The Impact of the Digital Economy on Food System Resilience: Insights from a Study across 190 Chinese Towns

Abstract

This study explores the impact of the digital economy on the resilience of the food system, employing data from 190 towns in China and a difference-in-differences (DID) model. The results indicate that, between 2011 and 2020, the progress of the digital economy was instrumental in driving continuous improvements in the food system’s resilience in China. This conclusion stands firm after accounting for endogenous issues and conducting comprehensive robustness tests. According to a mechanism test, the digital economy can impact the resilience of the food system through three avenues: digital technology, digital finance, and human capital. Further research indicates that the influence of the digital economy on the resilience of the food system varies across regions and different food functional zones. These findings offer fresh insights and empirical evidence into the linkages between the digital economy and food system resilience. Such insights may bolster the food system’s resilience in developing countries and promote sustainable food development.

1. Introduction

As the primary essential element underpinning all production activities, food is an important foundation for national economic construction and development. At present, the global scenario is experiencing significant transformations, and unstable factors such as financial and political collapses, health crises, and geopolitical disputes are increasing [1]. Both sudden shocks and long-term stressors increase the vulnerability of the food system [2]. The capacity of the food system to withstand external risks and strive for sustainable development has attracted more and more attention from governments [3]. According to the Food and Agriculture Organization of the United Nations, resilience is the ability of the food system to resist destructive factors, ensure people obtain adequate, safe, and nutritious food in a sustainable and long-term manner, and preserve the livelihood of individuals involved in the food system. To effectively deal with the various risks associated with food production, it is crucial to establish a resilient food system [4]. This will not only enhance the resilience of the food system, but also ensure the promotion of sustainable development in food production.

As a traditional food-producing country, China’s total food production has exceeded 650 million tonnes for eight consecutive years. While China’s capacity for food production continues to improve, there are significant challenges to the production of food. For example, there has been a decline in water and land resources for cultivation, an excessive use of chemical fertilisers and pesticides, the price of labour and land has risen sharply, and the volatility of the international food market has increased [5]. Facing double impacts at home and abroad, China’s food production must take into account not just the total output but also the imperative of sustainable development. Hence, China’s food system should move towards a more resilient direction to ensure a consistent food supply for its 1.4 billion population [6]. In 2021, the central government announced the need to improve the resilience and impact resistance of the food industry to ensure food security. The Party stressed the importance of improving the resilience of the food system, prioritising the growth of agriculture and ensuring food security in its report to the 20th CPC National Congress. Enhancing the resilience of the food system holds immense importance in maintaining China’s food security and promoting sustainable agricultural development.

Resilience originated as a concept in ecological research [7], but has been increasingly used to analyse social systems [8,9]. Some scholars also apply it to studies of the food system. Tendall introduced resilience into the food security system, constructing a framework for food system resilience, and proposing that food system resilience represents the capacity for eradicating weaknesses and dealing with future uncertainty [10]. Béné [11] suggested that the resilience of the food system should be constructed from many different aspects to enhance its resistance and adaptability to shocks. The operational mode of the food system varies from region to region. In large, developed countries such as the USA and Canada, the food supply chain relies on a complex web of interconnected systems [12]. The food system in Indonesia and other developing nations primarily relies on production and trade due to the complementary nature of their food items [13]. Indeed, due to the complexity of the food system [14], many factors can affect the resilience of the food system [14], such as organic farming [15], land access regimes [16], seed exchange [17], the COVID-19 epidemic [18,19], and so on.

In Oliver’s view, enhancing the resilience of the food system is an urgent challenge [20]. The prevalence and severity of natural hazards represent a significant peril to food systems, which undermines their function to provide food security and improved nutrition [21]. Worldwide shocks can adversely affect international supply chains, while insecure working conditions in the food system erode the resilience of the entire system [22]. There is increasing apprehension regarding the fragility of the European food system, which may result in adverse consequences when it is fractured [23,24,25,26]. If the performance of the food system is not improved quickly, it could become a leading cause of food crises in the near future [27]. Therefore, a comprehensive understanding of the factors affecting food system resilience is imperative to develop a system that can effectively overcome future challenges [28,29]. Generally speaking, scholars have conducted some research into the resilience of the food system, but, in relative terms, they have disregarded the impact of the digital economy on its resilience.

To improve the resilience of the food system and reduce its fragility, China has implemented various measures, including the development of the digital economy, which is an important measure. As the digital economy makes its way into the agricultural sector, there are expectations for harnessing digital technology to enhance the resilience of the food system [1]. Recently, the digital economy has undergone rapid expansion in China. In 2021, the scale of China’s digital economy reached CNY 45.5 trillion, accounting for 39.8% of GDP (China’s White Paper on Digital Economy Development). With the rapid expansion of information technology, the digital economy has gained increasing significance. It has eliminated barriers to information circulation, hastened the movement of resource elements, enhanced the effectiveness of the demand–supply correlation and agricultural trade [30], and facilitated agricultural modernization [31]. It has been observed that the digital economy can improve the efficiency of labour allocation for farmers [32], decrease capital mismatch [33], and significantly impact agricultural total factor productivity and income [34]. Additionally, the digital economy can reduce agricultural carbon emissions [35], facilitate the green development of agriculture, and have a positive impact on agricultural development [36,37]. Therefore, the digital economy may have an important impact on the resilience of the food system, providing the direction for the study within this paper.

Some academics have examined the resilience of the food system, but their research focus has mainly been on organic agriculture, seed exchange, and other aspects. The existing literature pays less attention to the influence of the digital economy on food system resilience. Consequently, the marginal contribution of this research lies in, firstly, the provision of causal evidence of the impact of the digital economy on food system resilience for the first time. Secondly, it analyses the influencing path of the digital economy on food system resilience whilst providing fresh ideas for realizing the sustainable development of food.

The remainder of the paper is organized as follows. Section 2 presents the mechanism analysis and research hypotheses. Section 3 introduces the data source, variable selection, and model selection. This study primarily utilises the DID model and the mediating effect model. The results are reported and discussed in Section 4. Section 5 introduces the main conclusions and policy recommendations. Section 6 introduces the final remarks.

2. Mechanism Analysis and Research Hypotheses

Due to the advancements in science and technology, the digitisation of all areas of society has emerged as a key trend for the future [38]. Food production is a crucial aspect of societal activities, and the digitisation of its processes is now inevitable [39]. The key determinants impacting food production encompass technology, capital, and labour [34]. The digital economy has witnessed a rapid growth in digital technologies, including the Internet of Things, big data, and artificial intelligence [40,41,42], leading to the emergence of digital inclusive finance and a significant increase in the number of digital professionals [43,44]. These advancements may have far reaching implications for food production, and could affect the resilience of the food system, as depicted in Figure 1. According to the referenced statement of theory, this thesis puts forward the following hypothesis:

Hypothesis 1 (H1). 

The digital economy can impact the resilience of the food system through three avenues: digital technology, digital finance, and human capital.

2.1. Digital Technology

The advancement of the digital economy has propelled the growth of digital technology [45,46], and significantly influenced the resilience of the food system. Firstly, digital technology has the potential to enhance food production efficiency by altering food production and management modes. Digital technology can assist farmers in developing food production plans that align with the agricultural production cycle, minimising the idle time of agricultural resources, and improving the comprehensive food production efficiency [47]. Secondly, digital technology has altered the operational mode of traditional food with the widespread application of big data [48]. The agricultural platform based on big data has introduced a new mode of operation in information sharing, improving profitability for farmers and optimizing their business decisions [49]. Food operators can use digital technologies, including the Internet of Things, to assess every aspect of food cultivation, processing, storage, and sales [50]. This approach can significantly reduce the insufficient information in food production and sales by enabling an accurate awareness of market conditions, improve the efficiency of the food supply chain, and enhance the resistance capacity of the food system [51]. Furthermore, the comprehensive utilisation of agricultural data results in a more extensive development scope for the modes of order production and precision marketing. The connection between the supply and demand of foods has been enhanced, which has positively impacted the market transaction of such products, thereby boosting the resilience of the food system [52]. According to the referenced statement of theory, this thesis puts forward the following hypothesis:

Hypothesis 2 (H2). 

The digital economy enhances the resilience of the food system by promoting the development of digital technology.

2.2. Digital Finance

Digital finance embodies the financial concept of a community of human destiny. As a more comprehensive and inclusive financial service, it bears the responsibility of ensuring food security [53]. The development of digital inclusive finance can overcome the limitations of traditional finance, which fails to cover agriculture and farmers, and provides superior financial services and products to Chinese farmers [54]. By implementing digital inclusive finance and leveraging its inclusive advantages, the digital economy can effectively alleviate the financial barriers encountered by food producers. There are numerous links in the food system between production and consumption, which involve various entities. When one entity experiences a shock, it not only affects itself but also radiates throughout the upper, middle, and lower reaches of the entire system. The effect of shocks on the food system relies on the weakest component of the food system. With limited assets and access to credit, small-scale producers are the most vulnerable link in China’s food system [55]. The small-scale producers often find it more difficult to shake off the negative effects of the shock. Hence, improving the resilience of the food system relies largely on enhancing the most vulnerable subjects, namely, small-scale producers. The advent of digital financial services and technologies has addressed the limitations of conventional financial institutions [56]. The digitisation of the rural financial infrastructure has enabled digital finance to play an efficient and precise role, resulting in expanded credit resources and channels to acquire information for small-scale producers [57]. This expansion has allowed small-scale producers to access financial services previously unavailable to them, securing adequate financial backing for expanding their farmland operation scale [58] and purchasing machinery [59]. Ultimately, due to the scale effect and the improvements in production efficiency [43], farmers in China have increased their income and food output [60]. The resistance capacity of the food system is enhanced. According to the referenced statement of theory, this thesis puts forward another hypothesis:

Hypothesis 3 (H3). 

The digital economy enhances the resilience of the food system by developing digital finance.

2.3. Manpower Capital

Human capital is the innate driving force behind the formation of food system resilience. The digital economy has significantly influenced the labour market reform [61]. On one hand, the expansion of the digital economy necessitates a significant number of proficient digital experts, leading to changes in the composition of the job market. The enhancement of digital literacy abilities is progressively crucial for numerous citizens to optimise their work productivity [62]. This shift will prompt employees to enhance their digital proficiency. Additionally, the proliferation of information channels, facilitated by the digital economy, could mitigate information acquisition expenses for the farming workforce. This development enables the labour force to acquire knowledge and skills at an improved rate. Thus, the digital economy increases the demand for and supply of high-quality talent [63]. The digitalization of economy even leads to the emergence of new professions [64]. On the other hand, the national government is blending talent education with the development of the digital economy, which hastens the preparation of digital professionals in rural regions. The government’s “Notice on the Pilot Rural Reform in 2023” highlights the importance of cultivating digital talent in rural areas as a critical task. The government is promoting information technology and digital tools to enhance the digital literacy and skills of farmers. This measure has positively impacted the overall quality of the rural workforce and has helped align it to the requirements of agricultural digital development.

According to Ahsan [65], human capital has the potential to have a positive impact when economic development is faced with external shocks. When food production experiences external shocks, digital talents can help manage risks and find new development paths. Digital professionals assist in creating a digital management platform for agriculture and anticipate market scenarios and risks by collecting or simulating data from food management. This capability is valuable in predicting risks and potential shocks, which can strengthen the food system’s compression resistance, namely, the capacity of the food system to mitigate the effects of disruptive events. Furthermore, China considers smart agriculture as an important engine to promote high-quality agricultural and rural development [66]. Smart agriculture views data as an essential production factor, facilitating the integration of digital technology and conventional farming practices. This integration not only transforms the traditional approach to food production but also enhances the transformation and upgrading capacity of the food system after the impact. Therefore, smart agriculture has created a novel pathway for future agricultural development. Building an intelligent agricultural production system requires talented people who know about both digital technology and food production. The cultivation of digital talents in the digital economy opens possibilities for intelligent agriculture. Human capital enhances the transformative capacity of the food sector, and offers an impetus for the adjustment of food production [67]. The rise of the digital economy has established digital talents as the new driving force for food system resilience. According to the referenced statement of theory, this thesis puts forward the following hypothesis:

Hypothesis 4 (H4). 

The digital economy improves the resilience of the food system by improving human capital development.

3. Data Source, Variable Selection, and Model Selection

3.1. Data Source

There exist regional disparities between the economic advancement of China and the food output levels, and the issue of uncoordinated development across regions persists. Towns play significant roles as both the vehicles of regional economic growth and decisive entities in the food production industry. Employing China’s towns as research subjects is a useful approach for better comprehending the resilience of the food system and the factors that influence it. The study sample are the data from 190 towns in China from 2011 to 2020, totalling 1900 samples. From a geographical perspective, the 190 towns are categorized into three regions, comprising 88 towns in East China, 47 towns in Central China, and 55 towns in West China. Furthermore, based on the food functional zoning, these 190 towns can be categorised into two groups, with 154 situated in major food-producing zones and 36 situated in non-major food-producing zones. The 190 towns account for 57.05% of all towns in China. Therefore, the samples used in this study are nationally representative. The data on Chinese towns between 2011 and 2020, presented in this study, have been predominantly sourced from the China Statistical Yearbook, the China Rural Statistical Yearbook, and the statistical yearbooks of various provinces and cities.

3.2. Variable Selection

3.2.1. Explained Variables

A resilient food system should possess the critical abilities of resistance, adaptation, and change to maintain normal operations amidst unforeseeable shocks [68]. On the basis of the aforementioned mechanism analysis, in conjunction with the current situation of food production in China, this article develops the index system for food system resilience, as shown in

Table 1. Index system of food system resilience.

First Tier Index	Second Tier Index	Third Tier Index	Indicator Attribute
Capability of resistance	Intrinsic stability	Area of cultivated land	Positive
		Irrigated area	Positive
	Robust productivity	Number of employees in primary industry	Positive
		Grain output per capita	Positive
Capability of adaptation	Sustainability	Amount of pesticide applied	Negative
		Amount of chemical fertilizer applied	Negative
	Restorability	Increase rate of agricultural output value	Positive
		Electric power consumption of agricultural production	Positive
Capability of change	Diversified cooperation	Gross power of agricultural machinery	Positive
		Total output value of agriculture, forestry, animal husbandry, and fishery	Positive
	Technologic advance	Input in agricultural scientific research	Positive
		Number of agricultural technicians (10,000)	Positive

.

3.2.2. Explanatory Variable

According to the methodology of Tian and Zhang [69], this study uses the “Broadband China” (BC) policy as a proxy variable for the digital economy. In August 2013, China implemented the BC policy to address slow Internet speeds and poor network coverage. In accordance with the BC policy, China planned to incrementally construct and enhance its broadband infrastructure throughout various regions. During the period between 2014 and 2016, 39 towns implemented the BC policy annually, resulting in a total of 117 towns implementing the policy over the course of three years. The government of China evaluated the BC policy in October 2020. Since the implementation of the BC policy, China’s broadband network infrastructure has undergone significant transformation, leading to a rapid surge in the number of broadband users. The application of broadband informatisation has extended to multiple sectors of society (White Paper on Broadband Development in China, 2020). Specifically, as of 2020, China’s rural fibre-optic broadband penetration has exceeded 98%, ranking second globally. Additionally, China has built the world’s largest 4G network and boasts 150 million 5G users. The BC policy promotes network construction whilst also supporting the constant growth of the network industry chain. Constantly expanding the network’s applications in production and utilizing information technology to transform traditional industries ensures networking and intelligence, promoting the optimization and upgrading of the industry, consistently innovating broadband application modes and cultivating novel markets, like the Internet of Things, online shopping, and intelligent terminals. The BC policy affects both the construction of infrastructure, like the Internet, and the use of the Internet for economic and social purposes. It improves people’s proficiency in using the Internet and leads to the emergence of new consumer patterns. So, the BC policy can effectively represent the development of the digital economy [69].

As a policy at the city level, the BC policy can be influenced by numerous unobservable factors. As a result, this variable may encounter endogenous problems. To address the potential endogenous problems, this study employs the number of fixed telephones in 1984 as an instrumental variable to establish the reliability of its findings. Specifically, the development of the digital economy depends on the widespread adoption of Internet technology, which initially arose from the popularity of fixed telephones. This is because fixed telephones form the foundation of the Internet and will influence the future development of the digital economy in terms of technical level and habit formation. Therefore, this instrumental variable satisfies the relevance requirements. On the other hand, the rapid development of Internet technology has led to the gradual disappearance of fixed telephones. The main purpose of fixed telephones was to provide communication services to society, and they do not directly impact the resilience of the food system. Therefore, the number of fixed telephones, being an instrumental variable of the digital economy’s development level, essentially satisfies the requirement of exclusivity. As the study sample consists of balanced panel data, the fixed telephone information in 1984 are cross-sectional data and cannot be used as a tool variable for panel data. Therefore, this paper utilises the interactive item between the quantity of fixed telephones in different regions in 1984 and the previous year’s national Internet penetration rate as the instrumental variable for the digital economy [70].

3.2.3. Mediator Variable

This study introduces three mediator variables: digital technology, digital finance, and human capital. Digital technology is evaluated using the entropy weight method, based on four dimensions: internet and mobile internet penetration rates, per capita telecom business volume, computer service, and the proportion of software professionals [71]. Digital finance has a significant impact on agricultural production through the provision of agricultural loans. For this reason, agricultural loan data are employed to measure digital finance. Human capital is measured by the average number of years of education received by the rural population.

3.2.4. Control variable

To eliminate the influence of other factors on the resilience of the food system, the control variables were determined according to the relevant literature [34,72]. In this study, the control variables influencing the food system have been selected from the perspectives of population, economy, transportation, environment, and disasters. Specifically, the urbanization level is related to agricultural production population and land. Furthermore, the industrial structure serves as an indicator of the development level of the secondary and tertiary industries. Additionally, the degree of openness pertains to the import and export of food. The definitions of these variables are shown in

Table 2. Definitions of main variables.

Variable Name	Variable Definition
Urban	Urbanization level (non-agricultural population/total regional population)
Way	Traffic facilities (highway length/provincial area)
IS	Industry structure (the combined output values of the secondary and tertiary sectors/regional GDP)
Open	Openness (total import and export/regional GDP)
GDP	Level of economic development (regional GDP/total regional population)
Environment	Eco-environment (soil erosion control area/regional area)
Disaster	Disaster rate (disaster area/grain planting area)

. The descriptive statistics of these vectors are indicated in

Table 3. Descriptive statistics.

Variables	(1)	(2)	(3)	(4)	(5)
	Obs	Mean	Std. Dev	Min	Max
Food system resilience	1900	0.154	0.086	0.034	0.555
Digital	1900	0.202	0.401	0	1
Urban	1900	0.554	0.125	0.112	0.958
Way	1900	1.084	0.558	0.030	2.872
IS	1900	0.875	0.084	0.230	0.992
Open	1900	0.269	0.301	0.001	0.511
GDP	1900	0.251	0.277	0.011	2.504
Environment	1900	0.205	0.102	0.074	0.469
Disaster	1900	0.189	0.125	0.016	0.617
Digital technology	1900	0.157	0.063	0.004	0.299
Digital finance	1900	1.274	0.897	0.105	4.690
Human capital	1900	7.902	0.448	4.359	8.869

.

3.3. Model Selection

3.3.1. DID Model

The DID method has been widely used in the natural and social sciences to evaluate the effect of policy implementation [73]. The fundamental principle of the DID method is to establish a quasi-natural experiment. Before the policy was implemented, the situation in both regions (such as A and B) was comparable. If a policy is implemented in Area A but not in Area B, we can evaluate the policy’s effect by comparing the changes in the target variables of Area A and B before and after implementation [74]. This study uses the DID model to identify the causal relationship between BC policy and food system resilience. The sample towns were divided into experimental and control groups according to the implementation of BC policy. The towns affected by BC policy were considered the experimental group, and the other towns were taken as the control group. Equation (1) is the DID model that examines the impact of BC policy on food system resilience. This paper applies the OLS method with Stata 16 software to perform regression analysis.

$$Y_{it}={\alpha }_0+{\alpha }_{1}Digita{l}_{it}+{\displaystyle \sum }_{j=1}^n{b}_j {Controls}_{\mathit{it}}+{\mu }_t+{\nu }_i+{\epsilon }_{it}$$

(1)

where $Y_{it}$ indicates the resilience index of food system in the t year of i town; $Digita{l}_{it}$ is the dummy variable of BC policy (proxy variable of the digital economy); if i town implemented BC policy in the t year, $Digita{l}_{it}=1$, otherwise, $Digita{l}_{it}=0$; $Control{s}_{it}$ denotes the control variables; ${\alpha }_0$ is the intercept; and ${\alpha }_1$ and $b_j$ are the model parameters. Our coefficient of interest is ${\alpha }_1$, which quantifies how much digital economy affected food system resilience. ${\mu }_t$ are year fixed effects, ${\nu }_i$ are individual fixed effects, and ${\epsilon }_{it}$ is the error term. The year fixed effects can control the influence of time on the research results. The individual fixed effects can eliminate the influence of individual fixation characteristics on the results.

3.3.2. Mediating Effect Model

The mechanism analysis indicates that the digital economy can impact the resilience of the food system through three avenues: digital technology, digital finance, and human capital. To verify the above mechanism, this study references Baron and Kenny [75] and Cheung and Lau [76], constructing the following mediating effect model:

$$Y_{it}=a_0+a_{1}Digita{l}_{it}+a_2 Control{s}_{it}+{\mu }_t+{\nu }_i+{\epsilon }_{it}$$

(2)

$$Medi{a}_{it}=b_0+Digita{l}_{it}+b_2 Control{s}_{it}+{\mu }_t+{\nu }_i+{\epsilon }_{it}$$

(3)

$$Y_{it}=c_0+c_{1}Digita{l}_{it}+c_{2}Medi{a}_{it}+c_{3}Control{s}_{it}+{\mu }_t+{\nu }_i+{\epsilon }_{it}$$

(4)

where $Medi{a}_{it}$ represents the mediating variables that this study seeks to test, which include digital technology, digital finance, and human capital; $Control{s}_{it}$ represents the control variable; $a_1$ in Equation (2) is the total effect of digital economy on food system resilience; $b_1$ in Equation (3) is the effect of the digital economy on the mediating variables; $c_1$ in Equation (4) is the direct effect of the digital economy on food system resilience after controlling the influence of intermediary variables; and $c_2$ is the effect of the mediating variables on food system resilience after controlling other variables. The mediating effect model in Equations (2)–(4) is indirect, indicating the product of $b_1{c}_2$. According to the mediating effect test process of the organic combination using stepwise regression and bootstrap methods, the coefficient $b_1$ in Equation (3) and the coefficient $c_2$ in Equation (4) are tested separately. If both $b_1$ and $c_2$ are significant, this indicates that the indirect effect is significant. If either $b_1$ or $c_2$ is not significant, the bootstrap method is used to directly test the null hypothesis ($b_1{c}_2=0$). If the original hypothesis is rejected, the indirect effect is significant. If $c_1$ is not significant, this indicates that there is a complete mediating effect. If $c_1$ is significant and $c_1$ and $b_1{c}_2$ are consistent, this indicates a partial mediating effect. If $c_1$ is significant and the sign of $c_1$ is opposite to $b_1{c}_2$, this indicates a masking effect.

4. Empirical Analysis

This section presents our empirical results regarding the issues of the digital economy and food system resilience.

4.1. Basic Regression Results

Table 4. The impact of digital economy on food system resilience.

Variable	Food System Resilience
	(1)	(2)
Digital	0.053 *** (0.016)	0.045 *** (0.015)
Urban		−0.456 *** (0.152)
Way		0.139 *** (0.047)
IS		−0.118 (0.110)
Open		0.015 (0.024)
GDP		0.169 ** (0.078)
Environment		0.006 (0.121)
Disaster		0.039 (0.026)
Year effects	Y	Y
Individual effects	Y	Y
Constant	1.450 *** (0.070)	1.596 *** (0.119)
Obs	1900	1900
R2	0.277	0.318

(1) *** and **, indicate significance at 1%, and 5%, respectively. (2) Robust standard errors in parentheses.

presents the impact of the digital economy on food system resilience. The results in column (1) demonstrate a significant positive effect of the digital economy on food system resilience, without controlling for other variables. In column (2) of Table 4, after all control variables are also added, the number of coefficients of the core explanatory variable, digital economy, is 0.045, and the absolute values of the estimation coefficients remain significant at the level of 1%. (Fisher put forward the idea of statistical hypothesis testing in 1925 and called it “tests of significance” [77]. He introduced the concept of P-value in hypothesis testing. Currently, in much of the literature, the level of significance is determined by the value of P. In general, the smaller the value of P, the more significant the result. The significance level in this paper is determined by the P value of the regression coefficient provided by Stata software.) This is a preliminary indication that the digital economy positively impacts farm food system resilience. The increase in resilience of the Chinese food system can be attributed to the development of the digital economy. The upsurge of network technologies, for instance big data and the Internet of Things, has been expedited by digital economy. Moreover, the digital inclusive finance and the education of digital specialists that are co-promoted by the digital economy uphold the system’s resilience.

The regression results of control variables show that the coefficient of urbanisation rate (Urban) is −0.456, which is significant at the 1% level. This finding is consistent with Zhang and Wang [78], implying that the growth in the urbanisation rate is unfavourable to enhancing the resilience of the food system. This may be due to the city expansion displacing agricultural production land and drawing numerous agricultural technicians to non-agricultural jobs, which has hampered agriculture’s progress. The traffic facilities (Way) coefficient is significant at the 1% level, with a value of 0.139. This finding is consistent with Li et al. [72], and suggests that building roads may improve the resilience of the food system. In fact, roads facilitate the transportation of goods and food products, thereby enhancing circulation efficiency. Consequently, food system resilience could potentially increase. Furthermore, the coefficient of GDP is significant at the 5% level. This finding is consistent with Xiao et al. [34], indicating that economic progress enhances the resilience of the food system. As the economy grows, the government’s investment in agriculture will likely increase in tandem, leading to better infrastructure development in rural areas and ultimately benefiting the development of agriculture.

4.2. Parallel Trend Test

While using the DID model, it is crucial for the experimental and control groups to meet the homogeneity and common trends hypotheses. This means that prior to the implementation of the BC policy, the food system resilience of both the experimental and control groups ought to have the same trend. To examine parallel trends, this paper establishes model (5), following Jacobson et al. [79], and assesses whether the coefficient of food system resilience for the years 2011 and 2012 differs significantly from 0:

$$Y_{it}=\alpha +{\displaystyle \sum }_{j=-m}^n{\delta }_j{\alpha }_{1}Digita{l}_{i,t-j}+\gamma Contro{l}_{it}+{ϵ}_i+{\nu }_i+{\epsilon }_{it}$$

(5)

where m and n represent the number of cycles prior to and post the execution of the BC policy. Additionally, ${\delta }_j$ is the discrepancy coefficient of food durability between the experimental and control groups in the year of the BC policy implementation.

The regression results are presented in Figure 2, revealing that the corresponding coefficient of food system resilience exhibited no significant difference from 0 during 2011 and 2012. This indicates that, prior to the implementation of the BC policy, there was no distinguishable difference in the changing trend of food system resilience between the experimental group and the control group. The DID model can be applied to examine the effect of the BC policy on the food system’s resilience, and further substantiate the causal link between the digital economy and the system’s resilience.

4.3. Placebo Test

To ascertain that the aforementioned findings are attributable to the BC policy and not to other unobservable and unknown or accidental factors, a placebo trial is necessary. Referring to He’s practice [80], this paper constructs a counterfactual framework, conducts benchmark regression, and then investigates the significance of the key variable coefficients. The paper utilises the Bootstrap methodology to randomly allocate the BC policy implementation time for each town, and performs 500 repetitions of the regression according to Formula (1). If the coefficients of the key variables are statistically significant, we can conclude that there is no correlation between the enhancement of food system resilience and the BC policy, as the timing and location of the BC policy implementation were randomly selected. If the coefficients of crucial variables do not show any statistical significance, it can be inferred that the rise in resilience of the food system is a consequence of the BC policy, and the effect of other factors on the said system can be neglected. Figure 3 presents the kernel density diagram of digital economic variables. It can be observed that the absolute value of the estimation coefficient t for the majority of samples is less than 2, indicating that the coefficients of crucial variables do not show any statistical significance. Therefore, the placebo test confirmed that the model setting was less disturbed by issues such as omitted variables or random factors.

4.4. Instrumental Variable Estimation

We employed the instrumental variables approach to mitigate potential endogeneity issues. The outcomes of the regression analysis incorporating instrumental variables are depicted in

Table 5. Instrumental variable estimation.

Variable	Food System Resilience
IV	0.034 * (0.020)
Urban	−0.019 * (0.012)
Way	0.001 (0.002)
IS	−0.098 *** (0.032)
Open	0.012 ** (0.006)
GDP	0.013 (0.011)
Environment	−0.012 (0.008)
Disaster	0.003 (0.002)
Year effects	Y
Individual effects	Y
R2	0.132
Kleibergen–Paap rk LM statistic	11.075 ***
F statistic	16.459
Obs	1422

(1) ***, **, and * indicate significance at 1%, 5%, and 10%, respectively. (2) Robust standard errors in parentheses. (3) Between 1984 and 2020, the municipal government of China made adjustments to the division of urban areas. Presently, there are 158 cities devoid of adjusted areas, meaning that only data from these cities can be matched, resulting in a corresponding sample size of 1422.

. The results demonstrate a statistically significant Anderson LM statistic at the 1% significance level, and a Cragg–Donald Wald F statistic exceeding the critical value at the 10% significance level. These results demonstrate that the instrumental variables chosen in this paper are robust, rejecting the likelihood of weak or unidentifiable identification. The estimation results indicate that the digital economy’s development has greatly improved food system resilience and confirmed the estimation results’ robustness.

4.5. Mediation Effect Test

Overall, the digital economy is proven to positively impact food system resilience; however, through what mechanisms does the digital economy affect food system resilience? Based on the model setting and mechanism analysis described above, this section uses the mediating effect model to empirically test the mechanisms by which the digital economy affects food system resilience and investigates whether the digital economy affects food system resilience through digital technology, digital finance, and human capital. Equation (3) is regressed and the estimated results of the influence of the digital economy on the mediating variables are presented in

Table 6. The influence of digital economy on mediator variables.

Variable	Digital Technology	Digital Finance	Human Capital
	(1)	(2)	(3)
Digital	0.091 * (0.052)	0.086 * (0.050)	0.070 ** (0.035)
Control variables	Y	Y	Y
Year effects	Y	Y	Y
Individual effects	Y	Y	Y
Constant	−0.724 * (0.369)	−0.068 (0.397)	8.436 ** (0.162)
Obs	1900	1900	1900
R2	0.406	0.634	0.189

(1) **, and * indicate significance at 5%, and 10%, respectively. (2) Robust standard errors in parentheses.

. A regression of Equation (4) is also conducted and the estimated results are presented in

Table 7. The direct effect test of digital economy on food system resilience.

Variable	Food System Resilience
	(1)	(2)	(3)
Digital	0.043 *** (0.015)	0.043 *** (0.014)	0.044 *** (0.014)
Digital technology	0.020 * (0.010)
Digital finance		0.028 * (0.016)
Human capital			0.019 *** (0.006)
Control variables	Y	Y	Y
Year effects	Y	Y	Y
Individual effects	Y	Y	Y
Constant	1.610 *** (0.121)	1.598 *** (0.124)	1.438 *** (0.123)
Obs	1900	1900	1900
R2	0.321	0.325	0.321

(1) ***, and * indicate significance at 1%, and 10%, respectively. (2) Robust standard errors in parentheses.

.

The first column of Table 6 illustrates that the digital economy has a noteworthy and positive influence on digital technology. This indicates that the growth of the digital economy drives the progress of digital technology. Moreover, the first column of Table 7 indicates that advancements in digital technology have bolstered the resilience of the food system. The fact that all coefficients have positive signs demonstrates that digital technology has a partial mediating effect on the resilience of the food system. The digital economy enhances the resilience of the food system by promoting the advancement of digital technology. Therefore, Hypothesis 2 of this paper is supported by empirical results.

Table 6’s second column demonstrates that the growth of the digital economy stimulates advancements in digital finance. Similarly, the second column of Table 7 illustrates that the advancements of digital finance have heightened the resilience of the food system. All coefficients indicate positive effects, indicating that digital finance has a partial mediating effect. The digital economy enhances the food system’s resilience by facilitating the growth of digital finance. Therefore, Hypothesis 3 of this paper is supported by empirical results.

Table 6’s third column demonstrates that the digital economy fosters human capital development. The third column in Table 7 clarifies the positive impact of human capital development on the food system’s resilience. Additionally, all coefficients indicate positive effects, signifying that human capital has a partially mediating effect. The digital economy enhances the resilience of the food system by fostering the growth of human capital. Therefore, Hypothesis 4 of this paper is supported by empirical results.

In conclusion, the digital economy can impact the resilience of the food system through three avenues: digital technology, digital finance, and human capital. Therefore, Hypothesis 1 of this paper is supported by empirical results.

4.6. Heterogeneity Analysis

4.6.1. Geospatial Locations

Due to differences in digital economic development levels within China, this study investigates the influence of the digital economy on food system resilience across different regions. Based on the varying levels of economic development and geographical location, China is divided into three regions: Eastern, Central, and Western. The regression results of regional heterogeneity are shown in

Table 8. Analysis heterogeneity of digital economy on food system resilience.

Variable	Eastern China	Central China	Western China
	(1)	(2)	(3)
Digital	0.042 * (0.023)	0.059 ** (0.035)	0.032 (0.022)
Control variables	Y	Y	Y
Year effects	Y	Y	Y
Individual effects	Y	Y	Y
Constant	2.133 *** (0.153)	0.619 (0.666)	1.072 *** (0.248)
Obs	880	470	550
R2	0.221	0.484	0.580

(1) ***, **, and * indicate significance at 1%, 5%, and 10%, respectively. (2) Robust standard errors in parentheses.

. The results in columns (1) and (2) indicate that the digital economy has a significant and positive impact on the resilience of the food system in Central and Eastern China, with a greater impact on the central area. Perhaps the reason for this is twofold: firstly, China’s central region utilises digital finance to advance the development of farmland water conservancy facilities and agricultural intelligent equipment, both of which are capable of effectively mitigating risks. Secondly, the government employs digital technology towards the establishment of an agricultural information platform, promoting land circulation, augmenting farmers’ planting acreage, fostering economies of scale, and ultimately enhancing the resilience of the food system. Hence, the effects of digital economy development are more apparent in the central region. The results in column (3) indicate that the digital economy does not have a significant impact on the resilience of the food system in Western China. This lack of impact is due to the comparatively underdeveloped nature of China’s western region and its slower pace of digital economy growth. It is possible that this slow growth has not affected the resilience of the food system.

4.6.2. Functional Zoning of Food

To guarantee food security and maintain sustainable development, Chinese towns are categorised into primary food-producing zones and non-primary food-producing zones based on a functional zoning of food. These zones have distinct functions. The major producing zones require an escalated input of production factors to continually improve their food output capacity. Furthermore, aside from catering to local demand, the harvested foods are also supplied to non-primary food-producing zones. The non-major food-producing zones develop the economy vigorously, while procuring food from the major food-producing zones, and supporting their further development. Therefore, to assess the impact of the digital economy on the resilience of food systems in various functional zones, this study divides the towns into two distinct parts. The results of heterogenous regression are shown in

Table 9. Analysis of the heterogeneity of digital economy on food system resilience.

Variable	Primary Food-Producing Zones	Non-Primary Food-Producing Zones
	(1)	(2)
Digital	0.037 ** (0.017)	0.057 ** (0.022)
Control variables	Y	Y
Year effects	Y	Y
Individual effects	Y	Y
Constant	1.670 *** (0.118)	0.405 ** (0.174)
Obs	1540	360
R2	0.318	0.636

(1) *** and ** indicate significance at 1%, and 5%, respectively. (2) Robust standard errors in parentheses.

. It is demonstrated that the digital economy has a positive and significant impact on the resilience of the food system in both the major food-producing areas and the non-major food-producing areas, and especially in the non-major food-producing areas. This could be due to the higher level of digital economy development, more advanced digital infrastructure, and mature digital technology in non-primary food-producing zones. The digital economy has the capacity to play a greater role and significantly improve the resilience of the food system in non-primary food-producing zones.

5. Conclusions and Discussion

5.1. Conclusions

In this paper, the impact of the digital economy on food system resilience is empirically tested. This paper draws the following conclusions. Firstly, the digital economy has a considerable positive impact on the resilience of the food system, a finding that remains valid even after considering endogenous issues and performing robustness tests. Secondly, the digital economy can impact the resilience of the food system through three channels: digital technology, digital finance, and human capital. Finally, there is regional heterogeneity in the impact of the digital economy on the resilience of the food system, with variations observed across different food functional zones.

5.2. Policy Recommendations

China should prioritise the development of a resilient food system due to resource and environmental challenges facing its food production, as well as exposure to international market impact. As a traditional food-producing country, China requires a more resilient food system to ensure food security. In the future, China must remain focused on the crucial role of digital economy development in enhancing the resilience of its food system. This paper recommends three follow-up policy measures based on its findings.

Firstly, China ought to enhance the utilization of digital technology in the food system. It should encourage the profound integration of the digital economy and agriculture, and enhance the level of applied agricultural modernization machinery and equipment, remote sensing natural disaster prediction technology, and Internet of Things agricultural management system. This will undoubtedly provide safeguarding for the food production system. China should also increase support for the “Internet+Agriculture” initiative, employ big data analytics to precisely manage market conditions, and offer assurances for the food management system.

Secondly, it is essential to improve the ability of farmers to use digital finance. On the one hand, it is necessary to enhance farmers’ understanding of digital finance by combining online and offline means of publicity. On the other hand, the government should provide greater financial subsidies and introduce modern digital equipment in rural areas to lower the threshold for farmers to use digital finance.

Thirdly, it is crucial to enhance the training of professionals, including technical innovation experts, production managers, and management service experts in digital agriculture. Furthermore, China should also aim to improve the cultural level of farmers, encouraging them to apply for vocational colleges through government subsidies, fee reductions, and other means, to elevate their education level and develop reserve talents for establishing a resilient food system.

5.3. Limitations and Further Research

The role of digital technology development and digital inclusive finance on agricultural production has been identified in previous studies, although the existence of a facilitating role of the digital economy on food system resilience has not been addressed. For example, Zheng et al. found that Internet use had a significant positive impact on farmers’ grain production [81]. He et al. suggested that digital inclusive finance can help farmers to increase their resilience to climate change [82]. Moreover, Hu et al. have confirmed the role of rural credit on food production [83]. The findings obtained in this study are a useful complement to the role of digital economy on the food system, which can be further explored in depth in the future. The available research, however, has yet to arrive at a uniform conclusion regarding the regional disparities in the effects of the digital economy. This paper found that the digital economy does not have a significant impact on the resilience of the food system in Western China. However, Jia found that the digital economy has a prominent impact on the sustainable agricultural development of the central region, followed by the western region [33]. Additionally, this paper found that the digital economy has a positive and significant impact on the resilience of the food system in both the major food-producing areas and the non-major food-producing areas. But Lin et al. found that digital inclusive finance has no significant impact on food security in non-major food-producing areas [58].

Although the findings of this study support the existence of an important role for digital economy in promoting food system resilience, the findings can only be considered as a preliminary validation and have some limitations. First, due to limited data availability, this study is confined to the analysis of 190 towns in China. These towns constitute 57.05% of all towns in the country, but the results may carry a certain degree of bias. Future research will investigate additional towns in China. Second, we have not examined the correlation between the digital economy and the resilience of the food system in developed nations, which have a different scenario compared to developing ones. As a result, future studies will encompass investigations in developed countries.

6. Final Remarks

This paper investigates the causal link between the digital economy and the resilience of the food system in China. No conclusive evidence of such a correlation has been found in other countries. Nevertheless, research shows that the influence of the digital economy transcends national boundaries [84], and has revolutionised various societal aspects, consequently becoming a key player in sustainable development [85]. Canada’s food system is currently embracing the emerging trend of agricultural digitalisation [86]. Highly mechanised precision agriculture and intelligent farms have been developed in both the United States and Russia [87,88]. Additionally, smartphone applications are utilised in Uganda to enhance public agricultural extension services [89]. Digital technology is widely regarded as the key factor in accelerating economic growth and enhancing labour productivity in modern times [90,91], resulting in high economic efficiency across different sectors [92]. The implementation of digital technology in Ukrainian agriculture has led to the development of efficient business processes, enhancing the overall efficiency of the agricultural companies [93]. More strikingly, the creation of online content in the UK in 2000 has had a substantial, enduring effect on regional productivity levels 16 years later [94]. Digitization is a social technology project, and it actively promotes the ideal future of agricultural food [95]. We believe that the digital economy could affect the resilience of food systems in other regions. However, further verification is necessary.