The impact of high-standard farmland construction (HSFC) on China's agricultural resilience

The impact of high-standard farmland construction (HSFC) on China's agricultural 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 The impact of high-standard farmland construction (HSFC) on China's agricultural resilience Feiwu Ren, Zihan Xia, Yi Huang, Jiangtao Chi, Gai He, Yanwei Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4495317/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract The development of high-standard farmland (HSF) constitutes a crucial strategy for enhancing agricultural infrastructure, which significantly contributes to increasing agricultural production and catalyzing agroeconomic growth. The present study devises an index system to evaluate agricultural resilience (AR) in China, anchored in the DPSIR framework, and analyzes data from 28 Chinese provinces over 2011–2021 to ascertain the effects of HSFC on AR employing fixed-effects and mediation-effects models. The study reveals that the HSFC markedly enhances AR. Specifically, in key grain production regions or the central area, the positive influence of such construction on resilience is more substantial than in non-key grain production or east-west regions. HSF fortifies AR chiefly by boosting labor productivity and increasing farmers’ income. Analysis of correlation coefficients and overall context confirms that enhanced income of farmers is the key driver in this equation. Although HSF encourages urbanization within the agricultural community, this trend may inadvertently hinder resilience improvements. While the indispensable role of HSF in promoting agricultural progress is recognized, it is crucial to address the concurrent issue of population outflow from agricultural sectors. This study contributes uniquely by integrating the DPSIR model into the exploration of AR, thereby offering a novel, proactive approach to sustainable agricultural development. Furthermore, it elucidates the mechanisms through which HSF impacts AR across three dimensions: labor productivity, farmer incomes, and population urbanization, from the farmers’ vantage point. This insight enables policymakers to refine resource allocation, enhancing the planning, design, and stewardship of sustainable agriculture. agricultural resilience DPSIR high-standard farmland food security rural development Figures Figure 1 Figure 2 1. Introduction The escalating frequency, intensity, and complexity of global natural disasters, extreme weather events, and the intensification of international geopolitical conflicts pose serious challenges to the global food supply. In an era marked by historic changes, significant shifts in the external environment, and an increase in risks and adverse factors, the need for resilient and high-quality agricultural development becomes paramount 1 . Resilience initially referred to the capacity of a system to revert to its original state following an external shock 2 . Presently, the concept has been extensively applied in research across ecology, economy, industrial development, and other fields. According to Folke's theory, agricultural resilience (AR) can be interpreted as the ability of an agricultural system to ensure its original key functions under external disturbances 3 . Fostering a resilient, high-quality agriculture is critical not only for China’s food security but also plays a pivotal role in global food security and building a shared future for humanity 4 . China, a major agricultural country with a large population 5 , led global food production in 2022 with a harvest of 686.53 billion kilograms. This achievement underscores China’s commitment to agricultural development, evidenced by the prioritization of agriculture in national policies. For instance, the Central Committee’s Document No. 1 has emphasized agricultural development as its primary theme for 21 consecutive years since 2004, highlighting farmland improvement as a critical area of focus. High-standard farmland construction (HSFC) plays a key role in advancing agricultural social services and governance in China 6 , contributing to poverty alleviation and rural economic development, thereby strengthening AR 7 . Defined as a modernized network of agricultural infrastructure, including irrigation systems, drainage systems, and road networks 8 , high-standard farmland (HSF) is fundamental to enhancing food security. Recent policies and plans, such as the reports from the 20th Party Congress and the National HSFC Plan (2021–2030), outline ambitious goals for the development and upgrading of HSF 9 , reflecting China’s dedication to stable food supplies and sustainable agricultural development 10 . However, the impact and mechanisms of HSF on agricultural development amid challenges like natural disasters, population growth, and diminishing land resources require further investigation to inform strategies for quality agricultural development. Existing studies have explored the effects of HSF on various aspects such as the rural environment 10–12 , food production 8,9 , and poverty reduction 12,13 . Firstly, there is a lack of research to assess the overall effects of the implementation of HSF as a vigorously pursued agricultural policy in China. Second, the number of quantitative studies exploring the direction of agricultural improvement from an external macro perspective is low in the context of increasing food security risks. In this context, this study attempts to make the following contributions: firstly, it enriches the research field of AR by incorporating the DPSIR model and considering multiple subjects in the indicator system. Secondly, we consider the key role of the farmer subject between arable land and agricultural growth, construct a theoretical model based on the role of farmers in the agricultural production system and business system, and conduct an in-depth analysis of the mechanism by which HSF affects AR, which enriches the research perspective of agricultural development. The rest of the article is organised as follows: the second part is the literature review, the third part is the theoretical analysis and hypotheses, the fourth part is the research methodology and data, the fifth part is the empirical results, and the last part is the conclusion and discussion. 2. Literature Review 2.1 Resilience Theory and the DPSIR Framework Introduced into ecology by Canadian ecologist C.S. Holling in the 1970s 14 , the concept of resilience has since expanded beyond its initial scope, encompassing fields such as urbanism. Initially, resilience research primarily addressed the capacity of systems to absorb shocks while maintaining their original functions, as highlighted in early stress model studies from the 1970s 15 . Scholarship, however, has emphasized resilience’s role in enabling systems to renew, reorganize, and evolve 16 —elements essential for sustainable development but previously underexplored 17 . This shift marks a significant deepening of resilience theory, acknowledging the necessity of a multifaceted approach including recovery, resistance, adaptation, and development. The Organisation for Economic and Co-operative Development (OECD) later evolved the stress model into the Pressure-State-Response (PSR) model, which was subsequently expanded by the European Environment Agency (EEA) through the integration of drivers and impacts 18 . This enhancement facilitates the identification of causal connections between human and natural systems and supports the evaluation of progress towards sustainable development 19 . The Driver-Pressure-State-Impact-Response (DPSIR) framework emerged as a valuable tool, linking conceptual inquiry across social and natural sciences and offering a systematic way to describe interactions between the environment and human activities 20–22 . The framework’s ability to merge knowledge from various domains and support diverse decision-making contexts underscores its potential to mediate between scientific disciplines and policy and management spheres 23 . DPSIR’s application in fields ranging from water resources 24–26 and ecology 27,28 to food security 29,30 , urbanization 31 , and regional development illustrates its broad utility 32,33 . Its adaptability to various research contexts reinforces the framework’s relevance, particularly in studying AR where the interaction between human activities and the natural environment plays a pivotal role. In this interaction, the development of agriculture acts as a driver of human activities, while human activities and changes in the environment bring about pressures on the development of agriculture, which lead to changes in the state of agricultural development and consequently impacts. Finally, humans can further respond and re-improve agriculture based on these impacts. As such, DPSIR offers a comprehensive perspective by incorporating human agency into the analysis, thereby enriching the discourse on AR—a field poised for further exploration. 2.2 Agricultural Resilience (AR) Emphasizing AR presents a novel approach toward addressing agrarian risk crises and effectively compensates for the shortcomings inherent in traditional risk management strategies, which are predominantly defensive. The concept of resilient agriculture is increasingly becoming a focal point within agricultural development and risk management discourse. In this context, substantial research from varied perspectives has been conducted on AR. Initially, concerning the conceptualization and quantification of AR, Liu et al. introduced a TOPSIS evaluation model, characterized by weighted martens’ distance and grey correlation analysis (MTS-GRA-TOPSIS), to assess the resilience of the Comprehensive Regional Agricultural Soil and Water Resource Systems (CRAWSR) 34 . Furthermore, Hao and colleagues formulated a tripartite model—encompassing economic, social, and environmental dimensions—and established 16 indicators for evaluating sustainable agricultural development capacity 8 . The discourse also extends to analyzing determinants of AR, notably land resources and policy frameworks. Lin contends that crop diversification constitutes a viable and cost-effective strategy for bolstering AR, offering farmers a range of scales and modes for both sustainability and economic gain 35 . Similarly, Bowles et al. observed that, under optimal conditions, diversified crop rotations led to a notable increase in maize yields, and even under adverse conditions, they significantly mitigated yield losses during dry years 36 . Additionally, research by Webb et al. 37 and Michler et al. 38 elucidates the interplay between land degradation, climate change, and AR, highlighting the urgent need for adaptive policies and conservation agriculture practices amidst environmental challenges. Notwithstanding the valuable insights offered by existing literature on measures of AR, which predominantly concentrate on static dimensions of agricultural development, there is a discernible gap in incorporating forward-looking stances on risk resilience and response mechanisms. Moreover, the focus has been overly skewed towards the impact of arable land, with insufficient attention to the role of the farmers themselves 39 . Considering farmers as pivotal agents in achieving Sustainable Development Goals (SDGs) within agriculture 40,41 , their ability to navigate and adapt within this intricate and dynamic sector is crucial for fostering an economically and environmentally resilient agricultural framework 42 . 2.3 Farmland Improvement and HSFC in China The HSFC plays a crucial role in advancing agricultural infrastructure in China. This process entails various activities such as land development, soil enhancement, water management, improvement of rural road networks, and the preservation of farmland and ecological systems. These efforts aim to address challenges such as the fragmentation of arable land, the inadequacy of water management facilities, and the poor quality of agricultural land. Moreover, they seek to improve the resilience of agriculture to disasters and foster sustainable agricultural advancement by refining the organization and distribution of farmland and augmenting the infrastructure of farmland and waterways 43 . Previous research has predominantly concentrated on the construction, advantages, and issues associated with HSF. Wen Song et al. investigated the decision-making and execution processes of HSF projects in administrative villages, focusing on the assessment of arable land quality and the organization of construction efforts 44 . Pu et al. evaluated the impact of ten select projects in Liaoning Province through on-site surveys and remote sensing, revealing that HSF significantly boosts grain productivity stability during severe drought conditions 45 . Utilizing Chinese provincial data and a continuous difference model, Ye et al. demonstrated that HSFC policies notably enhance agricultural total factor productivity, albeit with a delayed effect 46 . Moreover, Peng et al. highlighted that HSF can markedly decrease rural poverty by 7.4% 13 . In contrast, Hao et al. reported that such projects can substantially increase grain yield by mitigating yield loss due to droughts and floods and by upgrading medium- and low-yield plots 8 . While extensive studies have been conducted on the production benefits of HSF, a systematic examination of its multidimensional impacts on agriculture development remains lacking. The extant research on AR primarily focuses on the production or ecological dimensions, often overlooking the integration of resilience with risk resistance and response, as well as the broader examination from a farmers' and macro-perspective. This omission constrains theoretical depth and necessitates the refinement and enhancement of the corresponding index evaluation systems in terms of their rationality and applicability. Meanwhile, specialized investigations into the synergies between the development of HSF and AR, particularly concerning their effects and underlying mechanisms, remain scarce. A thorough exploration of these topics is crucial not only for ensuring the stability and vitality of agricultural advancement in China but also for invigorating the sector’s intrinsic growth motivations and reshaping its geographical development patterns to serve as a catalyst for progress. Utilizing panel data from China’s provincial administrative regions between 2011 and 2021, this study employs fixed-effects and mediation-effects models to analyze the influence of HSFC on AR and the mechanisms driving this impact. This endeavor aims to offer scientifically grounded recommendations for fostering high-quality agricultural development amid an increasingly challenging development context. 3. Theory and Hypothesis 3.1Research Theory 3.1.1Theory of production factors Farmers play a critical role in enhancing AR. The classical economists Adam Smith and David Ricardo were among the first to systematically formulate and analyze the theory of production factors, emphasizing the contributions of labor, land, and capital to productive activities 47–49 . Smith, in “The Wealth of Nations,” articulated how the division of labor and specialization lead to increases in labor productivity 50 . Ricardo extended the analysis to include the diminishing marginal returns of land in its contribution to labor production 48,51,52 . Within the agricultural sector, factor theory underscores the multifaceted influences of farmers: they provide essential labor for tasks such as planting and harvesting, and they enhance the use of resources including land, water, and capital through effective management and decision-making 53,54 . Furthermore, farmers’ roles manifest in their ability to adapt to market changes, embrace new technologies and practices, and innovate in product development 55,56 . In contemporary agriculture, farmers face the intricate challenge of balancing production efficiency with sustainability and ecological harmony. Analyzing the role of farmers through the lens of factor theory not only illuminates their contributions to agricultural production via labor and resource management but also sheds light on their innovative and adaptive strategies in addressing contemporary agricultural challenges. 3.1.2 Incentive Theory Incentive theory, a fundamental tenet of microeconomics, examines what motivates individuals to undertake certain actions. Herzberg’s two-factor theory posits that job satisfaction and motivation are bolstered by the presence of incentives 57 . Adams’ equity theory articulates that individual assess their input-to-reward ratio (e.g., effort, skill versus pay, recognition), comparing it to that of their peers 58 . Perceived disparities in this ratio can lead to decreased effort or an increased pursuit of rewards. Offering economic incentives to farmers could motivate them to enhance their agricultural investments, such as purchasing seeds, fertilizers, and advanced farming equipment. Such augmentations in inputs can lead to improved agricultural productivity and crop quality, thereby contributing to the overarching development of the sector. In conclusion, after considering the construction of HSF and the fortification of AR, a theoretical framework has been devised. It conceptualizes farmers as pivotal intermediaries within the agricultural production and business systems, which is depicted in the Fig. 1 . 3.2 Hypothesis 3.2.1 Food production increase effects Enhanced AR generally signifies a stable and sustainable food supply, as indicated by prior studies 35,59,60 . Development of premium-quality farmland encompasses an integrated approach that addresses land improvement, road access, and water management infrastructure 61 . Initiatives such as HSF development facilitate agricultural enhancements by consolidating fragmented farmlands, thereby promoting large-scale cultivation that can streamline smallholder farming operations into contiguous, high-quality land parcels 62 . This development not only augments the soil’s fertility and productive depth but also mitigates prominent constraints on cropland quality 63 . Furthermore, upgrading farm road networks support agricultural mechanization and modernization, fostering an uptick in food output 64 . In rural domains, crucial public provisions like irrigation and drainage systems bolster agricultural productivity and food availability by enhancing water resource management. Moreover, these systems permit the reallocation of rural labor from farming to other economic activities by enhancing the efficiency of labor usage. Additionally, they counteract the detriments of environmental calamities on capital and labor inputs, thus contributing to the expansion of the agrarian sector and associated industries. 3.2.2 Farmers' income-generating effects Growth in AR is also manifested, to some extent, in increased incomes for farmers. Evidenced by multiple research findings, the inception of rural infrastructure like transportation not only boosts productivity and economic expansion but also advances income levels, thereby diminishing poverty – an effect delineated as the trickle-down phenomenon 65 . Upgrading irrigation systems also empowers farmers to better handle adversities, which cumulatively improves rural living standards and perpetuates equilibrium in farmland use 66 . Investments in agricultural consolidation and research and development amplify production aptitude and efficacy 67 while nurturing regional agrarian economic prosperity 66 . Simultaneously, farmland management initiatives conserve labor, enabling reallocation of the workforce to non-farming sectors, thus elevating income potentials 68 . 3.3.3 Population urbanisation The increase in AR is often accompanied by the contradiction between agricultural development and urbanisation. After the reform and opening up, the allure of city life—with better employment, education, and healthcare—has lured substantial rural labor to urban centers. This exodus diminishes the agricultural labor pool’s aptitude, posing a formidable challenge to the country’s agronomy. Agriculture is fraught with uncertainties, including climatic variations, pests, and disasters. The labor shift impairs farmers’ capacity to respond to such adverse events promptly. Furthermore, the economic disparity between urban earnings and agricultural incomes may prompt farmers to overlook risks and neglect infrastructure maintenance in rural areas, imposing latent perils on agricultural continuity. Based on the above statements, we formulate the following hypotheses: Hypothesis 1 HSFC bolsters AR. Hypothesis 2 HSFC reinforces AR via the impact on boosting food production. Hypothesis 3 HSFC reinforces AR via the impact on boosting farmers’ incomes. Hypothesis 4 While HSFC can stimulate urbanisation, the latter may impede the growth of AR. 4. Data and Methodology 4.1 Entropy weight method Indicator assignment is divided into subjective assignment by expert scoring and AHP, and objective assignment represented by entropy weight method. In order to reduce the influence of subjective factors, this study adopts the entropy weight method to measure the weight of each indicator. The specific calculation steps are as follows: First, establish the evaluation matrix X=(X ij ) mn ,i = 1,2,⋯,m; j = 1,2,⋯,n, the original data of the matrix in accordance with the following method of dimensionless processing. \(x'ij=\frac{{xij - mij}}{{Mij - mij}}\) (Positive)( 1 ) \(x'ij=\frac{{Mij - xij}}{{Mij - mij}}\) (Negative)( 2 ) Where M ij and m ij are the maximum and minimum values of X, respectively. Normalisation is then performed. Then the entropy value is calculated: $$Pij=\frac{{x'ij}}{{\sum\limits_{{i=1}}^{n} {xij} }}$$ 3 $$eij= - \frac{1}{{\ln n}}\sum\limits_{{i=1}}^{n} {Pij\ln (Pij)}$$ 4 Next, the coefficient of variation is calculated: $$dj=1 - ej$$ 5 Finally, the weights are calculated and the matrix is obtained: $$wj=\frac{{dj}}{{\sum\limits_{{j=1}}^{m} {dj} }}$$ 6 $$Y=(x'ij)mn \times wj$$ 7 4.2 Baseline regression model In order to assess the impact of HSFC on AR, a baseline regression model ( 8 ) was constructed. The equations are as follows: $$ARit=\alpha 0+\alpha 1HSFit+\alpha i\sum\limits_{{i=1}}^{{28}} X it+\mu it+\gamma it+\varepsilon it$$ 8 Where i denotes a survey province (i = 1, 2, 3...28) and t denotes a study year. AR it denotes agricultural resilience, HSF it denotes the scale of HSFC, and X it denotes a series of control variables. α0 denotes a constant term, αi(i = 1,...,4) denotes the parameter to be estimated. µ it denotes a fixed effect for a single city, i.e., it reflects a fixed effect for the region; γ it is a time fixed effect; ε it denotes a random disturbance term. 4.3 Indirect effects modelling The basic regression model reflects the direct effect of HSFC on AR, but it is difficult to depict its propagation mechanisms and channels. Therefore, based on the methodology proposed by Baron and Kenny, a mediated effects model was constructed in Equations (9)-(10) to test the potential indirect effect mechanism of HSFC on AR 69 . Where LP denotes labour productivity, PU denotes population urbanisation, LN_RRI denotes rural residents' income, χ i , δ i , η i denotes the parameters to be estimated and ε it denotes the residual term. 4.4 Data 4.4.1 Explained variable The explanatory variable is agricultural resilience (AR). Building resilient agriculture is an inevitable requirement to ensure food security and achieve sustainable agricultural development at the present time. In order to scientifically, objectively and comprehensively measure the resilience of agriculture in each province and region, we draw on the concept of DPSIR model, follow the principles of wholeness, scientificity, hierarchy, operability and simplicity, and combine with the research results of the existing AR index system, as well as the characteristics of China's agricultural development, to construct a resilience index system of agriculture with 31 indexes at three levels, namely, the target level, the criterion level, and the index level (see Table 1 ). Table 1 Table of Explained Variables and Weights Target Criteria Indicator Unit Nature of the indicator Weights Drivers Economic drivers. Per capita gdp RMB 10000 + 0.039251594 Agriculture, forestry, animal husbandry and fishery output RMB 100 million + 0.049527945 Socially drivers. Population density (People/square kilometer) + 0.027661328 Total sown area of crops Thousand hectares + 0.047402778 Urbanisation rate % + 0.005250576 Energy pressures Agricultural diesel use 10000 tons - 0.007889076 Pressures Total agricultural water consumption Billion cubic meters + 0.044998961 Rural electricity consumption Billion kilowatt hours + 0.134690666 Environmental pressures Number of livestock Ten thousand heads - 0.017036084 Fertiliser use per unit area Tons per thousand hectares - 0.014130224 Plastic film use per unit area Tons per thousand hectares - 0.007750892 Pesticide use per unit area Tons per thousand hectares - 0.002853233 State Efficiency of agricultural production Grain output per unit area Tons per thousand hectares + 0.026116254 Total power of agricultural machinery 10000 kilowatts + 0.062192352 Leisure Agriculture Demonstration Counties % + 0.053854914 Agricultural output per unit area RMB 100 million/1000 hectares + 0.054067235 Living state of farmers Rural per capita net income RMB Yuan + 0.032131764 Birth rate % + 0.016072513 Engel's coefficient - - 0.010448334 Quality of agricultural production Fertiliser use per unit of production ton - 0.004721409 Number of green food certifications each + 0.081970048 Pesticide use per unit of production ton - 0.01024165 Social impacts Rural population each + 0.050484934 Impacts Natural population growth rate % + 0.010768882 Percentage of Crops Affected Area % - 0.003502045 Production impacts Affected area Thousand hectares - 0.005778898 Soil erosion control area Thousand hectares + 0.051228499 Responses Disaster Recovery and Control Forest cover rate % + 0.033813652 Local fiscal expenditure on agriculture, forestry and water RMB 100 million + 0.034264151 Policy response Local fiscal expenditure on agriculture, forestry and water/fiscal expenditure % + 0.02019977 Local social security and employment expenditure RMB 100 million + 0.039699338 4.4.2 Core explanatory variables The core explanatory variable is the area of HSFC (HSF). In 2017 and before, China's HSF was jointly handled by the departments of finance, land, water conservancy, and development and reform. In 2018, due to the national institutional reform, China established the Department of Agriculture and Rural Affairs to deal with agricultural and rural affairs, and the construction of HSF has been led by the newly-established administrative departments of agriculture and rural areas in each region since that year. As a result, data for 2018 are in a missing state, and the China Financial Yearbook has suspended the inclusion of data on HSFC for 2018 and later years. By collaborating with agricultural and rural departments across provinces and central municipalities, we gathered data on HSFC for 2019–2021. For the absent 2018 data, we utilized approved HSFC plans where available, and employed linear interpolation elsewhere. All data were subsequently log-transformed for analytical convenience. Table 2 presents descriptive statistics for the above variables. 4.4.3 Mediating Variables In line with hypotheses discussed in Section 3 , we have identified Labor Productivity (LP), Population Urbanisation (PU), and Rural Residents’ Income (RRI) as mediating variables. LP is quantified as the gross output value of agriculture, forestry, animal husbandry, and fisheries per employee in the primary sector 70 ; PU by the ratio of urban to total permanent residents 71,72 ; and RRI as the log-transformed per capita disposable income of rural households 73,74 , following methodologies established in prior research. 4.4.4 Control variables It has been shown that there is a significant correlation between agricultural development according to local government and policy regulation 75,76 . Therefore, government intervention should be controlled in agricultural research. In addition, agricultural development is affected by the local industrial structure 77 , so it is necessary to consider the upgrading of the industrial structure and the status of the agricultural industry in local development. Finally, agricultural development is related to the degree of agricultural mechanisation 78,79 . Therefore, with reference to existing studies, we expressed the above variables with the following indicators: ( 1 ) Government intervention (GI). It is expressed using the ratio of local financial expenditure on agriculture, forestry and water affairs to regional GDP 80 . ( 2 ) Degree of agricultural mechanisation (AM). Expressed using the logarithmic value of the number of agricultural large and medium-sized tractors (LN_AM) 81 . ( 3 ) Industrial upgrading (IU). Expressed as the ratio of tertiary industry to secondary industry 82,83 . ( 4 ) Agricultural Industry Structural Adjustment Index (AISAI). Expressed as 1-(agricultural output value/total agricultural, forestry, animal husbandry and fishery output value) 77 . Table 2 Descriptive statistics for variables variable name Number of variables mean standard deviation. Min Max AR 308 0.317 0.089 0.131 0.562 HSF 308 541.402 642.88 0 4084.415 LP 308 5.745 2.881 1.026 17.375 PU 308 0.581 0.115 0.095 0.942 RRI 308 12925.476 5431.176 3909 35247.398 GI 308 0.032 0.021 0.008 0.11 IU 308 1.315 0.732 0.527 5.297 AM 308 167968.22 205019.26 2025 1060600 AISAI 308 0.483 0.089 0.094 0.649 5. Results 5.1 Direct effects The Hausman test was first performed. Judging by the results of the Hausman test (see Table 3 ), a fixed effects model was used. Table 3 Hausmann test results AR FE HSF 0.0258*** (0.00149) GI -0.133 (0.212) AISAI 0.0178 (0.0223) LN_AM -0.00374 (0.00291) IU 0.0110 (0.00797) Constant 0.139*** (0.0374) Observations 308 Number of region 28 R-squared 0.822 Hausman 35.56 p-value 1.17e-06 Standard errors in parentheses *** p < 0.01, ** p < 0.05, * p < 0.1 Table 4 presents the outcomes of regression analyses examining the influence of HSF development on AR. Models ( 1 ) through ( 5 ) correspond, respectively, to ordinary least squares (OLS), random effects, fixed time effects, fixed area effects, and combined fixed effects for both time and area. Subsequently, Table 5 displays the outcomes of progressively including variables within the double fixed effects framework. Across all models, a robust positive association between HSF development and AR is evident, confirming Hypothesis 1 . Table 4 Benchmark regression results 1 OLS RE I. Year I. Region Double Fixed Effect AR AR AR AR AR HSF 0.032 *** 0.027 *** 0.009 *** 0.026 *** 0.007 *** (0.002) (0.001) (0.002) (0.001) (0.002) GI -2.129 *** -0.380 * -0.142 -0.133 0.078 (0.153) (0.199) (0.185) (0.212) (0.191) AISAI 0.034 0.020 -0.048 ** 0.018 -0.055 *** (0.038) (0.023) (0.021) (0.022) (0.020) LN_AM 0.013 *** -0.001 -0.002 -0.004 -0.004 (0.002) (0.003) (0.003) (0.003) (0.003) IU 0.006 0.009 -0.011 * 0.011 -0.010 (0.005) (0.007) (0.006) (0.008) (0.007) _cons -0.028 0.112 *** 0.270 *** 0.105 ** 0.287 *** (0.036) (0.037) (0.038) (0.044) (0.040) N 308.000 308.000 308.000 308.000 308.000 r2 0.620 r2_a 0.613 Standard errors in parentheses * p < 0.1, ** p < 0.05, *** p < 0.01 Table 5 Benchmark regression results 2 ( 1 ) ( 2 ) ( 3 ) ( 4 ) ( 5 ) AR AR AR AR AR HSF 0.007 *** 0.007 *** 0.006 *** 0.005 ** 0.007 *** (0.002) (0.002) (0.002) (0.002) (0.002) GI -0.018 0.014 -0.017 0.078 (0.180) (0.179) (0.180) (0.191) AISAI -0.052 ** -0.052 ** -0.055 *** (0.021) (0.020) (0.020) LN_AM -0.004 -0.004 (0.003) (0.003) IU -0.010 (0.007) _cons 0.175 *** 0.175 *** 0.209 *** 0.246 *** 0.287 *** (0.007) (0.007) (0.015) (0.028) (0.040) Standard errors in parentheses * p < 0.1, ** p < 0.05, *** p < 0.01 5.2 Heterogeneity analysis To align with the evolving grain production landscape, in 2001, China categorized the nation into 13 key grain production regions, facilitating the optimization of geographical resource advantages by dominant grain-producing areas. While benchmark regression reveals the overarching impact of HSF on AR, capturing regional disparities in the industrial specialization remains challenging. To address this, our analysis categorizes the 28 samples into regions with predominant agricultural production versus those without, conducting a heterogeneity analysis. The findings, outlined in Table 6 , showcase the differences between primary agricultural regions and non-primary regions in columns ( 1 ) and ( 2 ), respectively. In addition, since agriculture is affected by location factors, following Hong et al. we divided the sample into eastern, central, and western regions for heterogeneity analysis 84 , as shown in columns ( 4 ) and ( 5 ). Table 6 Heterogeneity analysis results ( 1 ) ( 2 ) ( 3 ) ( 4 ) ( 5 ) Key grain production regions Non-key grain production regions Eastern region Central region Western Region AR AR AR AR AR HSF 0.052 *** 0.004 * 0.004 0.030 *** 0.009 (0.008) (0.003) (0.003) (0.011) (0.007) GI 0.124 0.078 -1.342 -0.108 0.159 (0.314) (0.232) (0.848) (0.357) (0.224) AISAI 0.024 -0.069 *** -0.042 0.068 ** -0.090 *** (0.031) (0.026) (0.046) (0.034) (0.027) LN_AM 0.001 -0.010 *** 0.005 0.005 -0.026 *** (0.004) (0.003) (0.006) (0.003) (0.005) IU 0.003 -0.020 ** -0.012 0.039 ** 0.017 (0.013) (0.008) (0.011) (0.015) (0.015) _cons -0.105 0.404 *** 0.320 *** -0.047 0.498 *** (0.091) (0.045) (0.081) (0.111) (0.085) N 143.000 165.000 110.000 88.000 110.000 ar2 r2_a 0.970 0.976 0.983 0.974 0.980 Standard errors in parentheses * p < 0.1, ** p < 0.05, *** p < 0.01 The results of the heterogeneity analysis show that HSF significantly contributes to AR in the key grain production regions, but the significance and correlation coefficients of this contribution decrease substantially in the non-key grain production regions. This suggests that in the Non-key grain production regions, HSF can be constructed with more significant results. In eastern and western areas, HSF’s effect on AR is negligible, attributed to the eastern region’s focus on non-agricultural sectors and the tertiary industry’s dominance 85 , alongside the western region’s geographical challenges and a notable agricultural workforce outflow 86 . Conversely, the central region’s favorable geography and climate make it an optimal location for HSF’s positive impact on AR. Consequently, emphasizing HSF implementation in central and primary agricultural zones is recommended, while its expansion into eastern and western areas should be approached with caution. In economically disadvantaged western regions, HSF initiatives ought to be complemented with supportive agricultural policies to mitigate workforce outflow and ensure effective farm management. 5.3 Robustness Tests Following methodologies from previous research 87,88 , this study used three strategies for robustness testing, and the results of each robustness test are presented in Table 7 : First, the sample years were shortened. Shortening the sample years to 2012 to 2020. Studying samples from different time periods may have an impact on the stability of the model. Therefore, the robustness test can be conducted by shortening or lengthening the time interval of the sample. The regression results are shown in model ( 1 ). Second, we delay the key explanatory variables by one period to mitigate the potential endogeneity problem. The regression results are shown in model ( 2 ). Finally, we add control variables - agricultural production scale and developed level of agricultural transportation. In this case, the scale of agricultural production is measured using the logarithm of the total sown area of food crops (LN_SAP), and the level of agricultural transportation developement is measured using the logarithm of the mileage of graded highways - mileage of freeways - mileage of first-class highways - mileage of second-class highways - mileage of out-of-grade highways (LN_ATD). The regression results are shown in model ( 3 ) They reaffirm the strong link between HSFC and AR, thus proving the reliability of the findings. Table 7 Robustness test results ( 1 ) ( 2 ) ( 3 ) AR AR AR HSF 0.010 *** 0.007 *** (0.003) (0.003) L.HSF 0.006 ** (0.003) GI 0.124 0.216 0.074 (0.198) (0.206) (0.197) AISAI -0.045 ** -0.046 ** -0.057 *** (0.021) (0.021) (0.021) LN_AM -0.006 * -0.003 -0.004 (0.003) (0.003) (0.003) IU -0.013 * -0.011 -0.012 (0.007) (0.008) (0.008) LN_SAP -0.003 (0.007) LN_ATD -0.015 (0.014) _cons 0.336 *** 0.293 *** 0.449 *** (0.050) (0.043) (0.144) Standard errors in parentheses * p < 0.1, ** p < 0.05, *** p < 0.01 5.4 Indirect effects Section 4.1 disclosed the positive effects of HSFC on AR; yet, the exact mechanisms and pathways mediating this effect remain to be clarified. In response, this study utilized SPSS 16.0 and the PROCESS v4.1 software by Hayes for a mediated effects analysis, employing the bias-corrected non-parametric percentile Bootstrap approach to evaluate the significance of these mediated effects, and specifically adopting model 4 as proposed by Hayes. The results are presented in Table 8 . Table 8 Mediation effect regression results outcome variable predictor variable R R-sq F β t p BootSE BootLLCI BootULCI LP HSF 0.7095 0.5033 61.2123 1.0904 12.9974 *** 0.1159 0.8891 1.3375 GI -29.8348 -5.1265 *** 5.1193 -39.8964 -20.0029 AISAI 6.9083 4.8504 *** 1.2421 4.566 9.4767 IU 1.4508 8.3872 *** 0.2008 1.1241 1.9092 LN_AM -0.3909 -4.4184 *** 0.1019 -0.5992 -0.2007 constant -2.6854 -1.9666 ** 1.1633 -4.9608 -0.3436 PU HSF 0.5813 0.338 30.832 0.0196 5.0815 *** 0.0039 0.0039 0.0119 GI -1.6937 -6.3419 *** 0.2733 0.2575 -2.2392 AISAI 0.1527 2.3368 ** 0.0499 0.0508 0.0475 IU 0.0577 7.2712 *** 0.02 0.0195 0.0174 LN_AM -0.0124 -3.057 *** 0.0013 0.0033 -0.0188 constant 0.4712 7.5204 *** 0.0582 0.0586 0.3586 RRI HSF 0.8871 0.787 223.1444 0.1884 24.1036 *** 0.01 0.0131 0.1654 GI -7.3811 -13.6091 *** 0.6872 0.6085 -8.709 AISAI 0.598 4.5051 *** 0.1264 0.1201 0.3568 IU 0.2857 17.7238 *** 0.0172 0.0194 0.253 LN_AM -0.0686 -8.3202 *** 0.003 0.009 -0.0877 constant 8.2511 64.8398 *** 0.1249 0.1264 8.0169 AR hsf 0.8442 0.7127 92.7223 0.0199 5.3592 *** 0.0049 0.0106 0.0301 LP 0.0036 1.9155 ** 0.0022 -0.0006 0.008 PU -0.3182 -8.4443 *** 0.1072 -0.5399 -0.1828 RRI 0.075 3.3116 *** 0.0332 0.0203 0.1446 GI -2.0083 -11.054 *** 0.1792 -2.4111 -1.7061 AISAI 0.0131 0.3713 0.03 -0.0474 0.0713 IU -0.0018 -0.3021 0.007 -0.0146 0.0125 LN_AM 0.0152 6.4793 *** 0.0021 0.0108 0.0193 constant -0.4878 -2.6353 *** 0.2351 -0.9796 -0.0876 Standard errors in parentheses * p < 0.1, ** p < 0.05, *** p < 0.01 The results of indirect effects illustrates that the development of HSF positively influences labor productivity (LP), population urbanization (PU), Rural Residents’ Income (RRI), and Agricultural Resilience (AR) (see Table 8 and Fig. 2 Mediation model diagrams). It is evident that LP and RRI enhance AR, whereas PU adversely affects it, corroborating our hypothesis. Specifically, the establishment of HSF boosts labor productivity by augmenting agricultural infrastructure and ameliorating substandard agricultural conditions, as noted by Rajkhowa and Baumüller and Kijek et al., with farmers being the primary beneficiaries 89,90 . An increase in labor productivity and food production mitigates the effects of agricultural risks, such as natural disasters, thereby significantly bolstering AR. Conversely, PU poses significant challenges to AR, suggesting that population urbanization leads to a rural exodus. In the current demographic scenario characterized by decline and aging, the total labor force is either stagnant or diminishing. This demographic shift challenges the sustainability of Chinese agriculture, driven by high-quality agricultural workers seeking better-paying opportunities outside farming and difficulties in replenishing the rural labor force with youthful workers. This situation underscores the existing dilemma in Chinese agricultural development. The results further reveal that enhancing RRI has a more pronounced impact on improving AR than increasing LP. Consequently, it is crucial to augment subsidies for the agricultural population to counterbalance the adverse effects of population urbanization on AR. 6. Discussion and conclusions 6.1 Direct effects of HSF on AR The outcomes of direct effect analyses underscore that the HSFC is increasingly pivotal in enhancing China’s AR, a finding that robustness tests support. By establishing centralized and significantly enhanced farmland with accompanying facilities, HSF lays the groundwork for superior agricultural advancement. Moreover, comprehensive upgrades and regulatory measures for cropland quality, alongside the development of agricultural roads, water conservation, and electrical infrastructure, substantially refine land use strategies, ensure cropland preservation, and elevate concentrated land utilization levels 91 . Distinct levels of improvement are observed between key and non-key grain production regions. The former, being the cornerstone of China’s agricultural sector, benefit from strategic land use and a larger farming populace, thus optimizing HSF efficiency. Conversely, non-key grain production regions, often focused on manufacturing, energy, or services, exhibit a nascent agricultural foundation. Additionally, the central region witnesses significant gains from HSF, whereas the eastern and western regions do not exhibit marked improvements in AR. The eastern region, characterized by limited agricultural activity, and the economically fragile western region require a tailored approach focusing on demographic and income issues within the agricultural framework. These insights provide a theoretical foundation for the strategic, region-specific deployment of HSF projects. 6.2 Mechanism of HSF's effect on AR The mediation analysis indicates that HSF can bolster AR by enhancing labor productivity and increasing farmers’ incomes. Nonetheless, it also precipitates a rural labor exodus by fostering urbanization, which may, in turn, constrain resilience. As HSF raises agricultural efficiency, it reduces the requisite labor for equivalent food outputs. Per the Cobb-Douglas production function, the elasticity coefficients of capital and labor significantly influence growth rates 92–94 . This liberation of rural laborers from farm work allows for a reallocation of time towards more lucrative endeavors. However, it could concurrently lead to diminished food production due to a reduction in the labor force available to manage adverse events, such as pests, disease, and extreme weather, thereby eroding AR. Additionally, the migration of workers to urban centers contributes to local talent depletion and rural depopulation. Within these increasingly deserted villages, the impoverishment of the remaining population can deepen, potentially negating the financial advantages offered by HSF — a phenomenon herein termed the ‘masking effect’. Consequently, while the implementation of HSF has registered significant achievements, navigating the balance between urban and rural development remains a critical issue in the progression of agricultural advancement. 6.3 Conclusion This study applied the DPSIR framework to develop an evaluation index system for assessing China’s AR. It also investigated the effect of HSFC on AR, utilizing a dual fixed-effect model and a mediation effect model, grounded in theoretical and mechanistic analysis. The findings reveal that HSF noticeably enhances China’s AR, with a more pronounced effect in kye grain production regions. Moreover, HSF indirectly bolsters AR by boosting labor productivity and increasing farmers’ income, with the latter being a more significant factor. However, HSFC potentially reduces the agricultural population, thereby impeding AR improvement. Finally, HSFC accelerate the loss of agricultural population, thus hindering AR. 6.4 Theoretical Contributions This research integrates the DPSIR model into agricultural infrastructure studies from an external macro perspective, building on resilience theory. It establishes an AR indicator system and expands the scope of AR research. Additionally, employing the theory of production factors, it constructs a theoretical framework centered on the agricultural production and operation system from the farmers’ perspective. Through econometric methods, it systematically examines how HSFC influences AR, employing both a dual fixed-effect model and a mediation effect model. This approach extends the conceptual avenues for exploring AR. 6.5 Management Recommendations In light of China’s specific conditions, we suggest the following strategies: First, enhance HSF development and improve the quality of land resources comprehensively. Evidence from the study suggests that HSFC not only increases labor productivity but also elevates farmers’ incomes, aiding rural revitalization. Thus, standards for HSFC should be elevated. Simultaneously, implementing differentiated HSFC along with establishing a capital investment growth stability mechanism could spur governmental support at various levels for HSF development, ultimately enhancing agricultural production factor quality. Second, augment agricultural subsidies to bolster farmers’ business incomes. Experimental findings indicate that improving farmers’ income more significantly fosters AR than enhancing labor productivity does. Increasing farmers’ income boosts their willingness to farm and, based on the opportunity cost theory, makes staying in rural areas more appealing compared to urban employment, as it reduces the opportunity costs involved by not requiring them to forsake their familiar lifestyles and social ties for better pay, thus aiding in counteracting the reduced agricultural productivity linked to labor migration. Third, optimize agricultural factor allocation and expedite the accumulation of rural human capital. Leveraging the HSFC initiative, train a cadre of new professional farmers and refine the overall quality of agricultural management entities. HSF development, which involves various sectors like agriculture, finance, and water management, should be strategically coordinated with promoting agricultural mechanization, reducing fertilizer use, and cultivating high-quality farmers to achieve high-quality agricultural development. 6.6 Limitations and Prospects This paper offers an in-depth analysis of the influence and mechanisms of HSF on AR. Nonetheless, three areas warrant further exploration: expanding the research scale, delving into the interactions within the AR indicator system based on the DPSIR model, and considering overlooked uncertainties such as sudden policy shifts and potential trade disagreements. Future studies should aim to address these aspects for a more comprehensive understanding. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4495317","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":330954756,"identity":"3991b852-3fe7-47ac-8545-0b9238e4f3a1","order_by":0,"name":"Feiwu Ren","email":"","orcid":"","institution":"Yunnan Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Feiwu","middleName":"","lastName":"Ren","suffix":""},{"id":330954757,"identity":"94ea4621-b81e-41a9-a878-0f06b34bf21d","order_by":1,"name":"Zihan Xia","email":"","orcid":"","institution":"Yunnan Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Zihan","middleName":"","lastName":"Xia","suffix":""},{"id":330954758,"identity":"4aadf27c-8717-412a-9218-65905abc0ee0","order_by":2,"name":"Yi Huang","email":"","orcid":"","institution":"Yunnan Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Huang","suffix":""},{"id":330954761,"identity":"3429c630-383e-41b3-a040-75e39db5fb42","order_by":3,"name":"Jiangtao Chi","email":"","orcid":"","institution":"Yunnan Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Jiangtao","middleName":"","lastName":"Chi","suffix":""},{"id":330954763,"identity":"e445a360-d1f8-4de2-a39c-10c167fb223f","order_by":4,"name":"Gai He","email":"","orcid":"","institution":"Yunnan Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Gai","middleName":"","lastName":"He","suffix":""},{"id":330954764,"identity":"b66659d4-da70-4c99-9e15-3745986f9297","order_by":5,"name":"Yanwei Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYBACfmbGxgcJPyTkoHxmwlok25sPG3zssTEmXovBmWNpkjPY0hIbiNbCcCPHTJqH53D6htvNzyQYKqwTG9jPHsCrg3FGjrE1j8Xh3A13jplJMJxJT2zgyUvAq4VZIsfwNtCW3A03EswkGNsOJzZI8Bjg1cImkWMgzcN2ON3gRvo3CcZ/RGjh4TmWBPJ+ggHQUxKMDURokWCHBLLhzBs5xRYJx9KN23hy8GuxPwyJSnm+G+kbb3yosZbtZz+DXwscKBwAEgkg3xGnHgjkG4hWOgpGwSgYBSMNAABfc0e6uUcGngAAAABJRU5ErkJggg==","orcid":"","institution":"Yunnan Agricultural University","correspondingAuthor":true,"prefix":"","firstName":"Yanwei","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-05-29 08:06:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4495317/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4495317/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-22519-9","type":"published","date":"2025-11-04T15:57:52+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":61094064,"identity":"9a00a298-6d9f-4dc8-9729-78cc1c6de96c","added_by":"auto","created_at":"2024-07-25 13:43:35","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":122203,"visible":true,"origin":"","legend":"\u003cp\u003eTheoretical framework\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4495317/v1/f68b5ec3ceb77f007b4ebf8c.png"},{"id":61094063,"identity":"45bb4966-b4ae-4ece-969a-22563a7a14a8","added_by":"auto","created_at":"2024-07-25 13:43:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":55747,"visible":true,"origin":"","legend":"\u003cp\u003eMediation model diagrams\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4495317/v1/cce1b5271f09e7b8be4fa4a8.png"},{"id":95564058,"identity":"b2b831bb-0e48-4779-b9bf-49dcfa5fb0e3","added_by":"auto","created_at":"2025-11-10 16:07:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1930820,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4495317/v1/92b20269-0f15-4f95-a0df-8fab72006737.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The impact of high-standard farmland construction (HSFC) on China's agricultural resilience","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe escalating frequency, intensity, and complexity of global natural disasters, extreme weather events, and the intensification of international geopolitical conflicts pose serious challenges to the global food supply. In an era marked by historic changes, significant shifts in the external environment, and an increase in risks and adverse factors, the need for resilient and high-quality agricultural development becomes paramount \u003csup\u003e1\u003c/sup\u003e. Resilience initially referred to the capacity of a system to revert to its original state following an external shock \u003csup\u003e2\u003c/sup\u003e. Presently, the concept has been extensively applied in research across ecology, economy, industrial development, and other fields. According to Folke's theory, agricultural resilience (AR) can be interpreted as the ability of an agricultural system to ensure its original key functions under external disturbances \u003csup\u003e3\u003c/sup\u003e. Fostering a resilient, high-quality agriculture is critical not only for China\u0026rsquo;s food security but also plays a pivotal role in global food security and building a shared future for humanity \u003csup\u003e4\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eChina, a major agricultural country with a large population \u003csup\u003e5\u003c/sup\u003e, led global food production in 2022 with a harvest of 686.53\u0026nbsp;billion kilograms. This achievement underscores China\u0026rsquo;s commitment to agricultural development, evidenced by the prioritization of agriculture in national policies. For instance, the Central Committee\u0026rsquo;s Document No. 1 has emphasized agricultural development as its primary theme for 21 consecutive years since 2004, highlighting farmland improvement as a critical area of focus. High-standard farmland construction (HSFC) plays a key role in advancing agricultural social services and governance in China \u003csup\u003e6\u003c/sup\u003e, contributing to poverty alleviation and rural economic development, thereby strengthening AR \u003csup\u003e7\u003c/sup\u003e. Defined as a modernized network of agricultural infrastructure, including irrigation systems, drainage systems, and road networks \u003csup\u003e8\u003c/sup\u003e, high-standard farmland (HSF) is fundamental to enhancing food security. Recent policies and plans, such as the reports from the 20th Party Congress and the National HSFC Plan (2021\u0026ndash;2030), outline ambitious goals for the development and upgrading of HSF \u003csup\u003e9\u003c/sup\u003e, reflecting China\u0026rsquo;s dedication to stable food supplies and sustainable agricultural development \u003csup\u003e10\u003c/sup\u003e. However, the impact and mechanisms of HSF on agricultural development amid challenges like natural disasters, population growth, and diminishing land resources require further investigation to inform strategies for quality agricultural development.\u003c/p\u003e \u003cp\u003eExisting studies have explored the effects of HSF on various aspects such as the rural environment \u003csup\u003e10\u0026ndash;12\u003c/sup\u003e, food production \u003csup\u003e8,9\u003c/sup\u003e, and poverty reduction \u003csup\u003e12,13\u003c/sup\u003e. Firstly, there is a lack of research to assess the overall effects of the implementation of HSF as a vigorously pursued agricultural policy in China. Second, the number of quantitative studies exploring the direction of agricultural improvement from an external macro perspective is low in the context of increasing food security risks. In this context, this study attempts to make the following contributions: firstly, it enriches the research field of AR by incorporating the DPSIR model and considering multiple subjects in the indicator system. Secondly, we consider the key role of the farmer subject between arable land and agricultural growth, construct a theoretical model based on the role of farmers in the agricultural production system and business system, and conduct an in-depth analysis of the mechanism by which HSF affects AR, which enriches the research perspective of agricultural development. The rest of the article is organised as follows: the second part is the literature review, the third part is the theoretical analysis and hypotheses, the fourth part is the research methodology and data, the fifth part is the empirical results, and the last part is the conclusion and discussion.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Resilience Theory and the DPSIR Framework\u003c/h2\u003e \u003cp\u003eIntroduced into ecology by Canadian ecologist C.S. Holling in the 1970s \u003csup\u003e14\u003c/sup\u003e, the concept of resilience has since expanded beyond its initial scope, encompassing fields such as urbanism. Initially, resilience research primarily addressed the capacity of systems to absorb shocks while maintaining their original functions, as highlighted in early stress model studies from the 1970s \u003csup\u003e15\u003c/sup\u003e. Scholarship, however, has emphasized resilience\u0026rsquo;s role in enabling systems to renew, reorganize, and evolve \u003csup\u003e16\u003c/sup\u003e\u0026mdash;elements essential for sustainable development but previously underexplored \u003csup\u003e17\u003c/sup\u003e. This shift marks a significant deepening of resilience theory, acknowledging the necessity of a multifaceted approach including recovery, resistance, adaptation, and development.\u003c/p\u003e \u003cp\u003eThe Organisation for Economic and Co-operative Development (OECD) later evolved the stress model into the Pressure-State-Response (PSR) model, which was subsequently expanded by the European Environment Agency (EEA) through the integration of drivers and impacts \u003csup\u003e18\u003c/sup\u003e. This enhancement facilitates the identification of causal connections between human and natural systems and supports the evaluation of progress towards sustainable development \u003csup\u003e19\u003c/sup\u003e. The Driver-Pressure-State-Impact-Response (DPSIR) framework emerged as a valuable tool, linking conceptual inquiry across social and natural sciences and offering a systematic way to describe interactions between the environment and human activities \u003csup\u003e20\u0026ndash;22\u003c/sup\u003e. The framework\u0026rsquo;s ability to merge knowledge from various domains and support diverse decision-making contexts underscores its potential to mediate between scientific disciplines and policy and management spheres \u003csup\u003e23\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDPSIR\u0026rsquo;s application in fields ranging from water resources \u003csup\u003e24\u0026ndash;26\u003c/sup\u003e and ecology \u003csup\u003e27,28\u003c/sup\u003e to food security \u003csup\u003e29,30\u003c/sup\u003e, urbanization \u003csup\u003e31\u003c/sup\u003e, and regional development illustrates its broad utility\u003csup\u003e32,33\u003c/sup\u003e. Its adaptability to various research contexts reinforces the framework\u0026rsquo;s relevance, particularly in studying AR where the interaction between human activities and the natural environment plays a pivotal role. In this interaction, the development of agriculture acts as a driver of human activities, while human activities and changes in the environment bring about pressures on the development of agriculture, which lead to changes in the state of agricultural development and consequently impacts. Finally, humans can further respond and re-improve agriculture based on these impacts. As such, DPSIR offers a comprehensive perspective by incorporating human agency into the analysis, thereby enriching the discourse on AR\u0026mdash;a field poised for further exploration.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Agricultural Resilience (AR)\u003c/h2\u003e \u003cp\u003eEmphasizing AR presents a novel approach toward addressing agrarian risk crises and effectively compensates for the shortcomings inherent in traditional risk management strategies, which are predominantly defensive. The concept of resilient agriculture is increasingly becoming a focal point within agricultural development and risk management discourse. In this context, substantial research from varied perspectives has been conducted on AR. Initially, concerning the conceptualization and quantification of AR, Liu et al. introduced a TOPSIS evaluation model, characterized by weighted martens\u0026rsquo; distance and grey correlation analysis (MTS-GRA-TOPSIS), to assess the resilience of the Comprehensive Regional Agricultural Soil and Water Resource Systems (CRAWSR) \u003csup\u003e34\u003c/sup\u003e. Furthermore, Hao and colleagues formulated a tripartite model\u0026mdash;encompassing economic, social, and environmental dimensions\u0026mdash;and established 16 indicators for evaluating sustainable agricultural development capacity \u003csup\u003e8\u003c/sup\u003e. The discourse also extends to analyzing determinants of AR, notably land resources and policy frameworks. Lin contends that crop diversification constitutes a viable and cost-effective strategy for bolstering AR, offering farmers a range of scales and modes for both sustainability and economic gain \u003csup\u003e35\u003c/sup\u003e. Similarly, Bowles et al. observed that, under optimal conditions, diversified crop rotations led to a notable increase in maize yields, and even under adverse conditions, they significantly mitigated yield losses during dry years \u003csup\u003e36\u003c/sup\u003e. Additionally, research by Webb et al. \u003csup\u003e37\u003c/sup\u003e and Michler et al. \u003csup\u003e38\u003c/sup\u003e elucidates the interplay between land degradation, climate change, and AR, highlighting the urgent need for adaptive policies and conservation agriculture practices amidst environmental challenges. Notwithstanding the valuable insights offered by existing literature on measures of AR, which predominantly concentrate on static dimensions of agricultural development, there is a discernible gap in incorporating forward-looking stances on risk resilience and response mechanisms. Moreover, the focus has been overly skewed towards the impact of arable land, with insufficient attention to the role of the farmers themselves \u003csup\u003e39\u003c/sup\u003e. Considering farmers as pivotal agents in achieving Sustainable Development Goals (SDGs) within agriculture \u003csup\u003e40,41\u003c/sup\u003e, their ability to navigate and adapt within this intricate and dynamic sector is crucial for fostering an economically and environmentally resilient agricultural framework \u003csup\u003e42\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Farmland Improvement and HSFC in China\u003c/h2\u003e \u003cp\u003eThe HSFC plays a crucial role in advancing agricultural infrastructure in China. This process entails various activities such as land development, soil enhancement, water management, improvement of rural road networks, and the preservation of farmland and ecological systems. These efforts aim to address challenges such as the fragmentation of arable land, the inadequacy of water management facilities, and the poor quality of agricultural land. Moreover, they seek to improve the resilience of agriculture to disasters and foster sustainable agricultural advancement by refining the organization and distribution of farmland and augmenting the infrastructure of farmland and waterways \u003csup\u003e43\u003c/sup\u003e. Previous research has predominantly concentrated on the construction, advantages, and issues associated with HSF. Wen Song et al. investigated the decision-making and execution processes of HSF projects in administrative villages, focusing on the assessment of arable land quality and the organization of construction efforts \u003csup\u003e44\u003c/sup\u003e. Pu et al. evaluated the impact of ten select projects in Liaoning Province through on-site surveys and remote sensing, revealing that HSF significantly boosts grain productivity stability during severe drought conditions \u003csup\u003e45\u003c/sup\u003e. Utilizing Chinese provincial data and a continuous difference model, Ye et al. demonstrated that HSFC policies notably enhance agricultural total factor productivity, albeit with a delayed effect\u003csup\u003e46\u003c/sup\u003e. Moreover, Peng et al. highlighted that HSF can markedly decrease rural poverty by 7.4%\u003csup\u003e13\u003c/sup\u003e. In contrast, Hao et al. reported that such projects can substantially increase grain yield by mitigating yield loss due to droughts and floods and by upgrading medium- and low-yield plots \u003csup\u003e8\u003c/sup\u003e. While extensive studies have been conducted on the production benefits of HSF, a systematic examination of its multidimensional impacts on agriculture development remains lacking.\u003c/p\u003e \u003cp\u003eThe extant research on AR primarily focuses on the production or ecological dimensions, often overlooking the integration of resilience with risk resistance and response, as well as the broader examination from a farmers' and macro-perspective. This omission constrains theoretical depth and necessitates the refinement and enhancement of the corresponding index evaluation systems in terms of their rationality and applicability. Meanwhile, specialized investigations into the synergies between the development of HSF and AR, particularly concerning their effects and underlying mechanisms, remain scarce. A thorough exploration of these topics is crucial not only for ensuring the stability and vitality of agricultural advancement in China but also for invigorating the sector\u0026rsquo;s intrinsic growth motivations and reshaping its geographical development patterns to serve as a catalyst for progress. Utilizing panel data from China\u0026rsquo;s provincial administrative regions between 2011 and 2021, this study employs fixed-effects and mediation-effects models to analyze the influence of HSFC on AR and the mechanisms driving this impact. This endeavor aims to offer scientifically grounded recommendations for fostering high-quality agricultural development amid an increasingly challenging development context.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Theory and Hypothesis","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1Research Theory\u003c/h2\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1Theory of production factors\u003c/h2\u003e \u003cp\u003eFarmers play a critical role in enhancing AR. The classical economists Adam Smith and David Ricardo were among the first to systematically formulate and analyze the theory of production factors, emphasizing the contributions of labor, land, and capital to productive activities \u003csup\u003e47\u0026ndash;49\u003c/sup\u003e. Smith, in \u0026ldquo;The Wealth of Nations,\u0026rdquo; articulated how the division of labor and specialization lead to increases in labor productivity \u003csup\u003e50\u003c/sup\u003e. Ricardo extended the analysis to include the diminishing marginal returns of land in its contribution to labor production \u003csup\u003e48,51,52\u003c/sup\u003e. Within the agricultural sector, factor theory underscores the multifaceted influences of farmers: they provide essential labor for tasks such as planting and harvesting, and they enhance the use of resources including land, water, and capital through effective management and decision-making \u003csup\u003e53,54\u003c/sup\u003e. Furthermore, farmers\u0026rsquo; roles manifest in their ability to adapt to market changes, embrace new technologies and practices, and innovate in product development \u003csup\u003e55,56\u003c/sup\u003e. In contemporary agriculture, farmers face the intricate challenge of balancing production efficiency with sustainability and ecological harmony. Analyzing the role of farmers through the lens of factor theory not only illuminates their contributions to agricultural production via labor and resource management but also sheds light on their innovative and adaptive strategies in addressing contemporary agricultural challenges.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2 Incentive Theory\u003c/h2\u003e \u003cp\u003eIncentive theory, a fundamental tenet of microeconomics, examines what motivates individuals to undertake certain actions. Herzberg\u0026rsquo;s two-factor theory posits that job satisfaction and motivation are bolstered by the presence of incentives \u003csup\u003e57\u003c/sup\u003e. Adams\u0026rsquo; equity theory articulates that individual assess their input-to-reward ratio (e.g., effort, skill versus pay, recognition), comparing it to that of their peers \u003csup\u003e58\u003c/sup\u003e. Perceived disparities in this ratio can lead to decreased effort or an increased pursuit of rewards. Offering economic incentives to farmers could motivate them to enhance their agricultural investments, such as purchasing seeds, fertilizers, and advanced farming equipment. Such augmentations in inputs can lead to improved agricultural productivity and crop quality, thereby contributing to the overarching development of the sector.\u003c/p\u003e \u003cp\u003eIn conclusion, after considering the construction of HSF and the fortification of AR, a theoretical framework has been devised. It conceptualizes farmers as pivotal intermediaries within the agricultural production and business systems, which is depicted in the Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Hypothesis\u003c/h2\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Food production increase effects\u003c/h2\u003e \u003cp\u003eEnhanced AR generally signifies a stable and sustainable food supply, as indicated by prior studies \u003csup\u003e35,59,60\u003c/sup\u003e. Development of premium-quality farmland encompasses an integrated approach that addresses land improvement, road access, and water management infrastructure \u003csup\u003e61\u003c/sup\u003e. Initiatives such as HSF development facilitate agricultural enhancements by consolidating fragmented farmlands, thereby promoting large-scale cultivation that can streamline smallholder farming operations into contiguous, high-quality land parcels \u003csup\u003e62\u003c/sup\u003e. This development not only augments the soil\u0026rsquo;s fertility and productive depth but also mitigates prominent constraints on cropland quality \u003csup\u003e63\u003c/sup\u003e. Furthermore, upgrading farm road networks support agricultural mechanization and modernization, fostering an uptick in food output \u003csup\u003e64\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn rural domains, crucial public provisions like irrigation and drainage systems bolster agricultural productivity and food availability by enhancing water resource management. Moreover, these systems permit the reallocation of rural labor from farming to other economic activities by enhancing the efficiency of labor usage. Additionally, they counteract the detriments of environmental calamities on capital and labor inputs, thus contributing to the expansion of the agrarian sector and associated industries.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 Farmers' income-generating effects\u003c/h2\u003e \u003cp\u003eGrowth in AR is also manifested, to some extent, in increased incomes for farmers. Evidenced by multiple research findings, the inception of rural infrastructure like transportation not only boosts productivity and economic expansion but also advances income levels, thereby diminishing poverty \u0026ndash; an effect delineated as the trickle-down phenomenon \u003csup\u003e65\u003c/sup\u003e. Upgrading irrigation systems also empowers farmers to better handle adversities, which cumulatively improves rural living standards and perpetuates equilibrium in farmland use \u003csup\u003e66\u003c/sup\u003e. Investments in agricultural consolidation and research and development amplify production aptitude and efficacy \u003csup\u003e67\u003c/sup\u003e while nurturing regional agrarian economic prosperity\u003csup\u003e66\u003c/sup\u003e. Simultaneously, farmland management initiatives conserve labor, enabling reallocation of the workforce to non-farming sectors, thus elevating income potentials \u003csup\u003e68\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.3.3 Population urbanisation\u003c/h2\u003e \u003cp\u003eThe increase in AR is often accompanied by the contradiction between agricultural development and urbanisation. After the reform and opening up, the allure of city life\u0026mdash;with better employment, education, and healthcare\u0026mdash;has lured substantial rural labor to urban centers. This exodus diminishes the agricultural labor pool\u0026rsquo;s aptitude, posing a formidable challenge to the country\u0026rsquo;s agronomy. Agriculture is fraught with uncertainties, including climatic variations, pests, and disasters. The labor shift impairs farmers\u0026rsquo; capacity to respond to such adverse events promptly. Furthermore, the economic disparity between urban earnings and agricultural incomes may prompt farmers to overlook risks and neglect infrastructure maintenance in rural areas, imposing latent perils on agricultural continuity.\u003c/p\u003e \u003cp\u003eBased on the above statements, we formulate the following hypotheses:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 1\u003c/strong\u003e \u003cp\u003eHSFC bolsters AR.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 2\u003c/strong\u003e \u003cp\u003eHSFC reinforces AR via the impact on boosting food production.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 3\u003c/strong\u003e \u003cp\u003eHSFC reinforces AR via the impact on boosting farmers\u0026rsquo; incomes.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 4\u003c/strong\u003e \u003cp\u003eWhile HSFC can stimulate urbanisation, the latter may impede the growth of AR.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Data and Methodology","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Entropy weight method\u003c/h2\u003e \u003cp\u003eIndicator assignment is divided into subjective assignment by expert scoring and AHP, and objective assignment represented by entropy weight method. In order to reduce the influence of subjective factors, this study adopts the entropy weight method to measure the weight of each indicator. The specific calculation steps are as follows:\u003c/p\u003e \u003cp\u003eFirst, establish the evaluation matrix X=(X\u003csub\u003eij\u003c/sub\u003e)\u003csub\u003emn\u003c/sub\u003e,i\u0026thinsp;=\u0026thinsp;1,2,⋯,m; j\u0026thinsp;=\u0026thinsp;1,2,⋯,n, the original data of the matrix in accordance with the following method of dimensionless processing.\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(x\u0026#039;ij=\\frac{{xij - mij}}{{Mij - mij}}\\)\u003c/span\u003e \u003c/span\u003e(Positive)(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(x\u0026#039;ij=\\frac{{Mij - xij}}{{Mij - mij}}\\)\u003c/span\u003e \u003c/span\u003e(Negative)(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eWhere M\u003csub\u003eij\u003c/sub\u003e and m\u003csub\u003eij\u003c/sub\u003e are the maximum and minimum values of X, respectively. Normalisation is then performed. Then the entropy value is calculated:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$Pij=\\frac{{x\u0026#039;ij}}{{\\sum\\limits_{{i=1}}^{n} {xij} }}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$eij= - \\frac{1}{{\\ln n}}\\sum\\limits_{{i=1}}^{n} {Pij\\ln (Pij)}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eNext, the coefficient of variation is calculated:\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$dj=1 - ej$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eFinally, the weights are calculated and the matrix is obtained:\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$wj=\\frac{{dj}}{{\\sum\\limits_{{j=1}}^{m} {dj} }}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$$Y=(x\u0026#039;ij)mn \\times wj$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e7\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Baseline regression model\u003c/h2\u003e \u003cp\u003eIn order to assess the impact of HSFC on AR, a baseline regression model (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) was constructed. The equations are as follows:\u003cdiv id=\"Equ6\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ6\" name=\"EquationSource\"\u003e\n$$ARit=\\alpha 0+\\alpha 1HSFit+\\alpha i\\sum\\limits_{{i=1}}^{{28}} X it+\\mu it+\\gamma it+\\varepsilon it$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e8\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere i denotes a survey province (i\u0026thinsp;=\u0026thinsp;1, 2, 3...28) and t denotes a study year. AR\u003csub\u003eit\u003c/sub\u003e denotes agricultural resilience, HSF\u003csub\u003eit\u003c/sub\u003e denotes the scale of HSFC, and X\u003csub\u003eit\u003c/sub\u003e denotes a series of control variables. α0 denotes a constant term, αi(i\u0026thinsp;=\u0026thinsp;1,...,4) denotes the parameter to be estimated. \u0026micro;\u003csub\u003eit\u003c/sub\u003e denotes a fixed effect for a single city, i.e., it reflects a fixed effect for the region; γ\u003csub\u003eit\u003c/sub\u003e is a time fixed effect; ε\u003csub\u003eit\u003c/sub\u003e denotes a random disturbance term.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Indirect effects modelling\u003c/h2\u003e \u003cp\u003eThe basic regression model reflects the direct effect of HSFC on AR, but it is difficult to depict its propagation mechanisms and channels. Therefore, based on the methodology proposed by Baron and Kenny, a mediated effects model was constructed in Equations (9)-(10) to test the potential indirect effect mechanism of HSFC on AR \u003csup\u003e69\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/122228_c8a1650c59388082/122228_custom_files/img1721914672.png\"\u003e\u003cbr\u003e\u003c/p\u003e\u003cp\u003eWhere LP denotes labour productivity, PU denotes population urbanisation, LN_RRI denotes rural residents' income, χ\u003csub\u003ei\u003c/sub\u003e, δ\u003csub\u003ei\u003c/sub\u003e, η\u003csub\u003ei\u003c/sub\u003edenotes the parameters to be estimated and ε\u003csub\u003eit\u003c/sub\u003e denotes the residual term.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Data\u003c/h2\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e4.4.1 Explained variable\u003c/h2\u003e \u003cp\u003eThe explanatory variable is agricultural resilience (AR). Building resilient agriculture is an inevitable requirement to ensure food security and achieve sustainable agricultural development at the present time. In order to scientifically, objectively and comprehensively measure the resilience of agriculture in each province and region, we draw on the concept of DPSIR model, follow the principles of wholeness, scientificity, hierarchy, operability and simplicity, and combine with the research results of the existing AR index system, as well as the characteristics of China's agricultural development, to construct a resilience index system of agriculture with 31 indexes at three levels, namely, the target level, the criterion level, and the index level (see 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\u003eTable of Explained Variables and Weights\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTarget\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCriteria\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNature of the indicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWeights\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eDrivers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEconomic drivers.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePer capita gdp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRMB 10000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.039251594\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAgriculture, forestry, animal husbandry and fishery output\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRMB 100 million\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.049527945\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSocially drivers.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePopulation density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(People/square kilometer)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.027661328\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal sown area of crops\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThousand hectares\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.047402778\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUrbanisation rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.005250576\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eEnergy pressures\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAgricultural diesel use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10000 tons\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.007889076\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003ePressures\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal agricultural water consumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBillion cubic meters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.044998961\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRural electricity consumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBillion kilowatt hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.134690666\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eEnvironmental pressures\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of livestock\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTen thousand heads\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.017036084\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFertiliser use per unit area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTons per thousand hectares\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.014130224\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePlastic film use per unit area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTons per thousand hectares\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.007750892\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePesticide use per unit area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTons per thousand hectares\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.002853233\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eState\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eEfficiency of agricultural production\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrain output per unit area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTons per thousand hectares\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.026116254\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal power of agricultural machinery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10000 kilowatts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.062192352\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLeisure Agriculture Demonstration Counties\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.053854914\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAgricultural output per unit area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRMB 100\u0026nbsp;million/1000 hectares\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.054067235\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eLiving state of farmers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRural per capita net income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRMB Yuan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.032131764\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBirth rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.016072513\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEngel's coefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.010448334\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eQuality of agricultural production\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFertiliser use per unit of production\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eton\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.004721409\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of green food certifications\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eeach\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.081970048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePesticide use per unit of production\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eton\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.01024165\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSocial impacts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRural population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eeach\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.050484934\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eImpacts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNatural population growth rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.010768882\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage of Crops Affected Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.003502045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eProduction impacts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAffected area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThousand hectares\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.005778898\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSoil erosion control area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThousand hectares\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.051228499\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eResponses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDisaster Recovery and Control\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eForest cover rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.033813652\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLocal fiscal expenditure on agriculture, forestry and water\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRMB 100 million\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.034264151\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePolicy response\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLocal fiscal expenditure on agriculture, forestry and water/fiscal expenditure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.02019977\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLocal social security and employment expenditure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRMB 100 million\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.039699338\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=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e4.4.2 Core explanatory variables\u003c/h2\u003e \u003cp\u003eThe core explanatory variable is the area of HSFC (HSF). In 2017 and before, China's HSF was jointly handled by the departments of finance, land, water conservancy, and development and reform. In 2018, due to the national institutional reform, China established the Department of Agriculture and Rural Affairs to deal with agricultural and rural affairs, and the construction of HSF has been led by the newly-established administrative departments of agriculture and rural areas in each region since that year. As a result, data for 2018 are in a missing state, and the China Financial Yearbook has suspended the inclusion of data on HSFC for 2018 and later years. By collaborating with agricultural and rural departments across provinces and central municipalities, we gathered data on HSFC for 2019\u0026ndash;2021. For the absent 2018 data, we utilized approved HSFC plans where available, and employed linear interpolation elsewhere. All data were subsequently log-transformed for analytical convenience. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents descriptive statistics for the above variables.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e4.4.3 Mediating Variables\u003c/h2\u003e \u003cp\u003eIn line with hypotheses discussed in Section \u003cspan refid=\"Sec6\" class=\"InternalRef\"\u003e3\u003c/span\u003e, we have identified Labor Productivity (LP), Population Urbanisation (PU), and Rural Residents\u0026rsquo; Income (RRI) as mediating variables. LP is quantified as the gross output value of agriculture, forestry, animal husbandry, and fisheries per employee in the primary sector \u003csup\u003e70\u003c/sup\u003e; PU by the ratio of urban to total permanent residents \u003csup\u003e71,72\u003c/sup\u003e; and RRI as the log-transformed per capita disposable income of rural households\u003csup\u003e73,74\u003c/sup\u003e, following methodologies established in prior research.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e4.4.4 Control variables\u003c/h2\u003e \u003cp\u003eIt has been shown that there is a significant correlation between agricultural development according to local government and policy regulation \u003csup\u003e75,76\u003c/sup\u003e. Therefore, government intervention should be controlled in agricultural research. In addition, agricultural development is affected by the local industrial structure \u003csup\u003e77\u003c/sup\u003e, so it is necessary to consider the upgrading of the industrial structure and the status of the agricultural industry in local development. Finally, agricultural development is related to the degree of agricultural mechanisation \u003csup\u003e78,79\u003c/sup\u003e. Therefore, with reference to existing studies, we expressed the above variables with the following indicators:\u003c/p\u003e \u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Government intervention (GI). It is expressed using the ratio of local financial expenditure on agriculture, forestry and water affairs to regional GDP \u003csup\u003e80\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Degree of agricultural mechanisation (AM). Expressed using the logarithmic value of the number of agricultural large and medium-sized tractors (LN_AM) \u003csup\u003e81\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) Industrial upgrading (IU). Expressed as the ratio of tertiary industry to secondary industry \u003csup\u003e82,83\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) Agricultural Industry Structural Adjustment Index (AISAI). Expressed as 1-(agricultural output value/total agricultural, forestry, animal husbandry and fishery output value) \u003csup\u003e77\u003c/sup\u003e.\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\u003eDescriptive statistics for variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003evariable name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003emean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003estandard deviation.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.562\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHSF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e541.402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e642.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4084.415\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.881\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.375\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.581\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.942\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12925.476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5431.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35247.398\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.732\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.527\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.297\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e167968.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e205019.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1060600\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAISAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.483\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.649\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"},{"header":"5. Results","content":"\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Direct effects\u003c/h2\u003e \u003cp\u003eThe Hausman test was first performed. Judging by the results of the Hausman test (see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), a fixed effects model was used.\u003c/p\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\u003eHausmann test results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHSF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0258***\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(0.00149)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.133\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(0.212)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAISAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0178\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(0.0223)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLN_AM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.00374\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(0.00291)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0110\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(0.00797)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.139***\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(0.0374)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e308\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.822\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHausman\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.17e-06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eStandard errors in parentheses\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003e*** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the outcomes of regression analyses examining the influence of HSF development on AR. Models (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) through (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) correspond, respectively, to ordinary least squares (OLS), random effects, fixed time effects, fixed area effects, and combined fixed effects for both time and area. Subsequently, Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e displays the outcomes of progressively including variables within the double fixed effects framework. Across all models, a robust positive association between HSF development and AR is evident, confirming Hypothesis \u003cspan refid=\"FPar1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\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\u003eBenchmark regression results 1\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOLS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eI. Year\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eI. Region\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDouble Fixed Effect\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\u003eAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHSF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.032\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.027\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.009\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.026\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.007\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(0.002)\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(0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.002)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.129\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.380\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.078\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(0.153)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.199)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.185)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.212)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.191)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAISAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.048\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.055\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(0.038)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.022)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.020)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLN_AM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.013\u003csup\u003e***\u003c/sup\u003e\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-0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.004\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(0.002)\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(0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.011\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.010\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(0.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.007)\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\u003e-0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.112\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.270\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.105\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.287\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(0.036)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.037)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.038)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.044)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.040)\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\u003e308.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e308.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e308.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e308.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e308.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003er2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003er2_a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eStandard errors in parentheses\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003e*\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1, \u003csup\u003e**\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003csup\u003e***\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \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\u003eBenchmark regression results 2\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e)\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\u003eAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHSF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.007\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.007\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(0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.002)\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\u003e(0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.002)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.078\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 \u003cp\u003e(0.180)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.179)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.180)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.191)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAISAI\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-0.052\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.052\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.055\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(0.021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.020)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLN_AM\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.004\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIU\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.010\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.007)\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\u003e0.175\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.175\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.209\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.246\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.287\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(0.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.015)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.028)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.040)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eStandard errors in parentheses\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003e*\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1, \u003csup\u003e**\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003csup\u003e***\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Heterogeneity analysis\u003c/h2\u003e \u003cp\u003eTo align with the evolving grain production landscape, in 2001, China categorized the nation into 13 key grain production regions, facilitating the optimization of geographical resource advantages by dominant grain-producing areas. While benchmark regression reveals the overarching impact of HSF on AR, capturing regional disparities in the industrial specialization remains challenging. To address this, our analysis categorizes the 28 samples into regions with predominant agricultural production versus those without, conducting a heterogeneity analysis. The findings, outlined in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, showcase the differences between primary agricultural regions and non-primary regions in columns (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) and (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e), respectively. In addition, since agriculture is affected by location factors, following Hong et al. we divided the sample into eastern, central, and western regions for heterogeneity analysis \u003csup\u003e84\u003c/sup\u003e, as shown in columns (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) and (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\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=\"6\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e)\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\u003eKey grain production regions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-key grain production regions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEastern region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCentral region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWestern Region\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\u003eAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHSF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.052\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.030\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.009\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(0.008)\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(0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.011)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.007)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.159\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(0.314)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.232)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.848)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.357)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.224)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAISAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.069\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.068\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.090\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(0.031)\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(0.046)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.034)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.027)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLN_AM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.010\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.026\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(0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.005)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.020\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.039\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.017\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(0.013)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.011)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.015)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.015)\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\u003e-0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.404\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.320\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.498\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(0.091)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.045)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.081)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.111)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.085)\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\u003e143.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e165.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e110.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e88.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e110.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ear2\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003er2_a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.980\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eStandard errors in parentheses\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003e*\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1, \u003csup\u003e**\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003csup\u003e***\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe results of the heterogeneity analysis show that HSF significantly contributes to AR in the key grain production regions, but the significance and correlation coefficients of this contribution decrease substantially in the non-key grain production regions. This suggests that in the Non-key grain production regions, HSF can be constructed with more significant results. In eastern and western areas, HSF\u0026rsquo;s effect on AR is negligible, attributed to the eastern region\u0026rsquo;s focus on non-agricultural sectors and the tertiary industry\u0026rsquo;s dominance \u003csup\u003e85\u003c/sup\u003e, alongside the western region\u0026rsquo;s geographical challenges and a notable agricultural workforce outflow \u003csup\u003e86\u003c/sup\u003e. Conversely, the central region\u0026rsquo;s favorable geography and climate make it an optimal location for HSF\u0026rsquo;s positive impact on AR. Consequently, emphasizing HSF implementation in central and primary agricultural zones is recommended, while its expansion into eastern and western areas should be approached with caution. In economically disadvantaged western regions, HSF initiatives ought to be complemented with supportive agricultural policies to mitigate workforce outflow and ensure effective farm management.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Robustness Tests\u003c/h2\u003e \u003cp\u003eFollowing methodologies from previous research \u003csup\u003e87,88\u003c/sup\u003e, this study used three strategies for robustness testing, and the results of each robustness test are presented in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e: First, the sample years were shortened. Shortening the sample years to 2012 to 2020. Studying samples from different time periods may have an impact on the stability of the model. Therefore, the robustness test can be conducted by shortening or lengthening the time interval of the sample. The regression results are shown in model (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Second, we delay the key explanatory variables by one period to mitigate the potential endogeneity problem. The regression results are shown in model (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Finally, we add control variables - agricultural production scale and developed level of agricultural transportation. In this case, the scale of agricultural production is measured using the logarithm of the total sown area of food crops (LN_SAP), and the level of agricultural transportation developement is measured using the logarithm of the mileage of graded highways - mileage of freeways - mileage of first-class highways - mileage of second-class highways - mileage of out-of-grade highways (LN_ATD). The regression results are shown in model (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) They reaffirm the strong link between HSFC and AR, thus proving the reliability of the findings.\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\u003eRobustness test results\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\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\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\u003eAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHSF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.010\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.007\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(0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL.HSF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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\u0026nbsp;\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 \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.074\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(0.198)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.206)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.197)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAISAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.045\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.046\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.057\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(0.021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.021)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLN_AM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.006\u003csup\u003e*\u003c/sup\u003e\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-0.004\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(0.003)\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(0.003)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.013\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.012\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(0.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.008)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLN_SAP\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-0.003\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(0.007)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLN_ATD\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-0.015\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(0.014)\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\u003e0.336\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.293\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.449\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(0.050)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.043)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.144)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eStandard errors in parentheses\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003e*\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1, \u003csup\u003e**\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003csup\u003e***\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e5.4 Indirect effects\u003c/h2\u003e \u003cp\u003eSection \u003cspan refid=\"Sec15\" class=\"InternalRef\"\u003e4.1\u003c/span\u003e disclosed the positive effects of HSFC on AR; yet, the exact mechanisms and pathways mediating this effect remain to be clarified. In response, this study utilized SPSS 16.0 and the PROCESS v4.1 software by Hayes for a mediated effects analysis, employing the bias-corrected non-parametric percentile Bootstrap approach to evaluate the significance of these mediated effects, and specifically adopting model 4 as proposed by Hayes. The results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMediation effect regression results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eoutcome variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003epredictor variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eR-sq\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eBootSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eBootLLCI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eBootULCI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eLP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHSF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.7095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e61.2123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.0904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12.9974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.1159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.8891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.3375\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-29.8348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-5.1265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5.1193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-39.8964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-20.0029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAISAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.9083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.8504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.2421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e4.566\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e9.4767\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.4508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8.3872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.2008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.1241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.9092\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLN_AM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.3909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-4.4184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.1019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.5992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.2007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003econstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-2.6854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-1.9666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.1633\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-4.9608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.3436\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003ePU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHSF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5813\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30.832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.0815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.0039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.0119\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.6937\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-6.3419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.2733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.2575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-2.2392\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAISAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1527\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.3368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.0508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.0475\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0577\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7.2712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.0195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.0174\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLN_AM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.0124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-3.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.0033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.0188\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003econstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.4712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7.5204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.0586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.3586\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eRRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHSF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e223.1444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e24.1036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.0131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.1654\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-7.3811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-13.6091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.6872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.6085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-8.709\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAISAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.5051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.1264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.1201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.3568\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.2857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e17.7238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.0194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.253\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLN_AM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.0686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-8.3202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.0877\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003econstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.2511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e64.8398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.1249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.1264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e8.0169\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003eAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e92.7223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.3592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.0106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.0301\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.9155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.0006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.3182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-8.4443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.1072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.5399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.1828\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.3116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.0203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.1446\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-2.0083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-11.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.1792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-2.4111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-1.7061\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAISAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.3713\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.0474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.0713\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.0018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.3021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.0146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.0125\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLN_AM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.4793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.0108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.0193\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003econstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.4878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-2.6353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.2351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.9796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.0876\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eStandard errors in parentheses\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e\u003csup\u003e*\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1, \u003csup\u003e**\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003csup\u003e***\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe results of indirect effects illustrates that the development of HSF positively influences labor productivity (LP), population urbanization (PU), Rural Residents\u0026rsquo; Income (RRI), and Agricultural Resilience (AR) (see Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e Mediation model diagrams). It is evident that LP and RRI enhance AR, whereas PU adversely affects it, corroborating our hypothesis. Specifically, the establishment of HSF boosts labor productivity by augmenting agricultural infrastructure and ameliorating substandard agricultural conditions, as noted by Rajkhowa and Baum\u0026uuml;ller and Kijek et al., with farmers being the primary beneficiaries \u003csup\u003e89,90\u003c/sup\u003e. An increase in labor productivity and food production mitigates the effects of agricultural risks, such as natural disasters, thereby significantly bolstering AR. Conversely, PU poses significant challenges to AR, suggesting that population urbanization leads to a rural exodus. In the current demographic scenario characterized by decline and aging, the total labor force is either stagnant or diminishing. This demographic shift challenges the sustainability of Chinese agriculture, driven by high-quality agricultural workers seeking better-paying opportunities outside farming and difficulties in replenishing the rural labor force with youthful workers. This situation underscores the existing dilemma in Chinese agricultural development. The results further reveal that enhancing RRI has a more pronounced impact on improving AR than increasing LP. Consequently, it is crucial to augment subsidies for the agricultural population to counterbalance the adverse effects of population urbanization on AR.\u003c/p\u003e \u003c/div\u003e"},{"header":"6. Discussion and conclusions","content":"\u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e6.1 Direct effects of HSF on AR\u003c/h2\u003e \u003cp\u003eThe outcomes of direct effect analyses underscore that the HSFC is increasingly pivotal in enhancing China\u0026rsquo;s AR, a finding that robustness tests support. By establishing centralized and significantly enhanced farmland with accompanying facilities, HSF lays the groundwork for superior agricultural advancement. Moreover, comprehensive upgrades and regulatory measures for cropland quality, alongside the development of agricultural roads, water conservation, and electrical infrastructure, substantially refine land use strategies, ensure cropland preservation, and elevate concentrated land utilization levels \u003csup\u003e91\u003c/sup\u003e. Distinct levels of improvement are observed between key and non-key grain production regions. The former, being the cornerstone of China\u0026rsquo;s agricultural sector, benefit from strategic land use and a larger farming populace, thus optimizing HSF efficiency. Conversely, non-key grain production regions, often focused on manufacturing, energy, or services, exhibit a nascent agricultural foundation. Additionally, the central region witnesses significant gains from HSF, whereas the eastern and western regions do not exhibit marked improvements in AR. The eastern region, characterized by limited agricultural activity, and the economically fragile western region require a tailored approach focusing on demographic and income issues within the agricultural framework. These insights provide a theoretical foundation for the strategic, region-specific deployment of HSF projects.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section2\"\u003e \u003ch2\u003e6.2 Mechanism of HSF's effect on AR\u003c/h2\u003e \u003cp\u003eThe mediation analysis indicates that HSF can bolster AR by enhancing labor productivity and increasing farmers\u0026rsquo; incomes. Nonetheless, it also precipitates a rural labor exodus by fostering urbanization, which may, in turn, constrain resilience. As HSF raises agricultural efficiency, it reduces the requisite labor for equivalent food outputs. Per the Cobb-Douglas production function, the elasticity coefficients of capital and labor significantly influence growth rates \u003csup\u003e92\u0026ndash;94\u003c/sup\u003e. This liberation of rural laborers from farm work allows for a reallocation of time towards more lucrative endeavors. However, it could concurrently lead to diminished food production due to a reduction in the labor force available to manage adverse events, such as pests, disease, and extreme weather, thereby eroding AR. Additionally, the migration of workers to urban centers contributes to local talent depletion and rural depopulation. Within these increasingly deserted villages, the impoverishment of the remaining population can deepen, potentially negating the financial advantages offered by HSF \u0026mdash; a phenomenon herein termed the \u0026lsquo;masking effect\u0026rsquo;. Consequently, while the implementation of HSF has registered significant achievements, navigating the balance between urban and rural development remains a critical issue in the progression of agricultural advancement.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003e6.3 Conclusion\u003c/h2\u003e \u003cp\u003eThis study applied the DPSIR framework to develop an evaluation index system for assessing China\u0026rsquo;s AR. It also investigated the effect of HSFC on AR, utilizing a dual fixed-effect model and a mediation effect model, grounded in theoretical and mechanistic analysis. The findings reveal that HSF noticeably enhances China\u0026rsquo;s AR, with a more pronounced effect in kye grain production regions. Moreover, HSF indirectly bolsters AR by boosting labor productivity and increasing farmers\u0026rsquo; income, with the latter being a more significant factor. However, HSFC potentially reduces the agricultural population, thereby impeding AR improvement. Finally, HSFC accelerate the loss of agricultural population, thus hindering AR.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section2\"\u003e \u003ch2\u003e6.4 Theoretical Contributions\u003c/h2\u003e \u003cp\u003eThis research integrates the DPSIR model into agricultural infrastructure studies from an external macro perspective, building on resilience theory. It establishes an AR indicator system and expands the scope of AR research. Additionally, employing the theory of production factors, it constructs a theoretical framework centered on the agricultural production and operation system from the farmers\u0026rsquo; perspective. Through econometric methods, it systematically examines how HSFC influences AR, employing both a dual fixed-effect model and a mediation effect model. This approach extends the conceptual avenues for exploring AR.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec33\" class=\"Section2\"\u003e \u003ch2\u003e6.5 Management Recommendations\u003c/h2\u003e \u003cp\u003eIn light of China\u0026rsquo;s specific conditions, we suggest the following strategies:\u003c/p\u003e \u003cp\u003eFirst, enhance HSF development and improve the quality of land resources comprehensively. Evidence from the study suggests that HSFC not only increases labor productivity but also elevates farmers\u0026rsquo; incomes, aiding rural revitalization. Thus, standards for HSFC should be elevated. Simultaneously, implementing differentiated HSFC along with establishing a capital investment growth stability mechanism could spur governmental support at various levels for HSF development, ultimately enhancing agricultural production factor quality.\u003c/p\u003e \u003cp\u003eSecond, augment agricultural subsidies to bolster farmers\u0026rsquo; business incomes. Experimental findings indicate that improving farmers\u0026rsquo; income more significantly fosters AR than enhancing labor productivity does. Increasing farmers\u0026rsquo; income boosts their willingness to farm and, based on the opportunity cost theory, makes staying in rural areas more appealing compared to urban employment, as it reduces the opportunity costs involved by not requiring them to forsake their familiar lifestyles and social ties for better pay, thus aiding in counteracting the reduced agricultural productivity linked to labor migration.\u003c/p\u003e \u003cp\u003eThird, optimize agricultural factor allocation and expedite the accumulation of rural human capital. Leveraging the HSFC initiative, train a cadre of new professional farmers and refine the overall quality of agricultural management entities. HSF development, which involves various sectors like agriculture, finance, and water management, should be strategically coordinated with promoting agricultural mechanization, reducing fertilizer use, and cultivating high-quality farmers to achieve high-quality agricultural development.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec34\" class=\"Section2\"\u003e \u003ch2\u003e6.6 Limitations and Prospects\u003c/h2\u003e \u003cp\u003eThis paper offers an in-depth analysis of the influence and mechanisms of HSF on AR. Nonetheless, three areas warrant further exploration: expanding the research scale, delving into the interactions within the AR indicator system based on the DPSIR model, and considering overlooked uncertainties such as sudden policy shifts and potential trade disagreements. Future studies should aim to address these aspects for a more comprehensive understanding.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eData availability statement\u003c/p\u003e\n\u003cp\u003eThe data are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003eDisclosure statement\u003c/p\u003e\n\u003cp\u003eNo potential conflict of interest was reported by the author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eUrruty, N., Tailliez-Lefebvre, D. \u0026amp; Huyghe, C. Stability, robustness, vulnerability and resilience of agricultural systems. A review. \u003cem\u003eAgronomy for Sustainable Development\u003c/em\u003e \u003cstrong\u003e36\u003c/strong\u003e, doi:10.1007/s13593-015-0347-5 (2016).\u003c/li\u003e\n\u003cli\u003eMartin, R. 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A.\u003cem\u003e et al.\u003c/em\u003e Impact of Climate Smart Agriculture (CSA) Practices on Cotton Production and Livelihood of Farmers in Punjab, Pakistan. \u003cem\u003eSustainability\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, doi:10.3390/su10062101 (2018).\u003c/li\u003e\n\u003c/ol\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"agricultural resilience, DPSIR, high-standard farmland, food security, rural development","lastPublishedDoi":"10.21203/rs.3.rs-4495317/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4495317/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe development of high-standard farmland (HSF) constitutes a crucial strategy for enhancing agricultural infrastructure, which significantly contributes to increasing agricultural production and catalyzing agroeconomic growth. The present study devises an index system to evaluate agricultural resilience (AR) in China, anchored in the DPSIR framework, and analyzes data from 28 Chinese provinces over 2011\u0026ndash;2021 to ascertain the effects of HSFC on AR employing fixed-effects and mediation-effects models. The study reveals that the HSFC markedly enhances AR. Specifically, in key grain production regions or the central area, the positive influence of such construction on resilience is more substantial than in non-key grain production or east-west regions. HSF fortifies AR chiefly by boosting labor productivity and increasing farmers\u0026rsquo; income. Analysis of correlation coefficients and overall context confirms that enhanced income of farmers is the key driver in this equation. Although HSF encourages urbanization within the agricultural community, this trend may inadvertently hinder resilience improvements. While the indispensable role of HSF in promoting agricultural progress is recognized, it is crucial to address the concurrent issue of population outflow from agricultural sectors. This study contributes uniquely by integrating the DPSIR model into the exploration of AR, thereby offering a novel, proactive approach to sustainable agricultural development. Furthermore, it elucidates the mechanisms through which HSF impacts AR across three dimensions: labor productivity, farmer incomes, and population urbanization, from the farmers\u0026rsquo; vantage point. This insight enables policymakers to refine resource allocation, enhancing the planning, design, and stewardship of sustainable agriculture.\u003c/p\u003e","manuscriptTitle":"The impact of high-standard farmland construction (HSFC) on China's agricultural resilience","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-25 13:43:30","doi":"10.21203/rs.3.rs-4495317/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-02-04T08:15:10+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-02-03T02:10:51+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-01-21T14:06:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"93728908949606177959109133449398840176","date":"2025-01-20T13:24:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"207418637209283466414135782335603642966","date":"2025-01-02T10:31:55+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-04T04:54:49+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-04T04:54:14+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-07-03T14:24:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-03T09:51:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-05-29T08:04:11+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4adaeacd-f582-4cb5-a07a-e254ff7aebd9","owner":[],"postedDate":"July 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-10T16:02:42+00:00","versionOfRecord":{"articleIdentity":"rs-4495317","link":"https://doi.org/10.1038/s41598-025-22519-9","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-11-04 15:57:52","publishedOnDateReadable":"November 4th, 2025"},"versionCreatedAt":"2024-07-25 13:43:30","video":"","vorDoi":"10.1038/s41598-025-22519-9","vorDoiUrl":"https://doi.org/10.1038/s41598-025-22519-9","workflowStages":[]},"version":"v1","identity":"rs-4495317","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4495317","identity":"rs-4495317","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}