Policy Experimentation for Sustainable Agriculture: How China’s Policy-Based Agricultural Insurance Enhances Grain Production Resilience

Policy Experimentation for Sustainable Agriculture: How China’s Policy-Based Agricultural Insurance Enhances Grain Production Resilience | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Policy Experimentation for Sustainable Agriculture: How China’s Policy-Based Agricultural Insurance Enhances Grain Production Resilience Yifan Tang, Mengliang Zhou This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7477834/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Under the new normal of intensified climate change and overlapping systemic risks, building a resilient grain production system has become a core objective of national food security strategies. This study employs provincial panel data from 2013 to 2022 to measure grain production resilience using the Entropy Weight-Technique for Order Preference by Similarity to an Ideal Solution method and evaluates the impact of policy-oriented agricultural insurance through a quasi-natural experiment framework based on China’s full-cost and income insurance pilots. Key findings reveal that: (1) Policy-based agricultural insurance significantly enhances grain production resilience, a conclusion robust to placebo tests, subsample analyses, and Propensity Score Matching-Difference-in-differences validation. (2) The policy effect is more pronounced in northern China compared to southern regions, attributed to larger farming scales and higher climate risk exposure. (3) Mechanism analysis demonstrates that agricultural insurance strengthens resilience by promoting scale operations and increasing the grain replanting index. Accordingly, policy recommendations are proposed to refine insurance product design, enhance regional policy targeting, strengthen institutional coordination, and establish a dynamic monitoring framework, with the goal of improving the effectiveness of policy-based agricultural insurance in building a resilient and sustainable grain production system. Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Environmental social sciences Policy-based agricultural insurance Grain production resilience Difference-in-differences Quasi-natural experiment Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction The increasing frequency of extreme weather events driven by global climate change, compounded by heightened agricultural market volatility, which poses systemic threats to the stability and sustainability of grain production systems. Against this backdrop, developing resilient grain production systems has emerged as a strategic consensus for nations worldwide. According to the Food and Agriculture Organization (FAO) resilience assessment framework, grain production resilience encompasses three dimensions: buffering capacity against shocks, regulatory capacity to adapt to environmental changes, and transformative capacity for post-disaster recovery (FAO, 2021 ). This theoretical framework not only supports systematic evaluation of production resilience but also provides critical insights for policy formulation in China. As the world's largest grain producer and consumer, China prioritizes food security in its national development strategy and continuously enhances agricultural risk resistance through policy innovation. Since 2015, China's total grain output has remained stable at over 650 million metric tons for ten consecutive years, reaching a historic high of 706 million metric tons in 2024 (China, 2024 ). Per capita grain availability stands at 500 kg, exceeding the internationally recognized food security threshold of 400 kg by 25%. However, regional agricultural vulnerabilities persist, with major production areas exhibiting structural deficiencies in risk resistance due to path dependency on traditional farming practices and resource constraints(Yang & Pan, 2021 ). Furthermore, land scarcity, agricultural labor shortages, and market price fluctuations exacerbate uncertainties in food security (Liang et al., 2022 ). These challenges highlight the limitations of conventional production models, necessitating policy innovation to enhance systemic resilience. The inherent vulnerability of agriculture subjects its development to multifaceted risks. Agricultural production intertwines natural and economic reproduction processes, rendering it highly susceptible to environmental and market fluctuations. On one hand, climate-dependent cultivation systems remain vulnerable to natural disasters; on the other, volatile commodity prices and input costs amplify operational risks. This risk superposition critically threatens the stability and sustainability of grain production. Among policy instruments for enhancing resilience, policy-based agricultural insurance has gained prominence as an institutional mechanism for risk mitigation, owing to its risk transfer function and safety net role (Bielza, Stroblmair, Gallego, Conte, & Dittmann, 2007 ). By leveraging government subsidies to incentivize farmer participation, such insurance reduces systemic risk exposure in agricultural production and mitigates disaster-induced output losses. Compared to commercial agricultural insurance, policy-based programs demonstrate greater inclusivity and coverage, delivering timely post-disaster compensation to bolster farmers' production confidence and recovery capacity. Particularly in China's context-where smallholder farming dominates alongside diversified operations-policy-based agricultural insurance serves as a pivotal tool to address "market failures" and "institutional gaps" in agricultural risk management. 2. Literature Review With the dual uncertainties of natural and market forces increasing in agricultural production, grain production resilience has gradually become an important topic in the study of sustainable agricultural development, as it serves as a critical indicator of the agricultural system's capacity to withstand shocks. Since the concept of resilience was proposed by Holling (Holling, 1973 ), it has undergone a paradigm shift from engineering resilience to ecological resilience and then to evolutionary resilience (Hellige, 2019 ). With the deepening of research, the concept of resilience has become a key analytical perspective for understanding the capacity of complex socio-ecological systems to respond to escalating environmental and economic shocks, and has gradually been applied in the field of agricultural production (Bullock et al., 2017 ). In agricultural systems, resilience encompasses three interdependent dimensions: the buffering capacity to absorb disturbances, the adaptive capacity to reorganize production strategies, and the transformative capacity to recover after crises (Tendall et al., 2015 ). This three-part framework aligns with the FAO’s operational definition of resilience in grain production and provides a solid foundation for policy assessment. In agricultural resilience research, agricultural insurance is viewed as a key tool for mitigating external shocks and stabilizing the operation of agricultural systems (C. Xie, Kuang, Wen, & Wu, 2025 ). Existing literature has systematically assessed the economic effects of agricultural insurance on agricultural development from the perspectives of risk management, income stabilization, and production incentives. Hazell et al. ( 2010 ) found that agricultural insurance has a significant effect on alleviating the impacts of natural disasters on smallholder farmers, particularly when combined with other rural financial services, which can create a more comprehensive risk response mechanism (Hazell et al., 2010 ). Mahul and Stutley ( 2010 ) demonstrated that under a model combining appropriate government subsidies and market-driven operations, agricultural insurance can effectively expand coverage and enhance farmers' production efficiency and market competitiveness (Mahul & Stutley, 2010 ). Smith and Glauber ( 2012 ) noted that policy-based agricultural insurance, through government subsidies and risk-sharing mechanisms, significantly increases farmers' enrollment rates, thereby dispersing systemic risks and stabilizing production recovery capacity after disasters (Smith & Glauber, 2012 ). Carter et al. ( 2016 ) further revealed that in developing countries, agricultural insurance can motivate farmers to adopt high-yield agricultural technologies by alleviating their "risk rationing" issues, thereby indirectly enhancing the resilience of production systems (Carter, Cheng, & Sarris, 2016 ). Tan et al. found that agricultural insurance, through a risk compensation mechanism and synergy with technological advancements, significantly improves the stability of farmers' incomes, but its economic effects exhibit regional heterogeneity (Tan, Tao, Yi, He, & Huang, 2022 ). Xie et al. (2023) found that agricultural insurance significantly enhances the stability of farmers' incomes; post-disaster compensation directly reduces short-term income volatility and optimizes production decisions through a risk dispersion effect, promoting technology adoption and larger-scale operations, especially prominent in cash crop cultivation and among larger-scale farmers (S. Xie, Zhang, Li, Xia, & Chen, 2024 ). In terms of agricultural production behavior, Tang and Luo's research indicated that agricultural insurance not only promotes the use of biopesticides but also reduces the use of chemical pesticides, thereby increasing the proportion of biopesticides in the pesticide usage structure, which contributes to the advancement of green agricultural practices and sustainable agricultural development (Tang & Luo, 2021 ). The study by Fang et al. found that agricultural insurance, as an effective risk protection mechanism, can significantly enhance agricultural green total factor productivity by guiding farmers to adopt green agricultural technologies (Fang, Hu, Mao, & Chen, 2021 ). Dercon and Christiaensen ( 2011 ) noted that farmers, lacking risk protection mechanisms, tend to avoid high-risk but high-return agricultural technologies when faced with consumption risks, leading them into poverty traps (Dercon & Christiaensen, 2011 ). In production decision-making, Karlan et al. ( 2014 ) found through randomized controlled trials that rainfall index insurance significantly increases farmers' inclination to adopt high-risk, high-return agricultural inputs, pushing them towards more rewarding crops (Karlan, Osei, Osei-Akoto, & Udry, 2014 ). Cai ( 2016 ) pointed out that agricultural insurance alleviates risk constraints, expands the cultivated area and credit demand for insured crops, and optimizes resource allocation and production decisions, thereby enhancing the resilience of agricultural systems (J. Cai, 2016 ). Sun et al. ( 2024 ) discovered that climate change does not necessarily increase the coverage rate of agricultural insurance; farmers' enrollment behavior depends more on the trade-off between premiums and subsidy compensation, and is significantly influenced by government subsidies and claims mechanisms (Sun, Tao, Wang, Wang, & Li, 2024 ). Cai et al. ( 2024 ) noted that crop insurance optimizes the pesticide usage decisions of large-scale farmers by enhancing the marginal benefits of pesticide use, thereby reducing over-application (R. Cai, Ma, & Cai, 2024 ). In summary, existing research has yielded rich theoretical and empirical findings regarding the risk mitigation functions of agricultural insurance, safeguarding farmers' incomes, and achieving agricultural sustainability. However, there are still certain shortcomings in the existing research. First, while previous studies have revealed the impact of agricultural insurance on farmers' income stability and technology adoption, few studies have framed grain production resilience as an analytical framework to explore the comprehensive effects of agricultural insurance on the adaptability and recovery capacity of agricultural systems under external shocks. Second, current research lacks in-depth analysis of mechanisms related to grain replanting, changes in operational scale, and regional heterogeneity. Based on this, this paper attempts to achieve the following innovative breakthroughs on the foundation of existing research: first, to introduce resilience theory and construct a multidimensional measurement index system of grain production resilience encompassing "stability—recovery—adaptability"; second, to utilize the regional advancement characteristics of full-cost insurance and income insurance pilot policies in China to build a difference-in-differences (DID) model, forming a quasi-natural experiment framework to identify the causal impact of policies on agricultural resilience; third, to further explore the intrinsic mechanisms by which policy-based agricultural insurance influences grain resilience from dimensions such as grain replanting, operational scale, and regional heterogeneity. Through the above analysis, this paper seeks to fill the existing research gaps on both theoretical deepening and methodological breakthroughs, providing empirical evidence for improving agricultural support policies and enhancing national food security capacity. 3. Policy Background and Theoretical Analysis 3.1. Policy Background. China's agricultural insurance started relatively late, initially focusing primarily on low coverage level physical cost insurance, which only covered direct input costs such as seeds, fertilizers, and pesticides. The low coverage level made it difficult to effectively manage the multiple risks faced by agricultural production. In 2007, the central government began subsidizing agricultural insurance premiums, marking the start of a rapid development phase for agricultural insurance in China. Governments at all levels gradually increased their financial support for agricultural insurance, resulting in an expanded coverage area and a growing variety of insurance products; however, coverage primarily focused on physical costs, with insufficient protection against labor costs, land costs, and limited coverage for market risks such as price and yield fluctuations. To address the dual risks of frequent natural disasters and increased market volatility in grain production, in 2018, the Ministry of Finance, the Ministry of Agriculture and Rural Affairs, and the China Banking and Insurance Regulatory Commission jointly issued a notice on launching pilot projects for full-cost insurance and income insurance for the three major food crops. The pilots were initially implemented in six provinces: Inner Mongolia, Henan, Hubei, Shandong, Liaoning, and Anhui, in major grain-producing counties. This pilot program marked a pivotal shift in China's agricultural insurance from traditional cost coverage to income protection. Full-cost insurance covers all production costs, including physical costs, labor costs, and land costs, while income insurance further protects farmers' planting revenues, providing a more comprehensive risk protection for farmers. Together, these two insurance types address the narrow coverage and low payout limits of traditional agricultural insurance, significantly enhancing service effectiveness. 3.2. Theoretical Analysis As a risk management tool, agricultural insurance effectively reduces the risks of natural disasters and market fluctuations faced by farmers through a risk dispersion mechanism (Meuwissen, 2000 ). An effective risk hedging mechanism can reduce the distortion of risk in decision-making, thus aligning decision-making behavior more closely with the maximization of expected utility (Binswanger & Sillers, 1983 ; Shang & Xiong, 2021 ). Specifically, in the face of agricultural production uncertainties, farmers tend to improve their utility levels by participating in insurance programs, which makes them more proactive in expanding their production scale. This risk-sharing mechanism not only reduces production volatility and promotes stable and sustainable agricultural development but also alleviates the uncertainty of income expectations by safeguarding farmers' economic interests, thereby promoting the optimized allocation of production factors such as land, labor, and capital. Insurance compensation, as an external resource compensation mechanism, alleviates liquidity constraints, optimizes the structure of intertemporal production factor inputs for farmers, and promotes the efficient reallocation of resource factors, enhancing the resilience of the grain production system. Moreover, agricultural insurance lowers the risk costs associated with adopting new agricultural technologies. Under insurance coverage, farmers' perception of the risks associated with the application of new technologies diminishes, making them more inclined to invest in modern agricultural equipment, adopt environmentally friendly production technologies, and optimize the structure of agricultural inputs (Fu, Qin, Li, & Wang, 2024 ). The adaptive improvements brought about by such technological advancements can enhance the ability of the grain production system to respond to environmental changes and external shocks. Based on the analysis above, this paper proposes Hypothesis 1: H 1 : Policy-based agricultural insurance can significantly enhance grain production resilience. Policy-based agricultural insurance optimizes farmers' production decision-making through an institutional risk mitigation mechanism, with its core logic reflecting the dual effects of risk expectation correction and factor allocation optimization. Under uncertain conditions, farmers' production decisions are significantly suppressed by subjective biases in assessing risk probabilities (Cerroni, 2020 ). Insurance tools reduce farmers' risk exposure, thereby correcting their subjective probability assessments of production uncertainties and weakening the suppressive effect of risk aversion on scale expansion. According to expected utility theory, when marginal risk costs are internalized due to insurance coverage, farmers' decisions regarding scale operations will approach profit-maximizing equilibrium more closely. Secondly, subsidized insurance products lower farmers' liquidity constraints by reducing implicit risk pricing, improving their intertemporal budget constraints, and thus releasing potential demands for scaled production and technology adoption. Full-cost insurance and income insurance, as differentiated policy tools, construct risk buffering mechanisms from the dimensions of cost anchoring and income smoothing, respectively. The former transfers tail risks from disaster impacts into the insurance pool by covering all factor costs, thereby reducing the likelihood of production interruptions; the latter hedges income risks caused by market fluctuations through income security design, stabilizing farmers' long-term expectations. This composite insurance system reduces the variance of production functions, enhances farmers' investment confidence in economies of scale, and induces a reallocation of production factors from decentralized smallholder operations to intensive production modes. Based on this analysis, this paper proposes Hypothesis 2: H 2 : Policy-based agricultural insurance enhances grain production resilience by promoting the expansion of agricultural operating scales. The influence of policy-based agricultural insurance on the increase of grain replanting index reflects a comprehensive effect of weakened risk constraints and enhanced resource utilization. Farmers’ production decisions are constrained by both expected revenues and risk assessments (Kijima, 2019 ). In the absence of effective risk hedging tools, seasonal climate fluctuations and price uncertainties lead farmers to adopt conservative land use strategies, suppressing their willingness to rotate crops. Policy-based agricultural insurance establishes a cross-season risk dispersion mechanism that significantly reduces the seasonal disaster risk exposure faced by farmers in multi-cropping grain production. When single-season crop insurance transitions to year-round income protection, farmers' marginal utility assessments for crop rotation decisions become more positive, thereby promoting the allocation of resource factors towards multi-cropping production. Full-cost insurance fully covers the land input costs, alleviating resource exhaustion and output variability risks that may arise from intensive land use during crop rotation, enabling farmers to plan rotations based on the long-term productive potential of their land. Additionally, policy-based agricultural insurance alters farmers' input-output decision frameworks through its incentive effects. The price risk hedging mechanism provided by income insurance weakens the suppressive effect of seasonal market price fluctuations on farmers' crop rotation behaviors, enhancing the tendency of farmers to formulate rotation plans based on land suitability rather than short-term market signals. Simultaneously, the liquidity support from insurance payouts reduces farmers' funding constraints between different growing seasons, providing stable financial assurance for multi-season agricultural investments. This inter-seasonal resource smoothing capability significantly promotes annual land use efficiency. The multi-cropping planting system mitigates overall production risks throughout the year and optimizes the temporal efficiency of land resource use, forming a complementary resilience to single crop planting systems and enhancing the grain production system's adaptability to climate variability and market fluctuations. Based on the analysis above, this paper proposes Hypothesis 3: H 3 : Policy-based agricultural insurance enhances grain production resilience by increasing the grain replanting index. 4. Research Design and Data Sources 4.1. Measurement of Grain Production Resilience To measure grain production resilience, a measurement index system for grain production resilience needs to be constructed. The term resilience typically refers to a system's ability to maintain its functional stability, recovery capacity, and adaptability in the face of external shocks and pressures. In the agricultural field, grain production resilience emphasizes the ability of agricultural production systems to effectively respond, recover, and adapt to external pressures such as natural disasters, market fluctuations, and environmental changes. Based on the principles of scientificity, operability, and comprehensiveness in constructing the index system, and referencing the approaches of scholars such as Fan (FAN, QIN, & YU, 2024 ) and Zhang (Wei, Peng, Zhao, Li, & Wang, 2025 ), this paper constructs a grain production resilience measurement index system that includes three first-level indicators (resistance, recovery, adaptability) and eleven second-level indicators. According to the relationship between the indicators and grain production resilience, the indicators are classified into positive impact indicators and negative impact indicators, as detailed in Table 1 . Table 1 Index System for Measuring Resilience of Grain Production Primary Indicator Secondary Indicator Attribute Unit ​ Resistance Grain Cultivated Area Positive Impact Hectare (ha) Per Capita Grain Yield Positive Impact Tonnes/Person Soil and Water Conservation Area Positive Impact Hectare (ha) Natural Disaster Incidence Rate Negative Impact % ​ Recovery Land Productivity Positive Impact Tonnes/ha Per Capita Disposable Income of Rural Residents Positive Impact CNY/Person Per Capita Consumption Expenditure of Rural Residents Positive Impact CNY/Person ​ Adaptability Agricultural Machinery Intensity Positive Impact kWh/ha Pesticide Usage Intensity Negative Impact kg/ha Fertilizer Usage Intensity Negative Impact kg/ha Agricultural Film Usage Intensity Negative Impact kg/ha Engel coefficient for rural residents Negative Impact % 4.2. Variable Definitions (1) Dependent Variable Grain Production Resilience (GPR) is calculated using the Entropy Weight-Technique for Order Preference by Similarity to an Ideal Solution(EW-TOPSIS) method. (2) Core Independent Variable Policy Variable (DID): This variable is based on whether the area is a pilot region for full-cost insurance and income insurance. If it is a pilot region, it is set to 1 for the years 2018 and beyond; otherwise, it is set to 0. Based on the policy background described earlier, six provinces including Inner Mongolia, Henan, Hubei, Shandong, Liaoning, and Anhui were selected as the experimental group, while the rest of the provinces served as the control group. (3) Control Variables Per Capita GDP (GDP): Represented by the logarithm of per capita gross domestic product. Proportion of Financial Support in Agriculture (FSA): Represented by the ratio of agricultural, forestry, and water affairs expenditure to total general fiscal budget expenditure. Urbanization Level (UR): Represented by the ratio of the urban population to the total population. Proportion of Secondary Industry (SI): Represented by the ratio of value added by the secondary industry to gross domestic product. Proportion of Tertiary Industry (TI): Represented by the ratio of value added by the tertiary industry to gross domestic product. (4) Mediating Variables Based on the analysis above, the study selects operational scale (Scale) and the grain replanting index (GRI) as mediating variables. Scale is represented by the ratio of the sown area to the rural population, while the grain replanting index represents the ratio of grain sown area to total crop sown area. 4.3. Model selection. 4.3.1. EW-TOPSIS The study employs the EW-TOPSIS method to measure grain production resilience (GPR). The process begins by normalizing the original indicators into a [0,1] range to eliminate scale differences, distinguishing between positive indicators (e.g., grain yield) and negative indicators (e.g., pesticide usage) to ensure directional consistency. Next, the entropy weight method calculates objective weights for each indicator: the standardized data are transformed into proportional values to determine information entropy, which reflects the variability of each indicator. Lower entropy values indicate higher information content, and weights are assigned based on entropy redundancy (1–entropy value). Finally, TOPSIS evaluates resilience by constructing a weighted matrix, identifying optimal (positive ideal) and worst (negative ideal) solutions, and calculating the relative proximity of each province to these benchmarks. The resulting GPR index, ranging from 0 to 1, quantifies resilience levels, with higher values indicating stronger resilience. This integrated approach ensures a robust, multidimensional assessment of grain production resilience for subsequent empirical analysis. 4.3.2. Fixed effects model According to the theoretical analysis and research hypotheses presented in this paper, an empirical model is constructed to assess the impact of policy-based agricultural insurance on grain production resilience, replacing the concept of policy-based agricultural insurance with the pilot programs for full-cost insurance and income insurance. The model is set as follows: 1 Where GPR it represents the grain production resilience level of province in year (the dependent variable); DID it represents the policy variable (the core independent variable); Control it represents the control variables; η i and θ i represent the individual fixed effects and time fixed effects, respectively, while λ it represents the random disturbance term. To further analyze the mechanism of policy based agricultural insurance on the resilience of grain production, the following model is constructed: 2 Where Med it represents the mediating variables, specifically the operational scale and the grain replanting index for province i in year t, with all other components consistent with Eq. (5). 4.4. Data Sources. This study selects data from 30 provinces in China (excluding Hong Kong, Macau, Taiwan, and Tibet) from the years 2013 to 2022 as the research sample. Relevant data are sourced from the "China Statistical Yearbook," "China Rural Statistical Yearbook," and the China National Bureau of Statistics, with some indicators derived from the composite calculation of original indicators. The maps used in this study are based on the standard map with the review number GS(2024)0650, downloaded from the National Platform for Common Geospatial Information Services. The base map boundaries have not been modified. The vector data for China’s administrative boundaries are obtained from the Resource and Environment Science and Data Center. 5. Results and analysis 5.1. Current Status of Grain Production Resilience in China Figure 1 reflects the changes in the average grain production resilience in China. From the data, it can be seen that between 2013 and 2022, the overall trend of grain production resilience showed a steady increase. Although there were slight fluctuations in certain years, the overall upward trend was quite apparent. During the period from 2013 to 2016, grain production resilience increased from 0.298 to 0.310, with a relatively gentle growth rate. This stage may have been influenced by the gradual advancement of policies and continuous development in agricultural technologies, providing a certain level of stability for grain production. In 2017 and 2018, there were slight fluctuations in grain production resilience, but it remained at a high level. This may have been affected by market volatility and natural disasters; however, the adaptability and recovery capacity of the grain production system prevented significant declines in resilience. From 2019 to 2020, grain production resilience continued to rise. In 2021, although there was a slight decline, it rebounded in 2022, demonstrating that grain production resilience possesses a certain level of resistance and recovery capability. To further analyze the spatial distribution characteristics of grain production resilience across regions, this study utilizes ArcGIS 10.8 to visualize the provincial-level resilience scores in China for the years 2013 (Fig. 2 a) and 2022 (Fig. 2 b). The natural breaks classification method is applied to divide the resilience scores into five discrete intervals. As shown in Fig. 2 , the overall spatial pattern of grain production resilience across Chinese provinces exhibits a steady upward trend over the decade. Specifically, in 2013, Heilongjiang Province fell into the highest (fifth) interval, while Henan Province was at the upper bound of the fourth interval. Ten provinces, including Hebei, Shandong, and Inner Mongolia, were also in the fourth interval. Twelve provinces, such as Anhui and Beijing, were distributed within the third interval, while four provinces including Gansu and Fujian were in the second interval. The lowest (first) interval comprised four provinces, including Guizhou and Hainan. By 2022, Heilongjiang Province remained in the fifth interval, with its score reaching the upper bound. Henan Province advanced from the fourth to the fifth interval. Five provinces—including Anhui and Guangdong—improved from the third to the fourth interval. Gansu, Yunnan, and other provinces moved from the second to the third interval. Notably, Guizhou Province achieved a leapfrog advancement from the first to the third interval. The remaining 21 provinces maintained their original resilience intervals. Overall, approximately one-third of the provinces experienced an upgrade in resilience levels, with no province exhibiting a downgrade. High-resilience regions remained stable, while several mid- to low-resilience regions showed marked improvement, indicating that China’s grain production system has undergone overall enhancement in its capacity to withstand risks over the past decade. 5.2. Baseline Regression Before estimating the model using the difference-in-differences method, it is necessary to test whether the parallel trends assumption holds. The parallel trends assumption requires that before the policy implementation, the trends of the treatment group and the control group are similar. If the trends are similar before and the treatment group shows a significant change after the implementation, the parallel trends assumption is satisfied; otherwise, the results of the difference-in-differences estimates may be biased, and other methods should be considered. From Fig. 3 , it can be seen that the coefficients before the policy shock are not significant, indicating that there is no significant difference between the treatment group and the control group before the policy implementation, thus satisfying the parallel trends assumption. Table 2 reports the baseline regression results of the impact of policy-based agricultural insurance pilots on grain production resilience. Models (1) and (3) are the regression results without controlling for time fixed effects and individual fixed effects. In models (2) and (4), the estimated coefficients of the core independent variable policy variable (DID) are significantly positive, indicating that the implementation of the policy promotes grain production resilience. Moreover, in model (4), where control variables are included, there is a significant positive correlation between economic development level (GDP) and grain production resilience, maintaining significance at the 1% level. This indicates that an increase in the level of economic development contributes to enhancing grain production resilience. The coefficient of the proportion of financial support in agriculture (FSA) is also positive and significant at the 1% level, suggesting that financial support for agriculture significantly enhances grain production resilience. The coefficient for urbanization level (UR) is significantly positive, indicating that the advancement of urbanization is beneficial to the improvement of grain production resilience. The coefficients for the proportion of value added in the secondary industry (SI) and value added in the tertiary industry (TI) are both negative and significant at the 1% level, suggesting that an increase in the share of the secondary industry may have a negative impact on grain production resilience. Table 2 Baseline regression results Variable (1) (2) (3) (4) GPR GPR GPR GPR DID 0.078 *** 0.006 * 0.072 *** 0.010 *** (0.012) (0.003) (0.016) (0.003) GDP 0.002 0.122 *** (0.025) (0.021) FSA -0.101 0.207 *** (0.258) (0.072) UR 0.445 *** 0.280 *** (0.156) (0.086) SI -0.621 ** -0.517 *** (0.308) (0.131) TI -1.043 *** -0.312 *** (0.316) (0.112) _cons 0.300 *** 0.307 *** 0.793 ** -0.861 *** (0.006) (0.001) (0.336) (0.209) N 300 300 300 300 R-square 0.055 0.982 0.173 0.988 Time fixed effect NO YES NO YES Individual fixed effect NO YES NO YES Note: The values in the brackets are robust standard errors. *, **, and *** indicate that the regression results are statistically significant at the 10%, 5%, and 1% confidence levels, respectively. The same applies to the table below. 5.3. Robustness Tests. 5.3.1. Placebo Test Considering that the impact of the policy variable on grain production resilience may stem from some unobservable factors, a placebo test is constructed following related research methods to further verify the reliability of the policy effect. The specific approach is to create a "dummy" policy variable by randomly selecting the same number of provinces as the treatment group, forming a "pseudo-experimental group," and executing the regression 500 times. The dashed line in Fig. 4 represents the true estimated coefficients, while the points represent the dummy estimated coefficient. It can be observed that the regression coefficients are normally distributed and primarily located on both sides of the zero line. There are 47 results greater than the true estimated coefficient, accounting for 9.4% of the total sampling results, with most regression results distant from the true estimated coefficient. Therefore, the results can be considered robust. 5.3.2. Shortened Sample Period Table 3 Robustness test results Variable (5) (6) (7) (8) GPR GPR GPR GPR DID 0.075 *** 0.006 ** 0.079 *** 0.006 ** (0.016) (0.003) (0.017) (0.003) GDP -0.025 0.041 -0.002 0.127 *** (0.033) (0.026) (0.030) (0.021) FSA -0.270 0.177 *** -0.277 0.233 *** (0.289) (0.061) (0.257) (0.070) UR 0.445 ** 0.864 *** 0.473 *** 0.025 (0.204) (0.111) (0.165) (0.108) SI -0.681 * -0.246 -0.733 ** -0.488 *** (0.397) (0.175) (0.285) (0.123) TI -0.985 ** -0.202 -1.537 *** -0.276 *** (0.403) (0.148) (0.324) (0.105) _cons 1.109 ** -0.510 ** 1.124 *** -0.783 *** (0.443) (0.239) (0.363) (0.194) N 210 210 260 260 R-square 0.167 0.994 0.222 0.991 ​Time Fixed Effects NO YES NO YES ​Individual Fixed Effects NO YES NO YES Shortening the sample period can examine the stability and persistence of the policy's impact. If the policy variable still has a significant positive impact on grain production resilience within a shorter time frame, it suggests the robustness of the results. After shortening the sample period to 2016–2022, the results (see Table 3 ) indicate that in model (5), which does not control for fixed effects, the core independent variable is significantly positive at the 1% level. In model (6), which controls for fixed effects, the estimated coefficient of the policy variable (DID) remains significantly positive. This indicates that even with a shortened time frame, the policy variable continues to have a positive effect on grain production resilience. The coefficient for economic development level (GDP) changes to 0.041 but is not significant, suggesting that the impact of economic development level on grain production resilience is unstable in the shorter time frame. The coefficient for the proportion of financial support in agriculture (FSA) is 0.177, significant at the 1% level, indicating a clear enhancement of grain production resilience from financial support in agriculture within the shortened time frame. The coefficient for urbanization level (UR) is 0.86 and significantly positive, indicating that urbanization has a substantial promotional effect on grain production resilience within a short period. The coefficient for the proportion of value added in the secondary industry (SI) is -0.246 and not significant, suggesting that the negative impact of the secondary industry on grain production resilience is not evident in the new time frame. Similarly, the coefficient for the proportion of value added in the tertiary industry (TI) is -0.202 and not significant, indicating that the impact of the tertiary industry on grain production resilience is also insignificant within the shortened time frame. 5.3.3. Excluding Municipalities Municipalities have certain peculiarities in economic structure, resource allocation, and policy implementation compared to other regions. Excluding municipalities can better examine the impact of the policy variable on grain production resilience in more representative general regions, avoiding the interference of municipalities' uniqueness on the overall results. After excluding the four municipalities—Beijing, Shanghai, Chongqing, and Tianjin—the results (see Table 3 ) show that in model (7), which does not control for fixed effects, the core independent variable is significantly positive at the 1% level. In model (8), which controls for fixed effects, the estimated coefficient of the policy variable (DID) remains significantly positive, indicating that the positive impact of the policy on grain production resilience remains unchanged after excluding the municipalities. The coefficient for economic development level (GDP) becomes 0.127 and is significant at the 1% level, indicating that, after excluding the municipalities, the contribution of economic development to enhancing grain production resilience is more evident. The coefficient for the proportion of financial support in agriculture (FSA) is 0.233 and significant at the 1% level, showing that the promoting effect of financial support on grain production resilience further intensifies after excluding the municipalities. The coefficient for urbanization level (UR) becomes 0.025 and is not significant, indicating that the influence of urbanization on grain production resilience is not significant after excluding the municipalities. The coefficient for the proportion of value added in the secondary industry (SI) is -0.488 and significant at the 1% level, indicating that the negative impact of the secondary industry on grain production resilience has increased after excluding the municipalities. The coefficient for the proportion of value added in the tertiary industry (TI) is -0.276 and also significant at the 1% level, showing that the negative impact of the tertiary industry on grain production resilience has also intensified after excluding the municipalities. 5.3.4. PSM-DID Considering the potential selection bias in policy implementation, as well as the need to control for unobservable factors in evaluating policy effects, this study employs the propensity score matching-difference-in-differences (PSM-DID) method for robustness testing. The PSM method constructs a suitable control group through propensity score matching, which effectively eliminates sample selection bias; the DID method can control for unobservable factors that do not vary over time. The combination of the two methods can more accurately identify policy effects. The balance test results in Table 4 show that the differences between the treatment group and the control group across various variables have significantly improved before and after matching using PSM. Prior to matching, notable differences existed in economic development level (GDP), proportion of financial support in agriculture (FSA), urbanization level (UR), proportion of value added in the secondary industry (SI), and proportion of value added in the tertiary industry (TI), particularly with larger biases in SI and TI. The t-test results were significant, indicating systematic differences in these variables between the two groups. However, after PSM matching, the biases across the variables were significantly reduced, with t-values approaching 0 and p-values exceeding 0.05, indicating that the differences in these key variables between the treatment and control groups are no longer significant, achieving a balance. This suggests that the PSM method effectively weakened systematic differences between the groups, providing a reliable foundation for subsequent difference-in-differences analysis. The conditions of the matched treatment and control groups prior to policy implementation are closer, thus making the estimates of the policy's impact on grain production resilience (GPR) more accurate and credible. Consequently, the application of the PSM-DID method in this paper can more effectively identify policy effects and ensure a high level of reliability in the assessment of the policy variable's impact. Table 4 Balance test results Variable Matching Status Mean Bias (%) t-value p-value Treatment Group Control Group GDP Unmatched 10.948 10.945 0.6 0.03 0.972 Matched 10.948 10.955 -2.1 -0.13 0.894 FSA Unmatched 0.113 0.115 -7.8 -0.49 0.626 Matched 0.113 0.109 12.6 0.8 0.427 UR Unmatched 0.600 0.617 -16.8 -1.02 0.308 Matched 0.600 0.608 -7.4 -0.52 0.606 SI Unmatched 0.425 0.387 59.9 3.5 0.001 Matched 0.425 0.426 -0.8 -0.06 0.949 TI Unmatched 0.479 0.517 -54.3 -3.21 0.001 Matched 0.479 0.480 -2.4 -0.21 0.838 The study also visually compares the kernel density plots of the propensity scores before and after matching to intuitively demonstrate the matching effect. In Fig. 5 , the horizontal axis represents the propensity score, while the vertical axis indicates the kernel density. The results in Fig. 4 show that before matching (Fig. 5 . a), the distribution of propensity scores for the treatment group and control group diverged significantly, with distinctly different curve shapes. This indicates substantial heterogeneity between the two groups prior to policy implementation, which could lead to estimation bias when directly using the DID method. After PSM matching (Fig. 5 . b), the distribution of propensity scores for the treatment and control groups becomes noticeably aligned, with the curves becoming closer, demonstrating that after matching, the characteristics of the two groups are more similar and the group differences have been effectively eliminated. This result further verifies the effectiveness of PSM matching, providing a more reliable basis for subsequent DID analysis and ensuring more accurate estimates of policy effects. Table 5 reports the regression results after PSM matching. In the uncontrolled fixed effects model (9), the coefficient of DID is 0.072, which is significantly positive at the 1% level, indicating that policy implementation has a significant promoting effect on the resilience of food production. In the model (10) that controls for individual and time fixed effects, the coefficient of DID is 0.010, which is also significantly positive at the 1% level, further verifying the robustness of policy effects. In summary, hypothesis 1 is proven. Table 5 Regression results after PSM matching Variable (9) (10) GPR GPR DID 0.072 *** 0.010 *** (0.016) (0.003) GDP 0.002 0.122 *** (0.025) (0.021) FSA -0.101 0.207 *** (0.258) (0.072) UR 0.445 *** 0.280 *** (0.156) (0.086) SI -0.621 ** -0.517 *** (0.308) (0.131) TI -1.043 *** -0.312 *** (0.316) (0.112) _cons 0.793 ** -0.861 *** (0.336) (0.209) N 300 300 R-square 0.173 0.988 Time fixed effect NO YES Individual fixed effect NO YES 5.4. Heterogeneity Analysis There are significant geographic differences between the northern and southern regions of China. The northern regions have a relatively dry climate with less precipitation and notable seasonal variation, making grain production more susceptible to natural disasters such as droughts and frosts. In contrast, the southern regions have a more humid climate with abundant rainfall, although they may also face threats from disasters such as floods and typhoons. These differing geographic environments suggest that the mechanisms and degrees of effectiveness of policy-based agricultural insurance may vary across regions. To explore the differences in the impact of policy-based agricultural insurance on grain production resilience in different regions, this study categorizes the sample based on geography, dividing provinces south of the Qinling-Huaihe Line as southern provinces and those to the north as northern provinces. Specifically, Anhui and Jiangsu are classified as southern provinces, while Gansu, Shaanxi, and Henan are classified as northern provinces. The model (11) in Table 6 shows that the effect of policy-supported agricultural insurance on grain production resilience in northern provinces is more significant. Specifically, among the samples of northern provinces, the implementation of policy-supported agricultural insurance significantly improved the grain production resilience, with a coefficient of 0.018, which was significant at the 1% level. In model (12), although the policy effect coefficient was positive (0.006), it failed to pass the significance test. The reason for this may be that the northern regions are the main grain-producing areas of China, with larger planting scales and relatively higher enrollment rates and coverage for agricultural insurance(C. Xie et al., 2025 ). Additionally, the harsher climatic conditions in northern regions lead to a higher frequency of natural disasters, making the protective role of agricultural insurance more apparent, thereby more effectively enhancing the level of resilience in grain production(S. Xie et al., 2024 ). To further investigate the heterogeneous effects of grain production resilience across regions with varying agricultural development levels, this study divides the sample into two groups—high-resilience and low-resilience regions—based on the calculated grain production resilience scores. As shown in Table 6 , model (13) and (14), significant heterogeneity emerges between the two groups. Specifically, in high-resilience regions, the DID coefficient is 0.008 and fails to pass the significance test, indicating that policy-based agricultural insurance does not significantly improve grain production resilience in areas already exhibiting strong resilience. In contrast, the DID coefficient for low-resilience regions is 0.019 and statistically significant at the 5% level, suggesting that the positive effect of the policy is more pronounced in regions where resilience is relatively weak. These findings highlight the spatial differentiation of policy effects: low-resilience areas, characterized by weaker foundations, tend to respond more sensitively to policy interventions, and the marginal impact of insurance support is more readily observed. Conversely, high-resilience regions possess relatively robust risk-buffering mechanisms, making the incremental benefits of policy support less prominent. This evidence underscores the necessity of implementing differentiated agricultural insurance strategies tailored to regional resilience levels. Table 6 Heterogeneity analysis results Variable (11) (12) (13) (14) GPR GPR GPR GPR DID 0.018 *** 0.006 0.008 0.019 ** (0.004) (0.005) (0.005) (0.007) N 150 150 150 150 R-square 0.992 0.985 0.594 0.419 Control Variable YES YES YES YES Time fixed effect YES YES YES YES Individual fixed effect YES YES YES YES 5.5. Mechanism Testing From Table 7 , in model (15), the estimated coefficient of DID for scale operation (Scale) is 0.253, significant at the 5% level. This indicates that policy-based agricultural insurance promotes the scaling of grain production. Policy-based agricultural insurance can provide risk protection for farmers, reducing losses from risks such as natural disasters, thereby enhancing farmers' confidence in expanding production scales. Scaling up production can improve agricultural efficiency, reduce unit costs, and enhance the stability and resilience of grain production, thereby increasing grain production resilience, confirming Hypothesis 2. In model (16), the estimated coefficient of DID for the grain replanting index (GRI) is 0.025, significant at the 1% level. The implementation of policy-based agricultural insurance can alleviate farmers' risk concerns, making them more willing to increase planting frequency and improve land utilization rates. An increase in the grain replanting index signifies that more grain can be produced on the same land area, enhancing the sustainability and resilience of grain production, thereby confirming Hypothesis 3. Table 7 Mechanism test results Variable (15) (16) Scale GRI DID 0.253 ** 0.025 *** (0.107) (0.006) GDP -3.574 *** -0.064 * (0.540) (0.036) FSA 9.329 *** -0.396 *** (2.891) (0.101) UR 7.941 *** 0.075 (1.527) (0.160) SI -6.161 0.048 (6.600) (0.180) TI -10.952 ** 0.142 (5.468) (0.173) _cons 44.409 *** 1.263 *** (8.690) (0.395) N 300 300 R-square 0.983 0.987 Time fixed effect YES YES Individual fixed effect YES YES 6. Conclusions and policy recommendations 6.1. Conclusions Drawing on province-level panel data from 2013 to 2022, this study measures grain production resilience using the entropy weight TOPSIS method and employs a difference-in-differences (DID) approach to evaluate the effects of policy-based agricultural insurance. The key findings are as follows: First, grain production resilience in China has shown a steady upward trend over the past decade. Although minor fluctuations occurred due to market volatility and natural disasters, no significant downturn was observed, indicating a generally stable and improving resilience landscape. Second, policy-based agricultural insurance has a significant and positive impact on enhancing grain production resilience. This effect remains robust across multiple empirical tests, including placebo checks, shortened sample periods, and PSM-DID estimations. Third, the effects of policy-based agricultural insurance vary by region. The impact is more pronounced in northern provinces and in areas with lower baseline resilience levels, suggesting region-specific differences in the policy’s effectiveness. Fourth, mechanism analysis reveals that policy-based agricultural insurance enhances resilience primarily by promoting farm scale expansion and increasing the grain replanting index. 6.2. Recommendations Based on the findings of this study, and in light of the practical realities of grain production and the current implementation status of policy-based agricultural insurance in China, the following policy recommendations are proposed to further strengthen the institutional effectiveness of agricultural insurance and enhance the resilience of the national grain production system. First, it is essential to improve the structure and functionality of insurance products. Coverage should be expanded beyond direct input costs to include broader categories of production risks, such as labor, land use, and systemic risks arising from climate variability and market fluctuations. Insurance design should be more tailored to regional agricultural conditions to ensure accuracy and relevance. A multi-tiered risk-sharing framework involving government support, insurance institutions, and reinsurance mechanisms should be strengthened. Index-based insurance can be promoted to improve efficiency and reduce transaction costs, and regional risk pooling mechanisms should be encouraged to buffer the impacts of extreme events. Second, differentiated regional strategies should be adopted to address spatial disparities in policy effectiveness. In areas where agricultural insurance has shown limited impact, particularly in regions with frequent natural disasters, support should focus on improving product adaptability, increasing subsidy intensity, and enhancing coordination with local governments for disaster prevention and risk management. In regions where insurance has demonstrated stronger outcomes, especially key production zones, efforts should focus on expanding participation, improving claim efficiency, and integrating complementary instruments to manage both production and price risks. Third, insurance should be leveraged as a policy tool to support structural adjustment in agriculture. This includes promoting moderate-scale operations and improving land-use efficiency. Policy instruments such as premium subsidies and credit support should be aligned with efforts to expand farm size and consolidate production. Insurance should also play a role in encouraging more efficient land use, including practices that increase cropping intensity and resilience. Strengthening the link between insurance, mechanization, and infrastructure investment can amplify its role in stabilizing agricultural operations. Fourth, financial support mechanisms should be optimized, and stronger integration with related policies should be pursued. Public subsidies should be directed toward regions and producer groups with higher exposure and lower resilience, using a dynamic and targeted allocation system. Policy coherence should be enhanced by aligning agricultural insurance with credit systems, technology extension, and risk reduction programs, forming a coordinated support network for agricultural producers. This would help reduce transaction costs and ensure more effective risk management at the farm level. Finally, a comprehensive monitoring and adjustment mechanism is needed to ensure long-term policy effectiveness. A resilience-oriented evaluation framework should be developed, combining indicators that reflect the capacity to resist, recover, and adapt to shocks. The performance of agricultural insurance should be assessed regularly, with particular attention to underperforming regions. Based on evaluation results, insurance policies and subsidy structures should be reviewed and revised in a timely manner to maintain alignment with evolving risk profiles and development needs. Declarations Competing interests The author declares no competing interests. Ethical approval This article does not involve studies with human participants conducted by any of the authors. Informed consent No research involving human subjects was conducted for this paper. Author Contribution Conceptualization, Y.T.; methodology, Y.T.; formal analysis, Y.T.; investigation, M.Z.; resources, Y.T.; writing—original draft preparation, Y.T.; writing—review and editing, Y.T., M.Z.; visualization, Y.T.; supervision, Y.T., M.Z.; project administration, M.Z.; funding acquisition, M.Z.. Acknowledgement This research was funded in part by the National Social Science Fund of China (No. 23BJY167). Data Availability For access to the datasets generated or analyzed in the currentstudy, please feel free to contact the corresponding author. References Bielza M, Stroblmair J, Gallego J, Conte CG, Dittmann C (2007) Agricultural risk management in Europe. Binswanger HP, Sillers DA (1983) Risk aversion and credit constraints in farmers' decision-making: A reinterpretation. J Dev Stud 20(1):5–21 Bullock JM, Dhanjal-Adams KL, Milne A, Oliver TH, Todman LC, Whitmore AP, Pywell RF (2017) Resilience and food security: rethinking an ecological concept. J Ecol 105(4):880–884 Cai J (2016) The impact of insurance provision on household production and financial decisions. Am Economic Journal: Economic Policy 8(2):44–88 Cai R, Ma J, Cai S (2024) Can crop insurance help optimize farmers’ decisions on pesticides use? Evidence from family farms in East China. J Asian Econ 92:101735 Carter MR, Cheng L, Sarris A (2016) Where and how index insurance can boost the adoption of improved agricultural technologies. J Dev Econ 118:59–71 Cerroni S (2020) Eliciting farmers’ subjective probabilities, risk, and uncertainty preferences using contextualized field experiments. Agric Econ 51(5):707–724 China N (2024) B. o. S. o. Announcement of the National Bureau of Statistics on 2024 Grain Production Data. Retrieved from https://www.gov.cn/lianbo/bumen/202412/content_6992479.htm Dercon S, Christiaensen L (2011) Consumption risk, technology adoption and poverty traps: Evidence from Ethiopia. J Dev Econ 96(2):159–173 FAN Z, QIN Z, YU S (2024) Impact of extreme temperature on the resilience of grain production: perspectives on green finance. Chin J Eco-Agriculture 32(5):896–910 Fang L, Hu R, Mao H, Chen S (2021) How crop insurance influences agricultural green total factor productivity: Evidence from Chinese farmers. J Clean Prod 321:128977 FAO (2021) The State of Food and Agriculture 2021. Retrieved from https://www.fao.org/agrifood-economics/publications/detail/en/c/1457037/ Fu L-S, Qin T, Li G-Q, Wang S-G (2024) Efficiency of Agricultural Insurance in Facilitating Modern Agriculture Development: From the Perspective of Production Factor Allocation. Sustainability 16(14):6223 Hazell P, Anderson J, Balzer N, Clemmensen H, Hess A, U., Rispoli F (2010) The potential for scale and sustainability in weather index insurance for agriculture and rural livelihoods . Retrieved from Hellige HD (2019) The metaphorical processes in the history of the resilience notion and the rise of the ecosystem resilience theory. Handbook on resilience of socio-technical systems. Edward Elgar Publishing, pp 30–51 Holling CS (1973) Resilience and stability of ecological systems Karlan D, Osei R, Osei-Akoto I, Udry C (2014) Agricultural decisions after relaxing credit and risk constraints. Q J Econ 129(2):597–652 Kijima Y (2019) Farmers’ risk preferences and rice production: Experimental and panel data evidence from Uganda. PLoS ONE 14(7):e0219202 Liang X, Jin X, Han B, Sun R, Xu W, Li H, Li J (2022) China’s food security situation and key questions in the new era: A perspective of farmland protection. J Geog Sci 32(6):1001–1019 Mahul O, Stutley CJ (2010) Government support to agricultural insurance: challenges and options for developing countries. World Bank Meuwissen MPM (2000) Insurance as a risk management tool for European agriculture. Wageningen University and Research Shang Y, Xiong T (2021) The impact of farmers’ assessments of risk management strategies on their adoption willingness. J Integr Agric 20(12):3323–3338 Smith VH, Glauber JW (2012) Agricultural insurance in developed countries: where have we been and where are we going? Appl Economic Perspect Policy 34(3):363–390 Sun J-L, Tao R, Wang J, Wang Y-F, Li J-Y (2024) Do farmers always choose agricultural insurance against climate change risks? Econ Anal Policy 81:617–628 Tan C, Tao J, Yi L, He J, Huang Q (2022) Dynamic relationship between agricultural technology progress, agricultural insurance and farmers’ income. Agriculture 12(9):1331 Tang L, Luo X (2021) Can agricultural insurance encourage farmers to apply biological pesticides? Evidence from rural China. Food Policy 105:102174 Tendall DM, Joerin J, Kopainsky B, Edwards P, Shreck A, Le QB, Six J (2015) Food system resilience: Defining the concept. Global food Secur 6:17–23 Wei Z, Peng J, Zhao Y, Li X, Wang C (2025) Reform of agricultural land property rights system and grain production resilience: Empirical evidence based on China’s Three Rights Separation reform. PLoS ONE, 20(3), e0319387 Xie C, Kuang Y-p, Wen H-x, Wu X-q (2025) Agricultural agglomeration or industrial integration: how does agricultural insurance bolster agricultural resilience in China? Front Sustainable Food Syst 9:1531287 Xie S, Zhang J, Li X, Xia X, Chen Z (2024) The effect of agricultural insurance participation on rural households' economic resilience to natural disasters: evidence from China. J Clean Prod 434:140123 Yang R, Pan Y (2021) Rural vulnerability in China: Evaluation theory and spatial patterns. J Geog Sci 31(10):1507–1528 Additional Declarations No competing interests reported. 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18:25:34","extension":"html","order_by":39,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":149097,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7477834/v1/e5437108338937cb00ea84a8.html"},{"id":93712109,"identity":"a9e77471-054b-4663-8ff7-f33e6a0404db","added_by":"auto","created_at":"2025-10-16 18:25:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1896977,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in Grain Production Resilience in China from 2013 to 2022.\u003c/p\u003e","description":"","filename":"fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-7477834/v1/6772b611ddc4dbda2c7ce095.png"},{"id":93711465,"identity":"7d9a44ef-5f19-44ea-bfcf-034fddf45dd8","added_by":"auto","created_at":"2025-10-16 18:17:33","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":144849,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in Resilience of Grain Production in Various Provinces.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7477834/v1/3e123bc7ab5957665ce2cc19.jpg"},{"id":93712463,"identity":"54a73b67-df4b-4625-8836-39cde7fffe2f","added_by":"auto","created_at":"2025-10-16 18:33:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2061481,"visible":true,"origin":"","legend":"\u003cp\u003eParallel Trends Test.\u003c/p\u003e","description":"","filename":"fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-7477834/v1/5c7934a20b88ee81ceff3f3f.png"},{"id":93711468,"identity":"35ba8a43-cc7f-4563-96c6-d63374b54267","added_by":"auto","created_at":"2025-10-16 18:17:33","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":58006,"visible":true,"origin":"","legend":"\u003cp\u003ePlacebo Test.\u003c/p\u003e","description":"","filename":"fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-7477834/v1/51eb797eb64da4673e6c65b2.png"},{"id":93711467,"identity":"c6b2c361-0c46-4379-82e8-97cbc2e3d898","added_by":"auto","created_at":"2025-10-16 18:17:33","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":88434,"visible":true,"origin":"","legend":"\u003cp\u003eKernel Density Plot.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7477834/v1/27bf7b34ee930252a5fcc28b.jpg"},{"id":97249507,"identity":"ba1a6c7a-4693-41de-9f0f-4da4c86cec3e","added_by":"auto","created_at":"2025-12-02 13:12:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1495403,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7477834/v1/76f4d8f4-6a26-4b6f-8899-7769c7bed146.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Policy Experimentation for Sustainable Agriculture: How China’s Policy-Based Agricultural Insurance Enhances Grain Production Resilience","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe increasing frequency of extreme weather events driven by global climate change, compounded by heightened agricultural market volatility, which poses systemic threats to the stability and sustainability of grain production systems. Against this backdrop, developing resilient grain production systems has emerged as a strategic consensus for nations worldwide. According to the Food and Agriculture Organization (FAO) resilience assessment framework, grain production resilience encompasses three dimensions: buffering capacity against shocks, regulatory capacity to adapt to environmental changes, and transformative capacity for post-disaster recovery (FAO, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This theoretical framework not only supports systematic evaluation of production resilience but also provides critical insights for policy formulation in China.\u003c/p\u003e\u003cp\u003eAs the world's largest grain producer and consumer, China prioritizes food security in its national development strategy and continuously enhances agricultural risk resistance through policy innovation. Since 2015, China's total grain output has remained stable at over 650\u0026nbsp;million metric tons for ten consecutive years, reaching a historic high of 706\u0026nbsp;million metric tons in 2024 (China, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Per capita grain availability stands at 500 kg, exceeding the internationally recognized food security threshold of 400 kg by 25%. However, regional agricultural vulnerabilities persist, with major production areas exhibiting structural deficiencies in risk resistance due to path dependency on traditional farming practices and resource constraints(Yang \u0026amp; Pan, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Furthermore, land scarcity, agricultural labor shortages, and market price fluctuations exacerbate uncertainties in food security (Liang et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These challenges highlight the limitations of conventional production models, necessitating policy innovation to enhance systemic resilience.\u003c/p\u003e\u003cp\u003eThe inherent vulnerability of agriculture subjects its development to multifaceted risks. Agricultural production intertwines natural and economic reproduction processes, rendering it highly susceptible to environmental and market fluctuations. On one hand, climate-dependent cultivation systems remain vulnerable to natural disasters; on the other, volatile commodity prices and input costs amplify operational risks. This risk superposition critically threatens the stability and sustainability of grain production. Among policy instruments for enhancing resilience, policy-based agricultural insurance has gained prominence as an institutional mechanism for risk mitigation, owing to its risk transfer function and safety net role (Bielza, Stroblmair, Gallego, Conte, \u0026amp; Dittmann, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). By leveraging government subsidies to incentivize farmer participation, such insurance reduces systemic risk exposure in agricultural production and mitigates disaster-induced output losses. Compared to commercial agricultural insurance, policy-based programs demonstrate greater inclusivity and coverage, delivering timely post-disaster compensation to bolster farmers' production confidence and recovery capacity. Particularly in China's context-where smallholder farming dominates alongside diversified operations-policy-based agricultural insurance serves as a pivotal tool to address \"market failures\" and \"institutional gaps\" in agricultural risk management.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cp\u003eWith the dual uncertainties of natural and market forces increasing in agricultural production, grain production resilience has gradually become an important topic in the study of sustainable agricultural development, as it serves as a critical indicator of the agricultural system's capacity to withstand shocks. Since the concept of resilience was proposed by Holling (Holling, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1973\u003c/span\u003e), it has undergone a paradigm shift from engineering resilience to ecological resilience and then to evolutionary resilience (Hellige, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). With the deepening of research, the concept of resilience has become a key analytical perspective for understanding the capacity of complex socio-ecological systems to respond to escalating environmental and economic shocks, and has gradually been applied in the field of agricultural production (Bullock et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In agricultural systems, resilience encompasses three interdependent dimensions: the buffering capacity to absorb disturbances, the adaptive capacity to reorganize production strategies, and the transformative capacity to recover after crises (Tendall et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This three-part framework aligns with the FAO\u0026rsquo;s operational definition of resilience in grain production and provides a solid foundation for policy assessment.\u003c/p\u003e\u003cp\u003eIn agricultural resilience research, agricultural insurance is viewed as a key tool for mitigating external shocks and stabilizing the operation of agricultural systems (C. Xie, Kuang, Wen, \u0026amp; Wu, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Existing literature has systematically assessed the economic effects of agricultural insurance on agricultural development from the perspectives of risk management, income stabilization, and production incentives. Hazell et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) found that agricultural insurance has a significant effect on alleviating the impacts of natural disasters on smallholder farmers, particularly when combined with other rural financial services, which can create a more comprehensive risk response mechanism (Hazell et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Mahul and Stutley (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) demonstrated that under a model combining appropriate government subsidies and market-driven operations, agricultural insurance can effectively expand coverage and enhance farmers' production efficiency and market competitiveness (Mahul \u0026amp; Stutley, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Smith and Glauber (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) noted that policy-based agricultural insurance, through government subsidies and risk-sharing mechanisms, significantly increases farmers' enrollment rates, thereby dispersing systemic risks and stabilizing production recovery capacity after disasters (Smith \u0026amp; Glauber, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Carter et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) further revealed that in developing countries, agricultural insurance can motivate farmers to adopt high-yield agricultural technologies by alleviating their \"risk rationing\" issues, thereby indirectly enhancing the resilience of production systems (Carter, Cheng, \u0026amp; Sarris, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Tan et al. found that agricultural insurance, through a risk compensation mechanism and synergy with technological advancements, significantly improves the stability of farmers' incomes, but its economic effects exhibit regional heterogeneity (Tan, Tao, Yi, He, \u0026amp; Huang, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Xie et al. (2023) found that agricultural insurance significantly enhances the stability of farmers' incomes; post-disaster compensation directly reduces short-term income volatility and optimizes production decisions through a risk dispersion effect, promoting technology adoption and larger-scale operations, especially prominent in cash crop cultivation and among larger-scale farmers (S. Xie, Zhang, Li, Xia, \u0026amp; Chen, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn terms of agricultural production behavior, Tang and Luo's research indicated that agricultural insurance not only promotes the use of biopesticides but also reduces the use of chemical pesticides, thereby increasing the proportion of biopesticides in the pesticide usage structure, which contributes to the advancement of green agricultural practices and sustainable agricultural development (Tang \u0026amp; Luo, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The study by Fang et al. found that agricultural insurance, as an effective risk protection mechanism, can significantly enhance agricultural green total factor productivity by guiding farmers to adopt green agricultural technologies (Fang, Hu, Mao, \u0026amp; Chen, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Dercon and Christiaensen (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) noted that farmers, lacking risk protection mechanisms, tend to avoid high-risk but high-return agricultural technologies when faced with consumption risks, leading them into poverty traps (Dercon \u0026amp; Christiaensen, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). In production decision-making, Karlan et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) found through randomized controlled trials that rainfall index insurance significantly increases farmers' inclination to adopt high-risk, high-return agricultural inputs, pushing them towards more rewarding crops (Karlan, Osei, Osei-Akoto, \u0026amp; Udry, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Cai (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) pointed out that agricultural insurance alleviates risk constraints, expands the cultivated area and credit demand for insured crops, and optimizes resource allocation and production decisions, thereby enhancing the resilience of agricultural systems (J. Cai, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Sun et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) discovered that climate change does not necessarily increase the coverage rate of agricultural insurance; farmers' enrollment behavior depends more on the trade-off between premiums and subsidy compensation, and is significantly influenced by government subsidies and claims mechanisms (Sun, Tao, Wang, Wang, \u0026amp; Li, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Cai et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) noted that crop insurance optimizes the pesticide usage decisions of large-scale farmers by enhancing the marginal benefits of pesticide use, thereby reducing over-application (R. Cai, Ma, \u0026amp; Cai, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn summary, existing research has yielded rich theoretical and empirical findings regarding the risk mitigation functions of agricultural insurance, safeguarding farmers' incomes, and achieving agricultural sustainability. However, there are still certain shortcomings in the existing research. First, while previous studies have revealed the impact of agricultural insurance on farmers' income stability and technology adoption, few studies have framed grain production resilience as an analytical framework to explore the comprehensive effects of agricultural insurance on the adaptability and recovery capacity of agricultural systems under external shocks. Second, current research lacks in-depth analysis of mechanisms related to grain replanting, changes in operational scale, and regional heterogeneity.\u003c/p\u003e\u003cp\u003eBased on this, this paper attempts to achieve the following innovative breakthroughs on the foundation of existing research: first, to introduce resilience theory and construct a multidimensional measurement index system of grain production resilience encompassing \"stability\u0026mdash;recovery\u0026mdash;adaptability\"; second, to utilize the regional advancement characteristics of full-cost insurance and income insurance pilot policies in China to build a difference-in-differences (DID) model, forming a quasi-natural experiment framework to identify the causal impact of policies on agricultural resilience; third, to further explore the intrinsic mechanisms by which policy-based agricultural insurance influences grain resilience from dimensions such as grain replanting, operational scale, and regional heterogeneity. Through the above analysis, this paper seeks to fill the existing research gaps on both theoretical deepening and methodological breakthroughs, providing empirical evidence for improving agricultural support policies and enhancing national food security capacity.\u003c/p\u003e"},{"header":"3. Policy Background and Theoretical Analysis","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Policy Background.\u003c/h2\u003e\u003cp\u003eChina's agricultural insurance started relatively late, initially focusing primarily on low coverage level physical cost insurance, which only covered direct input costs such as seeds, fertilizers, and pesticides. The low coverage level made it difficult to effectively manage the multiple risks faced by agricultural production. In 2007, the central government began subsidizing agricultural insurance premiums, marking the start of a rapid development phase for agricultural insurance in China. Governments at all levels gradually increased their financial support for agricultural insurance, resulting in an expanded coverage area and a growing variety of insurance products; however, coverage primarily focused on physical costs, with insufficient protection against labor costs, land costs, and limited coverage for market risks such as price and yield fluctuations. To address the dual risks of frequent natural disasters and increased market volatility in grain production, in 2018, the Ministry of Finance, the Ministry of Agriculture and Rural Affairs, and the China Banking and Insurance Regulatory Commission jointly issued a notice on launching pilot projects for full-cost insurance and income insurance for the three major food crops. The pilots were initially implemented in six provinces: Inner Mongolia, Henan, Hubei, Shandong, Liaoning, and Anhui, in major grain-producing counties. This pilot program marked a pivotal shift in China's agricultural insurance from traditional cost coverage to income protection. Full-cost insurance covers all production costs, including physical costs, labor costs, and land costs, while income insurance further protects farmers' planting revenues, providing a more comprehensive risk protection for farmers. Together, these two insurance types address the narrow coverage and low payout limits of traditional agricultural insurance, significantly enhancing service effectiveness.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Theoretical Analysis\u003c/h2\u003e\u003cp\u003eAs a risk management tool, agricultural insurance effectively reduces the risks of natural disasters and market fluctuations faced by farmers through a risk dispersion mechanism (Meuwissen, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). An effective risk hedging mechanism can reduce the distortion of risk in decision-making, thus aligning decision-making behavior more closely with the maximization of expected utility (Binswanger \u0026amp; Sillers, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1983\u003c/span\u003e; Shang \u0026amp; Xiong, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Specifically, in the face of agricultural production uncertainties, farmers tend to improve their utility levels by participating in insurance programs, which makes them more proactive in expanding their production scale. This risk-sharing mechanism not only reduces production volatility and promotes stable and sustainable agricultural development but also alleviates the uncertainty of income expectations by safeguarding farmers' economic interests, thereby promoting the optimized allocation of production factors such as land, labor, and capital.\u003c/p\u003e\u003cp\u003eInsurance compensation, as an external resource compensation mechanism, alleviates liquidity constraints, optimizes the structure of intertemporal production factor inputs for farmers, and promotes the efficient reallocation of resource factors, enhancing the resilience of the grain production system. Moreover, agricultural insurance lowers the risk costs associated with adopting new agricultural technologies. Under insurance coverage, farmers' perception of the risks associated with the application of new technologies diminishes, making them more inclined to invest in modern agricultural equipment, adopt environmentally friendly production technologies, and optimize the structure of agricultural inputs (Fu, Qin, Li, \u0026amp; Wang, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The adaptive improvements brought about by such technological advancements can enhance the ability of the grain production system to respond to environmental changes and external shocks. Based on the analysis above, this paper proposes Hypothesis 1:\u003c/p\u003e\u003cp\u003eH\u003csub\u003e1\u003c/sub\u003e: Policy-based agricultural insurance can significantly enhance grain production resilience.\u003c/p\u003e\u003cp\u003ePolicy-based agricultural insurance optimizes farmers' production decision-making through an institutional risk mitigation mechanism, with its core logic reflecting the dual effects of risk expectation correction and factor allocation optimization. Under uncertain conditions, farmers' production decisions are significantly suppressed by subjective biases in assessing risk probabilities (Cerroni, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Insurance tools reduce farmers' risk exposure, thereby correcting their subjective probability assessments of production uncertainties and weakening the suppressive effect of risk aversion on scale expansion. According to expected utility theory, when marginal risk costs are internalized due to insurance coverage, farmers' decisions regarding scale operations will approach profit-maximizing equilibrium more closely. Secondly, subsidized insurance products lower farmers' liquidity constraints by reducing implicit risk pricing, improving their intertemporal budget constraints, and thus releasing potential demands for scaled production and technology adoption.\u003c/p\u003e\u003cp\u003eFull-cost insurance and income insurance, as differentiated policy tools, construct risk buffering mechanisms from the dimensions of cost anchoring and income smoothing, respectively. The former transfers tail risks from disaster impacts into the insurance pool by covering all factor costs, thereby reducing the likelihood of production interruptions; the latter hedges income risks caused by market fluctuations through income security design, stabilizing farmers' long-term expectations. This composite insurance system reduces the variance of production functions, enhances farmers' investment confidence in economies of scale, and induces a reallocation of production factors from decentralized smallholder operations to intensive production modes. Based on this analysis, this paper proposes Hypothesis 2:\u003c/p\u003e\u003cp\u003eH\u003csub\u003e2\u003c/sub\u003e: Policy-based agricultural insurance enhances grain production resilience by promoting the expansion of agricultural operating scales.\u003c/p\u003e\u003cp\u003eThe influence of policy-based agricultural insurance on the increase of grain replanting index reflects a comprehensive effect of weakened risk constraints and enhanced resource utilization. Farmers\u0026rsquo; production decisions are constrained by both expected revenues and risk assessments (Kijima, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In the absence of effective risk hedging tools, seasonal climate fluctuations and price uncertainties lead farmers to adopt conservative land use strategies, suppressing their willingness to rotate crops. Policy-based agricultural insurance establishes a cross-season risk dispersion mechanism that significantly reduces the seasonal disaster risk exposure faced by farmers in multi-cropping grain production. When single-season crop insurance transitions to year-round income protection, farmers' marginal utility assessments for crop rotation decisions become more positive, thereby promoting the allocation of resource factors towards multi-cropping production.\u003c/p\u003e\u003cp\u003eFull-cost insurance fully covers the land input costs, alleviating resource exhaustion and output variability risks that may arise from intensive land use during crop rotation, enabling farmers to plan rotations based on the long-term productive potential of their land. Additionally, policy-based agricultural insurance alters farmers' input-output decision frameworks through its incentive effects. The price risk hedging mechanism provided by income insurance weakens the suppressive effect of seasonal market price fluctuations on farmers' crop rotation behaviors, enhancing the tendency of farmers to formulate rotation plans based on land suitability rather than short-term market signals. Simultaneously, the liquidity support from insurance payouts reduces farmers' funding constraints between different growing seasons, providing stable financial assurance for multi-season agricultural investments. This inter-seasonal resource smoothing capability significantly promotes annual land use efficiency. The multi-cropping planting system mitigates overall production risks throughout the year and optimizes the temporal efficiency of land resource use, forming a complementary resilience to single crop planting systems and enhancing the grain production system's adaptability to climate variability and market fluctuations. Based on the analysis above, this paper proposes Hypothesis 3:\u003c/p\u003e\u003cp\u003eH\u003csub\u003e3\u003c/sub\u003e: Policy-based agricultural insurance enhances grain production resilience by increasing the grain replanting index.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Research Design and Data Sources","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e4.1. Measurement of Grain Production Resilience\u003c/h2\u003e\u003cp\u003eTo measure grain production resilience, a measurement index system for grain production resilience needs to be constructed. The term resilience typically refers to a system's ability to maintain its functional stability, recovery capacity, and adaptability in the face of external shocks and pressures. In the agricultural field, grain production resilience emphasizes the ability of agricultural production systems to effectively respond, recover, and adapt to external pressures such as natural disasters, market fluctuations, and environmental changes. Based on the principles of scientificity, operability, and comprehensiveness in constructing the index system, and referencing the approaches of scholars such as Fan (FAN, QIN, \u0026amp; YU, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and Zhang (Wei, Peng, Zhao, Li, \u0026amp; Wang, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), this paper constructs a grain production resilience measurement index system that includes three first-level indicators (resistance, recovery, adaptability) and eleven second-level indicators. According to the relationship between the indicators and grain production resilience, the indicators are classified into positive impact indicators and negative impact indicators, as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eIndex System for Measuring Resilience of Grain Production\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary Indicator\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSecondary Indicator\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAttribute\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUnit\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e​\u003cb\u003eResistance\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGrain Cultivated Area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePositive Impact\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHectare (ha)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePer Capita Grain Yield\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePositive Impact\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTonnes/Person\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSoil and Water Conservation Area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePositive Impact\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHectare (ha)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNatural Disaster Incidence Rate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNegative Impact\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e​\u003cb\u003eRecovery\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLand Productivity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePositive Impact\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTonnes/ha\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePer Capita Disposable Income of Rural Residents\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePositive Impact\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCNY/Person\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePer Capita Consumption Expenditure of Rural Residents\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePositive Impact\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCNY/Person\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e​\u003cb\u003eAdaptability\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAgricultural Machinery Intensity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePositive Impact\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ekWh/ha\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePesticide Usage Intensity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNegative Impact\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ekg/ha\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFertilizer Usage Intensity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNegative Impact\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ekg/ha\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAgricultural Film Usage Intensity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNegative Impact\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ekg/ha\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEngel coefficient for rural residents\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNegative Impact\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e4.2. Variable Definitions\u003c/h2\u003e\u003cp\u003e(1) Dependent Variable\u003c/p\u003e\u003cp\u003eGrain Production Resilience (GPR) is calculated using the Entropy Weight-Technique for Order Preference by Similarity to an Ideal Solution(EW-TOPSIS) method.\u003c/p\u003e\u003cp\u003e(2) Core Independent Variable\u003c/p\u003e\u003cp\u003ePolicy Variable (DID): This variable is based on whether the area is a pilot region for full-cost insurance and income insurance. If it is a pilot region, it is set to 1 for the years 2018 and beyond; otherwise, it is set to 0. Based on the policy background described earlier, six provinces including Inner Mongolia, Henan, Hubei, Shandong, Liaoning, and Anhui were selected as the experimental group, while the rest of the provinces served as the control group.\u003c/p\u003e\u003cp\u003e(3) Control Variables\u003c/p\u003e\u003cp\u003ePer Capita GDP (GDP): Represented by the logarithm of per capita gross domestic product. Proportion of Financial Support in Agriculture (FSA): Represented by the ratio of agricultural, forestry, and water affairs expenditure to total general fiscal budget expenditure. Urbanization Level (UR): Represented by the ratio of the urban population to the total population. Proportion of Secondary Industry (SI): Represented by the ratio of value added by the secondary industry to gross domestic product. Proportion of Tertiary Industry (TI): Represented by the ratio of value added by the tertiary industry to gross domestic product.\u003c/p\u003e\u003cp\u003e(4) Mediating Variables\u003c/p\u003e\u003cp\u003eBased on the analysis above, the study selects operational scale (Scale) and the grain replanting index (GRI) as mediating variables. Scale is represented by the ratio of the sown area to the rural population, while the grain replanting index represents the ratio of grain sown area to total crop sown area.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e4.3. Model selection.\u003c/h2\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e4.3.1. EW-TOPSIS\u003c/h2\u003e\u003cp\u003eThe study employs the EW-TOPSIS method to measure grain production resilience (GPR). The process begins by normalizing the original indicators into a [0,1] range to eliminate scale differences, distinguishing between positive indicators (e.g., grain yield) and negative indicators (e.g., pesticide usage) to ensure directional consistency. Next, the entropy weight method calculates objective weights for each indicator: the standardized data are transformed into proportional values to determine information entropy, which reflects the variability of each indicator. Lower entropy values indicate higher information content, and weights are assigned based on entropy redundancy (1\u0026ndash;entropy value). Finally, TOPSIS evaluates resilience by constructing a weighted matrix, identifying optimal (positive ideal) and worst (negative ideal) solutions, and calculating the relative proximity of each province to these benchmarks. The resulting GPR index, ranging from 0 to 1, quantifies resilience levels, with higher values indicating stronger resilience. This integrated approach ensures a robust, multidimensional assessment of grain production resilience for subsequent empirical analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\u003ch2\u003e4.3.2. Fixed effects model\u003c/h2\u003e\u003cp\u003eAccording to the theoretical analysis and research hypotheses presented in this paper, an empirical model is constructed to assess the impact of policy-based agricultural insurance on grain production resilience, replacing the concept of policy-based agricultural insurance with the pilot programs for full-cost insurance and income insurance. The model is set as follows:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cp\u003e\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAcgAAAAlCAYAAADGDHkCAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAAFiUAABYlAUlSJPAAAA9uSURBVHhe7Z1NbBtFFMf/4YRQAof1gdTigKsm6gFIGiWHlC/FQYppuCBaEBLmEgsBB0piJBBChCK1SDgfSFSiJEgVh7ooR8fkQBO1FbcodpULii1ShBSvD/Wh2b0Pl7zV7Hh217tefzTMT/IhOzszb99787FvZjY9jDEGhUKhUCgUNh4TLygUCoVCoVADpEKhUCgUUtQAqVAoFAqFBDVAKhQKhUIhQQ2QCoVCoVBIUAOkQqFQKBQS1ACpUCgUCoUENUAqFAqFQiFBDZAKheJ/SbVaxS+//IJEIoGFhQUxWaEINkBWq1UsLS1hcHAQPT091i+RSKBYLMI0TXz77bcwTdPKo+s6Tp48abtf/CUSCZTLZVtdjeRNJBL4/fffxWxdjWmauHnzpk2HkUgEc3NzNr11C83KWywWEYlE6mxHv7Nnz2JpaQnValXMauHkBydPnoSu66HU0W00q3cZpmliaWkJZ8+elZZZLBaxuroqZjtWLCwsoL+/HzMzM9je3sbExIR4i8IF0Seb8cewKZfLGBsbQ09PT/MTH+YDwzBYKpViAFgqlWKFQsGWns1mmaZpDADLZDK2NCKbzTIATNM0dufOHet6Pp9nmqYxTdPqymVHdc/OzjIALBaLsUqlwhhjrFQqsVgs5lpnt1EoFNjo6CgbHR1lpVLJup7JZBgAFo/HmWEYtjydJEx5k8mkNE8+n2ejo6MN2XFlZYUBYABYNpsVk0OpoxsIU+9MaENTU1O2dsanAWC5XM6W9zhiGAaLx+O+9fh/hnSWSqUsnywUCkzTNJZMJsXb2w7JQn7MjxVBaHiApIo1TWP5fF5MtqD7nBpYLpdzbNxuaXy6aAi63qwy2kGlUmGxWEz6jJTWTc8RtrzUucsGKCrPq4N28gMijDo6Tdh6L5VKbHR0lGmaJp1UEMlk0nGS2moMw2CXLl2qe95WQXqU+YminkqlwkZHR6XtLplM+vLHVmAcvcDRwE0vbM34ckMh1mKxiNdeew0A8Mcff+D1118Xb7E4deoU3nzzTYyMjIhJAIC9vT0AQDQaRW9vry0tGo1C0zTcv38fhmHY0sDlPX/+vO065Xv48GHXh88+//xz7O/v4+LFi3XPX61W8fDhQzz77LPo6+uzpflF13WMjY2hWCyKSb4IU17TNLGxsQFN06Qhrf7+fvzwww8AgOXlZceQzdraGiDxA4RYR1C6Ue+6rmNqagrb29u4fv063nnnHfEWi/Pnz2NoaAinTp0Sk1qKaZr4+OOP8cQTT9Q9b6vY2dnB/v4+BgcHxaRAhGX7VtGMfKZp4r333kOtVsN3330nJgNAU/1vM7IRhUIBH374oeW7r7zyCmKxmHibLzwHSF3X8dZbb6FWq+GLL77A8PCweEsdzzzzjLThUucFh87t4OAAtVpNvAxweWOxWN3gS/meeuopPP3007a0bkLXdfz555+Ix+N49dVXxWRsbW2hVqtJO8VOELa85XIZ9+7dc+2AR0ZGEIvFsLm5idu3b4vJME0TBwcHUj9ASHV0mjD1Th3b/v4+kskkpqenxVtsRKNRvPDCC57lhkm5XMbExATy+bx0UtMq1tbWHP1IYefatWvY3NzERx99hP7+fjG5K3j55ZfrxqdmxwTPAfLGjRvY399HPB7HBx98ICbX0dvbi6+++krawKjzcnJKejN48cUX64xAecU00zSxvLwMAF1tPHAz1kQiUaef9fV1XLlyBdls1rMTaxdhy0sduyx6IIMiBjxOfkCEUUenCVPvt2/fxubmJmKxmOPMX+T06dPiJVSrVczNzVmboCKRCG7evCnehmKxiKWlJSQSCWujD20KikQiiEQi1lsCvTUMDAxge3sbtVoNZ86csTZW8GWtr68DR5tDIpGIdPNFozKCm4Q4+VG7KBaL1oaSsbExlMtlm77oejNvVs1SLBZx5coVxGIxvPvuu2Kypctug9pRU4gxVx5+vSaMOD2tDcli2PwapyxmTHn5dSN+08Xs7Kzt/m6EX9+hxW4AbHx8nA0MDEifOyi0XtBMmWHKy+d3W/vj75P5nNv6Ylh1NEM36Z3PK2tzjUJrObOzs9b6oMwOtDkKR5vwSP50Os1KpZLVxkWdN1oWrT2L9zIfMhJUlpuf+MWv7QuFAsvn85adNE1j6XSazc/PM13XGfPoM/3iVz6CbCHTI+P67mbWIIPK5gTJJFvD94PrAElO5DRo+cGpUzIMgy0uLroOjnxe/kcOxe/wawZyxkZ/Tg4jw81g9HzNOJhIsw4XtryNNiK3SRnfkcieK4w6mqWb9E5loYmBwKmDJh2KcvLXdV23bbqh/kRs/0425cu6e/cuW1lZYblcru5evzKyFm0qCWp7klN8Luags6AEkY9vK14/Uf9+CCKbE7zfy2zvB9cQK4Wf3OK46+vrdefNerizaQSFxjRNw61bt6zwQV9fH3766Sd8+eWX+Oeff+piyHzeZDIJxhgKhQI0TcPQ0BC+/vprx7Umv8zNzeFo0tDQb25uTizCEQr9ycJmvb29SCQS2N/fx40bN2xpnSJseWmd2CukRRtQANRtnvBaXwyjjk4Tpt6pLKclDS/40JpTeFbcUEe6TSQSuHbtGmZmZqznoCUUXuduNqWyXnrpJZRKJczMzGB6ehoPHjyw+okgMnZLeJWgUKBsj4dMZ+2EZIvH4zAMo64PrFQq1kYY2b6SdkN7ZgDg0qVL0DRNvMUXrgPk7u4uALjulpuengZjDLlczhImm83i77//tjkfNdZz585hY2MDDx48sJS8t7eHTz/9tK5DICjv888/DxztlB0aGsK9e/ekHxboNrx2VvKQzv2wsLBQN0E5ceIEtre3cebMmbq0999/XyzCRivkddt5ykODnKxTdxs8EFIdfngU9N4My8vLqNVqvtb2t7a2AAClUgkjIyNWPhqURJ272ZTKeuyxxxx3zgeRkTp9Lz9xI0zbr62tSW3upLNGCEs+alMy+4DTpR8Zw5JNhN+Qdv36dQwPD+Pxxx+Xyt0w4islD4UuGnlNdQsNMS6O7TfU4xSCcVtf6Da8dMMaiPP7pZmQRdjyUpimkZAWlSuGa5z8gAijjjDoJr37ab8iXvqk0B9fNtkIkj0BsrOrbjbl05z6jCAyshaFV1lA29MziDIyB501g1/5eHvKbMCnN+KPbviVTYbYNnK5XNO6c32DpNd6MUQhw20m2MxMyCkEMzExAU3TsLGxEfpZtrBx0w04/chmkZ0gbHlplukV0ioWi8jn89A0DRcvXrSlOfkBEUYdnSZsvftpvyKkT6fokezNgt+lnk6npffzb22GYeD+/ftSm/L2lh11QUAZSYdOedqN245lmc7aCdlH0zREo1Ex2doh3egJh1aysLCAX3/9Fclk0lr62tvbs6KOQXEdIOm8mNeah1doqNHOS4ZTp9GKMKvs1d/tJ9tqLuKlG3BHac6dO4fh4WGYponJyUn09PRgcnKyrROAIPIWi0UMDg46bkVvpKGbponPPvsMtVoNP/74Y91ajJMfEGHU0UmC6B3CHgAxFMW3352dHVuajNXV1Tobyo7L0CRD3PZPoWsx3Ol0rtNtcKCyGjnr6UdGtzo7QSvCq2Ej24NCx+s0TcP333/fUV2ur68jnU4jHo/j6tWrYrKFruv+vzEsvlKK0Gt+LBZz3C1KoSFZ2MLrNd0NtxAMcwmV5XK5utDP6uqq7Z524RU2k6Unk0krTJDJZOqerxGChixk8rile+109gqDsQY+g+blB2HUEZbPtEvvdI1CmZQuhroozOrWftnR92359imrj3mEPp1Cl/xOTF3XbX5ONjUMwyafU1k8QWWkOu/cucMKR8dHOmF7J/mZR3hVlLdR/Mrn1u4ymYxUv+3SHeGmw2QyaV0vlUrswoUL7PLly75s7TlAMu6MEXUuoiDUCGXG5D9OLirTCypXZiDGOREcPlpNZLNZtrKyIl5uC/QM4+PjbJT76LRhGJZeU6mUpdPC0QeqqWOoVCpscnLStaOQEdTh/MrLXOoyDMOaxIgOrOs6y+fz1sfv+bp4DMNg8/PzViffijpkBPUZJ114EUTvd+/etf2dyWTqdMAYsz5CLmu/hUKBpVIpafshvVJaqVRy7DBpkiLrA2hQ+u2339j8/Lzt/vjRcZB0Om3zeaeyRILIGI/HrXOZMtple7K5OKlh3HP57TPd8Csf4/rY+fl5ZhgGM44+ai/TL2uj7pjHixmTHNuT6dlL3oYGSHbU2SwuLrKBgQFbpZqmsampKba6umodbmXC4MX/nB6Gxymv2PjJ4XlZLl++zHA0WFcqFRbnzk+G6WyNYAgzMOroSB7xPyowyeyQyvArexCHCyIvc6iLGrjbb3x83PUcq+jg9CP9hFEHE2brzfqMTBdeBNW7iOg7PKVSiaVSKVu5AwMDbHZ21tZueYyjyQnl0TSNLS4uSssnHcr0RQMk3xGRnjXJwO9WlogfGfk6aUDtlO1Fm/NQvybrK3l5/eJHPp6VlRVP/bZTd4zTkUx/BA2g5HtBbN3wAPkokUwmrQcuFArswoULdQZtB2Qgp05LhhhSDTpABiGIvCyAc3cjnfSZoHoXyWQy0lmywp1O2l6G1yDIy9tNdIPuvPBra9dNOo8iuq6jUqlYC9tbW1vNn4UJiNfGkm7jUZM3LDrtM2HoXdd17O3tdXw34aNGp20vw23DmShvN9ENunND1F0j8h67AXJnZwcnTpywdtHt7u5KHa3V0K5EcRedF4ODgzg4OGjrzlU0Ie9xoJM+E5bef/75Z3zzzTeujV1RTydtL8Nr96oobzfRad15IequEXmP3QC5trZmPbSu6/jrr7+kZ3haDZ0R8nu0JRqN4vDw0Dq3Vi6XcXh4KG0sYRJU3uNAJ30mDL0vLCzYvlqjaJxO2l6G15E4Xt5uoht050UQWx+rAZJeoaPRKEzTxM7ODp588kn09fW19Y2MPyPk9zD68PAwTp8+bZ073drawttvvy1tLGHRjLyPOp30mTD0Tv8Giv711fr6unVN4U4nbe8Eff9adsBdlLeb6AbduSHqrlF5j9UAWa1W8e+//0LXdfT29mJvbw+Hh4colUptDT319vbi1q1bto8q++Hq1avY2NhAT08Pdnd3fX0UPQjNyFssFvHcc89Z31F81DrnTvpMM3rH0ZvjG2+8gXQ6bX0s4JNPPml5tOG40EnbO0H/MEHW5kV5u4lu0J0bou4albeHMcbEiwqFQqFQ/N85Vm+QCoVCoVCEhRogFQqFQqGQoAZIhUKhUCgkqAFSoVAoFAoJ/wE4zEeMnN76UAAAAABJRU5ErkJggg==\"\u003e\u003c/p\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere \u003cem\u003eGPR\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e represents the grain production resilience level of province in year (the dependent variable); \u003cem\u003eDID\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e represents the policy variable (the core independent variable); \u003cem\u003eControl\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e represents the control variables; \u003cem\u003eη\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eθ\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e represent the individual fixed effects and time fixed effects, respectively, while \u003cem\u003eλ\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e represents the random disturbance term.\u003c/p\u003e\u003cp\u003eTo further analyze the mechanism of policy based agricultural insurance on the resilience of grain production, the following model is constructed:\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere \u003cem\u003eMed\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e represents the mediating variables, specifically the operational scale and the grain replanting index for province i in year t, with all other components consistent with Eq.\u0026nbsp;(5).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e4.4. Data Sources.\u003c/h2\u003e\u003cp\u003eThis study selects data from 30 provinces in China (excluding Hong Kong, Macau, Taiwan, and Tibet) from the years 2013 to 2022 as the research sample. Relevant data are sourced from the \"China Statistical Yearbook,\" \"China Rural Statistical Yearbook,\" and the China National Bureau of Statistics, with some indicators derived from the composite calculation of original indicators. The maps used in this study are based on the standard map with the review number GS(2024)0650, downloaded from the National Platform for Common Geospatial Information Services. The base map boundaries have not been modified. The vector data for China\u0026rsquo;s administrative boundaries are obtained from the Resource and Environment Science and Data Center.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Results and analysis","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e5.1. Current Status of Grain Production Resilience in China\u003c/h2\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e reflects the changes in the average grain production resilience in China. From the data, it can be seen that between 2013 and 2022, the overall trend of grain production resilience showed a steady increase. Although there were slight fluctuations in certain years, the overall upward trend was quite apparent. During the period from 2013 to 2016, grain production resilience increased from 0.298 to 0.310, with a relatively gentle growth rate. This stage may have been influenced by the gradual advancement of policies and continuous development in agricultural technologies, providing a certain level of stability for grain production. In 2017 and 2018, there were slight fluctuations in grain production resilience, but it remained at a high level. This may have been affected by market volatility and natural disasters; however, the adaptability and recovery capacity of the grain production system prevented significant declines in resilience. From 2019 to 2020, grain production resilience continued to rise. In 2021, although there was a slight decline, it rebounded in 2022, demonstrating that grain production resilience possesses a certain level of resistance and recovery capability.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo further analyze the spatial distribution characteristics of grain production resilience across regions, this study utilizes ArcGIS 10.8 to visualize the provincial-level resilience scores in China for the years 2013 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea) and 2022 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). The natural breaks classification method is applied to divide the resilience scores into five discrete intervals. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the overall spatial pattern of grain production resilience across Chinese provinces exhibits a steady upward trend over the decade.\u003c/p\u003e\u003cp\u003eSpecifically, in 2013, Heilongjiang Province fell into the highest (fifth) interval, while Henan Province was at the upper bound of the fourth interval. Ten provinces, including Hebei, Shandong, and Inner Mongolia, were also in the fourth interval. Twelve provinces, such as Anhui and Beijing, were distributed within the third interval, while four provinces including Gansu and Fujian were in the second interval. The lowest (first) interval comprised four provinces, including Guizhou and Hainan.\u003c/p\u003e\u003cp\u003eBy 2022, Heilongjiang Province remained in the fifth interval, with its score reaching the upper bound. Henan Province advanced from the fourth to the fifth interval. Five provinces\u0026mdash;including Anhui and Guangdong\u0026mdash;improved from the third to the fourth interval. Gansu, Yunnan, and other provinces moved from the second to the third interval. Notably, Guizhou Province achieved a leapfrog advancement from the first to the third interval. The remaining 21 provinces maintained their original resilience intervals.\u003c/p\u003e\u003cp\u003eOverall, approximately one-third of the provinces experienced an upgrade in resilience levels, with no province exhibiting a downgrade. High-resilience regions remained stable, while several mid- to low-resilience regions showed marked improvement, indicating that China\u0026rsquo;s grain production system has undergone overall enhancement in its capacity to withstand risks over the past decade.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e5.2. Baseline Regression\u003c/h2\u003e\u003cp\u003eBefore estimating the model using the difference-in-differences method, it is necessary to test whether the parallel trends assumption holds. The parallel trends assumption requires that before the policy implementation, the trends of the treatment group and the control group are similar. If the trends are similar before and the treatment group shows a significant change after the implementation, the parallel trends assumption is satisfied; otherwise, the results of the difference-in-differences estimates may be biased, and other methods should be considered.\u003c/p\u003e\u003cp\u003eFrom Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, it can be seen that the coefficients before the policy shock are not significant, indicating that there is no significant difference between the treatment group and the control group before the policy implementation, thus satisfying the parallel trends assumption.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e reports the baseline regression results of the impact of policy-based agricultural insurance pilots on grain production resilience. Models (1) and (3) are the regression results without controlling for time fixed effects and individual fixed effects. In models (2) and (4), the estimated coefficients of the core independent variable policy variable (DID) are significantly positive, indicating that the implementation of the policy promotes grain production resilience. Moreover, in model (4), where control variables are included, there is a significant positive correlation between economic development level (GDP) and grain production resilience, maintaining significance at the 1% level. This indicates that an increase in the level of economic development contributes to enhancing grain production resilience. The coefficient of the proportion of financial support in agriculture (FSA) is also positive and significant at the 1% level, suggesting that financial support for agriculture significantly enhances grain production resilience. The coefficient for urbanization level (UR) is significantly positive, indicating that the advancement of urbanization is beneficial to the improvement of grain production resilience. The coefficients for the proportion of value added in the secondary industry (SI) and value added in the tertiary industry (TI) are both negative and significant at the 1% level, suggesting that an increase in the share of the secondary industry may have a negative impact on grain production resilience.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline regression results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(4)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eGPR\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eGPR\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eGPR\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eGPR\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eDID\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.078\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.006\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.072\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.010\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.012)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.016)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.003)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eGDP\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.122\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.025)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.021)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eFSA\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.207\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.258)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.072)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eUR\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.445\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.280\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.156)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.086)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSI\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.621\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.517\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.308)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.131)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTI\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.043\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.312\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.316)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.112)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e_cons\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.300\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.307\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.793\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.861\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.006)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.336)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.209)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR-square\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.055\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.982\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.173\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.988\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTime fixed effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndividual fixed effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: The values in the brackets are robust standard errors. *, **, and *** indicate that the regression results are statistically significant at the 10%, 5%, and 1% confidence levels, respectively. The same applies to the table below.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e5.3. Robustness Tests.\u003c/h2\u003e\u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\u003ch2\u003e5.3.1. Placebo Test\u003c/h2\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eConsidering that the impact of the policy variable on grain production resilience may stem from some unobservable factors, a placebo test is constructed following related research methods to further verify the reliability of the policy effect. The specific approach is to create a \"dummy\" policy variable by randomly selecting the same number of provinces as the treatment group, forming a \"pseudo-experimental group,\" and executing the regression 500 times. The dashed line in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e represents the true estimated coefficients, while the points represent the dummy estimated coefficient. It can be observed that the regression coefficients are normally distributed and primarily located on both sides of the zero line. There are 47 results greater than the true estimated coefficient, accounting for 9.4% of the total sampling results, with most regression results distant from the true estimated coefficient. Therefore, the results can be considered robust.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\u003ch2\u003e5.3.2. Shortened Sample Period\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRobustness test results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(5)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(6)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(7)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(8)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGPR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eGPR\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eGPR\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eGPR\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eDID\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.075\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.006\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.079\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.006\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.016)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.017)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.003)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eGDP\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.041\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.127\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.033)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.026)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.030)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.021)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eFSA\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.270\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.177\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.277\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.233\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.289)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.061)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.257)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.070)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eUR\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.445\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.864\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.473\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.204)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.111)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.165)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.108)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSI\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.681\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.246\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.733\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.488\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.397)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.175)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.285)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.123)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTI\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.985\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.202\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.537\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.276\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.403)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.148)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.324)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.105)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e_cons\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.109\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.510\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.124\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.783\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.443)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.239)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.363)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.194)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e210\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e210\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e260\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e260\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR-square\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.994\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.222\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.991\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e​Time Fixed Effects\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e​Individual Fixed Effects\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eShortening the sample period can examine the stability and persistence of the policy's impact. If the policy variable still has a significant positive impact on grain production resilience within a shorter time frame, it suggests the robustness of the results. After shortening the sample period to 2016\u0026ndash;2022, the results (see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) indicate that in model (5), which does not control for fixed effects, the core independent variable is significantly positive at the 1% level. In model (6), which controls for fixed effects, the estimated coefficient of the policy variable (DID) remains significantly positive. This indicates that even with a shortened time frame, the policy variable continues to have a positive effect on grain production resilience. The coefficient for economic development level (GDP) changes to 0.041 but is not significant, suggesting that the impact of economic development level on grain production resilience is unstable in the shorter time frame. The coefficient for the proportion of financial support in agriculture (FSA) is 0.177, significant at the 1% level, indicating a clear enhancement of grain production resilience from financial support in agriculture within the shortened time frame. The coefficient for urbanization level (UR) is 0.86 and significantly positive, indicating that urbanization has a substantial promotional effect on grain production resilience within a short period. The coefficient for the proportion of value added in the secondary industry (SI) is -0.246 and not significant, suggesting that the negative impact of the secondary industry on grain production resilience is not evident in the new time frame. Similarly, the coefficient for the proportion of value added in the tertiary industry (TI) is -0.202 and not significant, indicating that the impact of the tertiary industry on grain production resilience is also insignificant within the shortened time frame.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section3\"\u003e\u003ch2\u003e5.3.3. Excluding Municipalities\u003c/h2\u003e\u003cp\u003eMunicipalities have certain peculiarities in economic structure, resource allocation, and policy implementation compared to other regions. Excluding municipalities can better examine the impact of the policy variable on grain production resilience in more representative general regions, avoiding the interference of municipalities' uniqueness on the overall results. After excluding the four municipalities\u0026mdash;Beijing, Shanghai, Chongqing, and Tianjin\u0026mdash;the results (see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) show that in model (7), which does not control for fixed effects, the core independent variable is significantly positive at the 1% level. In model (8), which controls for fixed effects, the estimated coefficient of the policy variable (DID) remains significantly positive, indicating that the positive impact of the policy on grain production resilience remains unchanged after excluding the municipalities. The coefficient for economic development level (GDP) becomes 0.127 and is significant at the 1% level, indicating that, after excluding the municipalities, the contribution of economic development to enhancing grain production resilience is more evident. The coefficient for the proportion of financial support in agriculture (FSA) is 0.233 and significant at the 1% level, showing that the promoting effect of financial support on grain production resilience further intensifies after excluding the municipalities. The coefficient for urbanization level (UR) becomes 0.025 and is not significant, indicating that the influence of urbanization on grain production resilience is not significant after excluding the municipalities. The coefficient for the proportion of value added in the secondary industry (SI) is -0.488 and significant at the 1% level, indicating that the negative impact of the secondary industry on grain production resilience has increased after excluding the municipalities. The coefficient for the proportion of value added in the tertiary industry (TI) is -0.276 and also significant at the 1% level, showing that the negative impact of the tertiary industry on grain production resilience has also intensified after excluding the municipalities.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section3\"\u003e\u003ch2\u003e5.3.4. PSM-DID\u003c/h2\u003e\u003cp\u003eConsidering the potential selection bias in policy implementation, as well as the need to control for unobservable factors in evaluating policy effects, this study employs the propensity score matching-difference-in-differences (PSM-DID) method for robustness testing. The PSM method constructs a suitable control group through propensity score matching, which effectively eliminates sample selection bias; the DID method can control for unobservable factors that do not vary over time. The combination of the two methods can more accurately identify policy effects.\u003c/p\u003e\u003cp\u003eThe balance test results in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e show that the differences between the treatment group and the control group across various variables have significantly improved before and after matching using PSM. Prior to matching, notable differences existed in economic development level (GDP), proportion of financial support in agriculture (FSA), urbanization level (UR), proportion of value added in the secondary industry (SI), and proportion of value added in the tertiary industry (TI), particularly with larger biases in SI and TI. The t-test results were significant, indicating systematic differences in these variables between the two groups. However, after PSM matching, the biases across the variables were significantly reduced, with t-values approaching 0 and p-values exceeding 0.05, indicating that the differences in these key variables between the treatment and control groups are no longer significant, achieving a balance. This suggests that the PSM method effectively weakened systematic differences between the groups, providing a reliable foundation for subsequent difference-in-differences analysis. The conditions of the matched treatment and control groups prior to policy implementation are closer, thus making the estimates of the policy's impact on grain production resilience (GPR) more accurate and credible. Consequently, the application of the PSM-DID method in this paper can more effectively identify policy effects and ensure a high level of reliability in the assessment of the policy variable's impact.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBalance test results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMatching Status\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eBias (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003et-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTreatment Group\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eControl Group\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGDP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnmatched\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10.948\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10.945\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.972\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMatched\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10.948\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10.955\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-2.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.894\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFSA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnmatched\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.113\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.115\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-7.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.626\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMatched\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.113\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.109\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e12.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.427\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnmatched\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.617\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-16.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-1.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.308\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMatched\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.608\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-7.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.606\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnmatched\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.425\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.387\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e59.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMatched\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.425\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.426\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.949\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnmatched\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.479\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.517\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-54.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-3.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMatched\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.479\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.480\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-2.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.838\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe study also visually compares the kernel density plots of the propensity scores before and after matching to intuitively demonstrate the matching effect. In Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the horizontal axis represents the propensity score, while the vertical axis indicates the kernel density. The results in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e show that before matching (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. a), the distribution of propensity scores for the treatment group and control group diverged significantly, with distinctly different curve shapes. This indicates substantial heterogeneity between the two groups prior to policy implementation, which could lead to estimation bias when directly using the DID method. After PSM matching (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. b), the distribution of propensity scores for the treatment and control groups becomes noticeably aligned, with the curves becoming closer, demonstrating that after matching, the characteristics of the two groups are more similar and the group differences have been effectively eliminated. This result further verifies the effectiveness of PSM matching, providing a more reliable basis for subsequent DID analysis and ensuring more accurate estimates of policy effects.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e reports the regression results after PSM matching. In the uncontrolled fixed effects model (9), the coefficient of DID is 0.072, which is significantly positive at the 1% level, indicating that policy implementation has a significant promoting effect on the resilience of food production. In the model (10) that controls for individual and time fixed effects, the coefficient of DID is 0.010, which is also significantly positive at the 1% level, further verifying the robustness of policy effects. In summary, hypothesis 1 is proven.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRegression results after PSM matching\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(9)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(10)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eGPR\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eGPR\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eDID\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.072\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.010\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.016)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.003)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eGDP\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.122\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.025)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.021)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eFSA\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.207\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.258)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.072)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eUR\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.445\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.280\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.156)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.086)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSI\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.621\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.517\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.308)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.131)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTI\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.043\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.312\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.316)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.112)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e_cons\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.793\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.861\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.336)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.209)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR-square\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.173\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.988\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTime fixed effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndividual fixed effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e5.4. Heterogeneity Analysis\u003c/h2\u003e\u003cp\u003eThere are significant geographic differences between the northern and southern regions of China. The northern regions have a relatively dry climate with less precipitation and notable seasonal variation, making grain production more susceptible to natural disasters such as droughts and frosts. In contrast, the southern regions have a more humid climate with abundant rainfall, although they may also face threats from disasters such as floods and typhoons. These differing geographic environments suggest that the mechanisms and degrees of effectiveness of policy-based agricultural insurance may vary across regions. To explore the differences in the impact of policy-based agricultural insurance on grain production resilience in different regions, this study categorizes the sample based on geography, dividing provinces south of the Qinling-Huaihe Line as southern provinces and those to the north as northern provinces. Specifically, Anhui and Jiangsu are classified as southern provinces, while Gansu, Shaanxi, and Henan are classified as northern provinces. The model (11) in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows that the effect of policy-supported agricultural insurance on grain production resilience in northern provinces is more significant. Specifically, among the samples of northern provinces, the implementation of policy-supported agricultural insurance significantly improved the grain production resilience, with a coefficient of 0.018, which was significant at the 1% level. In model (12), although the policy effect coefficient was positive (0.006), it failed to pass the significance test. The reason for this may be that the northern regions are the main grain-producing areas of China, with larger planting scales and relatively higher enrollment rates and coverage for agricultural insurance(C. Xie et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Additionally, the harsher climatic conditions in northern regions lead to a higher frequency of natural disasters, making the protective role of agricultural insurance more apparent, thereby more effectively enhancing the level of resilience in grain production(S. Xie et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo further investigate the heterogeneous effects of grain production resilience across regions with varying agricultural development levels, this study divides the sample into two groups\u0026mdash;high-resilience and low-resilience regions\u0026mdash;based on the calculated grain production resilience scores. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, model (13) and (14), significant heterogeneity emerges between the two groups. Specifically, in high-resilience regions, the DID coefficient is 0.008 and fails to pass the significance test, indicating that policy-based agricultural insurance does not significantly improve grain production resilience in areas already exhibiting strong resilience. In contrast, the DID coefficient for low-resilience regions is 0.019 and statistically significant at the 5% level, suggesting that the positive effect of the policy is more pronounced in regions where resilience is relatively weak. These findings highlight the spatial differentiation of policy effects: low-resilience areas, characterized by weaker foundations, tend to respond more sensitively to policy interventions, and the marginal impact of insurance support is more readily observed. Conversely, high-resilience regions possess relatively robust risk-buffering mechanisms, making the incremental benefits of policy support less prominent. This evidence underscores the necessity of implementing differentiated agricultural insurance strategies tailored to regional resilience levels.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eHeterogeneity analysis results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(11)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(12)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(13)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(14)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eGPR\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eGPR\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eGPR\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eGPR\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eDID\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.018\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.019\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.004)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.005)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.005)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.007)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e150\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e150\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e150\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e150\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR-square\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.992\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.985\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.594\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.419\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControl Variable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTime fixed effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndividual fixed effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e5.5. Mechanism Testing\u003c/h2\u003e\u003cp\u003eFrom Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, in model (15), the estimated coefficient of DID for scale operation (Scale) is 0.253, significant at the 5% level. This indicates that policy-based agricultural insurance promotes the scaling of grain production. Policy-based agricultural insurance can provide risk protection for farmers, reducing losses from risks such as natural disasters, thereby enhancing farmers' confidence in expanding production scales. Scaling up production can improve agricultural efficiency, reduce unit costs, and enhance the stability and resilience of grain production, thereby increasing grain production resilience, confirming Hypothesis 2.\u003c/p\u003e\u003cp\u003eIn model (16), the estimated coefficient of DID for the grain replanting index (GRI) is 0.025, significant at the 1% level. The implementation of policy-based agricultural insurance can alleviate farmers' risk concerns, making them more willing to increase planting frequency and improve land utilization rates. An increase in the grain replanting index signifies that more grain can be produced on the same land area, enhancing the sustainability and resilience of grain production, thereby confirming Hypothesis 3.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMechanism test results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(15)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(16)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eScale\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eGRI\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eDID\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.253\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.025\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.107)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.006)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eGDP\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-3.574\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.064\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.540)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.036)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eFSA\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.329\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.396\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(2.891)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.101)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eUR\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.941\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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colname=\"c3\"\u003e\u003cp\u003e1.263\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(8.690)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.395)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR-square\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.983\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.987\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTime fixed effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndividual fixed effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"6. Conclusions and policy recommendations","content":"\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003e6.1. Conclusions\u003c/h2\u003e\u003cp\u003eDrawing on province-level panel data from 2013 to 2022, this study measures grain production resilience using the entropy weight TOPSIS method and employs a difference-in-differences (DID) approach to evaluate the effects of policy-based agricultural insurance. The key findings are as follows:\u003c/p\u003e\u003cp\u003eFirst, grain production resilience in China has shown a steady upward trend over the past decade. Although minor fluctuations occurred due to market volatility and natural disasters, no significant downturn was observed, indicating a generally stable and improving resilience landscape.\u003c/p\u003e\u003cp\u003eSecond, policy-based agricultural insurance has a significant and positive impact on enhancing grain production resilience. This effect remains robust across multiple empirical tests, including placebo checks, shortened sample periods, and PSM-DID estimations.\u003c/p\u003e\u003cp\u003eThird, the effects of policy-based agricultural insurance vary by region. The impact is more pronounced in northern provinces and in areas with lower baseline resilience levels, suggesting region-specific differences in the policy\u0026rsquo;s effectiveness.\u003c/p\u003e\u003cp\u003eFourth, mechanism analysis reveals that policy-based agricultural insurance enhances resilience primarily by promoting farm scale expansion and increasing the grain replanting index.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e\u003ch2\u003e6.2. Recommendations\u003c/h2\u003e\u003cp\u003eBased on the findings of this study, and in light of the practical realities of grain production and the current implementation status of policy-based agricultural insurance in China, the following policy recommendations are proposed to further strengthen the institutional effectiveness of agricultural insurance and enhance the resilience of the national grain production system.\u003c/p\u003e\u003cp\u003eFirst, it is essential to improve the structure and functionality of insurance products. Coverage should be expanded beyond direct input costs to include broader categories of production risks, such as labor, land use, and systemic risks arising from climate variability and market fluctuations. Insurance design should be more tailored to regional agricultural conditions to ensure accuracy and relevance. A multi-tiered risk-sharing framework involving government support, insurance institutions, and reinsurance mechanisms should be strengthened. Index-based insurance can be promoted to improve efficiency and reduce transaction costs, and regional risk pooling mechanisms should be encouraged to buffer the impacts of extreme events.\u003c/p\u003e\u003cp\u003eSecond, differentiated regional strategies should be adopted to address spatial disparities in policy effectiveness. In areas where agricultural insurance has shown limited impact, particularly in regions with frequent natural disasters, support should focus on improving product adaptability, increasing subsidy intensity, and enhancing coordination with local governments for disaster prevention and risk management. In regions where insurance has demonstrated stronger outcomes, especially key production zones, efforts should focus on expanding participation, improving claim efficiency, and integrating complementary instruments to manage both production and price risks.\u003c/p\u003e\u003cp\u003eThird, insurance should be leveraged as a policy tool to support structural adjustment in agriculture. This includes promoting moderate-scale operations and improving land-use efficiency. Policy instruments such as premium subsidies and credit support should be aligned with efforts to expand farm size and consolidate production. Insurance should also play a role in encouraging more efficient land use, including practices that increase cropping intensity and resilience. Strengthening the link between insurance, mechanization, and infrastructure investment can amplify its role in stabilizing agricultural operations.\u003c/p\u003e\u003cp\u003eFourth, financial support mechanisms should be optimized, and stronger integration with related policies should be pursued. Public subsidies should be directed toward regions and producer groups with higher exposure and lower resilience, using a dynamic and targeted allocation system. Policy coherence should be enhanced by aligning agricultural insurance with credit systems, technology extension, and risk reduction programs, forming a coordinated support network for agricultural producers. This would help reduce transaction costs and ensure more effective risk management at the farm level.\u003c/p\u003e\u003cp\u003eFinally, a comprehensive monitoring and adjustment mechanism is needed to ensure long-term policy effectiveness. A resilience-oriented evaluation framework should be developed, combining indicators that reflect the capacity to resist, recover, and adapt to shocks. The performance of agricultural insurance should be assessed regularly, with particular attention to underperforming regions. Based on evaluation results, insurance policies and subsidy structures should be reviewed and revised in a timely manner to maintain alignment with evolving risk profiles and development needs.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003eThe author declares no competing interests.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003cp\u003eThis article does not involve studies with human participants conducted by any of the authors.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eInformed consent\u003c/strong\u003e\u003cp\u003eNo research involving human subjects was conducted for this paper.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization, Y.T.; methodology, Y.T.; formal analysis, Y.T.; investigation, M.Z.; resources, Y.T.; writing\u0026mdash;original draft preparation, Y.T.; writing\u0026mdash;review and editing, Y.T., M.Z.; visualization, Y.T.; supervision, Y.T., M.Z.; project administration, M.Z.; funding acquisition, M.Z..\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThis research was funded in part by the National Social Science Fund of China (No. 23BJY167).\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eFor access to the datasets generated or analyzed in the currentstudy, please feel free to contact the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBielza M, Stroblmair J, Gallego J, Conte CG, Dittmann C (2007) Agricultural risk management in Europe.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBinswanger HP, Sillers DA (1983) Risk aversion and credit constraints in farmers' decision-making: A reinterpretation. J Dev Stud 20(1):5\u0026ndash;21\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBullock JM, Dhanjal-Adams KL, Milne A, Oliver TH, Todman LC, Whitmore AP, Pywell RF (2017) Resilience and food security: rethinking an ecological concept. J Ecol 105(4):880\u0026ndash;884\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCai J (2016) The impact of insurance provision on household production and financial decisions. Am Economic Journal: Economic Policy 8(2):44\u0026ndash;88\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCai R, Ma J, Cai S (2024) Can crop insurance help optimize farmers\u0026rsquo; decisions on pesticides use? Evidence from family farms in East China. J Asian Econ 92:101735\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCarter MR, Cheng L, Sarris A (2016) Where and how index insurance can boost the adoption of improved agricultural technologies. J Dev Econ 118:59\u0026ndash;71\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCerroni S (2020) Eliciting farmers\u0026rsquo; subjective probabilities, risk, and uncertainty preferences using contextualized field experiments. Agric Econ 51(5):707\u0026ndash;724\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChina N (2024) B. o. S. o. Announcement of the National Bureau of Statistics on 2024 Grain Production Data. Retrieved from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gov.cn/lianbo/bumen/202412/content_6992479.htm\u003c/span\u003e\u003cspan address=\"https://www.gov.cn/lianbo/bumen/202412/content_6992479.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDercon S, Christiaensen L (2011) Consumption risk, technology adoption and poverty traps: Evidence from Ethiopia. J Dev Econ 96(2):159\u0026ndash;173\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFAN Z, QIN Z, YU S (2024) Impact of extreme temperature on the resilience of grain production: perspectives on green finance. Chin J Eco-Agriculture 32(5):896\u0026ndash;910\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFang L, Hu R, Mao H, Chen S (2021) How crop insurance influences agricultural green total factor productivity: Evidence from Chinese farmers. J Clean Prod 321:128977\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFAO (2021) The State of Food and Agriculture 2021. Retrieved from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.fao.org/agrifood-economics/publications/detail/en/c/1457037/\u003c/span\u003e\u003cspan address=\"https://www.fao.org/agrifood-economics/publications/detail/en/c/1457037/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFu L-S, Qin T, Li G-Q, Wang S-G (2024) Efficiency of Agricultural Insurance in Facilitating Modern Agriculture Development: From the Perspective of Production Factor Allocation. Sustainability 16(14):6223\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHazell P, Anderson J, Balzer N, Clemmensen H, Hess A, U., Rispoli F (2010) \u003cem\u003eThe potential for scale and sustainability in weather index insurance for agriculture and rural livelihoods\u003c/em\u003e. Retrieved from\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHellige HD (2019) The metaphorical processes in the history of the resilience notion and the rise of the ecosystem resilience theory. Handbook on resilience of socio-technical systems. Edward Elgar Publishing, pp 30\u0026ndash;51\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHolling CS (1973) Resilience and stability of ecological systems\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKarlan D, Osei R, Osei-Akoto I, Udry C (2014) Agricultural decisions after relaxing credit and risk constraints. Q J Econ 129(2):597\u0026ndash;652\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKijima Y (2019) Farmers\u0026rsquo; risk preferences and rice production: Experimental and panel data evidence from Uganda. PLoS ONE 14(7):e0219202\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiang X, Jin X, Han B, Sun R, Xu W, Li H, Li J (2022) China\u0026rsquo;s food security situation and key questions in the new era: A perspective of farmland protection. J Geog Sci 32(6):1001\u0026ndash;1019\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMahul O, Stutley CJ (2010) Government support to agricultural insurance: challenges and options for developing countries. World Bank\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMeuwissen MPM (2000) Insurance as a risk management tool for European agriculture. Wageningen University and Research\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShang Y, Xiong T (2021) The impact of farmers\u0026rsquo; assessments of risk management strategies on their adoption willingness. J Integr Agric 20(12):3323\u0026ndash;3338\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSmith VH, Glauber JW (2012) Agricultural insurance in developed countries: where have we been and where are we going? Appl Economic Perspect Policy 34(3):363\u0026ndash;390\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSun J-L, Tao R, Wang J, Wang Y-F, Li J-Y (2024) Do farmers always choose agricultural insurance against climate change risks? Econ Anal Policy 81:617\u0026ndash;628\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTan C, Tao J, Yi L, He J, Huang Q (2022) Dynamic relationship between agricultural technology progress, agricultural insurance and farmers\u0026rsquo; income. Agriculture 12(9):1331\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTang L, Luo X (2021) Can agricultural insurance encourage farmers to apply biological pesticides? Evidence from rural China. Food Policy 105:102174\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTendall DM, Joerin J, Kopainsky B, Edwards P, Shreck A, Le QB, Six J (2015) Food system resilience: Defining the concept. Global food Secur 6:17\u0026ndash;23\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWei Z, Peng J, Zhao Y, Li X, Wang C (2025) Reform of agricultural land property rights system and grain production resilience: Empirical evidence based on China\u0026rsquo;s Three Rights Separation reform. PLoS ONE, 20(3), e0319387\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXie C, Kuang Y-p, Wen H-x, Wu X-q (2025) Agricultural agglomeration or industrial integration: how does agricultural insurance bolster agricultural resilience in China? Front Sustainable Food Syst 9:1531287\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXie S, Zhang J, Li X, Xia X, Chen Z (2024) The effect of agricultural insurance participation on rural households' economic resilience to natural disasters: evidence from China. J Clean Prod 434:140123\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang R, Pan Y (2021) Rural vulnerability in China: Evaluation theory and spatial patterns. J Geog Sci 31(10):1507\u0026ndash;1528\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Policy-based agricultural insurance, Grain production resilience, Difference-in-differences, Quasi-natural experiment","lastPublishedDoi":"10.21203/rs.3.rs-7477834/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7477834/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUnder the new normal of intensified climate change and overlapping systemic risks, building a resilient grain production system has become a core objective of national food security strategies. This study employs provincial panel data from 2013 to 2022 to measure grain production resilience using the Entropy Weight-Technique for Order Preference by Similarity to an Ideal Solution method and evaluates the impact of policy-oriented agricultural insurance through a quasi-natural experiment framework based on China\u0026rsquo;s full-cost and income insurance pilots. Key findings reveal that: (1) Policy-based agricultural insurance significantly enhances grain production resilience, a conclusion robust to placebo tests, subsample analyses, and Propensity Score Matching-Difference-in-differences validation. (2) The policy effect is more pronounced in northern China compared to southern regions, attributed to larger farming scales and higher climate risk exposure. (3) Mechanism analysis demonstrates that agricultural insurance strengthens resilience by promoting scale operations and increasing the grain replanting index. Accordingly, policy recommendations are proposed to refine insurance product design, enhance regional policy targeting, strengthen institutional coordination, and establish a dynamic monitoring framework, with the goal of improving the effectiveness of policy-based agricultural insurance in building a resilient and sustainable grain production system.\u003c/p\u003e","manuscriptTitle":"Policy Experimentation for Sustainable Agriculture: How China’s Policy-Based Agricultural Insurance Enhances Grain Production Resilience","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-16 18:17:28","doi":"10.21203/rs.3.rs-7477834/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"244ff91e-7bd6-4a38-9ae8-db0d96e9961d","owner":[],"postedDate":"October 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":56285013,"name":"Earth and environmental sciences/Environmental sciences"},{"id":56285014,"name":"Earth and environmental sciences/Environmental social sciences"}],"tags":[],"updatedAt":"2025-12-01T18:53:19+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-16 18:17:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7477834","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7477834","identity":"rs-7477834","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}