Does Labor Aging Inhibit Farmers’ Straw-Returning Behavior? Evidence from Rural Rice Farmers in Southwest China

Abstract

As a typical green production technology, straw return affects environmental pollution control and waste recycling. However, in reality, farmers are not active in returning straw to the field. This study constructed a theoretical analysis of farmers’ straw-returning behavior under the conditions of labor aging, socialization service, and environmental regulation. Based on the survey data from 540 households in the Province of Sichuan, we empirically study the relationship between labor aging and farmers’ straw-returning behavior by using the binary logistic regression model and explore the moderating effects of socialization service and environmental regulation on labor aging and straw-returning behavior. The results show that: (1) Aging laborers in rural households constitute a higher proportion, accounting for 29% of the rural household labor force. However, there is limited enthusiasm among farmers to adopt straw returning to the field, with only 65% of farmers adopting this technology. (2) The labor aging hinders farmers’ straw-returning behavior. Specifically, under other fixed conditions, the behavior of straw returning decreases by 0.647 units when the labor aging increases by one unit. (3) Socialization services and economic incentives can mitigate the adverse effects of labor aging on straw-returning behavior, while mandatory constraints do not. (4) The heterogeneity analysis shows that labor aging has a stronger inhibitory effect on straw-returning behavior when the land scale of farmers is lower than the average level and the area is not plain.

1. Introduction

As a type of agricultural solid waste, the resource utilization of crop straw has been widely discussed in the world. As the largest agricultural country in the world, China is rich in crop straw resources [1]. According to statistics, China produces 600–800 million tons of corn, wheat, and rice straw every year [2], accounting for about one-third of the global straw output [3]. In the past, straw was often used for heating, cooking, and feeding livestock, and has a great potential utilization value [4,5]. However, with the change in farmers’ lifestyles and the use of various clean energy sources, such as natural gas, straw is no longer used for cooking, which leads to the phenomenon of straw being casually abandoned and illegally burned [6,7]. This inevitably leads to a variety of serious consequences, such as reduced air quality, increased health risks for farmers, and environmental pollution [8,9]. Therefore, how to achieve the resource utilization of straw has become the focus of the entire society.

In recent years, straw returning has become one of the important ways to centralize the utilization of straw resources [10]. Straw has a great potential utilization value and is a rich biomass waste [11,12]. The reasonable treatment of straw not only improves soil fertility [13], but also increases crop yield [14]. More importantly, the application of crop straw return has obvious environmental benefits and can reduce environmental pollution [15]. However, straw mulching also has negative effects, such as pests and diseases, soil acidification, and difficulties in field management [16,17]. However, in general, the advantages of straw mulching outweigh the disadvantages. However, despite the multiple benefits of crop straw returning, the straw utilization rate of farmers in rural areas of China is still very low [17]. Therefore, the driving factors of straw-returning behavior have been studied extensively. However, the existing studies mostly focus on the socioeconomic characteristics of individual households [18], land-use status [19], cost and benefit [20,21], and environmental regulation [22,23] on farmers’ straw-returning behavior. As an important factor affecting farmers’ straw-returning behavior, labor aging is often included in the model as a control variable [7]. Few empirical studies directly focus on the impact of labor aging on farmers’ straw-returning behavior.

Although labor aging is an inevitable trend of agricultural development in various countries, the potential impact of aging on agricultural production cannot be ignored [24]. In particular, the impact of aging on agricultural green products should be paid enough attention to. Against the background of increasing population aging and the large-scale transfer of rural young people, labor aging in China is accelerating [25]. Results from the seventh National Census have shown that, by the end of 2021, there were 267 million aging people in China, accounting for 18.9% of the entire population. At the same time, in recent years, due to the rural labor force leaving the area, the rural population has more elderly population.

In recent years, some scholars have studied the impact of aging on agricultural green production, but no consensus has been reached. Most scholars believe that the elderly labor force is in a weak position in physical strength, learning ability, and cognitive ability, which is not conducive to the application of advanced technologies and production factors in agricultural production [26]. Some scholars believe that labor aging does not affect the green transformation of agriculture, and older farmers are more inclined to adopt green agricultural technologies [27]. In addition, some scholars have found that aging does not necessarily hinder agricultural green production, because the weakening of human capital caused by aging can be fully compensated by external environmental factors [24,28]. For example, Hu and Zhong [28] found that the improvement in village public good supply and collective decision making could alleviate the negative impact of aging on agricultural production. In addition, socialization services and environmental regulation are also important factors in farmers’ production decisions. However, the issue of whether socialization services and environmental regulation can effectively alleviate the negative impact of aging on straw returning has not received sufficient attention. On the one hand, a large number of rural labor force has left the area for work, and agricultural production presents an aging trend, which leads to the main body of agricultural socialization services. On the other hand, government departments realized the disadvantages of straw burning and the advantages of resource utilization and tried to explore the resource utilization of straw from the perspective of environmental regulation. For example, a series of relevant policies, such as “Environmental Protection Law”, “Air Pollution Prevention Law”, and “Straw Burning and Comprehensive Utilization Management Measures”, have been introduced to restrict and supervise straw-burning behavior. Based on this, it is necessary to systematically explore how farmers make rational decisions on straw returning under the realistic conditions of the coexistence of aging agriculture, socialization service, and government environmental regulations. However, there are few theoretical and empirical studies on this aspect.

Based on this, this study utilizes survey data from 540 rural households of rural rice farmers in southwest China. From the micro-perspective of labor force aging, the binary logistic model is used to empirically analyze the impact of labor force aging on farmers’ straw-returning behavior and its mechanism.

2. Theoretical Analysis and Research Hypotheses

2.1. Influence of Labor Aging on Straw-Returning Behavior

With the large-scale transfer of an abundance of rural young people to urban areas and non-agricultural industries, the aging problem of the rural population has become increasingly serious [29]. The aging of the rural population is accompanied by the aging of agricultural personnel, and the “aging” of production echelons has become the most obvious feature in agricultural production [30]. Many scholars believe that aging is accompanied by a decline in human capital [31]. According to the life cycle theory of human capital, individual human capital is different in different stages and shows an “inverted U-shaped” change trend with age [3]. In other words, individuals have abundant physical strength and strong learning abilities but lack experience when they are young and their human capital stock is generally low. With the increase in knowledge and experience, the stock of human capital will rise rapidly after middle age until it reaches its peak. Later, with the further growth of individual age, especially in the old age stage, individual physical strength and learning abilities decline, and the stock of human capital will gradually decline or even stagnate [31]. As a direct reflection of individual production capacity, human capital has an important impact on farmers’ production decisions and behaviors [32]. Therefore, the decline in human capital brought by aging seriously affects the popularization and promotion of green agricultural technology. The research frame diagram is shown in Figure 1.

The labor aging makes agricultural production face the constraint of labor quantity supply and labor quality supply [31]. First of all, with the increase in age, the health, physical strength, endurance, and other aspects of the elderly labor force rapidly decline [33]. In this case, most of the elderly will choose to transfer their land and withdraw from agricultural production to ensure their welfare in their later years. As a result, this leads to a reduction in the number of household workers. For example, Guo [34] found that aging leads to 58.53% of agricultural personnel quitting agricultural production. However, in reality, although aging leads to the withdrawal of some elderly labor force from agricultural production, most rural elderly labor force insist on participating in agricultural activities [24]. For example, the research of Li [35] shows that some rural elderly people choose to continue to engage in agricultural production for reasons of livelihood, consumption, and household subsidies. However, limited by their physical, cognitive, and learning abilities, the aging labor force has some detrimental effects on agricultural production processes [24]. First of all, the physiological functions of the elderly labor force decline, making it difficult to be competent in heavy agricultural labor [3]. In particular, straw returning, as a labor-intensive production activity, has a high labor intensity and requires a large amount of labor input [26]. To save labor, effort, and time, the elderly labor force chooses to burn the straw directly in the open air in the farmland after harvesting. Secondly, the older labor force typically has a low level of education, which often leads to the conservative thinking of farmers [36]. Therefore, elderly farmers tend to have an aversion to new technologies and new skills, which greatly hinders the popularization and promotion of new agricultural technologies [27]. At the same time, a reduced learning ability makes elderly farmers unable to apply the knowledge they have learned in actual production, thus reducing the utilization efficiency and allocation efficiency of agricultural resources [24]. In addition, aged farmers have limited physical capital and weak anti-risk ability, while straw-returning technology is an intertemporal agricultural technology with great uncertainty [37]. Finally, some scholars believe that, due to their rich experience in agricultural production, the elderly labor force is often higher than the young labor force in terms of production efficiency [28]. However, straw-returning technology is a new technology, and the elderly labor force has not had relevant experience. Therefore, in general, labor aging damages the straw-returning behavior of farmers. Based on this, hypothesis H1 is proposed in this study.

H1: 

Labor aging inhibits the straw-returning behavior of farmers.

2.2. The Moderating Effect of Socialization Service and Environmental Regulation

The theory of induced technological innovation holds that, when a production factor becomes relatively scarce, its relative price will rise. Then, farmers will look for other alternative factors, which will bring about agricultural technological innovation [38]. Due to the scarcity of labor resources, aging forces the improvement in agricultural mechanization, agricultural technology research and development, and application, which provides a vast market space for China’s agricultural socialization service industry [39]. The socialization service refers to the service that runs through the agricultural production operation chain and directly completes or assists in the completion of agricultural pre-production, production, and post-production operations. The essence of agricultural socialization service is to greatly improve the efficiency of agricultural production by introducing human capital into the production process. On the one hand, agricultural hire services and agricultural machinery rental services can effectively solve the problem of insufficient labor supply for elderly farmers [24]. On the other hand, the older the labor force, the more difficult it is to master the complex straw-returning technology [27]. With the rapid development of agricultural socialization services, more and more agricultural professional organizations have become the main force of agricultural technology promotion [40]. Machine transplanting rice, soil testing formula fertilization, unmanned plant protection machines, and other professional technology and equipment are widely used in agricultural production. This effectively alleviates the technical constraints in the agricultural production of older workers. Based on this, hypothesis H2 is proposed in this study.

H2: 

Socialization service can alleviate the inhibitory effect of labor aging on straw-returning behavior.

The change in farmers’ environmental governance behavior is often conducted against a certain background, and environmental regulation is an important means of achieving this [7,41]. Environmental regulation includes economic incentives and compulsory constraints. Economic incentive means that the government encourages farmers to actively participate in environmental governance activities using economic subsidies and ecological compensation. Compulsory constraint means that the government intervenes in the allocation of environmental resources and restrains the behavior of farmers through administrative orders and administrative penalties. According to economic theory, whether farmers participate in environmental governance can be regarded as the problem of maximizing interests under certain constraints. Only when the farmers believe that the penalty cost or reward income is greater than the cost of implementing environmental policies, the actors will follow environmental policies [42]. The existence of environmental regulations forces farmers to re-examine the situation of costs and benefits so that they can maintain or change their original behavior [43]. On the one hand, the elderly have a high dependence on agriculture [27]; so, they pay more attention to the costs and benefits of agricultural production [13,44]. Therefore, the economic incentive to return straw to the field is more likely to stimulate the middle-aged and elderly farmers to take action. On the other hand, farmers’ behavior of polluting the environment can be restrained by formulating relevant laws and regulations and village regulations [32]. Farmers who stray away from the regulation target will be held accountable and penalized. Farmers will be compelled to comply with the regulation objective and progressively shift their operations toward green production once they have considered the cost of violation [43]. For example, Wang [10] found that the experience of punishment may lead to farmers’ fear of straw burning, and rural families’ unfavorable perception of burning may restrict their behavior. Based on this, hypothesis H3 is proposed in this study.

H3: 

Environmental regulation can alleviate the inhibitory effect of the aging labor force on straw-returning behavior.

At the same time, studies have found that there are differences in the straw-returning behavior of farmers in different regions and land management areas [19,45]. Firstly, terrain condition is an important factor affecting the development of agricultural machinery. A flat terrain will improve the field accessibility and operation convenience of agricultural machinery and reduce the cost of returning agricultural machinery straw to the field [46]. Generally speaking, the farmland in the plain area is suitable for returning agricultural machinery straw to the field, but not in the non-plain area, where it is difficult to return agricultural machinery straw to the field. Secondly, farmers have different agricultural production behaviors with different land scales [45]. Small-scale farmers have a weak risk resistance and make behavioral decisions based on the necessity and cost minimization of survival [47]. Large-scale farmers, on the other hand, care more about the protection and sustainability of land because their investment in land is long-term [7]. Therefore, in theory, large-scale farmers are more inclined to adopt environmentally friendly technologies. Based on this, hypothesis H4 is proposed in this study.

H4: 

Under the constraints of different regions and land management areas, the straw-returning behavior of the aged labor force is heterogeneous.

3. Data and Methods

3.1. Data Source

The study’s data come from a questionnaire survey that the research team conducted in the counties of Jiajiang, Yuechi, and Gaoxian, the main rice-producing areas in Sichuan Province, in August 2021. As a large producer of corn and rice, Sichuan has abundant straw resources and its straw production ranks among the top in China. At the same time, Sichuan has less arable land per capita, the compound seed index is high, there is a short time gap of double cropping rice, and natural rot does not occur easily. Accordingly, the straw resource problem needs to be solved urgently. The reasons for choosing these three districts and counties are as follows: In terms of straw yield, Jiajiang, Yuechi, and Gaoxian are all large corn- and rice-producing counties. From the perspective of topography and landform, Jiajiang is mainly plain, Yuechi is mainly hilly, and Gaoxian is mainly mountainous. The investigation of these three counties can provide insights into the situation of farmers’ straw returning to the field in different landforms. In addition, the object of this study was farmers engaged in straw production.

The contents of the survey mainly include basic information about individual families, straw disposal and utilization, government incentives and constraints on straw returning to the field, recognition of straw-returning technology, and land conditions. Specific questions refer to the large-scale survey of rural fixed observation sites of Peking University. Before the formal survey, the research group conducted a pre-survey in rural areas around Chengdu, Sichuan Province, and made changes to the questionnaire in light of the results of the pre-survey. To ensure the typicality and representativeness of the selected samples, the method of general random sampling and stratified probability random sampling was mainly adopted to determine the survey samples, and the counties of Jiajiang, Yuechi, and Gaoxian were finally selected as the survey districts and counties. Specifically, taking into account variations in economic development and topography, a total of 183 counties in Sichuan were categorized into three distinct groups. Subsequently, one county was randomly selected from each group to serve as a representative sample. Consequently, we obtained three sample counties: Jiajiang County, Yuechi County, and Gaoxian County. Furthermore, the sample towns were chosen based on variations in economic development levels within the county and their proximity to the county government center. By randomly categorizing the towns in the sample area into 3 groups, we then selected one town from each group as a representative sample, resulting in a total of 9 towns. Furthermore, the process of choosing sample villages followed a similar approach to that of selecting sample towns. By considering factors such as economic development level and proximity to the town center, a total of 27 sample villages were obtained through random selection from the pool of 9 sample towns. After identifying the sample villages, the previous station members acquired a list of these villages from local officials and utilized a pre-determined random number table to select 20 households at random from each village as part of the sample group. Ultimately, a group of 16 extensively trained researchers, guided by local village officials, conducted individualized surveys at the residences of farmers. As a result, a total of 540 valid questionnaires were successfully collected from 27 villages spanning across 9 townships within 3 districts and counties. The map of sample districts and counties is shown in Figure 2.

3.2. Selection of the Model Variables

(1) Dependent variable. The dependent variable was the farmers’ straw-returning behavior. Straw returning refers to a technology that uses machinery to crush straw and bury it in the soil to improve soil fertility and crop yield [48]. In the investigation, by asking farmers “Do you use straw returning technology?”, if the farmer answered yes, the value was 1; otherwise, the value was 0. In general, only 65% of farmers used straw-returning technology (Table 1).

(2) Independent variables. The independent variable was labor aging. This paper defined labor aging as the population that is beyond the working age but participates in agricultural production. In this study, the proportion of the elderly labor force in the family was used to measure labor aging, that is, the proportion of the labor force older than or equal to 64 years old in the total labor force of the family. Overall, the proportion of older household workers was 29% (Table 1).

(3) Moderating variables: environmental regulations and socialization services. The economic incentive was measured by the question “Would you be willing to use straw returning technology if the government provides subsidies”, which was measured on the Likert scale. The mandatory constraint was measured as “if you burn straw, you think it is likely to be seized”, which was measured on a Likert scale. This study measured socialization services by asking “whether to buy socialization service”. If the farmer answered yes, the value was 1; otherwise, the value was 0. In general, the mean values of economic incentives and mandatory constraints were 4.14 and 4.31, respectively, and 46% of farmers purchased socialization services (Table 1).

(4) Control variables. In the study of Jiang [1], respondent characteristics, family characteristics, and cultivated land characteristics were introduced as the control variables. The definitions and descriptive statistics of each variable are shown in Table 1. The proportion of males (60%) was higher than that of females (40%). The age of the respondents tended towards aging, with an average age of 58 years. The average length of schooling was only 6.55 years, and 91% of the households were married families. The average health degree of a household head was 3.67. The average household size in 2020 was 4.54. The logarithm of the distance between the household and the main road was 7.74. The average annual cash income per household was CNY 19,463. The average of family per capita arable land area was 1.43 mu.

3.3. Methods

Firstly, the explained variables in this study were discrete binary choice variables; so, the discrete choice model was used to analyze the causal relationship between the variables. Since the dependent variable of this study was a dichotomous variable (whether farmers use straw-returning technology), the independent variable was a continuous variable. Therefore, the study used the binary logistic model to explore the impact of labor aging on farmers’ straw-returning behavior. The formula is as follows:

(1)${\mathrm{Y}=\mathsf{\beta }}_0+{\mathsf{\beta }}_1\mathrm{X}+{{\mathsf{\beta }}_2{\mathrm{C}\mathrm{o}\mathrm{n}}_{\mathrm{i}}+\mathsf{\epsilon }}_{\mathrm{i}}$

In Formula (1), Y is the explained variable of whether farmers use straw-returning technology; X is the explanatory variable labor aging; ${\mathrm{C}\mathrm{o}\mathrm{n}}_{\mathrm{I}}$ is the control variable; ${\mathsf{\beta }}_0, {\mathsf{\beta }}_1$, and ${\mathsf{\beta }}_2$ represent the parameters of the model to be estimated; and ${\mathsf{\epsilon }}_{\mathrm{i}}$ is the residual term of the model.

Secondly, to further explore whether socialization services and environmental regulation have a buffer effect on the straw-returning behavior of the elderly agricultural labor force, based on Equation (1), this study introduced the interaction terms of labor aging and socialization services, labor aging, and environmental regulations to answer this question.

(2)${\mathrm{Y}=\mathsf{\beta }}_0+{\mathsf{\beta }}_1\mathrm{X}+{\mathsf{\beta }}_2{\mathrm{Z}}_{\mathrm{i}}+{\mathsf{\beta }}_3{\mathrm{Z}}_{\mathrm{i}}\times \mathrm{X}+{{\mathsf{\beta }}_4{\mathrm{C}\mathrm{o}\mathrm{n}}_{\mathrm{i}}+\mathsf{\epsilon }}_{\mathrm{i}}$

In Formula (2), Y is the explained variable of whether farmers use straw-returning technology; X is the explanatory variable of labor aging; ${\mathrm{Z}}_{\mathrm{i}}$ is the moderating variable, including socialization services, economic incentives, and mandatory constraints; ${\mathrm{Z}}_{\mathrm{i}}$ × X is the interaction term between the explanatory variable and the moderating variable; ${\mathrm{C}\mathrm{o}\mathrm{n}}_{\mathrm{i}}$ is the control variable; ${\mathsf{\beta }}_0, {\mathsf{\beta }}_1, {\mathsf{\beta }}_2$, and ${\mathsf{\beta }}_3$ represent the parameters of the model to be estimated; and ${\mathsf{\epsilon }}_{\mathrm{i}}$ is the residual term of the model.

4. Results

4.1. Model Results

Table 2 shows the regression results of labor aging as a core explanatory variable. Among them, Model 1 is the general estimation result that only includes the correlation between labor aging and the straw-returning behavior of farmers. Model 2 is the result of adding the control variables based on Model 1. To further explore whether socialization services and environmental regulations alleviate the inhibitory effect of labor aging on farmers’ straw-returning behavior, the interaction terms of labor aging and socialization services, labor aging, and environmental regulations were introduced to verify their effects, and the regression results are shown in Table 3, Model 5. The Model 3–Model 5 examine the moderating effects of social services, economic incentives, and mandatory constraints, respectively.

As shown in Model 2, labor aging is negatively correlated with the straw-returning behavior of farmers, and the regression coefficient is significant. Specifically, under other fixed conditions, the behavior of straw returning decreases by 0.647 units when the labor aging increases by one unit. According to the regression results of Model 3, the interaction term between labor aging and socialization services is robust and significant at the 5% level of probability, as well as having a positive estimated coefficient. This indicates that socialization services can alleviate the inhibitory effect of labor aging on farmers’ straw-returning behavior. Moreover, after adding the moderating variables, the main effect of labor aging on straw-returning behavior becomes insignificant. To explain the moderating effect of socialization services on labor aging and straw-returning behavior more clearly, an interaction diagram was further used (Figure 3). The behavior of straw returning is different with different socialized services for the aging labor force. Specifically, when the elderly labor force uses socialization services, the possibility of straw returning increases. However, for the elderly workers that did not use social services, their straw returning to the field decreased with increasing age. According to the regression results of Model 4, at a statistical threshold of 10%, the terms for the interplay between economic incentives and labor force aging are both positive and significant. At the same time, at a statistical level of 10%, the labor aging regression coefficient is negative and significant, but it becomes smaller as time progresses. This indicates that economic incentives can alleviate the inhibitory effect of labor aging on farmers’ straw-returning behavior. The interaction diagram in Figure 4 shows that, when the economic incentive is low, the elderly labor force has a weak enthusiasm to adopt straw-returning technology. However, with the strengthening of economic incentives, the enthusiasm for straw-returning behavior gradually improves. This shows that the higher the aging degree of the labor force, the more sensitive the farmers are to the economic stimulus of the government. According to the regression results of Model 5, labor aging is robust and significant at the statistical level of 5%, and the estimated coefficient is negative; the estimated coefficient of the interaction term between labor aging and the mandatory constraints is negative but insignificant. The results indicate that mandatory constraints do not play a moderating role in labor aging and the straw-returning behavior of farmers.

4.2. Heterogeneity Analysis

As mentioned above, there are considerable differences in straw-returning behavior among farmers in different regions and different land management areas. Therefore, according to the area, the samples were divided into plain areas and non-plain areas. According to the area of land that the farmers operate, the sample was divided into small-scale and large-scale farmers (whether the land scale is larger than the village average). Furthermore, a binary logistic regression model was used to explore the heterogeneity of labor aging within different groups on the straw-returning behavior of farmers.

The results in Table 3 show that labor aging has uneven effects on farmers’ straw-returning behavior. That is to say, the effect of the aging labor force on the straw-returning behavior of farmers is also different when the area of farmers and land management area are different. The Model 1 results show that labor aging has a negative and significant relationship with straw-returning behavior in small-scale land farmers. However, there is no significant correlation between labor aging and straw-returning behavior in large-scale land farmers. The Model 2 results show that, in non-plain areas, labor aging was negatively correlated with the straw-returning behavior of farmers. However, there is no significant correlation between labor aging and straw-returning behavior in plain areas.

5. Discussion

Based on the survey data of rice farmers in southwest China, this study analyzed the characteristics of labor aging and farmers’ straw-returning behavior, and then used econometric models to analyze the relationships among labor aging, socialization services, environmental regulations, and farmers’ straw-returning behavior. Compared with previous studies, the marginal contributions are as follows: First, the theoretical analysis of labor aging, socialization services, environmental regulations, and straw-returning behavior was established in theory. The second is the empirical analysis of the relationship between labor aging, socialization services, environmental regulations, and straw-returning behavior using survey data of rural rice farmers in southwest China. In addition, there are some similarities and differences between the results of this study and those of existing studies.

This study found that labor aging is an important factor affecting farmers’ straw-returning behavior. This is consistent with the research hypothesis H1 and the results of Liu [27]. A possible explanation is that, with the deepening of the aging degree of the labor force, farmers have problems, such as the weakening of human capital and conservative agricultural production decisions, which affect farmers’ understanding of green agricultural technologies and their acceptance of new technologies. Therefore, as the direct decision maker and implementer of agricultural activities, labor aging has a hindering effect on the straw-returning behavior of farmers. This is consistent with the research hypothesis H2 and Hu and Zhong [28]. Socialization services are also an important factor affecting farmers’ straw-returning behavior. This study found that, for farmers who purchased socialization services, the higher the score of environmental regulations, the more active the straw-returning behavior of the elderly labor force. The possible explanation is that, as farmers grow older, their ability to learn and apply new technologies weakens. Socialized agricultural services include a stable labor force and professional agricultural production knowledge, which has a good effect on alleviating agricultural labor shortages and extensive production mode.

Consistent with hypothesis H3, this study found that economic incentives can alleviate the inhibitory effect of labor aging on farmers’ straw-returning behavior. A possible explanation is that the straw-returning operation subsidy, as an incentive method, can reduce the operating costs of farmers and alleviate the financial constraints of the aged agricultural labor force. This helps to avoid short-sighted behavior and thus improve the efficiency of straw resource utilization to solve the externality problem of straw-returning technology [13,45]. Meanwhile, it is inconsistent with the research hypothesis H3 and the results of Su [37]. Possible explanations are as follows: On the one hand, due to the low degree of industrialization in rural areas and the scattered living situation, implementing the government’s obligatory restrictions on straw being returned to the fields will require significant human, material, and financial resources. On the other hand, the straw ban policy implemented by the government is simply to force the ban on burning and lacks relevant guidance policies. Consistent with hypothesis H4, this study found that the effects of labor aging on the straw-returning behavior of farmers in different regions and different land management areas were inconsistent. Possible explanations are as follows: On the one hand, farmers with different land management scales will make different production decisions due to different management objectives. Farmers with larger land holdings are more likely to use straw-returning technology because they have more negotiating power with the service providers of that technology and because the long-term average total cost is lower [49]. On the other hand, in different regions, due to the different resource conditions and information convenience between regions, labor aging will also have different effects on the behavior of farmers. Plain areas are flat, arable land that is concentrated and contiguous, suitable for mechanical farming. Therefore, farmers can increase agricultural machinery to compensate for the lack of labor supply. However, in non-plain areas, agricultural mechanization is difficult to implement because of the undulating terrain and scattered cultivated land.

There are some limitations to this study. For instance, this paper only discussed the mechanisms of socialization services and environmental regulations on the straw-returning behavior of aging labor, and further empirical tests are needed to determine whether there are other mechanisms. The relationship between the labor force aging and the straw-returning behavior of rural rice farmers in southwest China was the exclusive focus of this paper. Whether the research results apply to other regions needs to be further explored. It is important to broaden the range of research objects in the future to explore the relationship between aging labor and the straw-returning behavior of farmers in different regions.

6. Conclusions and Implications

6.1. Conclusions

Based on the realistic background of aging in China, this paper analyzed the effect of labor aging on farmers’ straw-returning behavior by using the sample data of 540 rice farmers in Jiajiang, Yuechi, and Gaoxian of Sichuan Province, using a binary logistic model, and further analyzed the moderating role of socialization services and environmental regulations. The results show that:

(1) In general, the proportion of farmers who adopt straw-returning technology is 65%, and the proportion of the elderly labor force in the total family labor force is 29%. (2) Socialization services and environmental regulations can alleviate the inhibitory effect of labor aging on farmers’ straw-returning behavior to a certain extent. Specifically, socialization services and economic incentives alleviate the inhibitory effect of aging labor on straw returning; the effect of mandatory constraints on the straw-returning behavior of aged labor is not obvious. (3) The heterogeneity analysis showed that aging has a stronger inhibitory effect on straw-returning technology adoption behavior when the farmer’s land size is lower than the average level and the farmer lives in non-plain areas.

6.2. Policy Implications

(1) In the process of agricultural green production transformation, we should fully recognize the limited role of the aging labor force. Firstly, in the process of agricultural production, targeted and operable agricultural technology training should be conducted for the aged agricultural labor force to improve their knowledge level and application abilities of new technology. Secondly, due to the aggravation of aging, the working ability level of the rural households that are left behind is greatly reduced. It is urgent to cultivate a group of new professional farmers to make full use and optimize the allocation of land resources.

(2) The government should accelerate the cultivation and expansion of agricultural social services. The labor force restriction brought by aging has a great impact on traditional land management. It is necessary to improve the level of mechanization through the development of socialization services, to reduce the high-density and high-intensity demand for the agricultural labor force, and to further reduce the impact of labor force restrictions on agricultural production.

(3) From the perspective of environmental regulations, the impact of each environmental regulation is different, and different policies should be implemented according to different regions. First, the government should further implement the straw-farming subsidy policy. In areas where no subsidy policy has been implemented, farmers can be encouraged to adopt straw-returning technology through subsidies. In areas where straw-returning subsidies have been implemented, subsidy standards should be moderately increased to ensure that the increased costs brought by straw returning can be compensated for, and the enthusiasm of farmers for the rational disposal of crop straw can be maximally encouraged. Secondly, for rural households in remote mountainous areas or poor households in non-plain areas, the publicity and popularization of the “punishment for straw burning” system should be further strengthened.

(4) From the perspective of heterogeneity, first of all, the land operation area should be expanded to achieve the appropriate scale of land operation. On the one hand, by facilitating the migration of rural populations for employment opportunities, land transfer can be effectively achieved, thereby promoting agricultural operations on an appropriate scale. On the other hand, there is still a wide range of small farmers who have not conducted land transfers. The shortage of cultivated land can be made up for by strengthening socialization services to promote moderate-scale agricultural operations and the organic connection between small farmers and green agricultural technology. Secondly, the policy of returning straw to the field should be formulated according to the local conditions. For example, in plain areas with a high level of agricultural mechanization, intensification, and specialized production, attention should be paid to the popularization and application of agricultural science and technology to promote the efficient adoption of straw-returning technology.

Author Contributions

Conceptualization, W.Z. and J.H.; methodology, Y.Y. and D.X.; formal analysis, W.Z.; investigation, J.H.; writing—original draft preparation, J.H. and D.X.; writing—review and editing, W.Z.; supervision, D.X.; funding acquisition, D.X. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

Footnotes

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Figures and Tables

Figure 1. Conceptual framework.

Figure 2. Location map of the sample districts and counties and sample towns.

Figure 3. The moderating role of socialization services.

Figure 4. The moderating effect of economic incentives.

Table 1

Variable setting and descriptive statistics.

Variable		Mean	SD a
Straw returning	Does your home use straw return to the field? (No = 0, Yes = 1)	0.650	0.480
Labor aging	The age of 64 years is a proportion of the entire family workforce	0.290	0.330
Gender	Gender of the respondent (Male = 0, Female = 1)	0.400	0.490
Age	Age of the respondents (Year)	58.48	11.84
Education	The educational level of the respondents (Year)	6.550	3.440
Marry	Whether the respondent was married or not? (No = 0, Yes = 1)	0.910	0.280
Healthy	The health level of the respondents (1 = very unhealthy–5 = very healthy)	3.670	1.140
Population	Total family population in 2020 (Person)	4.540	1.970
Ln(distance)	Your distance from the nearest main road (Meters)	7.740	1.110
Person_income	Per capita household income in 2020 (CNY)	19,463	33,420
Person_land	Per capita family operating in the area in 2020 (Mu)	1.430	4.260
Socialization services	Does your home buy socialization services? (No = 0, Yes = 1)	0.460	0.500
Economic incentive	If you have a government subsidy, are you willing to return the straw to the field? (1 = Very Unwilling–5 = Very willing)	4.140	1.080
Mandatory constraints	If you burn straw, do you think of the possibility of being seized? (1 = Very impossible–5 = Very likely)	4.310	1.150
Plain	Is it a plain area? (No = 0, Yes = 1)	0.330	0.470
Hill	Is it a hilly area? (No = 0, Yes = 1)	0.330	0.470
Mountain	Is it a mountain area? (No = 0, Yes = 1)	0.330	0.470

Note: ^a SD = Standard deviation

Table 2

Influence of the aging labor force on straw returning to the field.

	Model 1	Model 2	Model 3	Model 4	Model 5
Labor aging	−0.470 *	−0.647 *	−0.553	−0.642 *	−0.697 **
	(0.269)	(0.348)	(0.355)	(0.354)	(0.351)
Socialization services			0.402 **
			(0.195)
Economic incentives				−0.037
				(0.089)
Mandatory constraints					−0.069
					(0.089)
Aging × Socialization services			1.316 **
			(0.588)
Aging × Economic incentives				0.528 *
				(0.276)
Aging × Mandatory constraints					−0.145
					(0.219)
Gender		−0.098	−0.092	−0.096	−0.103
		(0.201)	(0.204)	(0.203)	(0.202)
Age		0.001	−0.001	0.004	0.001
		(0.010)	(0.010)	(0.010)	(0.010)
Education		0.021	0.009	0.029	0.022
		(0.031)	(0.031)	(0.031)	(0.032)
Marry		0.236	0.223	0.222	0.261
		(0.326)	(0.338)	(0.324)	(0.323)
Healthy		−0.072	−0.074	−0.068	−0.070
		(0.088)	(0.090)	(0.090)	(0.089)
Population		0.077	0.071	0.076	0.081
		(0.053)	(0.053)	(0.053)	(0.053)
Ln(person_distance)		0.135 *	0.145 *	0.127	0.126
		(0.082)	(0.083)	(0.083)	(0.082)
Ln(person_income)		−0.186	−0.227 *	−0.212 *	−0.176
		(0.126)	(0.128)	(0.126)	(0.127)
Ln(person_land)		0.898 ***	0.864 ***	0.903 ***	0.905 ***
		(0.242)	(0.237)	(0.241)	(0.245)
_cons			0.855	0.750	0.678
			(1.596)	(1.586)	(1.588)
chi2	3.057	26.262	34.549	29.090	27.525
N	540	540	540	540	540

Note: Robust standard errors are in parentheses, and odds ratio are reported in the regression results; *, **, and *** refer to p < 0.1, p < 0.05, and p < 0.01, respectively.

Table 3

Heterogeneity analysis.

Classification	Land Scale (Model 1)		Terrain (Model 2)
	Large-Scale	Small-Scale	Not Plain	Plain
Labor aging	−0.173	−0.791 *	−1.521 **	−0.461
	(0.570)	(0.437)	(0.701)	(0.436)
_cons	−0.161	1.818	0.484	0.780
	(2.671)	(1.793)	(3.419)	(1.904)
Control	Yes	Yes	Yes	Yes
chi2	8.237	11.002	15.680	17.954
N	207	333	360	180

Note: Robust standard errors are in parentheses, and odds ratio are reported in the regression results; *, ** refer to p < 0.1, p < 0.05, respectively.

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