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Article

The Impact of Social Security on Farmers’ Green Agricultural Technology Adoption: Empirical Evidence from Rural China

by
Yilan Xu
and
Kuan Zhang
*
College of Economics, Sichuan Agricultural University, Chengdu 611130, China
*
Author to whom correspondence should be addressed.
Agriculture 2025, 15(5), 498; https://doi.org/10.3390/agriculture15050498
Submission received: 6 February 2025 / Revised: 24 February 2025 / Accepted: 24 February 2025 / Published: 26 February 2025
(This article belongs to the Section Agricultural Economics, Policies and Rural Management)
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Abstract

:
To ensure food safety and support sustainable development of agriculture, it is critical to accelerate the transition of agricultural production methods and develop green agriculture. This study employs the Probit model with survey data gathered from households growing rice in 13 cities in Jiangsu Province, China, to investigate how social security affects green agricultural technology adoption. Key findings from the study include the following: (1) Social security significantly promotes farmers’ green agricultural technology adoption. (2) Social security promotes green agricultural technology adoption by alleviating farmers’ credit constraints. (3) Farmers with higher education levels are more impacted by social security than farmers with lower education levels. (4) The impact of social security is more positively significant for young and middle-aged farmers than for older groups. (5) Risk-averse farmers are more inclined to acquire social security and receive a more significant boost. These findings provide micro-evidence for improving China’s rural social security mechanisms and ensuring agricultural ecosystem security.

1. Introduction

Governments and the global community have increasingly prioritized green agriculture as a strategic approach to achieve sustainable agricultural transformation [1,2]. In an attempt to halt the deterioration of the agricultural ecological environment, China has established a comprehensive policy framework for green agricultural development since the 18th National Congress. Despite steady improvements in the degree of agricultural green development, significant challenges persist in production practices. The “Second National Pollution Source Census Bulletin” states that one of the primary causes of water pollution is non-point source pollution from agriculture. Total nitrogen (TN), ammonium nitrogen (NH4+-N), total phosphorus (TP), and chemical oxygen demand (COD) emissions were agricultural non-point source pollutants that contributed to 91.30%, 90.33%, 89.52%, and 88.89% of the nation’s total pollutant discharges from water bodies, respectively. In addition to resulting in financial losses, agricultural non-point source pollution and declining soil fertility also threaten the green, sustainable development of rural areas by lowering agricultural output and creating issues with the safety of agricultural products [3]. Substantial agricultural non-point source pollution and ecosystem degradation are caused by the loss of significant amounts of organic or inorganic pollutants. Traditional agriculture, characterized by high input and high energy consumption, has caused serious damage to China’s agricultural ecological environment. Accelerating the use of green agricultural technologies and achieving the green revolution of agriculture are crucial.
Green agricultural technology combines the benefits of traditional, conventional, and modern agricultural systems, characterized by modernity, resource efficiency, and less pollution. It involves a set of technologies that ensure the quality of agricultural goods in agricultural production, processing, and transportation systems. Each subsystem contains a number of technological systems, including testing technology, soil measuring formula fertilization, agricultural product quality standard systems, and green pest prevention and control technologies. It is a fundamental method for putting sustainable development policies into practice and resolving outstanding problems involving environmental degradation and agricultural resource constraints [4,5,6]. The implementation and enforcement of green agricultural technology are crucial to their advancement. One key strategy to hasten the green growth of agriculture is to actively encourage and support farmers to adopt green agricultural technologies.
Nevertheless, smallholder-dominated agricultural systems in developing economies exhibit persistent adoption barriers, with suboptimal enthusiasm and low uptake rates for green agricultural technologies, thereby hampering agricultural green transformation [7]. As one of the most significant micro-subjects for promoting green agricultural development, it is critical to analyze elements which affect farmers’ green technology adoption [8,9]. Previous studies have shown that a variety of factors, including risk factors, production characteristics, individual traits, cognitive traits, and government incentives, might impact farmers’ green agricultural technology adoption. The use of land conservation technology, for instance, will be hampered by the aging of farmers, and the impact on rental land will be greater than that on owned land [10]. More educated and experienced farmers are more likely to apply pesticides correctly [11]. Time preferences have significantly reduced the amount of technical adoption among farmers because farmers who have lower rates of discount are more sensitive to future revenue [12]. The area of the farm has a positive impact on decision-making, and farmers along with a larger farm are more inclined to finance innovative technologies [13]. The government’s use of financial incentives and technical assistance for green agriculture, as well as the creation of technical service organizations for green agricultural production, greatly increase farmers’ green agricultural technology adoption [3].
Furthermore, when farmers are confronted with agricultural production, the primary manifestations of external shocks are the market and natural risks brought by global climate change. However, the capacity of farmers to tolerate unforeseen hazards, such as natural disasters, in agricultural productivity is limited. To avoid risks in production decision-making, they tend to adopt conservative production behavior and are reluctant to adopt new agricultural production technologies [14]. As a key tool for managing agricultural risk, social security can effectively transfer losses resulting from natural disasters while also incentivizing producers to embrace green agricultural technology, which is critical to green agriculture development [15]. Green agricultural technology necessitates more funding and technology than conventional production methods. Enhancing risk aversion mechanisms and developing a social security system suitable for China’s agricultural output are imperative. Therefore, considering the contemporary background and practical challenges, the study explores the effect of social security on green agriculture technology adoption.
China’s rural areas are typical of a smallholder economy, and farmers have a stronger tendency to be risk-averse than ordinary economic subjects. Agricultural green transformation is positively impacted by the establishment of a comprehensive social security system. Nonetheless, the majority of earlier studies focused on how agricultural insurance affects a particular kind of agricultural output behavior [16,17]. The marginal contributions of this study are as follows: (1) This study examines the comprehensive effects of social security system composed of medical insurance, endowment insurance, planting insurance, and pig insurance on farmers’ green agricultural technology adoption, which complements the existing evidence on the factors influencing the adoption of green agricultural technologies by farmers. (2) By analyzing the mechanism of social security on the adoption of green agricultural technologies by farmers, this study finds that the productive credit behavior of farmers has a mediating role in social security and the adoption of green agricultural technologies. (3) Heterogeneity tests are conducted for farmers with different education levels, different ages, and different risk preferences. Based on the results, useful suggestions were made for the construction of China’s rural social security system and the green development of agriculture.

2. Theory and Hypotheses

2.1. The Impact of Social Security on Farmers’ Green Agricultural Technology Adoption

The risk shock theory suggests that there are various uncertainties and risk factors in the economic system (political risks, natural disasters, financial market fluctuations, technical changes, etc.). These factors can impact the production, consumption, and investment of the economic system, thereby causing instability and fluctuations in the economic system. Existing studies indicate that farmers in developing nations face multiple production risks, with climate variability emerging as a predominant threat due to their reliance on rain-fed agriculture [18]. Production failure is one of the main sources of small-scale and self-sufficient farmers’ income changes. Moreover, while modern agricultural investments offer potential benefits, they simultaneously entail greater risks. One of the primary causes of fluctuations in the revenue of small-scale and self-sufficient farmers is production failure [19]. Thus, the problem of slow technological diffusion caused by risks has become a “bottleneck problem” that restricts agricultural development and farmers’ living standards improvement in developing countries such as China [20].
Social security is regarded as a useful instrument for lowering agricultural risks and encouraging farmers to embrace production technologies, both of which are critical to the development of agricultural sustainability [21,22,23]. The rural social security system is a state-provided social system that aims to promote economic development and social stability while also providing material help in the form of pensions, medical care, and life assistance to rural households. It mainly manifests in three aspects: social insurance, social aid, and social welfare [24]. A well-developed social security system can greatly enhance the risk-taking capacity of farmers. In addition to sharing the cost of planting, breeding, medical care, and pensions [25], social security can also offer farmers prompt social assistance in the event of risky situations, lessening the detrimental effects of risk events on farmers through social insurance and agricultural insurance subsidies [26]. Furthermore, social security facilitates green technology adoption by improving credit access. Credit institutions typically assess farmers’ income levels, health status, and production risks when approving loans [27]. Farmers possessing insurance coverage and good health demonstrate stronger household creditworthiness, enabling easier access to financing that alleviates capital constraints during technology adoption processes, ultimately promoting sustainable agricultural practices [28].
A strong social security system in rural areas can significantly improve farmers’ ability to mitigate risk [29,30], which enhances their openness to technological innovations and consequently increases the adoption of green agricultural adoption.
Thus, the study presents the hypothesis H1: Social security significantly promotes farmers’ green agricultural technology adoption (H1 in Figure 1).

2.2. Social Security Promotes Green Agricultural Technology Adoption by Relieving Farmers’ Credit Constraints

Farmers’ credit behavior affects their green agricultural technology adoption [31,32]. The adoption of investment-based green agricultural technologies by farmers requires large amounts of capital to support the use of new equipment and technologies compared to traditional production methods [33]. Existing studies have shown that farmers’ green agricultural technology adoption is related to their credit behavior [16]. The higher the amount of credit received by farmers, the more capable they are of adopting green agricultural technologies with a certain level of risk [34].
The theory of information asymmetry holds that in actual economic life there is no complete information, the information actually possessed by economic participants is incomplete, and the distribution of information among different participants is uneven, which is known as information asymmetry [35]. This information asymmetry hinders financial institutions’ ability to monitor farming practices, resulting in distrust in loan repayment capacity and consequent reluctance to extend credit [36]. Such financial constraints ultimately impede green technology adoption and agricultural sustainability. However, social security can improve farmers’ credit accessibility [37]. Social security has a pledging role in lending [38]. Farmers participating in social security can use the policy as collateral to obtain loans from financial institutions [39], which can effectively alleviate the credit constraints of farmers [40], thus solving the financial constraints they face when adopting green agricultural technologies [41].
In conclusion, social security can facilitate farmers’ green agricultural technology adoption by alleviating their credit constraints and solving their financial needs dilemma. Thus, the study presents the hypothesis H2: Social security promotes green agricultural technology adoption by alleviating farmers’ credit constraints (as indicated in Figure 1).

2.3. Heterogeneous Effects of Social Security on Farmers’ Green Agricultural Technology Adoption

2.3.1. Impacts of Different Education Levels on Farmers’ Green Agricultural Technology Adoption

According to Schultz’s human capital theory, human capital is a sort of capital that employees possess. It is defined by a worker’s level of knowledge, technical proficiency, workability, and fitness. It is the total of these factors’ values. Human capital is created through investment and is an essential component of social production [42]. Existing research has found that greater education levels can increase farmers’ willingness to purchase social security [43,44]. Farmers with higher levels of education are more inclined to utilize social security because they can better appreciate its value and use it to minimize potential financial losses and avoid unclear risks in the process of manufacturing and operating [45,46]. As a result, they are more inclined to purchase social security. Farmers with less education are less likely to get insurance because they are less aware of its function.
Furthermore, agricultural producers in rural areas with high human capital have higher educational and cultural qualifications, which increases their likelihood of realizing that the value premiums of agricultural goods provided by green farming techniques outweighs the decrease in manufacturing profits caused by the reduction in chemical elements [47,48]. They are also highly conscious of environmental protection and the issues of environmental contamination brought by agricultural production [47], and they tend to generate high-quality, eco-friendly agricultural goods, which contributes to green agricultural development [49].
The high education levels of farmers can improve the driving effect of social security on green agricultural technology development. Thus, the study presents the hypothesis H3: Farmers with higher levels of education are more impacted by social security than on those with lower levels of education (as indicated in Figure 1).

2.3.2. Impacts of Different Ages on Farmers’ Green Agricultural Technology Adoption

According to peasant economic theory, small farmers are economic entities that rely primarily on family labor rather than paid labor for agricultural production and management. In addition to engaging in specific production activities, household members also serve as decision-makers in agricultural operations [50]. Per the World Bank’s definition of smallholder farmers as those possessing landholdings of less than 2 hectares, the vast majority of Chinese farmers have an average household business area of only a quarter of this standard, which is typical of smallholder farmers [51,52].
Farmers of various ages have varying intentions to acquire social security as decision-makers in agricultural management, owing to their differing comprehension of agricultural production conditions or social security awareness [53]. Young and middle-aged farmers will know more about agricultural output since they are more likely to be the family’s primary workers and agricultural management decision-makers. Consequently, they tend to mitigate risks associated with green technology adoption by strategically purchasing agricultural insurance and other social security products aligned with current production levels [54].
Based on the life cycle of human capital stocks theory, agricultural human capital stock is a dynamic change process with an “inverted U-shaped” change trend [55]. That is, as people become older, their human capital stock in terms of labor supply ability, cognitive ability, and learning ability increases until it reaches a peak at a specific age, after which it gradually drops, demonstrating the effect of human capital weakening [56]. Given that green agricultural technologies are both capital- and labor-intensive [57], elderly farmers face dual constraints: diminished capacity to adopt innovations and physical limitations in mastering technical knowledge. They may not have enough physical strength and energy to master professional knowledge related to green agricultural technology [58]. For example, in reality, most elder farmers still maintain the older thinking that the more fertilizers and pesticides they apply, the better. They are even unwilling to come into contact with and learn new agricultural production technology and apply it to production practice [14], thereby hindering green agricultural technology adoption.
Because middle-aged and young farmers are more willing to purchase social security, and elderly farmers possess a declining overall physical condition and a more traditional way of thinking, social security may have a greater promotion effect on green agricultural technology adoption by middle-aged and young farmers.
Therefore, the study presents the hypothesis H4: The impact of social security is more positively significant for young and middle-aged farmers than for older groups (as indicated in Figure 1).

2.3.3. Impacts of Different Risk Preferences on Farmers’ Green Agricultural Technology Adoption

Bounded rationality theory states that human rationality is constrained. Due to internal or external constraints in the decision-making process, humans may pursue a “satisfactory solution” rather than an “optimal solution”. While farmers operate as rational actors seeking benefit maximization, their bounded rationality often leads them to adopt conservative risk-management strategies aimed at minimizing uncertainty [59]. Therefore, to boost farmers’ technology adoption, one crucial step is to reduce their level of risk cognition [60].
Faced with production uncertainties and information asymmetry, farmers balance profit maximization against risk aversion in production decisions [61]. Farmers’ risk attitudes vary, with the majority being risk-averse [62]. Previous research has demonstrated that risk-averse farmers are more inclined to buy social security than risk-preferring farmers [63,64]. This is because risk-preferring farmers tend to be more aggressive in their risky decision-making [65]. However, risk-averse farmers are more sensitive to the uncertainty of future income and the fluctuations of production processes [66]. To enhance their risk-taking ability and to obtain stable psychological guarantees, they are usually more willing to purchase social security [67]. In addition, green agricultural technology requires more labor input and capital investment than traditional technology. It is difficult for farmers to receive relevant information concerning novel technologies, which generates information asymmetry. However, the acquisition of conventional technical information is often more convenient and stable, making the cost of adopting new technologies higher than conventional technology [68]. Risk-averse farmers tend to choose conventional technologies for stability of returns and to avoid loss of funds. As an important risk management tool, social security can not only disperse the risks and uncertainty brought by farmers’ new technology adoption but also promote risk-averse farmers to adopt green agricultural technology.
Therefore, risk-averse farmers may be more willing to buy social security and be more affected by its impact on green agricultural technology adoption. In view of this, the study presents the hypothesis H5: Social security affects risk-averse farmers more than risk-preferring and risk-neutral farmers (as indicated in Figure 1).

3. Material and Methods

3.1. Data Collection

As an agricultural innovation hub in China’s lower Yangtze River basin, Jiangsu Province demonstrates exemplary leadership in agricultural insurance development. The sustained advancement of macroeconomic policies and social security system construction has not only created strategic opportunities for the insurance sector but has also elevated its agricultural insurance framework to a national demonstration benchmark. This first-mover advantage in institutional innovation establishes Jiangsu as an ideal research context for investigating the impact of social security on farmers’ green agricultural technology adoption.
Therefore, this study employs data from the China Land Economy Survey (CLES) conducted by Nanjing Agricultural University. The survey implemented a multi-stage Probability Proportional to Size (PPS) sampling design across 13 prefecture-level cities in Jiangsu Province, following a hierarchical structure of 2 counties per city, 2 villages per county, and 50 households per village, ultimately encompassing 52 administrative villages and 2600 rural households. The questionnaire comprehensively addresses six key dimensions: agricultural production, factor markets, green development, financial insurance, rural governance, and village construction.
The survey timeline comprised a 2020 baseline investigation followed by subsequent tracking surveys in 2021 and 2022. Given the COVID-19-induced sampling bias potentially compromising 2020 data accuracy and significant data deficiencies in the 2022 wave, this analysis prioritizes the 2021 CLES data. Through screening the data multiple times to eliminate missing data, lacking crucial information, and invalid questionnaires, 2265 valid data are obtained. The adoption of green agricultural technologies, land features, household factors, and personal traits constitute the majority of the selected data.

3.2. Variables Definitions

3.2.1. Dependent Variable

The dependent variable of this study is green agricultural technology adoption. A technology package made up of several sub-technologies is recognized as green agricultural technology. There may also be correlation effects between multiple sub-technologies. Farmers can use several technologies to solve the problems faced in the production process.
In this study, the questionnaire asked farmers: What are the types of agricultural technology services you have used? And farmers were provided with 18 options to choose from (as shown in Table 1).
According to the definition of green agricultural technology, this study classified nine of these technologies under the category of green agricultural technology: (1) seed improvement services; (2) soil testing and fertilization; (3) crop cultivation and management; (4) pest control; (6) energy-saving and efficient facilities agricultural technology; (9) water-saving irrigation technology; (10) healthy and hygienic livestock breeding technology; (11) crop straw comprehensive utilization technology; (12) agricultural clean and renewable energy technology.
If a farmer’s answer includes any one of the nine technologies, the dependent variable is assigned a value of “1”, otherwise it is assigned a value of “0”.

3.2.2. Core Independent Variable

The core independent variable is social security. A comprehensive social security system can greatly enhance the farmers’ resilience against risks during the production process, and their adoption of new things and technologies will also be improved [21]. The survey in the study inquired about the family expenditure of plant insurance, endowment insurance, medical insurance, and pig insurance. Social security was measured through the number of insurance types held by farmers.

3.2.3. Control Variables

Based on the research of Li, Fan, Jiang, and Quan [3], Yu, Zhang, Zhang, Xu, Qi, and Deng [10], Mao, Zhou, Ying, and Pan [12], and Zeng et al. [69], the study appends some control variables to this model, including personal traits—age, health status, gender, education level, and risk preference; household features—households with village cadres (village cadres refer to grassroots officials in rural areas who manage village affairs and implement national policies in China), agricultural labors, and non-agricultural income; and land features—distance, land area, land slope, and mechanical operating costs. Table 2 shows the definitions and descriptive statistics for each variable.

3.2.4. Mediation Variable

Farmers with social security are more likely to obtain loans from credit institutions [16]. As a result, the financial constraints they face in their production can be alleviated, thereby facilitating the adoption of green agricultural technologies by farmers [34]. Thus, this study introduces the productive credit behavior of farmers as a mediation variable. Referring to the study of Makate, Makate, Mutenje, Mango, and Siziba [39], the study measures productive credit behavior by whether household members have productive borrowing behavior. If the productive borrowing in the questionnaire is greater than 0, the famers’ productive credit behavior is assigned the value of 1, otherwise it is assigned the value of 0.

3.3. Model

Green agriculture technology adoption by farmers is a discrete, binary variable. The Probit model is built to assess the influence of social security on green agricultural technology adoption. The basic model is configured as the following:
Y i c = β 0 + β 1 S o c i a l _ s e c u r i t y i c + β 2 X i c + δ c + ε i c
where individual I and city C are identified by signs i and c, respectively. Y represents the individual’s level of green agricultural technology adoption. Social security indicates how many different types of insurance farmers own. Other control variables are indicated by X. The city dummy variable is indicated by δ, which is the city effect of each city. The constant term is shown by β0. The parameters to be calculated are indicated by β1 and β2. The error term is indicated by ε.

4. Results Analysis

4.1. Descriptive Statistics

The features of participant farmers are displayed in the descriptive statistics of Table 2. The majority of household decision-makers are male, elder, lower educated, healthy, and generally risk-averse farmers, according to an assessment of their personal traits. With an average age of roughly 62 years, an average education level of roughly 8 years, and an average risk preference level of 2.715, 86.2% of the farmers in the sample are men.
Regarding household features, 15.6% of households have village cadres, and the average household in the sample includes one to two agricultural laborers. In total, 5.9% of households have productive credit behavior. In regard to land features, 92.3% of the examined land is on the plains, and the mean area of family-operated land is 0.506 ha. The average distance of the land from the nearest hardened concrete road is 1.489 km.
In terms of social security, each person has 2–3 types of insurance on average (Figure 2 depicts the percentage of each type of insurance purchased). In terms of adopting green production, just 18.3% of the sample farmers have used green agricultural technology. Each individual typically adopts 0–1 different types of green farming technology (the number of adopters for each category is depicted in Figure 3). Overall, the sample data perform reasonably well and are somewhat reflective of the general public (Table 2 contains the precise descriptive statistics for every variable).

4.2. The Impact of Social Security on Farmers’ Green Agricultural Technology Adoption

In order to prevent problems such as unstable parameter estimation and unreliable model results due to covariance, this study uses the variance inflation factor (VIF) to diagnose the covariance of the model before conducting the benchmark regression model test. The results showed that the VIF values for each explanatory variable ranged from 1.01 to 2.02 with a mean value of 1.47, and the maximum value was also less than 10. According to the VIF test rule [70], this indicated that the model did not have serious multicollinearity problems.
Table 3 presents the empirical findings about how social security affects farmers’ green agricultural technology adoption. To counteract any omitted variable bias, stepwise regression was utilized. Only the city dummy variable and the core independent variable (social security) are included in Model (1). Five control variables associated with personal features (the decision-maker’s age, education, health level, gender, and risk preference) are included in Model (2). Three household-related control variables (number of household agricultural laborers, non-agricultural income, and households with village cadres) are included in Model (3). Four control variables concerning land features (land area, land slope, distance, and mechanical operating costs) are included in Model (4). Lastly, Model (5) uses the estimation in Model (4) to compute the marginal effects because the Probit model is nonlinear.
The findings in Table 3 indicate that in Models (1) through (5), the social security variables’ coefficients continue to be positively significant at the 1% level. The estimated marginal effects, after adjusting for other factors, suggest that for each 1 percent rise in social security, green agricultural technology adoption rises by 2.5 percent. One explanation is that the capacity of farmers to tolerate unforeseen risks, including natural disasters, in agricultural production is inadequate because of unpredictable production hazards and information asymmetry. To avoid risks, conservative production behaviors are often adopted [62]. The several forms of insurance covered by social security can lower the production risks that farmers face and encourage green agricultural technology adoption as an agricultural support strategy to make up for financial losses. Thus, the hypothesis H1, which asserts that social security significantly promotes farmers’ adoption of green agricultural technology, gains approval.
Estimates of how control variables affect green agricultural technology adoption are also provided in Table 3. At the 5% level, the adoption of green agricultural technology is negatively correlated with the age of household decision-makers, indicating that farmers are less likely to select green agricultural technology as household decision-makers get older. One potential explanation could be that farmers’ general state of health function may deteriorate with age [71]. The green transformation of agriculture will also be hampered by older farmers’ increased reliance on conventional production techniques because they have less information about green agricultural technology [56]. The health of household decision-makers has a positive correlation with their green agricultural technology adoption at a 1% level. This means that farmers are more likely to adopt green agricultural technology if they are healthier family decision-makers. One rationale is that farmers who are in better health may be able to adhere to stringent environmental supervision regulations and embrace a range of agricultural technologies [72]. The number of household agricultural laborers and the adoption of green agricultural technology are positively correlated at the 1% level, suggesting that farmers are more likely to embrace green agricultural technology when there are more household agricultural laborers. The reason could be that green agricultural technologies are more demanding in terms of intensive management and require more indispensable labor inputs [73]. Therefore, the number of household labor forces will affect farmers’ decision-making, which will encourage the usage of green agricultural technologies. At a level of 1%, household cadres and the use of green agricultural technologies are positively correlated, suggesting that household cadres can motivate farmers to take up this technology. Households with village cadres may have greater social assets and relations compared to ordinary families [74]. This implies timely access to knowledge and assets on green agricultural technologies, which will increase their adoption [69]. There is a positive correlation between the total area of family-operated land and green agricultural technology adoption at the 1% level, suggesting that farmers are more inclined to adopt green technology when the scale of land operations are wider. A possible reason is that the likelihood that agriculture will be the family’s primary source of income increases as the amount of land under operation grows. Farmers tend to be more inclined to expand their operations and think more deeply about agricultural management [75]. Consequently, their likelihood of adopting green agricultural technologies increases. Mechanical operating costs and green agricultural technology adoption have a positive relationship at a level of 5%, meaning that farmers tend to be more inclined to adopt green technologies when their mechanical operating costs are higher. One possible explanation is that farmers’ level of agricultural mechanization increases with the mechanical operating costs. Higher levels of agricultural mechanization can boost labor productivity, enhance the utilization of agricultural resources, lower production costs [69], and improve green agricultural technology adoption, thereby supporting sustainable agricultural development.
It should be noted that the lower R2 values for the benchmark regression in this study are due to the fact that pseudo-R2 has a different meaning and interpretation in the Probit model than R2 in traditional linear regression. In the Probit model, pseudo-R2 is not a direct measure of the predictive accuracy of the model but is used to measure the degree of improvement of the model relative to the benchmark model. Therefore, a smaller pseudo-R2 does not mean that the model has poor explanatory power. Although the pseudo-R2 value is relatively modest, our model achieves statistical significance through likelihood ratio tests and other significance examinations. It effectively explains how critical socioeconomic factors shape farmers’ decisions to adopt green agricultural technologies. Moreover, the estimated coefficients demonstrate robust empirical relevance, carrying both substantial economic significance and actionable policy implications.

4.3. Mechanism Analysis

The results of the above study proved that social security has a significant positive impact on the adoption of green agricultural technologies by farmers. Further exploration of the theoretical analysis reveals that farmers’ productive credit behavior plays a mediating role in social security and green agricultural technology adoption [39].
Table 4 reports the results of the regression with farmers’ productive credit behavior as a mediation variable. In Table 4, Model (1) is the baseline model that represents the effect of social security on the adoption of green agricultural technologies by farmers, Model (2) represents the effect of social security on the productive credit behavior of farmers, and Model (3) represents the results of the regression that incorporates both social security and the productive credit behavior of farmers. In Model (2), the estimated coefficient of the social security variable is significantly positive, indicating that social security positively affects the productive credit behavior of farmers, which is similar to the findings of Farrin and Miranda [28] and Belissa et al. [76]. The result of Model (3) shows that the coefficient of productive credit is significantly positive and the coefficient of social security is still significantly positive. The above results indicate that the productive credit behavior of farmers has a mediating effect on social security and green agricultural technology adoption. The possible reason for this is that social security can increase the availability of credit to farmers through pledges, effectively alleviating the problem of credit constraints, which in turn promotes the adoption of green agricultural technologies by farmers. The findings in Table 4 provide empirical evidence for the hypothesis H2.

4.4. Robustness Analysis

Endogenous issues may arise from a reciprocal causal connection between social security and farmers’ green agricultural technology adoption. For one thing, social security serves the purpose of reducing risks, ensuring the income of farmers, and then encouraging farmers to use green agricultural technology. For another, farmers who adopt green agricultural technology have better equipment to produce and are better able to participate in social security. To reduce the insurance compensation rate for their interests, the insurance company is more inclined to sign a contract with farmers who have superior production circumstances. Thus, social security and farmers’ green agricultural technology adoption may have a reciprocal causation issue. Furthermore, there might be a “self-selection” problem of “simultaneous decision-making” between farmers’ green agricultural technology adoption and their participation in social security to some extent [77]. Therefore, this study tests for robustness using an instrumental variable methodology.
The correlation and exogeneity requirements must be met when choosing the instrumental variable. The “peer effect”—the quantity of insurance types that city farmers own outside of their homes—is selected as an instrumental variable for social security in this study. Family decisions will exhibit specific correlations within the same city. When deciding whether or not to enroll individual families in insurance, farmers will consider other people’s participation [78]. Theoretically, there is a link between the amount of insurance kinds carried by farmers in other rural families in the city and the number of insurance kinds carried by farmers in the family. However, it does not have an obvious connection to the household’s adoption of green agricultural technology, meeting the instrumental variable’s correlation and exogeneity conditions. Therefore, the IV-Probit model is utilized in this research to test for robustness. Furthermore, the IV-Tobit model is used to change the type of model. It is useful to adjust the model setting to see if a particular model has an impact on the findings. This study also uses total family insurance expenditure as a substitute for the core independent variable. Moreover, this study regresses on the amount of green agricultural technology types used by farmers via the O-Probit model, replacing the binary dependent variable of whether or not green agricultural technologies are adopted.
The outcomes of robustness tests are shown in Table 5. The regression outcomes of the IV-Probit model are represented in Model (1), and the regression outcomes of the IV-Tobit model are represented in Model (2). Model (3) shows the outcomes of substituting the insurance expenditure for the independent variable, and Model (4) shows the outcomes of substituting the number of different kinds of green agricultural technologies for the dependent variable in the O-Probit model. In the models mentioned above, the regression coefficient for each variable shows a high degree of agreement with the basic regression results shown in Table 3. Social security and green agricultural technology adoption appear to be strongly positively correlated, as evidenced by their statistical significance and identical signals. The more insurance types farmers carry, the more inclined they are to adopt green agricultural technologies. The overall consistency of different models and proxy variable regression results indicates that the empirical evaluation produced reliable results in the study.

4.5. Heterogeneity Analysis

4.5.1. Grouped by Education Levels

Based on subgroups by education level, Table 6 displays the estimated impact of social security on farmers’ green agricultural technology adoption. Specifically, the population being studied is split into two groups according to whether the nine-year compulsory education is completed: the higher-education-level group (household decision-makers with more than or equal to nine years of education) and the lower-education-level group (household decision-makers with less than nine years of education).
The findings of the heterogeneity analysis are displayed in Table 6. Models (1) and (2) provide the regression results for the groups with higher and lower levels of education, respectively. These coefficients show how social security affects each farmer group’s green agricultural technology adoption.
The findings indicate that social security has a significant positive effect on green agricultural technology adoption by farmers with both higher and lower education levels, showing coefficients of 0.028 and 0.020, respectively. This suggests that social security has a greater impact on green agricultural technology adoption by groups with higher education levels. Farmers with education higher levels possess greater cognitive capacity and more knowledge reserves. By actively searching for the use of biological green agricultural technology, it is easier for them to master technology and the economic benefits behind it [79]. These findings are consistent with existing research. One crucial component of human capital that reflects the features of labor in rural areas is levels of education. Higher levels of education are more conducive to increase farmers’ willingness and capacity to accept new agricultural knowledge and technology, as well as the green development of agriculture [11]. Thus, Hypothesis 2, which asserts that farmers with higher education levels benefit more from social security than farmers with lower education levels., is supported.
The results of the study demonstrate that enhancing rural communities’ human capital accumulation can encourage the adoption of green agricultural technologies. The United Nations Development Programme’s HDI (Human Development Index) indicates that the average number of years of education for individuals worldwide in 2022 was 8.7 years, and 7.6 years in developing countries. However, the average education level of the household decision-maker in the study was just 7.591 years. Growing agricultural production is crucial for meeting predicted rising requirements, and improved technology adoption is regarded as a fundamental role in the success of Asia’s green revolution [80]. Therefore, the majority of developing countries, including China, could promote the adoption of green agricultural technology by increasing human capital investment in rural regions, thereby boosting sustainable agricultural development.

4.5.2. Grouped by Ages

Table 7 displays the outcomes of the estimated impacts of social security on green agricultural technology adoption among farmers by age subgroups. Concerning the previous literature [81,82], this study divides farmers into two groups: the older age group (household decision-makers aged 60 years or older) and the young and middle-aged group (household decision-makers aged less than 60 years).
The findings of the heterogeneity testing are displayed in Table 7. The regression outcomes for the older age group are provided by Model (1), whereas the regression results for the younger and middle-aged groups are provided by Model (2). These coefficients show how social security affects green agricultural technology adoption among farmers of different ages.
These findings show that social security significantly boosts the adoption of green agricultural technologies among young and middle-aged farmers, while it has no significant effect on older farmers. This suggests that social security has a greater influence on the adoption of green agricultural technologies by young and middle-aged farmers. The aging of the agricultural labor force will unavoidably hinder the green production transformation fueled by green production technology implementation because of the physical strength and health decline of older farmers, as well as their vulnerability to a number of chronic diseases [10]. Furthermore, people’s specialized job abilities become less removable as they age. Compared with young people, older people may have difficulty meeting new work requirements, and it becomes more difficult to maintain the latest agricultural skills or to adopt higher levels of technology [83]. Young and middle-aged farmers are currently at a higher level of physical fitness, learning ability, and acceptance of new things [83]. They generally prefer high-risk, high-yield production and management strategies, which leads to a greater acceptance of green agricultural technologies.
Thus, Hypothesis 3, which asserts that the impact of social security is more positively significant for young and middle-aged farmers than for older groups, is supported. According to the study’s findings, in addition to social security, other more targeted strategies can be used to enhance the adaptability to new technologies of older farmers, which can subsequently boost the transition of green production.

4.5.3. Grouped by Risk Preferences

Table 8 displays the estimated impacts of social security on farmers’ green agricultural technology adoption based on risk preference subgroups. Farmers are split into three groups in this study: risk-preferring group, risk-neutral group, and risk-averse group. The outcomes of the heterogeneity analysis are presented in Table 8. These regression results for the risk-preferring, risk-neutral, and risk-averse groups are provided by Models (1), (2), and (3), respectively. These coefficients show how social security influences green agricultural technology adoption among farmers with varying risk preferences.
Based on these findings, social security has a significant positive impact on risk-averse farmers’ green agricultural technology adoption, but not on risk-preferring or risk-neutral farmers, suggesting that social security has a greater influence on risk-averse farmers’ green agricultural technology adoption. Because farmers also consider risk aversion in the production process in addition to maximizing profits [62]. To maintain the stability of income and avoid the loss of funds, farmers with different risk preferences will have different attitudes in the face of new agricultural production technology, and ultimately show different behaviors and degrees of green agricultural technology adoption. Risk-averse farmers tend to be conservative in their overall thinking and are more cautious and sensitive to the unknown risks associated with the new agricultural technology adoption. They might be reluctant to adopt green agricultural technologies when combined with the uncertainties around future earnings from agricultural production [84]. However, social security can reduce the economic burden of farmers in various aspects. It provides timely assistance and subsidies to farmers when risk events occur, and it offers stable psychological expectations for risk-averse farmers. This, in turn, promotes the adoption of green agricultural technologies. Thus, Hypothesis 4, which asserts that social security affects risk-averse farmers more than risk-preferring and risk-neutral farmers, is supported.

5. Conclusions and Implications

5.1. Conclusions

This investigation data from CLES in 2021 served as the basis for this study. Using 2265 sample data from 13 cities, the study examines the association across social security and green agricultural technology adoption. The conclusions of this study are as follows:
(1)
The findings demonstrate that social security significantly influences farmers’ green agricultural technology adoption.
(2)
Mechanism analysis demonstrates the mediating role of farmers’ productive credit behavior in social security and adoption of green agricultural technologies.
(3)
Compared to previous research findings, additional heterogeneity analysis shows that social security benefits farmers with higher educational levels more than it does farmers with lower education levels, and the positive impact on young and middle-aged farmers is greater than that on elderly farmers. Because risk-averse farmers tend to be more inclined to acquire social security than risk-neutral or risk-preferring farmers, social security has a more significant effect on risk-averse farmers’ green agricultural technology adoption.

5.2. Implications

Building on these findings, this study proposes five policy recommendations to facilitate agricultural green transformation:
(1)
Strengthen rural social security systems: Governments should enhance subsidy mechanisms for green technology adoption (e.g., improved seeds and machinery) while alleviating farmers’ financial burdens through improved medical and pension coverage. To improve farmers’ comprehension of how social security distributes the hazards of farming and boost their willingness to acquire social security, grassroots governments should implement educational campaigns to regularly explain the operating mechanisms and specific functions of social security.
(2)
Address capital constraints: Policy support should encourage agricultural credit institutions to develop insurance-backed mortgage products through tax incentives and targeted subsidies. Concurrently, incentive mechanisms should be implemented to ensure timely loan repayments.
(3)
Attract skilled agricultural talent: Given the labor-intensive and knowledge-driven nature of green technologies, governments should reshape agricultural perceptions through vocational training, university scholarships, and improved rural career development opportunities.
(4)
Differentiate technology extension approaches: It is important to establish dedicated technical support teams for elderly farmers while collaborating with agricultural colleges to develop tiered training programs. Regular on-site guidance should be maintained to enhance technology comprehension.
(5)
Enhance risk awareness: Village-level demonstration plots should be developed tailored to farmers’ risk preferences. Visual demonstrations of green technology benefits, combined with systematic risk education programs, can strengthen security system participation and technology adoption confidence.

6. Limitations of the Study

The study exhibits several limitations that can be addressed and enhanced in subsequent studies:
(1)
There may be a dynamic connection between social security and farmers’ green agricultural technology adoption. Panel data can be created and dynamic connections between them can be discussed in future studies. For example, longitudinal studies tracking the impact of social security on the adoption of green agricultural technology by farmers over a period of 5–10 years can shed more light on the temporal dynamics of technology diffusion, e.g., whether social security has a lagged effect on the adoption of green agricultural technology.
(2)
The world’s largest developing nation is China. To find out whether the results of this study can be applied to other nations, more research is still necessary. Emerging economies like China and India demonstrate unique advantages in green technology exports, whereas most developing nations face challenges competing globally in manufacturing sectors such as renewable energy equipment and eco-friendly appliances. For these countries, pragmatic strategies emphasizing the localized adaptation of mature green technologies prove more viable than pursuing industrial substitution or export-oriented green leapfrogging [85].

Author Contributions

Conceptualization, Y.X.; methodology, Y.X.; software, Y.X.; validation, Y.X.; formal analysis, Y.X.; investigation, Y.X.; resources, Y.X.; data curation, Y.X.; writing—original draft preparation, Y.X.; writing—review and editing, K.Z.; visualization, K.Z.; supervision, K.Z.; project administration, K.Z.; funding acquisition, K.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Natural Science Foundation of Sichuan Province, China (Grant No. 2025ZNSFSC1148), and Sichuan Philosophy and Social Key Laboratory of Monitoring and Assessing for Rural Land Utilization (Grant No. NDZDSC2023003).

Institutional Review Board Statement

Not applicable.

Data Availability Statement

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Acknowledgments

The authors extend their great gratitude to the anonymous reviewers and editors for their helpful review and critical comments.

Conflicts of Interest

The author declares no conflicts of interest.

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Agriculture 15 00498 g001
Figure 1. Study structure.
Figure 1. Study structure.
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Agriculture 15 00498 g002
Figure 2. Percentage of each type of insurance purchased.
Figure 2. Percentage of each type of insurance purchased.
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Figure 3. Number of adopters of each green agricultural technology.
Figure 3. Number of adopters of each green agricultural technology.
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Table 1. Answers to this question.
Table 1. Answers to this question.
Answers
(1) Seed improvement services
(2) Soil testing and fertilization
(3) Crop cultivation and management
(4) Pest control
(5) Mechanized production technology
(6) Energy-saving and efficient facilities agricultural technology
(7) Agricultural product processing, packaging, and preservation technology
(8) Network information technology services
(9) Water-saving irrigation technology
(10) Healthy and hygienic livestock breeding technology
(11) Crop straw comprehensive utilization technology
(12) Agricultural clean and renewable energy technology
(13) Disaster prevention and mitigation technology
(14) Agriculture-friendly policy information services
(15) Agricultural product market information services
(16) Agricultural credit fund services
(17) Agricultural operation and management services
(18) Other
Table 2. Variable definitions and descriptive statistics.
Table 2. Variable definitions and descriptive statistics.
VariableDefinitionMeanS.D.
TechnologyWhether the farmer adopts green agricultural technology (1 = yes; 0 = no) 0.1890.392
Number of technologiesNumber of types of green agricultural technologies adopted by farmers0.5121.326
Insurance expensesLogarithm of household insurance expenses7.1442.404
Social securityNumber of insurance types held by farmers2.2731.035
Age Age of the household decision-maker (years) 61.86410.829
HealthHealth status of household decision-maker
(1 = loss of labor; 2 = poor; 3 = medium; 4 = good; 5 = excellent) 		4.0721.052
GenderGender of household decision-maker (1 = male; 0 = female) 0.8620.345
Education levelEducation level of the household decision-maker (years) 7.5914.314
Risk preference1 = risk-preferring; 2 = risk-neutral; 3 = risk-averse2.7150.550
CadreWhether the household has cadres (1 = yes; 0 = no) 0.1560.363
LaborsNumber of household agricultural labors1.4421.004
Non-agricultural incomeLogarithm of total household non-agricultural income8.4194.904
DistanceDistance between land and nearest hardened concrete road (km) 1.48915.701
Land areaArea of family-operated land (ha) 0.5062.894
Land slopeWhether the land is located in a plain (1 = yes; 0 = no) 0.9230.267
Mechanical operating costsCost of mechanical land operations (yuan/mu) 137.359329.076
Farmers’ productive credit behaviorWhether household members have productive borrowing behavior
(1 = yes; 0 = no) 		0.0590.236
Table 3. Regression results of the probit model.
Table 3. Regression results of the probit model.
Category(1) (2) (3) (4) (5)
Social security0.183 ***0.139 ***0.115 ***0.107 ***0.025 ***
(5.48) (3.97) (3.19) (2.95) (2.96)
Age −0.010 ***−0.009 **−0.008 **−0.002 **
(−3.03) (−2.45) (−2.20) (−2.20)
Education level 0.0100.0070.0060.001
(1.29) (0.83) (0.70) (0.70)
Health 0.139 ***0.113 ***0.111 ***0.026 ***
(3.93) (3.11) (3.02) (3.03)
Gender 0.206 **0.1490.1250.029
(2.11) (1.48) (1.24) (1.24)
Risk preference −0.034−0.0220.0070.002
(−0.59) (−0.38) (0.12) (0.12)
Labors 0.219 ***0.198 ***0.046 ***
(6.66) (5.88) (5.98)
Non-agricultural income −0.005−0.003−0.001
(−0.64) (−0.37) (−0.37)
Cadre 0.490 ***0.498 ***0.117 ***
(5.87) (5.93) (6.05)
Land area 0.004 ***0.001 ***
(3.76) (3.77)
Land slope 0.0970.023
(0.74) (0.74)
Distance −0.003−0.001
(−0.75) (−0.75)
Mechanical operating costs 0.000 **0.000 **
(2.16) (2.17)
Constant−0.944 ***−0.994 ***−1.200 ***−1.437 ***
(−7.14) (−2.79) (−3.22) (−3.66)
City dummiesYesYesYesYesYes
Ll−1029.625−1005.642−967.891−954.691−954.691
Chi2133.593 ***181.558 ***257.061 ***283.461 ***283.461 ***
R20.0610.0830.1170.1290.129
Observation22652265226522652265
Note: With the t-value in parentheses, the symbols **, and *** denote significance at the 5% and 1% statistical levels, respectively.
Table 4. Results of mechanism analysis.
Table 4. Results of mechanism analysis.
(1) (2) (3)
Green Agricultural TechnologyFarmers’ Productive Credit BehaviorGreen Agricultural Technology
Social security0.107 ***0.117 **0.103 ***
(2.95) (2.15) (2.82)
Farmers’ productive credit behavior 0.291 **
(2.26)
Constant−1.437 ***0.396−1.546 ***
(−3.66) (0.72) (−3.90)
Control variablesYesYesYes
City dummiesYesYesYes
Ll−954.691−413.037−952.166
Chi2283.461 ***191.604 ***288.510 ***
R20.1290.1880.132
Observation226522652265
Note: With the t-value in parentheses, the symbols **, and *** denote significance at the 5% and 1% statistical levels, respectively.
Table 5. Results of robustness analysis.
Table 5. Results of robustness analysis.
(1) (2) (3) (4)
IV-Probit IV-Tobit Technology O-Probit
Social security0.097 ***Social security0.129 ***Insurance expenses0.030 *Social security0.102 ***
(2.61) (2.80) (1.84) (2.94)
Control variablesYes Yes Yes Yes
Wald test6.79 **Wald test7.86 **Chi2278.188 ***Chi2253.216 ***
Observation2265 2265 2265 2265
Note: With the t-value in parentheses, the symbols *, **, and *** denote significance at the 10%, 5%, and 1% statistical levels, respectively.
Table 6. Heterogeneity analysis of groups by education level.
Table 6. Heterogeneity analysis of groups by education level.
(1) (2)
Higher Education LevelLower Education Level
Social security0.028 **0.020 *
(2.17) (1.81)
Control variablesYesYes
City dummiesYesYes
Ll−563.575−370.505
Chi2142.942 ***165.143 ***
R20.1130.182
Observation12061059
Note: With the t-value in parentheses, the symbols *, **, and *** denote significance at the 10%, 5%, and 1% statistical levels, respectively.
Table 7. Heterogeneity analysis of groups by age.
Table 7. Heterogeneity analysis of groups by age.
(1) (2)
OlderYoung and Middle-Aged
Social security0.0060.060 ***
(0.65) (3.73)
Control variablesYesYes
City dummiesYesYes
Ll−487.052−444.086
Chi2166.460 ***138.017 ***
R20.1460.134
Observation1332933
Note: With the t-value in parentheses, the symbols *** denote significance at the 1% statistical levels, respectively.
Table 8. Heterogeneity analysis of groups by risk preference.
Table 8. Heterogeneity analysis of groups by risk preference.
(1) (2) (3)
Risk-PreferringRisk-NeutralRisk-Averse
Social security0.0410.0290.017 *
(0.95) (1.29) (1.80)
Control variablesYesYesYes
City dummiesYesYesYes
Ll−33.928−176.395−703.857
Chi244.917 ***81.519 ***225.920 ***
R20.3980.1880.138
Observation974211732
Note: With the t-value in parentheses, the symbols * and *** denote significance at the 10% and 1% statistical levels, respectively.
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Xu, Y.; Zhang, K. The Impact of Social Security on Farmers’ Green Agricultural Technology Adoption: Empirical Evidence from Rural China. Agriculture 2025, 15, 498. https://doi.org/10.3390/agriculture15050498

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Xu Y, Zhang K. The Impact of Social Security on Farmers’ Green Agricultural Technology Adoption: Empirical Evidence from Rural China. Agriculture. 2025; 15(5):498. https://doi.org/10.3390/agriculture15050498

Chicago/Turabian Style

Xu, Yilan, and Kuan Zhang. 2025. "The Impact of Social Security on Farmers’ Green Agricultural Technology Adoption: Empirical Evidence from Rural China" Agriculture 15, no. 5: 498. https://doi.org/10.3390/agriculture15050498

APA Style

Xu, Y., & Zhang, K. (2025). The Impact of Social Security on Farmers’ Green Agricultural Technology Adoption: Empirical Evidence from Rural China. Agriculture, 15(5), 498. https://doi.org/10.3390/agriculture15050498

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