widely connected. A closer distance between regions elicits closer connections. Digital technology can overcome the spatiotemporal limitations of production factors (Chen and Yao, 2024; Ma et al., 2024) and enhance the correlation of agricultural production activities between regions. In addition, digital technology can have spatial spillover effects (Tao et al., 2024), and the technology in one region will have demonstration effects and increase the use of digital technology in neighboring regions, which subsequently improves the efficiency of agricultural production in neighboring regions and reduces agricultural carbon emissions. This study contends that the effect of the digital economy on the coordinated development of green agriculture and food security may have positive spatial spillover effects.

3 Methodology

3.1 Variables

3.1.1 Explained variable

Coordinated development of green agriculture and food security (COR) refers to the coordinated development of the two systems, which is calculated in this paper using a coupling coordination model. Among them, based on the definition of green agriculture and previous studies (Han et al., 2023; Shao et al., 2024), this paper constructs a comprehensive evaluation system to examine the development of green agriculture from four dimensions of resource conservation, environmental friendliness, ecological conservation, and economic growth. Then, this study constructs a comprehensive evaluation system of food security considering food supply security, food access security, food production stability, and food production sustainability according to previous studies (Hadley et al., 2023; Liu and Ren, 2023; Lee et al., 2024). Table 1 presents the specific evaluation system of green agriculture and food security.

Table 1

Table 1. Comprehensive evaluation index of green agriculture and food security.

3.1.2 Explanatory variable

Digital economy development (DED). As noted previously, the digital economy is a new form of economic development that is manifested in digital industrialization and industrial digitization. This study references previous research (Zhang and Li, 2023; Chen et al., 2024; Tao et al., 2024) and constructs a comprehensive evaluation system from the dimensions of digital industrialization and industrial digitization. Table 2 presents the comprehensive digital economy evaluation index.

Table 2

Table 2. Comprehensive of digital economy evaluation index.

3.1.3 Control variables

Based on previous studies (Deng et al., 2023; Lee et al., 2023; Jin et al., 2024; Yang et al., 2024), factors such as financial support for agriculture, the level of openness to the outside world, the structure of industry, urbanization level, and urban–rural income gap are known to have an impact on green agriculture and food security. Therefore, this study uses the following controls variables. Financial support for agriculture (Gov) is expressed as the ratio of government expenditure on agriculture, forestry, and water to general public budget expenditure. The level of openness to the outside world (Open) is expressed as the ratio of total import and export of goods to GDP. The structure of industry (Is) is expressed as the ratio of value added of primary industry to GDP. Urbanization level (Urb) is expressed as the ratio of the year-end population of cities and towns to the year-end population of the region. Urban–rural income gap (Gap) is expressed as the ratio of urban residents' per capita disposable income to the disposable income of rural residents.

3.2 Model

3.2.1 Entropy weighting method

The entropy weight method is a multi-indicator decision-making technique that provides a basis for evaluating multiple indicators, offering greater accuracy and adaptability. Therefore, this paper employs the entropy weight method to assess the development level of green agriculture and food security. The specific steps are as follows:

In the first step, the indicator data were standardized using the methods described in Equations 1, 2. And Equation 1 illustrates the method for calculating positive indicators, while Equation 2 illustrates the method for calculating negative indicators.

$\begin{array}{ll}{y}_{ij}=\frac{x_{ij}-min{x}_{ij}}{max x_{ij}-min{x}_{ij}}+1& (1)\end{array}$

$\begin{array}{ll}{y}_{ij}=\frac{max x_{ij}-x_{ij}}{max x_{ij}-min{x}_{ij}}+1& (2)\end{array}$

where x_it represents the observed value of the j indicator in area i, maxx_ij is the maximum observed value of indicator j in area i, minx_ij is the minimum observed value of indicator j in area i.

In the second step, the entropy value for each indicator is calculated, as shown in Equations 3, 4.

$\begin{array}{ll}{H}_j=ln\frac{1}{n}{\sum }_{i=i}^n{f}_{ij}*ln{f}_{ij}& (3)\end{array}$

$\begin{array}{ll}{f}_{ij}=\frac{y_{ij}}{{\sum }_{i=1}^n{y}_{ij}}& (4)\end{array}$

In the third step, the weight of each indicator is determined, as shown in Equation 5.

$\begin{array}{ll}{w}_j=\frac{1-H_j}{{\sum }_{j}1-H_j}& (5)\end{array}$

In the fourth step, the index value is calculated, as shown in Equation 6.

$\begin{array}{ll}{X}_i={\sum }_j{w}_j*y_{ij}& (6)\end{array}$

3.2.2 Coupling coordination model

The Coupling coordination model is a mathematical tool used to evaluate the interaction and coordination between two or more systems. Its core function is to quantify the degree of coupling and coordination among these systems. This model offers several advantages: it quantifies the degree of coordination, helps identify and address issues of misalignment within the system, and ultimately facilitates more effective resource allocation and optimal management (Wang et al., 2017). Currently, it is applied across various fields, including but not limited to social sciences, economics, environmental sciences, and engineering. Considering that the coordinated development of green agriculture and food security represents the interaction between two systems, this study references previous research (Han et al., 2023; Liu and Ren, 2023) and uses the coupling coordination degree model to measure the degree of coordinated development of green agriculture and food security, which is calculated as follows, with the first step calculating coupling:

$\begin{array}{ll}C={\left\{\frac{G\times S}{\left(\frac{G+S}{2}\right)}\right\}}^{\frac{1}{2}}& (7)\end{array}$

where C is the coupling degree, G is the levels of green agriculture,⁶ and S is the level of food security.⁷

The second step calculates the coupling coordination development index as follows:

$\begin{array}{ll}T=\alpha \times G+\beta \times S& (8)\end{array}$

where T is the index of coupling coordination development, and α and β are the coefficients to be determined. Because green agriculture and food security are in the same significant position, this study references previous research (Liu and Ren, 2023) and assigns 0.5 as the value of both α and β.

The third step calculates the coupling coordination degree as follows:

$\begin{array}{ll}COR=\sqrt{C\times T}& (9)\end{array}$

where COR is the degree of coupling coordination, quantifying the degree of coordinated development of green agriculture and food security.

3.2.3 Baseline regression model

To investigate the impact of the digital economy on green agriculture and food security coordinated development, we construct the following regression model:

$\begin{array}{ll}CO{R}_{it}=a_0+a_{1}DE{D}_{it}+a_{j}Contro{l}_{it}+{\mu }_{it}+{\epsilon }_{it}& (10)\end{array}$

where COR_it denotes the degree of coordinated development of green agriculture and food security in region i in year t, DED_it represents the level of development of the digital economy in region i in year t, Control_it is a series of control variables, μ_i denotes regional fixed effects, and ε_it is a random error term.

3.2.4 Spatial spillover effects model

Considering the spatial interaction of production factors, local digital economy development also affects the coordination of green agriculture development and food security in neighboring regions. To explore this effect, this study constructs the following equation for regression analysis:

$\begin{array}{l}CO{R}_{it}=\alpha +\rho {\sum }_{i=1,j\ne 1}^n{w}_{ij}CO{R}_{it}+{\beta }_{1}DE{D}_{it}+{\beta }_{2}Contro{l}_{it}\\ +\theta {\sum }_{i=1,j\ne 1}^n{w}_{ij}(DE{D}_{it}+Contro{l}_{it})+{\mu }_i+{\vartheta }_t+{\epsilon }_{it}& (11)\end{array}$

where COR_it denotes the degree of coordinated development of green agriculture and food security in region i in year t, DED_it represents the level of development of the digital economy in region i in year t, Control_it is a series of control variables, w_ij is the adjacency (0–1) spatial weight matrix, μ_i denotes spatial fixed effects, ϑ_t represents time fixed effects, and ε_it denotes the random error term. Equation 11 reduces to a spatial lag model if the value of ρ is not 0 and the value of θ is 0. If the value of ρ is 0 and the value of θ is 0, Equation 11 reduces to a spatial error model.

3.3 Data sources and descriptive statistics

Based on the availability and timeliness of data, this study uses data from 30 provinces (municipalities directly under the central government and autonomous regions) in China from 2014 to 2021 for empirical analysis. The data in this study are obtained from the China Statistical Yearbook, the China Rural Statistical Yearbook, the China Population and Employment Statistical Yearbook, and the China Information Industry Yearbook. In addition, Linear interpolation is an effective tool for smoothing gaps in data and is commonly used to fill in missing values in statistical yearbooks. Thus, this study references previous research (Han et al., 2023; Wan et al., 2024) and uses linear interpolation to impute the missing values of individual variables. Table 3 presents the descriptive statistics of the variables.

Table 3

Table 3. Variable definitions and descriptive statistics.

4 Results and discussion

4.1 The dynamic evolution of digital economy and the coordinated development of green agriculture and food security

This study uses MATLAB2020b software and kernel density to investigate the dynamic evolution of digital economy and the coordinated development of green agriculture and food security, as shown in Figure 2. Figure 2A shows the dynamic evolution of the digital economy in China. The center of the kernel density curve gradually moves to the right, indicating that China's digital economy is growing rapidly. In terms of the wave peak, the width of the wave peak of the kernel density curve of digital economy in China gradually becomes narrower, indicating that the development level of digital economy in various regions is equalizing. Furthermore, the distribution of kernel density curve has been single peak, indicating no polarization in digital economy development in China. Figure 2B illustrates the dynamic evolution of green agriculture and food security coordination in China. The center position of the kernel density curve first moves slightly to the left and then to the right, indicating that the degree of green agriculture and food security coordination in China first declines, then rises; however, the overall coordinated development trend is positive. In terms of wave peaks, the kernel density curve gradually transitions from a broad peak to a sharp peak, indicating that differences are gradually narrowing in China, and the distribution of the kernel density curve has always been single-peaked. The findings suggest that the degree of coordinated development is balanced in all regions.

Figure 1

Figure 1. Methodological framework.

Figure 2

Figure 2. (A,B) Dynamic evolution of the digital economy and the coordination of green agriculture and food security.

4.2 Baseline results

The results of F and Hausman tests confirm that the fixed-effects model is appropriate for the study's regression, and the baseline regression results are presented in Table 4. Column (1) demonstrates the effect of the digital economy on the coordinated development of green agriculture and food security. Column (2) demonstrates the effect of digital industrialization as a subdimension of the digital economy on the coordinated development of green agriculture and food security. Finally, Column (3) demonstrates the effect of industrial digitization as a subdimension of the digital economy on the coordinated development of green agriculture and food security.

Table 4

Table 4. Baseline regression results.

According to the regression results, the digital economy coefficient in Column (1) is 1.523 (p < 0.01), indicating that a 1-unit increase in digital economy development results in 1.523 units of green agriculture and food security coordination, confirming that the digital economy can significantly promote coordinated development. The coefficient of digital industrialization in Column (2) is 1.119, and the coefficient of industrial digitization in Column (3) is 0.839, both of which pass the significance test. The results imply that a 1-unit increase in digital industrialization promotes the coordinated development of green agriculture and food security by 1.119 units. Furthermore, a 1-unit increase in industrial digitization promotes green agriculture and food security coordination by 0.839 units. In contrast, digital industrialization contributes more to the effect of the digital economy on coordinated development than industrial digitization.

4.3 Robustness tests

The baseline regression results confirm that the digital economy can significantly promote the coordinated development of green agriculture and food security. To ensure the robustness of the results, this study adopts three methods for robustness tests. The first is shrinkage treatment, in which the upper and lower 1% of the sample is reduced to exclude the influence of outliers on the regression results. The second uses the lagged period of digital economic development as an instrumental variable. The lagged period of digital economic development is highly correlated with digital economy development, which satisfies correlation. Furthermore, the lagged period of digital economic development cannot affect green agriculture and food security coordination through other ways than the digital economy. In addition, the lagged period of digital economic development is not correlated with the random error term, meeting exogeneity. In the third test, this study adds control variables. This study introduces farmers' education (Edu) as a control variable to the model for regression. The results of the robustness test are presented in Table 5, revealing that the digital economy coefficients are all significantly positive. The findings indicate that the digital economy can effectively drive the coordinated development of green agriculture and food security, which is consistent with the benchmark regression results; therefore, the empirical results are robust.

Table 5

Table 5. Robustness test.

4.4 Heterogeneity analysis

Because of differing geographic characteristics, the significance of agricultural production, and agricultural development patterns among regions of China, the following heterogeneity analysis is conducted to further explore the effect of the digital economy on the coordinated development of green agriculture and food security in different regions.

4.4.1 Heterogeneity of geographical characteristics

Digital economy development can be constrained by geographic features. In addition, its impact on the coordinated development of green agriculture and food security may subsequently differ in various regions. Therefore, this study divides the sample into mountainous-hilly areas and plains areas according to geographical features.⁸ The regression results are presented in Table 6. The digital economy coefficient in Column (1) is 1.500 and passes the significance test, indicating that a 1-unit increase in digital economy development can promote the coordinated development of green agriculture and food security by 1.500 units in hilly areas. The digital economy coefficient in Column (2) is 1.545 (p < 0.01), indicating that a 1-unit increase in digital economy development can promote the coordinated development of green agriculture and food security by 1.545 units in plains areas.

Table 6

Table 6. Results of heterogeneity analysis.

In summary, the impact of the digital economy on the coordinated development of green agriculture and food security is greater in the plains than in hilly areas. The rationale for this is that digital economy development, technology, and facilities in mountainous-hilly areas is relatively lagging compared with that in plains areas, which slows digital economy development.

4.4.2 Heterogeneity of agricultural production

Considering the different emphasis on agricultural production between large agricultural provinces and nonagricultural provinces, differences are expected in the impact of the digital economy on the coordinated development of green agriculture and food security. Therefore, this study divides the sample into agricultural and nonagricultural provinces according to functional areas, and the results are shown in Table 6. The digital economy coefficient in Column (3) is 1.601, passing the significance test. A 1-unit increase in the digital economy can promote coordinated development of green agriculture and food security by 1.601 units in large agricultural provinces. The digital economy coefficient in Column (4) is 1.579 (p < 0.01), indicating that a 1-unit increase in the digital economy can promote coordinated development of green agriculture and food security by 1.579 units in nonagricultural provinces. Therefore, the impact of the digital economy on the coordinated development of green agriculture and food security is greater in large agricultural provinces than nonagricultural provinces. This is attributable to the fact that large agricultural provinces prioritize agricultural production and have greater advantages in realizing large-scale agricultural management and adopting green production technology.

4.4.3 Heterogeneity of functional areas

Main grain producing areas are better equipped for scale operation and green technology adoption than the main nongrain producing areas (Lee et al., 2024). Therefore, the effect of the digital economy on the coordinated development of green agriculture and food security will be impacted by changes in agricultural functional areas. This study divides the sample into grain and nongrain producing areas, presenting the regression results in Table 6. The digital economy coefficient in Column (5) is 1.564, passing the significance test. A 1-unit increase in the digital economy promotes the coordinated development of green agriculture and food security by 1.564 units in main grain producing areas. The digital economy coefficient in Column (6) is 1.561 (p < 0.01), indicating that a 1-unit increase in the digital economy can promote the coordinated development of green agriculture and food security by 1.561 units in nonmain grain producing areas. Therefore, the impact of the digital economy on the coordinated development of green agriculture and food security is greater in main grain producing areas than nonmain grain producing areas. The rationale for this is that mousere conditions for large-scale operation exist in main grain producing areas, and the cost of agricultural digital transformation and agricultural green production is lower.

4.5 Analysis of spatial spillover effects

4.5.1 Spatial autocorrelation

This study next uses a spatial econometric model to test the spatial spillover effect of the digital economy on the coordinated development of green agriculture and food security. Before conducting spatial regression, it is necessary to test whether spatial dependence exists in the data by performing a spatial autocorrelation test. This study uses the global Moran's index (Moran's I) to test the spatial correlation of digital economy and the coordinated development of green agriculture and food security, presenting the results in Table 7. The global Moran's I of the digital economy is >0 in all years, passing the significance test (except in 2017). This indicates a positive spatial coefficient correlation for the digital economy as a whole. The values of global Moran's I of the coordinated development of green agriculture and food security are all >0 in all years and significant at the 1% level. This indicates a positive spatial coefficient correlation between the coordinated development of green agriculture and food security overall. The local Moran's I plot of digital economy and coordinated development of green agriculture and food security in 2021 are illustrated in Figure 3, and the slope of the straight line is positive, indicating significant spatial autocorrelation in digital economy and coordinated development of green agriculture and food security.

Table 7

Table 7. Global Moran's I.

Figure 3

Figure 3. Localized Moran's I map of the digital economy and the harmonization of green and green agriculture with food security in 2020.

4.5.2 Spatial Durbin model regression results

To determine the specific form of the spatial regression model, this study applies Wald and LR tests, presenting the results in Table 8. According to the test results, the p-values of the tests are < 0.05, indicating that it is more appropriate to select the spatial Durbin model for regression estimation. In addition, the chi-square value of Hausman test is 75.31 and p-value is 0.0000. Therefore, the spatial Durbin model with fixed effects is selected for estimation.

Table 8

Table 8. Results of Wald and LR tests.

Based on the above analysis, this study uses the spatial Durbin model with fixed effects for the regression estimation, presenting the results in Table 9. From the perspective of the core explanatory variables, the coefficient of digital economy is positive and significant at the 1% level, indicating that digital economy development can effectively promote the coordinated development of green agriculture and food security. Furthermore, the spatial lagged coefficient of digital economy is positive and passes the significance test, demonstrating that the digital economy has a positive spatial spillover effect on the coordinated development of green agriculture and food security. In other words, local digital economy development can effectively promote the coordinated development of green agriculture and food security in neighboring areas. In addition, the coefficients of openness to the outside world, industrial structure, and urbanization are all positive and pass the significance test, indicating positive effects on the coordinated development of green agriculture and food security. The coefficient of financial support for agriculture is significantly negative, indicating that the impact of financial support for agriculture on green agriculture and food security coordination is negative. The rationale for this may be that financial support for agriculture has deviations and does not have an influence. Therefore, the government should establish and enhance the supervision mechanism, increase transparency and public participation in agricultural financial support, and ensure the precision of these financial resources.

Table 9

Table 9. Results of spatial Durbin model regression.

4.5.3 Decomposition of spatial spillover effects

To further explore total, direct, and indirect effects, this study uses the partial differential method to decompose the spatial spillover effect of the digital economy on the coordinated development of green agriculture and food security, presenting the results in Table 10. According to the total effect, the coefficient of the digital economy is positive and highly significant, showing that the digital economy can promote green agriculture and food security coordination. Regarding the direct effect, the coefficient of the level of the digital economy is positive and significant, indicating that the digital economy can effectively promote the local green agriculture and food security coordination. In terms of the indirect effect, the coefficient of the digital economy is positive and significant, showing that digital economy development can effectively promote green agriculture and food security coordination in neighboring regions; however, the indirect effect is smaller than the direct effect.

Table 10

Table 10. Decomposition of effects.

5 Discussion

The digital economy, green agriculture, and food security are popular Research Topics. However, in the context of global economic development facing challenges of climate warming and ecological environment deterioration, it is particularly important to promote the coordinated development of green agriculture and food security.

Focusing on how the digital economy affects the coordinated development of green agriculture and food security, we measure the development of digital economy, green agriculture and food security through the entropy weight method, and apply the Coupling coordination model to measure the coordinated development level of green agriculture and food security, which is similar to the existing related research in research methods (Han et al., 2023; Liu and Ren, 2023; Shao et al., 2024; Tao et al., 2024). Our research found that the digital economy can effectively enhance the coordinated development of low-carbon agriculture and food security by optimizing factor allocation efficiency (Hao et al., 2023; Liu and Hao, 2023), improving agricultural production methods (Yang et al., 2024; Lu et al., 2024), and fostering agricultural technology innovation (Hao et al., 2023; Li and Gao, 2024; Lv and Chen, 2024). This conclusion aligns with existing studies on how the digital economy promotes low-carbon development and ensures food security (Deng et al., 2023; Shi et al., 2024), but there are some differences. Compared to previous studies, this research not only examines the individual impact of the digital economy on green agriculture and food security but also integrates these elements into a unified framework. It provides an in-depth analysis of how the digital economy affects the coordinated development of green agriculture and food security, specifically through the lens of spatial spillover effects. Our findings can still offer valuable insights for countries with similar agricultural practices, such as India, to help ensure food security and promote sustainable agricultural development.

But, this study also has the following limitations. First, this study analyzes the impact of the digital economy on the coordinated development of green agriculture and food security solely from the perspective of spatial effects. Future research should build on this foundation to explore the mechanisms underlying the digital economy's influence on the coordinated development of green agriculture and food security in greater depth. Second, this study only examined the effect of China's overall digital economy on the coordinated development of green agriculture and food security. Future research should build on this foundation to further investigate the nonlinear aspects of this impact and derive more policy-oriented conclusions. Third, due to data limitations, this study only examined the impact of the digital economy on the coordinated development of green agriculture and food security at the provincial level in China. Future research should collect county-level data through field surveys and other methods to further explore the impact of the digital economy on green agriculture and food security in different counties.

6 Conclusion and recommendations

Based on the above analysis, we draw three relevant conclusions. First, the development trend of digital economy and coordinated development of green agriculture and food security in China is positive, with no polarization phenomenon. Second, the digital economy can effectively promote the coordinated green agriculture and food security development, with notable heterogeneity. The positive effect of the digital economy on the coordinated development is larger in plains areas, agricultural provinces, and main grain producing areas. Third, digital economy development exhibits positive spatial spillover effects on the coordinated green agriculture and food security development. In other words, digital economy development promotes the coordinated development of green agriculture and food security in a region as well as neighboring regions. This study proposes the following relevant policy implications.

First, the development of rural digital economy should be promoted. We have shown that the digital economy can significantly enhance the coordinated green agriculture and food security development. On the one hand, infrastructure is crucial for economic development. More efforts should be directed toward advancing digital rural infrastructure, such as speeding up the deployment of 5G networks in rural areas and broadening network coverage in these regions. On the other hand, policy support should be formulated to vigorously promote the development of the Internet of Things, improve the integration of digital technology and traditional technology facilities, accelerate the digital transformation of traditional infrastructure, and promote agricultural business entities to use digital technology to promote the coordinated development of green agriculture and food security.

Second, we should support the adoption of technology by agricultural business entities. Research shows that digital economy can promote the coordinated development of green agriculture and food security through technological innovation in agriculture. On the one hand, the government should provide financial incentives and subsidies, not only providing grants, low-interest loans or tax breaks for farmers and agricultural enterprises to purchase digital technology and software, but also developing subsidy programs for the purchase of advanced equipment such as drones, automated machinery and precision farming tools. On the other hand, a group of high-quality digital agricultural talents should be cultivated to promote the research and development and application of agricultural digital technology, and promote agricultural technology innovation, so as to realize the coordinated development of green and low-carbon agriculture and food security.

Third, evaluation and monitoring systems should be established. On the one hand, indicators and benchmarks should be developed to assess the effectiveness of digital initiatives in improving green agriculture and food security. On the other hand, there should also be regular monitoring and review, creating a system of regular review and feedback to refine policies and ensure that they respond to changing challenges and opportunities.

Fourth, promoting coordinated regional development of the digital economy. We have shown that the digital economy has positive spatial spillover effects on the balanced development of green agriculture and food security. The government should encourage inter-regional exchanges and cooperation, establish unified policies for digital economy development, and facilitate the movement of capital, technology, and other production factors between regions. By promoting the coordinated development of the digital economy across regions.

In summary, our study has both theoretical and practical implications for achieving agricultural sustainability and safeguarding food security. However, since the research focuses on China, the results may vary with different data sets and contexts. It is necessary for further research to determine whether our conclusions are applicable to other countries. Additionally, the policy recommendations in this paper are tailored to China's specific situation and are more suitable for scenarios where the government plays a leading role. They may not be relevant in other contexts with more liberal approaches of the digital economy, green agriculture and food security.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Author contributions

JT: Writing – original draft, Methodology, Formal analysis, Data curation, Conceptualization. CL: Writing – review & editing, Supervision, Project administration, Funding acquisition. GM: Project administration, Resources, Supervision, Investigation, Writing – review & editing.

Funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by the National Social Science Foundation of China (grant number 22BGL175).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Footnotes

1. ^http://eu.mofcom.gov.cn/article/sqfb/202207/20220703323912.shtml

2. ^https://www.greenplan.gov.sg/

3. ^https://www.greeninitiatives.gov.sa/#:~:text=Climate%20action,%20energy%20security%20and%20economic%20prosperity%20must

4. ^https://www.gov.cn/xinwen/2022-10/25/content_5721685.htm

5. ^Source: China Academy of Information and Communication Research http://www.caict.ac.cn/kxyj/qwfb/bps/202207/t20220708_405627.htm.

6. ^Calculated using the entropy weight method based on the comprehensive evaluation index of green agriculture shown in Table 1.

7. ^Calculated using the entropy weight method based on the comprehensive evaluation index of food security shown in Table 1.

8. ^This study categorizes provinces where the combined area of mountains and hills exceeds 70% as mountainous-hilly areas, and remaining areas are categorized as plains.

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