Climate Change and Sustainable Agriculture: Assessment of Climate Change Impact on Agricultural Resilience

Abstract

[Introduction] Climate change is a serious global challenge that is currently being faced and could intensify in the future. The resulting climate risks will have varying degrees of impact on sustainable agricultural development. To cope with climate change and achieve sustainable agricultural development, there is an urgent need to enhance agricultural resilience. [Methods] This paper employs fixed effects modeling to explore the impacts of climate change on agricultural resilience (production, economy, society, and ecology) using China’s regional data and examines the moderating roles of digital finance and agricultural infrastructure in the relationship between the two. [Results] The findings indicate the following: first, climate change has a negative impact on agricultural resilience, which constrains sustainable agriculture; second, both digital finance and agricultural infrastructure can mitigate the adverse effects of climate change on agricultural resilience; and third, the heterogeneity analysis further reveals that agricultural resilience in grain functional areas and regions with low levels of agricultural industrial integration is more significantly affected by climate change. [Discussion] Climate change threatens sustainable agriculture as the frequency of extreme climate events increases. Assessing the impact of climate change on agricultural resilience is of profound strategic significance for promoting sustainable agriculture, addressing climate risks, and ensuring food security. Policymakers should take adequate measures to strengthen agricultural resilience, including promoting digital finance in agriculture and increasing targeted infrastructure investments for vulnerable areas.

1. Introduction

In recent years, the increasing frequency of extreme weather events and climate disasters has led to a growing number of uncertainties and instabilities in the agricultural system [1]. According to the Intergovernmental Panel on Climate Change (IPCC), human activities have caused global warming of approximately 1 degree Celsius compared to the pre-industrial era. Meanwhile, data from the National Oceanic and Atmospheric Administration (NOAA) indicate that 2023 was the most severe year on record for climate disasters. By the end of December 2023, more than 30 climate disaster events with individual losses exceeding USD 1 billion had occurred globally. Agriculture is highly dependent on and sensitive to climate [2], and climate change undoubtedly increases the uncertainty of agricultural activities, also threatening sustainable agriculture. Central Document No. 1 in 2022 first focused on the impact of medium- and long-term climate change on agriculture. In the same year, the National Strategy for Climate Change Adaptation 2035, which was released, included “enhancing the agricultural production’s capacity to adapt to climate change” in the “Vision 2035 Goals.” In response to the current situation, the Central Committee of the Communist Party of China and the State Council emphasized in their Opinion on Doing a Good Job in Promoting Rural Revitalization in 2023 the need to build a strong agricultural country with “strong industrial resilience [3].” Meanwhile, Central Document No. 1 in 2023 and 2024 also proposed, respectively, to “strengthen the capacity building for agricultural disaster prevention and mitigation” and “enhance short-term early warning of meteorological disasters and medium- and long-term trend analysis, and improve the long-term mechanism for agricultural disaster prevention and mitigation [4].” Therefore, enhancing the resilience of the agricultural system to withstand climate change is not only a necessary measure to adapt to climate change but also an urgent need to ensure national food security, maintain social stability, and promote sustainable agriculture [5].

Agriculture, due to its reliance on natural resources and weather conditions, is highly susceptible to climate impacts. With the significant increase in the frequency and intensity of extreme weather events, sustainable agriculture is facing severe challenges. As a global issue, climate change and its impacts on agricultural development have garnered considerable attention from the academic community. Many scholars have explored the influence of climate change over agricultural production. Ortiz-Bobea et al. demonstrated that global agricultural productivity has slowed down by about 21% since 1961 as a result of climate change [6]. Fanzo et al. concluded that climate change poses significant threats to the food system, increasing the risk of uncertainty in production [7]. Chen et al. discovered that extreme weather events significantly induce a reduction in rice and wheat yields [8]. Liu et al. suggested that climate change directly impacts food supply, such as by altering precipitation patterns and temperature conditions, which in turn affect the crop production cycle and yield [9]. In addition to agricultural production, some scholars pay attention to the influence of climate change on the agricultural economy. For example, a study by Ma et al. verified that climate change, which is mainly characterized by warming, is not conducive to enhancing agricultural economic resilience [10]. In addition, several studies have explored the effects of climate change on agricultural ecology. Overall, scholars generally agree that it constitutes threats to the stability of agricultural ecosystems. Specifically, Zhang et al. demonstrated that climate risks are significantly negatively correlated with agricultural ecological efficiency [11]. Dai and Yu argued that climate change exacerbates the uneven distribution of water resources, bringing water stress effects to crop production [12]. Zhou et al. suggested that climate change will lead farmers to increase pesticide use, which in turn will pose a threat to arable land soil [13].

The concept of “resilience” originates from the concept of physics and has gradually expanded to social and economic studies. The resilience of cities, industrial chains, and enterprises has been widely studied. At present, economic resilience is a focal point of research for numerous domestic and international scholars, including national economic resilience, regional economic resilience, urban economic resilience, and household economic resilience. In the agricultural sector, academic research on resilience remains predominantly concentrated on agricultural economic resilience, with studies conducted on its connotation, measurement, and driving mechanisms. However, agriculture is a complex system that integrates multiple scales and elements, and agricultural economics can only represent one aspect of the agricultural system [14]. Moreover, there is a clear distinction between agricultural economic resilience and agricultural resilience in essence. The former emphasizes the agricultural economic system’s resistance to external shocks such as market fluctuations [15], while the latter focuses on the adaptive development capacity of subsystems, including economic, social, and ecological aspects, after being subjected to external shocks [2]. Currently, direct research on agricultural resilience in the academic community is still relatively rare. The limited existing literature either revolves around the construction of indicator systems [16] or explores the influence of digital elements on agricultural resilience [17]. Rooted in the natural ecological domain, agricultural systems are highly susceptible to the negative impacts of climate change. However, theoretical discussions and quantitative estimates of the impact of climate change on agricultural resilience remain unclear.

By reviewing the existing literature, we find that while previous studies have provided a theoretical basis for understanding the relationship between climate change and agricultural systems, they still have limitations. First, most studies focus on the influence of climate change on one particular aspect of agricultural systems, like production or economics, lacking a comprehensive perspective that integrates economic, social, and ecological factors. Second, although existing studies do pay attention to resilience capacity in the agricultural sector, they are limited to agricultural economic resilience. However, there are essential differences between the two. Agricultural economic resilience primarily reflects the risk resistance and recovery capacity of economic activities when facing shocks, with a focus on the stability of economic returns for agricultural enterprises, farmers, and other entities. In contrast, agricultural resilience looks at the overall stability of agricultural systems when facing external disturbances, comprehensively measuring the resistance and recovery capacity of multiple subsystems, including agricultural production, economy, society, and ecology, after disturbances. In addition, existing studies mainly focus on the adverse influence of climate change, lacking in-depth discussions on measures to cope with climate change.

In summary, this paper will, given the circumstances of climate change, dissect the theoretical mechanisms through which it affects agricultural resilience. Also, it examines the influence of climate change on agricultural resilience. Furthermore, this paper will delve further into the potential roles of digital finance and agricultural infrastructure in mitigating the impacts of climate change. The marginal contributions are as follows: First, incorporating characteristics of the agricultural system, this paper constructs an evaluation index system that covers four dimensions, production, economy, society, and ecology, to accurately measure agricultural resilience and systematically examine the comprehensive impact of climate change on agricultural resilience, rather than a single impact. Second, this paper expands the literature on the influencing factors of agricultural resilience from the perspective of climate change. There are few direct studies on agricultural resilience, and no research has revealed the impact of uncertain factors on it. This paper takes climate change as the starting point and provides a new research perspective for exploring the adaptive capacity of agricultural systems in the face of uncertain factors. Third, it provides a scientific basis for agricultural systems to cope with the challenges of climate change, which reveals the moderating role of digital finance and agricultural infrastructure. The relevant conclusions provide empirical evidence for preventing and resolving climate risks, improving agricultural resilience, and promoting sustainable agriculture. Fourth, it further explores the heterogeneous impact of climate change on agricultural resilience from the two angles of agricultural functional zones and the level of industrial integration. This helps to more precisely understand the sensitivity of different functional zones or levels of industrial integration to climate change and the changes in agricultural resilience.

2. Theoretical Analysis and Research Hypotheses

2.1. The Theoretical Connotation of Agricultural Resilience

In the field of economics, there is a consensus among scholars regarding the interpretation of resilience, which is defined as the capacity of economic agents to resist, absorb, adapt to, and maintain functionality in the face of external disturbances and to achieve recovery, transformation, renewal, and development [18,19]. Thus, resilience is the capability to swiftly recover and transition to a new growth trajectory when subjected to external shocks. This capability encompasses three characteristics, namely resistance, recovery, and regenerative capacity. In defining the connotation of agricultural resilience, on the one hand, it should be consistent with the basic consensus in the field of economics; on the other hand, it should also fully take into account the fundamental characteristics of the agricultural sector. According to the above considerations, this paper defines agricultural resilience as the capacity of an agricultural system to achieve basic defense when subjected to external shocks, to reduce losses in production, economic, social, and ecological aspects, to rapidly self-recover, and to achieve adaptive development.

2.2. The Mechanism of Climate Change’s Impact on Agricultural Resilience

Comprehensively assessing climate change risks is crucial for sustainable agriculture. Climate change, as an external environmental factor, is primarily characterized by the long-term trends in climatic elements such as temperature and precipitation. Agriculture, being a natural reproductive activity that relies on various environmental elements such as light, heat, water, and soil, is highly susceptible to the impacts of climate change. Climate change poses a threat to the stability of agricultural production and economic, social, and ecological subsystems, making it difficult for them to recover to their original state in a short period, thereby exerting a negative impact on agricultural resilience [20]. The theoretical mechanism of climate change impact on agricultural resilience can be seen in Figure 1.

First, climate change poses a significant threat to the resilience of agricultural production by reducing agricultural output. The increasing frequency and intensity of extreme weather events, such as droughts, directly damage crop growth environments, leading to reduced outputs and disrupted growth cycles. These events not only destroy crops but also degrade soil quality and water resources, further weakening the agricultural system’s capacity for recovery and adaptation [21]. Additionally, changes in temperature and precipitation patterns alter the growing conditions for crops, making it difficult for traditional varieties to thrive and increasing the unpredictability of agricultural output. The spread of pests, diseases, and invasive weeds, facilitated by changing climates, adds another layer of complexity, as these biological threats can devastate crops and reduce biodiversity. This, in turn, increases the uncertainty and risks related to production activities, further undermining the resilience of agricultural production [22].

Second, climate change hampers the resilience of agricultural economies by disrupting the stability of the agricultural supply chain. The increasing frequency and severity of extreme weather events could result in large-scale fluctuations in crop yields and quality, which in turn trigger sharp fluctuations in market prices [23]. This weakens the risk resistance capacity of the agricultural economic system, making it more vulnerable to climate change. Price volatility not only affects farmers’ enthusiasm for agricultural activities, increasing the probability of land abandonment, but also impacts enterprises throughout the agricultural industry chain. For example, agricultural product processing companies face the risk of production interruptions due to unstable raw material supplies, while traders struggle to keep pace with market rhythms amid price fluctuations, resulting in profit declines. As the efficiency of agricultural product production and circulation decreases, the virtuous cycle of the agricultural economic system is disrupted.

Third, climate change undermines the social resilience of agriculture by threatening the livelihoods of farmers. To mitigate the risks of pests and extreme weather disasters caused by climate change, farmers must increase their investment in water facilities, fertilizers, pesticides, and other essential resources to ensure yields [10]. This will cause the marginal cost of agricultural production to rise and the net benefit of farmland production to fall, which significantly reduces the quality of farmers’ incomes and jeopardizes the stability of their livelihoods [24]. Moreover, the inputs for post-disaster management will further exacerbate the livelihood difficulties of farmers after extreme weather disasters. When crops are damaged by disasters such as hail, heavy rain, and typhoons, farmers not only bear the economic losses caused by reduced or total crop failure but also need to invest in human, material, and financial resources for post-disaster reconstruction. Furthermore, specific adaptive measures in response to extreme climatic conditions might result in counterproductive outcomes, which in turn generate negative externalities that can give rise to social issues like “environmental migration”, which will have an impact on the social resources and infrastructure of the destination areas.

Ultimately, climate change undermines the ecological resilience of agriculture by exacerbating the degradation of water and soil resources, as well as the environment. By intensifying extreme weather events, climate shocks weaken the self-restoration capacity of the ecosystems in agricultural sectors [25]. These extreme climatic events not only alter the availability of water resources but also damage the structure and quality of soil, causing arable land to become parched, leading to the degradation of surface vegetation and worsening soil salinization and desertification, thereby reducing the stability of agricultural ecosystems [12]. Moreover, the prevalence of pests and diseases and the spread of weeds due to global warming necessitate the use of higher doses of pesticides, which in turn cause more severe non-point source pollution in agriculture and greatly damage the water quality of river basins, thus eroding the resilience of agricultural ecosystems. Thus, the following hypothesis is proposed:

Hypothesis 1.

Climate change has a significant negative impact on agricultural resilience.

2.3. Moderating Role of Digital Finance and Agricultural Infrastructure

Digital finance, the use of digital technologies to deliver financial services and products, offers new opportunities to empower agricultural resilience when facing climate change. Climate change exacerbates the scarcity and instability of agricultural production resources, making traditional agricultural resource allocation methods inadequate. Digital finance, leveraging technologies, could more accurately assess the credit status of agricultural production entities [26]. It provides them with more convenient and low-cost financial support, thereby increasing production efficiency and promoting the reorganization of factors. It also facilitates the innovation of agricultural production technologies in the direction of adapting to climate change. In addition, it could guide the flow of funds to more climate-resilient agricultural sectors, reducing the agricultural industry’s reliance on limited natural resources and thereby enhancing the overall risk resistance capacity of the agricultural system [27]. Moreover, the “inclusive” nature of digital finance is stronger, providing robust financial support for the agricultural sector to withstand uncertain shocks [28]. By offering convenient and efficient financing channels, it can provide financial support for agricultural technological innovation, accelerating the development and application of agricultural technologies. Agricultural technological innovation can overcome the adverse spillover effects of climate change within the agricultural system, boosting the system’s capacity to withstand and recover from shocks, thereby improving agricultural resilience. In other words, as a risk-sharing tool, digital finance can effectively alleviate the financial constraints faced by the agricultural system when dealing with external shocks such as climate change. This implies that digital finance may play a significant role in the adaptation of agriculture to climate change.

Agricultural infrastructure is conducive to resisting the uncertainty brought to the agricultural system by natural factors. It refers to the physical structures, facilities, and systems that support agricultural production, processing, storage, and distribution. The increase in climate risks will raise the probability of related natural disasters, thereby threatening agricultural resilience. Agricultural infrastructure is the sum of various public facilities that ensure the smooth operation of agricultural production and management activities, and its types are diverse [29]. As an external investment for guarding against natural risks, agricultural infrastructure can effectively overcome constraints imposed by natural conditions and reduce the adverse effects of climate change on the resilience of the agricultural sector. Moreover, agricultural infrastructure, by facilitating the input of essential factors, can support the adoption of innovative technologies in agriculture, thereby enhancing the system’s ability to adapt and adjust to shocks. It can be seen that agricultural infrastructure can weaken the adverse effects of climate change on agricultural resilience. Thus, the following hypothesis is proposed:

Hypothesis 2.

Digital finance and agricultural infrastructure can mitigate the negative impact of climate change on agricultural resilience.

3. Research Design

3.1. Model Construction

With the continuous intensification of climate change, significant impacts have been exerted on the agricultural system’s stability. Understanding and assessing the complex dynamic relationships between climate change and the subsystems of agricultural production, economy, society, and ecology have become important research topics today. This paper centers on examining the influence of climate change on the resilience of agricultural systems, aiming to explore ways to reduce its shocks on agricultural resilience. To minimize the impact of unobservable unit-specific and time-specific factors, this paper employs the fixed effects model for baseline regression [30], which is specified as follows:

$${Agri\_res}_{it}={\beta }_o{Clim}_{it}+{\beta }_1{X}_{it}+{\mu }_i+{\theta }_t+{\epsilon }_{it}$$

(1)

In the above equation, ${Agri\_res}_{it}$ denotes the agricultural resilience of region $i$ in year $t$; the core explanatory variable ${Clim}_{it}$ is the normalized accumulated temperature and precipitation variables, which characterize climate change. To avoid issues such as omitted variable bias, a set of control variables $X_{it}$ will be selected in this part. Additionally, ${\mu }_i$ denotes the provincial fixed effects, ${\theta }_t$ represents the year fixed effects, ${\epsilon }_{it}$ is the random disturbance term, and ${\beta }_o$ and ${\beta }_1$ are the parameters to be estimated.

This paper constructs a moderating effects model based on the baseline model:

$${Agri\_res}_{it}={\beta }_o{Clim}_{it}+{\phi }_1{M}_{it}+{\phi }_2{Clim}_{it}\times M_{it}+{\beta }_1{X}_{it}+{\mu }_i+{\theta }_t+{\epsilon }_{it}$$

(2)

In Equation (2), $M_{it}$ represents the moderating variable, indicating the level of digital finance (${Dfi}_{it})$ or agricultural infrastructure construction (${Infras}_{it}$) in regions $i$ in years $t$; ${Climate}_{it}\times M_{it}$ is the interaction term between climate change and the moderating variable, and its estimated coefficient ${\phi }_2$ reflects the moderating role of digital finance or agricultural infrastructure. Understandably, coefficient ${\phi }_2$ plays a pivotal role in discerning the nature of this moderating effect. If the sign of ${\phi }_2$ is the same as that of ${\beta }_o$, this indicates that digital finance or agricultural infrastructure positively moderates the influence of climate change on agricultural resilience; otherwise, it suggests a negative moderating (mitigating) effect.

3.2. Variable Selection

3.2.1. Dependent Variable

Agricultural resilience (${Agri\_res}_{it}$). This section constructs an evaluation index system and uses the entropy method for calculation. This paper takes the production, economic, social, and ecological subsystems as the criterion layer to assess the degree of resilience each subsystem possesses when facing external shocks. From the perspective of supply and demand capabilities and input–output, and by referring to important domestic and international studies [4,14,17], a total of 26 indicators are finally selected to measure agricultural resilience (see

Table 1. The evaluation index system of agricultural resilience.

Dimensions	Indicators	Measurement	Indicator Attribute
Agricultural Production Resilience	Production Conditions	Per capita arable land area (hm2)	+
		Labor productivity (ten thousand CNY/person)	+
		Proportion of crop disaster area (%)	−
	Production Capacity	Per capita grain yield (kg)	+
		Crop planting structure (%)	+
	Production Inputs	Effective irrigation rate per unit of arable land area (%)	+
		Total power of agricultural machinery per unit of sown area (kW)	+
Agricultural Economic Resilience	Economic Foundation	Total output value of agriculture (CNY billion)	+
		Disposable income of rural residents (CNY/person)	+
		Proportion of primary industry (%)	+
	Economic Stability	Percentage growth in the value added of the primary sector (%)	+
		Per capita consumer expenditure of rural residents (CNY/person)	+
	Economic Transformation	Outstanding balance of agricultural loans (CNY billion)	+
		Fiscal expenditure on science and technology (CNY billion)	+
Agricultural Social Resilience	Social Stability	Ratio of per capita disposable income between urban and rural residents (%)	+
		Proportion of urban population at year end (%)	+
		Number of agricultural workers obtaining minimum living allowances (ten thousand people)	−
	Social Security	Per capita expenditure on medical and health care in rural areas (CNY/person)	+
		Number of health technicians per thousand people	+
		Average educational level among rural populations (years)	+
Agricultural Ecological Resilience	Chemical Inputs	Quantity of agricultural fertilizers per unit of sown area	−
		Quantity of agricultural plastic film used per unit of sown area	−
		Quantity of pesticides used per unit of sown area	−
	Ecological Governance	Forest coverage rate (%)	+
		Area of soil and water loss control per capita (hm2/person)	+
		Proportion of investment in environmental pollution control (%)	+

). Among them, production resilience focuses on the stability of the production process and the efficiency of resource use to ensure the continuous and stable output of agricultural products; economic resilience focuses on coping with market fluctuations and the development of the industry to ensure the stability of the income of the business entities; social resilience focuses on the integrity of the rural social system to ensure the stability of rural communities and the quality of life of residents; and ecological resilience focuses on ecological restoration and stability to ensure the health and sustainable development of the agricultural ecosystem.

3.2.2. Core Explanatory Variables

Climate change (${Clim}_{it}$): Drawing the method of Guo et al. [31], this paper utilizes the Climate Physical Risk Index (CRPI) to characterize climate change. This index comprises four sub-indices, namely LTD (the number of extreme low-temperature days), HTD (the number of extreme high-temperature days), ERD (the number of extreme rainfall days), and EDD (the number of extreme drought days). The calculation process of the CRPI is as follows: First, the period from 1973 to 1992 was selected as the climate baseline period, and extreme values were determined. Second, the number of extreme weather days for each weather station was calculated; that is, if the temperature, precipitation, or humidity on a given day exceeded the threshold, that day was counted as an extreme weather day. The calculation formulas for LTD, HTD, ERD, and EDD are shown in Equations (3)–(6). Next, using the geographical coordinates of each weather station, meteorological data were mapped to specific regions, and the average number of extreme weather days for all weather stations within a region was calculated. Finally, the min–max method was used to standardize the four sub-indices, and the standardized sub-indices were weighted averaged to obtain the overall degree of climate physical risk. In this study, the weights of the four sub-indices were set to 0.25, and the CRPI was log-transformed.

$${LTD}_{i,n}={\sum }_{t=1}^{365}{LT}_{i,n,t}{LT}_{i,n,t}=\left\{\begin{array}{c}1if{T}_{i,n,t}<T_i^{10}\\ 0if{T}_{i,n,t}\ge T_i^{10}\end{array}\right.$$

(3)

$${HTD}_{i,n}={\sum }_{t=1}^{365}{HT}_{i,n,t}{HT}_{i,n,t}=\left\{\begin{array}{c}1if{T}_{i,n,t}>T_i^{90}\\ 0if{T}_{i,n,t}\le T_i^{90}\end{array}\right.$$

(4)

$${ERD}_{i,n}={\sum }_{t=1}^{365}{ER}_{i,n,t}{ER}_{i,n,t}=\left\{\begin{array}{c}1if{R}_{i,n,t}>R_i^{95}\\ 0if{R}_{i,n,t}\le R_i^{95}\end{array}\right.$$

(5)

$${EDD}_{i,n}={\sum }_{t=1}^{365}{ED}_{i,n,t}{ED}_{i,n,t}=\left\{\begin{array}{c}1if{H}_{i,n,t}<H_i^5\\ 0if{H}_{i,n,t}\ge H_i^5\end{array}\right.$$

(6)

Note: In the equations, $T_{i,n,t}$, $R_{i,n,t}$ and $H_{i,n,t}$ represent the average temperature, precipitation, and humidity at weather station $i$ on day $t$ of year$n$, respectively.

3.2.3. Moderating Variables

Digital finance (${Dfi}_{it}$) and agricultural infrastructure ($Infras$): Digital finance is represented by the “Digital Inclusive Finance Index” jointly compiled by Peking University and other institutions. This index comprehensively measures the development status of digital finance in China. In this paper, the original data are divided by 1000 for processing. Agricultural infrastructure is measured by the fixed asset investment in agricultural sector. The variable is logarithmically transformed in this paper.

3.2.4. Control Variables

This paper selects industrial structure, the degree of opening up to the outside world, the proportion of employees in the primary industry, the intensity of financial support, the transportation level, etc., as control variables. Specifically, the industrial structure ($Indus$) is measured by the ratio of the output value of the tertiary industry to that of the secondary industry. The degree of opening up to the outside world ($Open$) is reflected by the proportion of foreign direct investment to the regional gross domestic product. The proportion of workers in primary industry ($Prim$) is represented by the ratio of the number of workforces engaged in the primary industry. The intensity of financial support ($Fis$) is calculated by the proportion of expenditure on agriculture to the local financial general public budget expenditure. The transportation level ($Trans$) is indicated by the number of kilometers of roads within the province and is logarithmically transformed.

3.3. Data Sources

Panel data from 30 provinces in China over the period 2013–2022 are selected as the sample (excluding the Hong Kong and Macao Special Administrative Regions, Taiwan Province, and Tibet Autonomous Region). The data are primarily sourced from the China Statistical Yearbook, China Rural Statistical Yearbook, China Environmental Statistical Yearbook, Peking University Digital Inclusive Finance Index, and statistical yearbooks of individual provinces. For missing data, this paper employs the linear interpolation method for imputation. The descriptive statistics of all variables are shown in

Table 2. Descriptive statistical analysis.

Variables	Symbol	Mean	Standard Deviation	Minimum	Maximum
Agricultural Resilience	$\mathrm{A}\mathrm{g}\mathrm{r}\mathrm{i}\_\mathrm{r}\mathrm{e}\mathrm{s}$	0.3135	0.0619	0.1884	0.5646
Climate Change	$\mathrm{C}\mathrm{l}\mathrm{i}\mathrm{m}$	3.7929	0.1972	3.2506	4.4348
Digital Finance	$\mathrm{D}\mathrm{f}\mathrm{i}$	0.2786	0.0802	0.1180	0.4607
Agricultural Infrastructure	$\mathrm{I}\mathrm{n}\mathrm{f}\mathrm{r}\mathrm{a}\mathrm{s}$	6.2973	1.2822	0.4729	8.5070
Industrial Structure	$\mathrm{I}\mathrm{n}\mathrm{d}\mathrm{u}\mathrm{s}$	0.4754	0.0899	0.0940	0.6489
Degree of Opening Up to the Outside World	$\mathrm{O}\mathrm{p}\mathrm{e}\mathrm{n}$	0.2359	0.2584	0.0002	1.3418
Proportion of Employees in the Primary Industry	$\mathrm{P}\mathrm{r}\mathrm{i}\mathrm{m}$	0.2915	0.1334	0.0156	0.5926
Intensity of Financial Support	$\mathrm{F}\mathrm{i}\mathrm{s}$	0.2532	0.1027	0.1066	0.6430
Transportation Level	$\mathrm{T}\mathrm{r}\mathrm{a}\mathrm{n}\mathrm{s}$	11.7262	0.8523	9.4441	12.9126

.

4. Empirical Results and Analysis

4.1. The Characteristics of Climate Change in China

Affected by the global warming trend, the average temperature increase in China is approximately 0.5–0.8 °C over the past 100 years, while the temperature increase in China is about 1.1 °C over the past 50 years. According to statistics from national meteorological observation stations, from 2012 to 2023, there were a total of 503 heavy rain weather processes in China. The frequency, duration, and impact range of extreme precipitation have all shown a clear increasing trend, with stronger precipitation extremes and more severe disasters. Also, the frequency of extreme weather events has increased. Specifically, the intensity and duration of extreme high-temperature events have both intensified. For example, in 2024, southern China was hit by a heatwave that lasted as long as 74 days. Extreme low-temperature events have also occurred with increasing frequency, with new record lows for minimum temperatures being set repeatedly. In 2023, for instance, the lowest temperature in Mohe, Heilongjiang Province, plummeted to minus 53 °C, breaking China’s historical record for the lowest temperature. Furthermore, the occurrence and strength of extreme rainfall events have risen markedly. In 2023, the Beijing–Tianjin–Hebei region experienced a sporadic heavy rainfall weather process, with a maximum cumulative rainfall of 1003 mm in some localized areas. Additionally, regional meteorological droughts have been occurring frequently, exerting a significant impact on agricultural production, water resource supply, and other aspects.

Based on the calculation results of the Climate Physical Risk Index (CRPI), this paper uses the ArcGIS 10.2 software to reveal the characteristics of climate change. Following the approach of Zhang and Xie [32], this paper uses the natural breaks classification method and divides the CRPI into five quintiles, namely low (0–1.5789), lower (1.5790–1.6328), medium (1.6329–1.6728), higher (1.6728–1.7111), and high (1.7112–1.9260). Figure 2a–c show its spatial evolution. The color in the maps represents the level of climate risk. In the figure, the closer the color of a region is to red, the higher the climate risk it indicates; conversely, the closer to green, the lower the climate risk. During the observation period, the overall climate risk in China shows an upward trend. In 2015, the CRPI in the vast majority of regions in China was at a low or lower level, with only Yunnan, Ningxia, and Xinjiang at a medium level. Between 2015 and 2019, the climate risk in several provinces changed, characterized by varying degrees of level increase. Among them, the western region experienced the most significant jump in climate risk levels. For example, the climate risk in Yunnan and Xinjiang increased from a medium level to a high-risk level; Qinghai and Gansu jumped from a low or lower level to a high-risk level. The climate risk level changes in the eastern coastal regions were relatively minor, such as in Jiangsu, Fujian, and Zhejiang. Between 2019 and 2022, the climate risk level in the vast majority of provinces in China reached medium and above, indicating that the climate risk in China is further intensifying, and this trend is widespread across the country. Given the inherent dependence of agriculture on climate, frequent climate changes will pose a threat to agricultural systems.

4.2. The Evolutionary Trend of Agricultural Resilience

This paper employs the entropy-weight method to quantify provincial-level agricultural resilience and visualizes its spatial evolution for 2015, 2019, and 2022 using ArcGIS (Figure 3). Based on a quintile classification, resilience values are categorized into five levels: low (0–0.2599), lower (0.2600–0.2945), medium (0.2946–0.3216), higher (0.3217–0.3546), and high (0.3547–0.5646). Color intensity corresponds directly to resilience strength—warmer red hues indicate stronger resilience, while deeper green tones denote weaker performance. Overall, agricultural resilience improved significantly during the study period. In 2015, only a few provinces fell into the higher or high categories, with most eastern coastal and border regions exhibiting low to lower resilience. Between 2015 and 2019, a marked upward shift occurred, particularly in eastern provinces. Notably, Hebei and Jilin advanced from higher to high resilience, Liaoning progressed from medium to higher, and several coastal provinces improved from lower to medium. From 2019 to 2022, central provinces continued rising into the higher and high categories, while western regions remained comparatively weak, lagging behind in resilience development.

With severe climate change threatening China’s food security, the 30 samples in this paper were grouped into “grain-producing areas” and “non-grain-producing areas”, and we compared the evolution of mean agricultural resilience between 2013 and 2022 (see Figure 4). Such a classification criterion was based on the policy document issued by the Ministry of Finance in 2003. Taking into account indicators such as grain output, 13 provinces were designated as grain-producing areas, clarifying their positioning and responsibilities in the national agricultural development work. Figure 4 shows that the level of agricultural resilience was positive throughout the observation period in China, indicating that China’s agricultural system could withstand external risks and shocks. However, in terms of numerical value, it is at a medium–low level, with considerable room for improvement. Overall, the agricultural resilience in China exhibited a continuous upward trend during the sample period. Specifically, the level of agricultural resilience in China was only 0.2750 in 2013, which increased to 0.3515 over the 10 years, a rise of 21.76%. At the regional level, the level of agricultural resilience in grain-producing areas was consistently higher than that in non-grain-producing areas. The level of agricultural resilience in grain-producing areas increased from 0.3048 in 2013 to 0.4021 in 2022, a significant change of 31.92%. In contrast, the level of agricultural resilience in non-grain-producing areas was always below the national average, and the gap with grain-producing areas widened in the later period of the sample. At the beginning of the sample period, the gap between the two was 0.2085, while by the end of the sample period, it expanded to 0.2855.

4.3. Results of Baseline Effects

This section conducts an empirical analysis based on the baseline model designed in the previous section. The findings are detailed in

Table 3. Baseline model results.

Variables	(1)	(2)
	$\mathit{A}\mathit{g}\mathit{r}\mathit{i}\_\mathit{r}\mathit{e}\mathit{s}$	$\mathit{A}\mathit{g}\mathit{r}\mathit{i}\_\mathit{r}\mathit{e}\mathit{s}$
$\mathrm{C}\mathrm{l}\mathrm{i}\mathrm{m}$	−0.0130 *** (−2.41)	−0.0121 *** (−2.43)
$\mathrm{I}\mathrm{n}\mathrm{d}\mathrm{u}\mathrm{s}$		−0.0839 *** (−4.38)
$\mathrm{O}\mathrm{p}\mathrm{e}\mathrm{n}$		0.0317 *** (3.15)
$\mathrm{P}\mathrm{r}\mathrm{i}\mathrm{m}$		0.1064 *** (3.45)
$\mathrm{F}\mathrm{i}\mathrm{s}$		0.1338 *** (4.17)
$\mathrm{T}\mathrm{r}\mathrm{a}\mathrm{n}\mathrm{s}$		0.0192 (1.35)
$\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{s}$	0.3255 *** (4.09)	0.0611 (0.35)
Fixed Effects	Control	Control
Sample Size	300	300
${\mathrm{R}}^2$	0.1442	0.3218

Note: *** denotes statistical significance at the 1% levels, respectively, with the t-values presented in parentheses.

. According to the results of the F-test and the Hausman test, the fixed-effects model was selected for the baseline regression. The test results for multicollinearity indicate that the variance inflation factor (VIF) value is 3.57, which is significantly lower than the empirical critical value of 10, suggesting that the model does not suffer from severe multicollinearity issues. In Column (1), only climate change is included in the regression, while in Column (2), both climate change and control variables are incorporated. The results indicate that, before adding control variables, the regression coefficient of climate change on agricultural resilience is −0.0130, which is significant at the 1% level. When including control variables such as industrial structure, the regression coefficient of climate change becomes −0.0121, which also satisfies the significance threshold at the 1% level. Consequently, it can be observed that climate change has a negative impact on agricultural resilience, which constrains sustainable agriculture, thereby verifying hypothesis 1.

Among the control variables, the degree of opening up to the outside world, the proportion of employees in the primary industry, and financial support all play a significant positive role. A one-unit rise in opening up to the outside world raises agricultural resilience by 3.17%. This indicates that a higher degree of opening up can promote the inflow of production factors into the agricultural sector, thereby strengthening the agricultural system’s capacity to withstand external uncertainties. A one-unit increase in the proportion of employees in the agricultural sector results in a 10.64% enhancement in agricultural resilience. The expansion of the workforce in the primary industry is conducive to providing labor security for diversified agricultural operations, which can serve as a buffer for the agricultural system against external shocks. A one-unit increase in the intensity of financial support leads to a 13.38% improvement in agricultural resilience. This suggests that financial support for the agricultural sector can compensate for the constraints imposed by natural resource endowments on the agricultural system, enhance its risk resistance and self-organization recovery capabilities, and elevate the level of agricultural resilience. The industrial structure also exerts an adverse influence on agricultural resilience. A possible reason could be that the tilt of resources towards the service industry generates a resource crowding-out effect that weakens support for the agricultural sector, thereby reducing the agricultural system’s risk resistance capacity. Finally, the coefficient of the transportation level is positive but not significant.

4.4. Robustness Tests

This paper employs four distinct methods for robustness testing. Firstly, considering that the agricultural resilience level ranges from 0 to 1, aligning with the conditions for a limited dependent variable model, the Tobit model is utilized for re-regression, with the outcomes summarized in

Table 4. Results of robustness tests.

Variables	(1)	(2)	(3)	(4)	(5)	(6)
	Tobit Model	Alternative Indicator Measurement Method	Winsorization	Introducing Linear and Quadratic Terms of Time Trend
	$\mathit{A}\mathit{g}\mathit{r}\mathit{i}\_\mathit{r}\mathit{e}\mathit{s}$	$\mathit{A}\mathit{g}\mathit{r}\mathit{i}\_\mathit{r}\mathit{e}\mathit{s}$	$\mathit{A}\mathit{g}\mathit{r}\mathit{i}\_\mathit{r}\mathit{e}\mathit{s}$	$\mathit{A}\mathit{g}\mathit{r}\mathit{i}\_\mathit{r}\mathit{e}\mathit{s}$	$\mathit{A}\mathit{g}\mathit{r}\mathit{i}\_\mathit{r}\mathit{e}\mathit{s}$	$\mathit{A}\mathit{g}\mathit{r}\mathit{i}\_\mathit{r}\mathit{e}\mathit{s}$
$\mathrm{C}\mathrm{l}\mathrm{i}\mathrm{m}$	−0.0121 *** (−3.24)	−0.0152 *** (−3.22)	−0.0102 ** (−2.46)	−0.0142 *** (−2.62)	−0.0115 *** (−2.61)	−0.0142 *** (−3.32)
$\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}\mathrm{V}\mathrm{a}\mathrm{r}\mathrm{i}\mathrm{a}\mathrm{b}\mathrm{l}\mathrm{e}\mathrm{s}$	Yes	Yes	Yes	Yes	Yes	Yes
$\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{s}$	0.0776 (0.57)	0.4564 *** (3.45)	0.1551 (0.89)	−0.7140 *** (3.19)	−7.5009 *** (3.39)	5.301 *** (4.97)
Provincial Fixed Effects	Yes	Yes	Yes	Yes	Yes	Yes
Year Fixed Effects	Yes	Yes	Yes	Yes	No	No
Linear Term of Time Trend	——	——	——	Yes	Yes	Yes
Quadratic Term of Time Trend	——	——	——	No	No	Yes
Sample Size	300	300	300	300	300	300
${\mathrm{R}}^2$	——	0.2957	0.2331	0.2560	0.2713	0.2238

Note: *** and ** denote statistical significance at the 1% and 5%, levels, respectively, with the t-values presented in parentheses.

. The findings indicate that the estimated coefficient for climate change is −0.0121 (p < 1%). Secondly, acknowledging the limitations of the entropy method, this paper recalculates the dependent variable utilizing the entropy weight–TOPSIS approach. After changing the indicator measurement method, the outcomes are shown in Column (2), demonstrating that climate change continues to exert a significant negative impact. Thirdly, to mitigate the influence of data outliers, the main variables are subjected to a bilateral 1% winsorization process before re-regression. The results, presented in Column (3), show that the estimated coefficient for climate change is −0.0102, statistically significant at the 1% level, suggesting that the estimation conclusions still support the validity of the original hypothesis after winsorization. Fourth, following the approach of Burke et al. [33], we include both the linear and quadratic terms of time trends in the regression to control some other unobserved factors that vary over time, thereby more comprehensively mitigating endogeneity issues arising from omitted variables. The results are shown in Columns (4) to (6). Column (4) introduces the linear term of the time trend on the basis of regional and time fixed effects. Column (5) includes only the linear term of the time trend, excluding the quadratic term of the time trend and year fixed effects. Column (6) incorporates both the linear and quadratic terms of the time trend, excluding year fixed effects. Under the above scenarios, the coefficient of climate change remains significantly negative.

4.5. Moderating Effects

This paper employs Model (2) to verify the moderating effects of digital finance and agricultural infrastructure, with the results presented in

Table 5. Estimation results of moderating effects.

Variables	(1)	(2)
$\mathrm{C}\mathrm{l}\mathrm{i}\mathrm{m}$	−0.0109 *** (−3.73)	−0.0211 *** (−5.04)
$\mathrm{D}\mathrm{f}\mathrm{i}$	0.2387 *** (3.57)	——
$\mathrm{D}\mathrm{f}\mathrm{i}\ast \mathrm{C}\mathrm{l}\mathrm{i}\mathrm{m}$	0.0028 *** (2.96)	——
$\mathrm{I}\mathrm{n}\mathrm{f}\mathrm{r}\mathrm{a}\mathrm{s}$	——	0.0026 *** (3.23)
$\mathrm{I}\mathrm{n}\mathrm{f}\mathrm{r}\mathrm{a}\mathrm{s}\ast \mathrm{C}\mathrm{l}\mathrm{i}\mathrm{m}$	——	0.0034 *** (3.90)
$\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}\mathrm{V}\mathrm{a}\mathrm{r}\mathrm{i}\mathrm{a}\mathrm{b}\mathrm{l}\mathrm{e}\mathrm{s}$	Yes	Yes
$\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{s}$	0.2259 *** (5.73)	0.2489 *** (4.79)
Fixed Effects	Control	Control
Sample Size	300	300
${\mathrm{R}}^2$	0.2677	0.2991

Note: *** denotes statistical significance at the 1% levels, respectively, with the t-values presented in parentheses.

. From Column (1), it is evident that the main effect coefficient of climate change on agricultural resilience remains significantly negative, remaining at the 1% significance level. The interaction term between digital finance and climate change is significantly positive (p < 1%), with a sign opposite to that of the main effect, indicating that digital finance mitigates the adverse effects of climate change on agricultural resilience. Moving to Column (2), the coefficient of the main effect is still significantly negative. After controlling for time and regional fixed effects, the interaction term between agricultural infrastructure and climate change is significantly positive, and its value is 0.0034. This demonstrates that increasing the intensity of agricultural infrastructure construction can significantly weaken the adverse influence of climate change on agricultural resilience. Consequently, Hypothesis 2 is validated.

4.6. Heterogeneity Analysis

The above research indicates that climate change exerts a negative influence on agricultural resilience. Also, an examination of moderating effects reveals that both digital finance and agricultural infrastructure can moderate the adverse influence of climate change. Analyzing the effects of the two in more detail, it is necessary to further analyze other factors that may affect climate change and agricultural resilience, taking into account the differences in the external environment.

4.6.1. Heterogeneity in Agricultural Functional Zones

The selection of agricultural functional zones as a variable for heterogeneity analysis is based on both theoretical and practical considerations. Theoretically, different agricultural functional zones are characterized by distinct land use patterns, farming practices, and resource availability. Grain-producing areas, for instance, are typically more focused on staple crops and may have different vulnerabilities to climate change compared to non-grain-producing areas, which may include a mix of cash crops, livestock, and other agricultural activities. Practically, understanding the differential impacts of climate change on these zones is crucial for targeted policy interventions and resource allocation. By dividing the sample into grain-producing and non-grain-producing areas, we can better identify the specific needs and resilience strategies for each zone. Drawing on the method of Gong et al. [34], the sample is divided into grain-producing areas and non-grain-producing areas. This section further explores the effect of climate change on agricultural resilience among distinct functional zones and the variations in the moderating effects of digital finance and agricultural infrastructure. The findings are presented in Columns (1) and (4) of

Table 6. Heterogeneity analysis of agricultural functional zones.

Variables	Grain-Producing Areas			Non-Grain-Producing Areas
	(1)	(2)	(3)	(4)	(5)	(6)
$\mathrm{C}\mathrm{l}\mathrm{i}\mathrm{m}$	−0.0172 ** (2.25)	−0.0521 ** (−2.29)	−0.0256 *** (−2.82)	−0.0094 (−1.62)	−0.0028 (−0.17)	−0.0156 *** (−3.68)
$\mathrm{D}\mathrm{f}\mathrm{i}$	——	0.0002 *** (2.82)	——	——	0.0003 *** (4.64)	——
$\mathrm{D}\mathrm{f}\mathrm{i}\ast \mathrm{C}\mathrm{l}\mathrm{i}\mathrm{m}$	——	0.0039 ** (1.99)	——	——	0.0002 (0.19)	——
$\mathrm{I}\mathrm{n}\mathrm{f}\mathrm{r}\mathrm{a}\mathrm{s}$	——	——	0.0040 *** (3.73)	——	——	0.0038 ** (2.11)
$\mathrm{I}\mathrm{n}\mathrm{f}\mathrm{r}\mathrm{a}\mathrm{s}\ast \mathrm{C}\mathrm{l}\mathrm{i}\mathrm{m}$	——	——	0.0030 *** (3.97)	——	——	0.0030 *** (4.59)
$\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}\mathrm{V}\mathrm{a}\mathrm{r}\mathrm{i}\mathrm{a}\mathrm{b}\mathrm{l}\mathrm{e}\mathrm{s}$	Yes	Yes	Yes	Yes	Yes	Yes
$\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{s}$	0.3450 *** (4.85)	0.3642 *** (3.90)	0.3056 *** (4.43)	0.3063 *** (4.21)	0.2551 *** (3.91)	0.2324 *** (5.52)
Fixed Effects	Control	Control	Control	Control	Control	Control
Sample Size	130	130	130	170	170	170

Note: *** and ** denote statistical significance at the 1% and 5%, levels, respectively, with the t-values presented in parentheses.

. The regression coefficients for climate change are negative in both grain-producing and non-grain-producing areas, indicating that climate change has an adverse impact on agricultural resilience in different functional zones. However, this impact is only significant in the grain-producing areas, and the coefficient value is significantly higher in the grain-producing areas. This indicates there are distinct characteristics at play in different agricultural areas that lead to these varying impacts on agricultural resilience. Therefore, the influence of climate change on agricultural resilience exhibits heterogeneity across functional zones.

The heterogeneity results of the moderating effect of digital finance across different agricultural functional zones are shown in Columns (2) and (5) of Table 6. Similarly to the baseline regression, the main effect regression coefficients are also negative, and they only pass the significance test in the grain-producing group. Additionally, the moderating effect regression coefficients are only significant in grain-producing areas and have an opposite sign to the main effect, suggesting its negative moderating role in the influence of climate change on agricultural resilience, thereby alleviating the adverse effects of climate change on agricultural resilience in grain-producing areas. However, the symbol of the moderating effect is not significant in the other group, suggesting its moderating role does not hold in the agricultural functional zone.

The heterogeneity results of the moderating effect of agricultural infrastructure are presented in Columns (3) and (6). Notably, across these different groupings analyzed for heterogeneity, the underlying relationship patterns show consistency in terms of the direction of influence. The main effect regression coefficients are significantly negative in both groups. The moderating effect regression coefficients are significantly positive and have an opposite sign to the main effect. This suggests that agricultural infrastructure can mitigate the adverse effects of climate change on resilience across various agricultural functional zones.

4.6.2. Heterogeneity of Agricultural Industrial Integration

The selection of agricultural industrial integration levels as a variable for heterogeneity analysis is also grounded in both theoretical and practical significance. Theoretically, higher levels of industrial integration can lead to a more diversified agricultural economy, which may enhance resilience by spreading risks across different sectors. This is supported by studies that argue that extending the agricultural industry chain can reduce the vulnerability of the agricultural system to natural risks [35]. Practically, understanding how industrial integration affects the impact of climate change on agricultural resilience can inform policies aimed at promoting sustainable agricultural development and enhancing economic resilience. By measuring the level of industrial integration using the proportion of agricultural service industries and dividing the sample based on the median value, we could assess the differential influence of climate change on agricultural resilience across regions with varying levels of industrial integration.

This paper examines the impact of climate change heterogeneity resulting from agricultural industrial integration, measuring industrial integration through the proportion of agricultural service industries. Subsequently, the samples are divided into two groups with levels of agricultural industrial integration in line with its median value. The findings are detailed in Columns (1) and (4) of

Table 7. Heterogeneity analysis of agricultural industrial integration.

Variables	High Level Industry Integration			Low Level Industry Integration
	(1)	(2)	(3)	(4)	(5)	(6)
$\mathrm{C}\mathrm{l}\mathrm{i}\mathrm{m}$	−0.0035 (−0.49)	−0.0189 (−1.28)	−0.0256 *** (−2.82)	−0.0139 ** (2.19)	−0.0026 (−0.09)	−0.0156 *** (−3.68)
$\mathrm{D}\mathrm{f}\mathrm{i}$	——	0.0002 *** (4.36)	——	——	0.0004 *** (3.89)	——
$\mathrm{D}\mathrm{f}\mathrm{i}\ast \mathrm{C}\mathrm{l}\mathrm{i}\mathrm{m}$	——	0.0012 (1.09)	——	——	0.0004 (0.16)	——
$\mathrm{I}\mathrm{n}\mathrm{f}\mathrm{r}\mathrm{a}\mathrm{s}$	——	——	0.0040 * (1.79)	——	——	0.0038 (1.11)
$\mathrm{I}\mathrm{n}\mathrm{f}\mathrm{r}\mathrm{a}\mathrm{s}\ast \mathrm{C}\mathrm{l}\mathrm{i}\mathrm{m}$	——	——	0.0003 *** (3.97)	——	——	0.0002 (1.29)
$\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}\mathrm{V}\mathrm{a}\mathrm{r}\mathrm{i}\mathrm{a}\mathrm{b}\mathrm{l}\mathrm{e}\mathrm{s}$	Yes	Yes	Yes	Yes	Yes	Yes
$\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{s}$	0.2556 *** (5.45)	0.2660 *** (4.40)	0.3056 *** (4.43)	0.2772 *** (6.75)	0.1874 * (1.76)	0.2324 *** (9.52)
Fixed Effects	Control	Control	Control	Control	Control	Control
Sample Size	151	151	151	149	149	149

Note: ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively, with the t-values presented in parentheses.

. In both groups, the regression coefficients for climate change are negative, confirming its negative impact on agricultural resilience. However, this impact is only significant in regions with a low level of agricultural industrial integration. It can be seen that the impact of climate change on agricultural resilience exhibits heterogeneity in terms of agricultural industrial integration.

The heterogeneity results of the moderating effect of digital finance across regions with different levels of industry integration are shown in Columns (2) and (5). The main effect regression coefficients are negative but not significant. The estimated coefficients for the moderating effect are opposite to those of the main effect, yet they are not significant, suggesting that the moderating role of digital finance is not significant in either group characterized by varying degrees of industrial integration.

The heterogeneity results of the moderating effects of agricultural infrastructure are presented in Columns (3) and (6). It is obvious that main effect regression coefficients are negative and pass the significance test in both groupings. However, the moderating effect regression coefficient is only significant in the high-level agricultural industry integration grouping, with a symbol opposite to that of the main effect, indicating that agricultural infrastructure exerts a stronger moderating role in regions with high levels of industry integration.

5. Discussion

Climate change, as a significant external environmental factor, poses a severe threat to sustainable agriculture. Given the continuous intensification of climate change, enhancing agricultural resilience has become a pressing global issue. Current discussions on the relationship between climate change and agricultural development have only focused on one aspect of the agricultural system (such as production), and no studies have comprehensively revealed the impacts of climate change on different subsystems of agriculture. In addition, research on resilience has mainly focused on micro-level entities such as corporate resilience. Although some studies have looked at the macro level, they have also focused on the manufacturing sector [36], with few linking it to the agricultural sector. This paper explores the mechanism by which climate change affects agricultural resilience and constructs a measurement framework for agricultural resilience from the dimensions of agricultural production, economy, society, and ecology, empirically testing the impacts of climate change on agricultural resilience. Existing studies have shown that climate change has a negative impact on crop yields and farmers’ incomes, and the results of this paper also confirm the negative shock that climate change has caused to the agricultural system. China is both a major agricultural country and a sensitive and significantly affected area of climate change. This requires China’s agricultural system to strengthen its ability to cope with climate change, enhance climate monitoring and early warning systems, and promptly detect and respond to possible natural disasters.

The moderating effect model further reveals the role of digital finance and agricultural infrastructure in moderating the relationship between climate change and agricultural resilience. This finding not only provides a new perspective for understanding how agricultural systems cope with climate change but also offers specific directions for policymakers. However, the moderating effects of these two factors are not fully realized in all cases. In some regions, the limited penetration of digital technology restricts the application of digital finance, preventing it from fully realizing its potential to enhance agricultural resilience. Similarly, the construction of agricultural infrastructure faces issues such as insufficient funding and low construction standards, which affect its effectiveness in responding to climate change. Therefore, to better leverage the moderating role of digital finance and agricultural infrastructure, it is necessary to accelerate the promotion and application of digital technology, improving the quality and standards of agricultural infrastructure construction. At the same time, policy guidance should be enhanced to encourage financial institutions and enterprises to increase their investment in digital finance and agricultural infrastructure, creating a favorable situation with the joint participation of multiple stakeholders, including the government, market, and society.

Theoretically, this paper enriches research on the relationship between climate change and agricultural resilience. On the one hand, by constructing a measurement framework for agricultural resilience from the dimensions of agricultural production, economy, society, and ecology, it provides a new method for a comprehensive assessment of agricultural resilience, which helps to more accurately grasp the overall stability of the agricultural system when facing external shocks such as climate change. On the other hand, it reveals the moderating mechanisms of digital finance and agricultural infrastructure between climate change and agricultural resilience, expanding the research perspective on the influencing factors of agricultural resilience and providing a new theoretical basis for further in-depth research on the pathways to enhance agricultural resilience.

In practice, the research findings of this paper provide a specific action guide for the agricultural sector to cope with climate change. First, the construction of the agricultural resilience measurement framework can help policymakers and agricultural practitioners more comprehensively identify the vulnerable links of the agricultural system under climate change, so as to formulate targeted strategies to enhance agricultural resilience. Second, clarifying the moderating role of digital finance and agricultural infrastructure provides a clear direction for the agricultural sector in terms of financial support and infrastructure construction, which helps to guide resources to more effective areas. Finally, this research also provides theoretical support for the sustainable development of the agricultural sector, emphasizing the importance of enhancing the adaptive capacity of the agricultural system through technological innovation and infrastructure improvement in the background of climate change, which is of great significance for ensuring national food security and promoting rural economic development.

Due to limitations in data availability and statistical scope, this paper’s sample is confined to provincial-level data from 2013 to 2022. The research team will continue to monitor this area, extending the timeframe of the study to more comprehensively assess the relationship between climate change and agricultural resilience. Additionally, the research team will explore the relationship between the two at various spatial scales, such as the municipal and county levels. Moreover, this paper assumes a linear relationship between climate change and agricultural resilience, which may not always hold true. Non-linear relationships and threshold effects could exist. Future research should employ some other econometric techniques, such as threshold models or non-linear regression analysis, to investigate these possibilities.

6. Conclusions and Suggestions

6.1. Conclusions

This paper employs panel data from 30 provinces in China over the period 2013–2022 to empirically analyze the impact of climate change on agricultural resilience, as well as the moderating effects of digital finance and agricultural infrastructure, and conducts heterogeneity analysis based on agricultural functional zones and the level of agricultural industrial integration. The main conclusions are as follows: There is a significant negative correlation between climate change and agricultural resilience. The moderating effects indicate that both digital finance and agricultural infrastructure can mitigate the adverse effects of climate change on agricultural resilience. Further analysis based on agricultural functional zones and the level of agricultural industrial integration shows that the impact of climate change on agricultural resilience is more pronounced in grain-producing areas and regions with a low level of industrial integration. Moreover, the moderating effects also exhibit regional heterogeneity. Specifically, digital finance plays a greater moderating role in grain-producing areas, while agricultural infrastructure exerts a more significant moderating effect in regions with a high level of industrial integration.

6.2. Policy Recommendations

In light of the conclusions drawn above, this paper puts forward the following recommendations for policymakers and stakeholders.

First, improve the climate risk monitoring and early warning system. The Ministry of Agriculture should collaborate with the Meteorological Bureau and other relevant agencies to establish specialized agricultural climate monitoring stations, particularly in climate-sensitive zones and key agricultural production regions. This will enable real-time data collection and sharing. Additionally, regular assessments of agricultural climate adaptability should be conducted to evaluate the impacts of climate change across different regions and sectors. Tailored adaptation plans and response strategies should then be formulated to guide rational agricultural planning and structural adjustments, thereby strengthening overall climate resilience.

Second, strengthen the support of digital finance for agriculture. Financial regulatory authorities should introduce guiding policies to encourage insurance companies to develop agricultural insurance products based on climate indices, reducing the economic losses of farmers caused by climate disasters. By leveraging digital technology, these insurance products can more accurately assess and manage the risks associated with climate change. This approach would not only help to mitigate the economic losses that farmers face due to climate-related disasters but also provide a more robust and responsive financial safety net.

Third, increase investment in agricultural infrastructure construction. For example, in areas frequently affected by droughts and floods, reservoirs, irrigation canals, and drainage systems should be built to enhance the disaster resistance of farmland. Another example is the promotion of intelligent agricultural equipment. By utilizing the Internet of Things technology, the development of intelligent irrigation, precision fertilization, and pest and disease monitoring systems can be advanced, thereby improving the efficiency of agricultural production and resource utilization.

Finally, implement differentiated agricultural climate adaptation strategies. For grain-producing areas, the focus should be on strengthening farmland water conservancy construction and the promotion of climate-adaptive crop varieties to enhance the stability and self-sufficiency of grain production. For non-grain-producing areas, the development of specialty and ecological agriculture should be encouraged. By leveraging the advantages of climate resources, the development of sightseeing agriculture, organic agriculture, and other forms of high-value-added agriculture can be promoted.