Could the Aging of the Rural Population Boost Green Agricultural Total Factor Productivity? Evidence from China

Abstract

The aging of the rural population is one of the important social problems facing China and the world. To provide strategic support for coping with the challenges brought by an aging society, this study examined the impact of aging of the rural population on agricultural green total factor productivity (AGTFP) and the mechanism of transmission between them, based on the panel data of 31 provinces in China from 2000 to 2022. The results showed that, first, the aging of the rural population had a negative inhibitory effect on AGTFP, a conclusion that remained valid after a series of robustness tests. Second, the heterogeneity analysis showed that the aging of the rural population in western China had a significant negative impact on AGTFP, while the effect was less significant in eastern and central regions. The intensity of environmental regulation will increase the negative impact of an aging rural population on AGTFP. Third, the analysis of the mechanism showed that the aging of the rural population had a negative impact on AGTFP by inhibiting labor productivity, scientific research and innovation, and farmland transfer.

1. Introduction

Marx posited that labor is the foundation of all material and spiritual sustenance, as well as a driving force behind social and historical change [1]. Consequently, the labor force, as the externalized form of labor, occupies a pivotal position in economic and social advancement, with shifts in the composition and quantity of the labor force inevitably exerting a far-reaching influence on economic and social growth.

China is a large country with a population that accounts for approximately one-sixth of the world’s population. Over the past few decades, thanks to the demographic dividend, China’s average growth rate of annual GDP once exceeded 10%, realizing leaps in economic development and becoming the second largest economy in the world [2]. Nevertheless, along with the continuous improvement in people’s living standards and education level, people’s mindset has begun to change, and the concept of having fewer births and sound child rearing practices is taking over the thoughts of the majority of women of childbearing age, causing the number of new births to fall continuously. Since 2016, the number of births in China has been in decline for seven consecutive years. By the end of 2022, the number of births had fallen to 9.56 million, representing the first time that the number of births had fallen below 10 million. This resulted in a negative population growth rate, which continued to decline at the end of 2023. Conversely, the number of individuals over the age of 65 in China has been on the rise (according to data from China’s seventh census). China’s seventh national census in 2021 showed that the total population aged 65 years old and above has exceeded 190 million, accounting for 13.5% of the total population of the country, an increase of 4.63% compared with 2010 and 6.54% compared with 2000, nearly doubling the number of people aged 65 and over in China in 20 years (according to the data collated from the fifth, sixth, and seventh Chinese census). Internationally, the criterion for a country or region to be considered an aging society is typically either that the proportion of people aged 60 and above in the total population exceeds 10%, or that the proportion of people aged 65 and above exceeds 7% [3]. According to China’s census data, China is significantly beyond the standard of an “aging society”. In 2000, the proportion of people aged over 65 in rural China accounted for 7.4% of the total rural population, while this figure rose to 18.6% in 2021. In comparison, the urban proportion was 6.3% in 2000 and increased to 11.4% in 2021. The dual economic system of urban and rural China has resulted in a significant migration of prime-working-aged rural laborers to urban areas, which has exacerbated the aging of the rural population relative to that of the urban population. The rural labor force is a crucial factor in agricultural production, and the participation, labor efficiency, and acceptance of new technologies and models of the rural elderly population in agricultural production directly affect the sustainable development of agriculture.

The data released by the International Energy Agency (IEA) indicated that global carbon dioxide emissions in 2023 increased by 1.1% year-on-year, reaching a record high of 37.4 billion tons. Of the total amount of carbon dioxide emissions, 12.6 billion tons were recorded in China, representing approximately one-third of the global total. This evidence demonstrates that China is confronted with a significant challenge in addressing climate change (the data come from CO₂ Emissions, released by the International Energy Agency (IEA) in 2023). Currently, China has proposed a “dual carbon” objective, namely, to achieve peak carbon emissions by 2030 and carbon neutrality by 2060. To achieve this goal, China is actively promoting the transformation of its energy mix, vigorously developing renewable energy sources, improving the efficiency of energy use, and enhancing cooperation with the international community to jointly address the challenge of climate change.

China’s greenhouse gas emissions are mainly attributable to five main sectors: energy, industry, transport, construction, and agriculture and land use [4]. If calculated on the basis of the agricultural food system, China’s total carbon emissions from agriculture account for a relatively low proportion of the country’s total emissions at 16.7%. However, with the gradual advancement of the “2030 peak carbon” and “2060 carbon neutral” targets, the agricultural sector must play a greater role in the reduction of emissions [5]. Furthermore, scholars have emphasized that China’s average application intensity of fertilizer (pure) per mu of arable land is 2.5 times the global average. The extensive use of fertilizers, pesticides, mulch, etc. has led to several problems, such as the aggravation of nonpoint-source pollution in agriculture, the decline in soil fertility, and the pollution of water resources [6]. Therefore, there is an urgent need to accelerate the promotion of green development of agriculture.

In the process of promoting the green development of agriculture, “elderly agriculture”, as a common form of agricultural production in rural China, will inevitably have an impact on the green development of agriculture. Including the aging of the population as one of the significant factors for research contributes to a more comprehensive assessment of its impact on the development of green agriculture. Does it have a positive or a negative effect? What is the mechanism of the effect? These questions deserve our intensive study. Therefore, we need to pay attention to the impact of aging of the rural population on AGTFP and address the question of how to promote AGTFP against the background of an aging rural labor force. On this basis this, the study attempted to scientifically analyze the impact of aging rural populations on AGTFP, as it is important for the Chinese government to formulate scientifically sound population and agricultural policies. The marginal contributions of this study are as follows. First, by incorporating more mechanism variables, it comprehensively examined the impact and mechanism of an aging population on AGTFP. Second, it analyzed the impact of aging of the rural population on AGTFP in different regions, providing a basis for formulating regionally differentiated policies.

The remainder of the article is organized as follows. Section 2 introduces the current research status. Section 3 introduces the theoretical background and research hypotheses. Section 4 introduces the design of the study, including the research methods, the model’s settings, selection of the variables, data processing, and sources. Section 5 presents the doubly time- and province-fixed model used to study the results, and the robustness test and heterogeneity analysis that were carried out. Section 6 conducts an analysis of the mechanisms. Section 6 is the discussion of the article, and Section 7 discusses the results and policy implications of this study.

2. Literature Review

2.1. The Impact of Aging Rural Populations on Agricultural Development

2.1.1. The Effect of Aging Rural Populations on Agricultural Output

There is a considerable body of research on the impact of aging rural populations on agricultural output, but no consensus has been reached. Two opposing views have emerged, with one group of academics arguing that aging of the rural population will reduce agricultural output, and another group arguing the opposite. Scholars who believe that aging of the rural population will reduce agricultural output on the basis of the perspective of labor productivity and the supply rate have conducted research. They believe that China’s current labor force involved in agricultural production is mainly the elderly in rural areas. The aging of the rural population will inevitably bring about a shortage of supply in the rural labor force. At the same time, there is an inverted “U”-type relationship between the age of the rural labor force and labor productivity. As the age of rural labor force grows, their labor productivity will eventually show a downward trend, thus restricting agricultural output [7,8,9]. Those scholars who posit that the aging of the rural population will enhance agricultural output rely primarily on the tenets of land transfer and socialized services for agriculture. They posited that while the aging of the rural population will result in a reduction in the productivity and supply rate of agricultural workers, aging of the rural population will increase the existing level of agricultural production by promoting the demand for land transfer or agricultural socialized services in rural areas, thus positively affecting agricultural output [10,11].

2.1.2. The Impact of Aging Rural Populations on AGTFP

Similarly, scholars have produced two opposing views on the study of aging rural populations and its impact on AGTFP. Among the studies showing that aging of the rural population inhibits AGTFP, some scholars believe that aging of the rural population hinders the adoption behavior of green production technology of farmers, thus hindering agricultural green production [12,13]. Other scholars have studied the impact of aging of the rural population on AGTFP, based on the mediation of levels of innovation. Their findings suggested that aging rural populations have a marked inhibitory effect on AGTFP by reducing the level of innovation [14].

Regarding the studies showing that aging of the rural population promotes the level of agricultural green development, some scholars have found that aging of the rural population can significantly increase AGTFP, based on China’s provincial panel data from 2000 to 2020. The main mechanism is that an aging population promotes AGTFP by facilitating agricultural land transfers [15]. Some scholars have found through empirical studies that aging of the rural population can improve the level of high-quality agricultural development by improving the level of agricultural development, resource conditions, economic conditions, and technical conditions [16].

2.2. Measurement of AGTFP

Measures of total green factor productivity date back to 1997. Chung and other scholars first used nonparametric methods to introduce undesirable outputs into the directional distance function method and realized the measurement of green total factor productivity [17]. In the early days, the measurement of green total factor productivity mainly focused on the industrial field, and then some scholars introduced the measurement of green total factor productivity into the agricultural field, and the measurement of AGTFP gradually began to emerge, and the methods of measurement became endless. For example, Oh measured the green development efficiency of OECD member countries using the Global Malmquist–Luenberger (GML) method [18]. Later, some scholars introduced the GML method into the field of agricultural production to measure the growth rate of AGTFP in China, and the results of the study found that green technological progress was the main factor affecting the growth of AGTFP [19]. Some scholars used the Super-SBM model method to measure China’s AGTFP and analyzed the distribution and regional differences in the level of AGTFP [20]. In addition, some scholars used the Meta-SBM-Luenberger, SMB-DEA, and MetaFrontier Malmquist–Luenberger (MML) methods to measure AGTFP [21,22,23,24]. While scholars measure AGTFP in slightly different ways, its core is measured by finding the input factors, desired output factors, and undesired output factors in agricultural production.

Through combing the existing literature, scholars have a wealth of research on the impact of aging of the rural population on AGTFP from the impact mechanism to the measurement, but now scholars have not yet formed a consistent view. At the same time, the impact mechanism of aging of the population on AGTFP needs to be further explored. Based on this, this study first used the Super-SBM model to measure the AGTFP of each province in China, then used the fixed-effect model to study the impact of the aging of the rural population on AGTFP, and finally used the mediating effect model to explore the impact mechanism of farmland transfer and innovations in scientific research on the aging of the rural population and AGTFP.

The marginal contributions of this study are as follows. First, it further investigates the impact of the aging of the rural population on AGTFP and its mechanisms, and expands the boundaries of research in this area. Second, we introduced three mediating variables, namely, farmland transfer, innovation in scientific research, and labor productivity, to explore the mechanism of the impact of the aging of the rural population on AGTFP from multiple perspectives.

3. Research Mechanism and Hypothesis

3.1. The Labor Productivity Effect of the Aging of the Rural Population on AGTFP

According to life cycle theory, the human life cycle can be roughly divided into three stages: growth, maturation, and decline. In the early stages of growth, people gradually accumulate knowledge and skills through learning and practice, and labor productivity gradually increases. After entering the mature phase, people’s knowledge and skills peak and labor productivity is at its highest; after that, as people age, their physical strength and energy gradually decline, and labor productivity starts to decline [25]. To some extent, life cycle theory explains the “inverted U” relationship between age and labor productivity.

At present, China’s agricultural production is mainly engaged in by middle-aged and elderly rural workers. Most of the workers are in the maturity and declining stages of the life cycle. With the aging of the rural population continuing to increase, participation in the agricultural production by laborers is gradually entering the declining stage. In the declining stage of the labor force, the acceptance of new knowledge and new technologies will decline alongside the ability, physical function, physical strength, energy, and other aspects of the young labor force, and there is an obvious gap, so that the elderly workers will reduce the demand for new technologies. The efficiency of resource utilization, production technology, and the management level will also decline, which will inhibit the improvement of AGTFP [26]. At the same time, laborers need to spend more time on medical care and old age after entering old age, which will squeeze the time put into agricultural production, which also inhibits the improvement in AGTFP to a certain extent. Based on this, the following hypothesis was proposed:

H1. 

The aging of the rural population will have a negative impact on AGTFP by reducing labor productivity.

3.2. The Farmland Transfer Effect of Aging Rural Populations on AGTFP

From the perspective of farmland transfer, the aging of the rural population will have an impact on AGTFP through the size of farmland transfers. The withdrawal of older workers from agricultural production is not only conducive to solving the problem of farmland fragmentation and facilitating the large-scale operation of farmland, but also facilitates mechanized operations and greatly increases the efficiency of agricultural production [27]. Specifically, for older workers, their physical function and management level will decline differently with increasing age. To compensate for the loss of benefits caused by the decline of physical function, some workers will choose to transfer the land out. Such a choice can not only allow them to obtain certain land rents and other job opportunities, but also provides more land resources and business space for young workers and new agricultural business entities. Under the operation and management of young workers, agricultural production will inevitably move in the direction of scale, mechanization, and greening. By introducing modern agricultural technology and management models, they can help improve the efficiency and quality of agricultural production and effectively reduce the negative impact on the environment, thereby promoting the improvement in AGTFP [28]. At the same time, the transfer of land from elderly laborers to new agricultural business entities has also increased the speed of promoting agricultural technology. New agricultural business entities typically have stronger absorptive capacity for technology and innovation ability. They can better apply modern agricultural technology to actual production and promote the technological progress of the entire agricultural industry through demonstration and driving effects, and further promote the improvement in AGTFP. According to the analysis above, the following hypothesis was proposed:

H2. 

The aging of the rural population will have a positive impact on AGTFP by increasing farmland transfer.

3.3. The Innovation in Scientific Research Effect of Aging of the Rural Population on AGTFP

Once an individual reaches old age, they increase their need for old-age pensions and medical care, among other things, due to the decline in physical function and resilience. To meet the basic public service needs of people’s pensions, medical care, health care, and other aspects, the government will inevitably increase financial expenditure on social security services [29]. Since total government spending is limited, the increase in fiscal spending on social security services will somewhat crowd out the increase in fiscal spending on research and innovation and other programs, thus dampening AGTFP. From the perspective of labor supply, the aging of the rural population will exacerbate the shortage of the rural labor force, rendering the supply of labor force engaged in agricultural production in rural areas insufficient. The theory of induced technological change posits that, under certain conditions of resource endowment, technological progress and development paths in different regions will choose their own technological paths according to their resource endowments [30].

In the context of an aging population, technological advances in agriculture tend to move in the direction of reducing the use of labor resources due to the relative scarcity of labor. Therefore, to offset the negative impact on labor supply caused by the aging of the rural population, labor producers will continue to increase investments into and use of new technologies and mechanized agricultural equipment, which will have a positive impact on scientific research and innovation. According to the analysis above, the following hypotheses were proposed:

H3a. 

The aging of the rural population will have a positive impact on AGTFP by improving innovation in scientific research.

H3b. 

The aging of the rural population will have a negative impact on AGTFP by inhibiting innovation in scientific research.

4. Research Design

4.1. Model Settings

4.1.1. Super-SBM Model

Data envelopment analysis (DEA) is a method commonly used to measure efficiency. This approach has the advantage of not having a pre-set functional form, being able to handle multi-input and multi-output problems, and being insensitive to the data’s distribution and dimensions. It has been widely used by scholars in the field of efficiency measures. In the conventional DEA-CCR or DEA-BCC models, the efficiency of the effective DMUs is limited to 1, which means that these DMUs are already at the optimal production frontier and cannot be improved. In practical applications, however, we may want to further distinguish between DMUs that are already at the production frontier but still have “surplus” efficiency, that is, efficiency beyond the production frontier. The super-SBM is also a DEA model, but it takes relaxation into account and allows the efficiency value of an effective decision unit (DMU) to exceed 1. Compared with the traditional efficiency measurement model, the Super-SBM model can further distinguish the differences in performance between these effective DMUs and provide more detailed and accurate information for decision makers. Therefore, the Super-SBM model with undesired outputs was used in this study to measure the green productivity of Chinese agriculture. The specific calculation of the Super-SBM model was as follows [31]:

$$min{\rho }^{*}={\displaystyle \frac{1-\frac{1}{m}{\sum }_{i=1}^m\frac{s_i^{-}}{X_{ik}}}{1+\frac{1}{s_1+s_2}\left({\sum }_{i=1}^{s_1}\frac{s_{ik}^g}{y_{ik}^g}+{\sum }_{i=1}^{s_2}\frac{s_{ik}^b}{y_{ik}^b}\right)}}$$

(1)

$$s.t.\left\{\begin{array}{c}\begin{array}{c}{\sum }_{j=1}^n{\lambda }_j{x}_{ij}+s_i^{-}=x_{ik} i=1, 2,\cdots , m\\ {\sum }_{j=1}^n{\lambda }_j{y}_{uj}^g+s_u^g=y_{uk}^b u=1, 2,\cdots , s_1\end{array}\\ {\sum }_{j=1}^n{\lambda }_j{y}_{vj}^b+s_v^b=y_{vk}^b u=1, 2,\cdots , s_2\\ {\lambda }_j\ge 0, s_i^{-}\ge 0, s_u^g\ge 0, s_v^b\ge 0\end{array}\right.$$

The Super-SBM model requires no 0 in both the input and output data. In the model, the efficiency value of the evaluated DMU is represented as $min{\rho }^{*}$, and the larger the $min{\rho }^{*}$, the higher the efficiency. Let $m$,${ s}_1$, and $s_2$ denote the number of input variables, the expected output, and the unexpected output variables for each decision element or DMU, respectively; $s_i^{-}$, $s_u^g$, and $s_v^b$ represent the slack variables of input, expected output, and undesirable output, respectively; $x_{ij}$, $y_{uj}^g$, and $y_{vj}^b$, respectively, represent the input vector, the expected output vector, and the unexpected output vector; ${\lambda }_j$ stands for the weight vector.

4.1.2. Benchmark Measurement Model

To explore the impact of aging of the rural population on AGTFP, a panel fixed-effect model was constructed as follows

$${AFTFP}_{it}={\alpha }_0+{\alpha }_1{nold}_{it}+{\sum }_{i=1}^n{\beta }_i{Control}_{it}+{\eta }_{it}+{\mu }_{it}+{\epsilon }_{it}$$

(2)

where $i$ denotes the province, $t$ denotes the time, ${AGTFP}_{it}$ is AGTFP index of province $i$ in year $t$, ${nold}_{it}$ is the degree of aging of the rural population in province $i$ in year $t$, ${Control}_{it}$ is the control scalar in the study, ${\eta }_{it}$ is the individual fixed effect, ${\mu }_{it}$ is the time fixed effect, and ${\epsilon }_{it}$ is the random error term.

4.1.3. Mediating Effect Model

According to the analysis of the mechanism, the aging of the rural population can affect AGTFP through labor productivity, land transfer, and innovation in scientific research. Therefore, this study tested the mechanism of the impact of an aging rural population on AGTFP through a mediating effect model. The process of the test process was as follows [32]:

$$AGTFP=a_1+c_{1}nold+CV+e_1$$

(3)

$$M=a_2+c_{2}nold+CV+e_2$$

(4)

$$AGTFP=a_3+c_{3}nold+bM+CV+e_3$$

(5)

The total effect of the explanatory and mediator variables was tested first. If $c_1$ was significant, we proceeded to test the significance of the explanatory and mediator variables. If $c_2$ was also significant, the final step was to test the significance of the coefficient $b$. If $b$ was significant, it meant that there was a mediating effect. There was a partial intermediary effect if both the coefficients $b$ and $c_3$ were significant, and a full intermediary effect if only $b$ was significant. Here, $ntfp$ is the explained variable, $nold$ is the explanatory variable, $M$ is the mediator variable, $CV$ is the control variable, and $e$ is the random error term.

4.2. Selection of the Variables

4.2.1. Explained Variables

This study choses AGTFP as the explained variable.

The AGTFP index needs to be measured by the Super-SBM model with undesired outputs. Before the measurement, the input, output, and undesired output variables needed to be determined. According to the references of other scholars, the input indicators mainly include the input of factors in agricultural production such as land, pesticides, fertilizers, labor, etc. The output indicator was the value of the output of primary industry, and the indicator of undesirable output was agricultural carbon emissions [33,34]. Specific metrics for input, output, and undesired output are shown in

Table 1. Measurement indicators of AGTFP.

Index	Variable	Definition of the Variable	Unit
Input indicators	Land investment	Agricultural total sown area	Thousand hectares
	Labor input	Number of persons employed in the primary industry	Million people
	Mechanical inputs	Total mechanical power	Ten thousand kilowatts
	Pesticide input	Pesticide usage (pure)	Ton
	Fertilizer input	Fertilizer application amount (pure)	Million tons
	Agricultural film input	Agricultural plastic film usage	Ton
	Irrigation input	Effective irrigation area	Thousand hectares
Output indicator	First industrial output	Total output of farming, forestry, raising stock, and fishery	Billion yuan
Undesirable output indicator	Total agricultural carbon emissions	Based on the calculation of six types of carbon emission sources in agriculture.	Tons

below.

The total agricultural carbon emissions for the undesired output indicator were calculated on the basis of six major sources of agricultural carbon. The calculation was as follows.

$$\begin{array}{c}{E}_t={\displaystyle \sum _{i=1}^6}{E}_i={\displaystyle \sum _{i=1}^6}{T}_i{\eta }_i\end{array}$$

(6)

In Equation (6), $E_t$ represents the total carbon emissions, $E_i$ represents the carbon emissions of each carbon source, $T_i$ represents the input of each carbon source, and ${ \eta }_i$ represents the carbon emission coefficient of each carbon source. The carbon emission coefficient of chemical fertilizer is 0.89 kg/kg, the carbon emission coefficient of pesticides is 4.95 kg/kg, the carbon emission coefficient of agricultural film is 5.184.95 kg/kg, the carbon emission coefficient of diesel oil is 0.59 kg/kg, the carbon emission coefficient of sowing is 312.60 kg/km², and the carbon emission coefficient of agricultural irrigation is 266.48 kg/hm². The carbon emission coefficients of chemical fertilizers and pesticides were derived from the data of the Oak Ridge National Laboratory in the United States [35]. The carbon emission coefficient of agricultural film was derived from the data of the laboratory of Nanjing Agricultural University [36]. The carbon emission coefficient of diesel oil was derived from the data of the Intergovernmental Panel on Climate Change (IPCC) 2013 [37]. The carbon emission coefficient of agricultural sowing was derived from the data of the laboratory of China Agricultural University [38]. The carbon emission coefficient of agricultural irrigation was derived from the relevant research of scholars [39].

4.2.2. Explanatory Variables

This study choses the aging of the rural population ($nold$) as the explanatory variable. Referring to the research of other scholars, and taking the difficulty of data acquisition into account, the population aged 65 and over was defined as an aging population [40,41]. Aging of the rural population is the degree to which the population of a rural area is aging, as measured by the ratio of the number of people aged 65 and older in the rural area to the total population of the rural area.

4.2.3. Control Variables

With reference to other scholars’ research [42,43], this study selected the control variables listed below.

• The proportion of primary industry $\left(ppi\right)$

This was expressed in terms of the proportion of the output value of primary industries in GDP. The larger the ratio, the more important the relative position of agriculture in the national economy. Changes in the economic structure may have an impact on the green factor productivity of agriculture. Therefore, it can be eliminated or reduced as a control variable.

2.

The rural per capita disposable income $\left(inc\right)$

Agriculture is the main productive activity in rural areas. The economic capacity of farmers directly affects their investment and decision-making in agricultural production. An increase in these inputs may increase the efficiency and quality of agricultural production, which would then affect AGTFP. To remove the effect of inflation, the per capita disposable income in rural areas was deflated in this study based on a base period of 2000.

3.

The area of crop disasters $\left(caa\right)$

The area of crop disasters reflects the extent of the external shocks faced by agricultural laborers in agricultural production. These shocks may have a direct impact on agricultural production, including reduced yields, reduced quality or increased production costs.

4.

Government intervention $\left(gi\right)$

This was expressed as a proportion of GDP within fiscal expenditure. The government can influence the development of the agricultural economy by formulating and implementing a range of policies, including agricultural subsidies, construction of agricultural infrastructure, etc., which can affect AGTFP.

5.

The degree of opening up $\left(ope\right)$

This was measured as a percentage of total foreign direct investment within regional GDP. Foreign investment and trade tend to be accompanied by technology transfers and industrial upgrades, which may help increase AGTFP. The use of the foreign investment budget as a control variable helped to eliminate the potential impact of capital flows on AGTFP. Among them, the total FDI was calculated on the basis of the average exchange rate of the renminbi against the US dollar during the year.

4.2.4. Mediating Variables

The mediating variables in this study were labor productivity $\left(lp\right)$, farmland transfer $\left(ft\right)$, and innovation in scientific research $\left(sri\right)$. Labor productivity was expressed as the ratio of the number of employees in the primary industry to the value of the primary industry’s output. The transfer of farmland was expressed in terms of the total area of farmland transferred, including rentals, equity, and other forms of transfer of land; innovation in scientific research was indicated by the number of patents for inventions.

4.3. Data Sources

On the basis of the panel data of 31 provinces (autonomous regions and municipalities) in China from 2000 to 2022, this study examined whether aging of the rural population promote AGTFP. Considering the availability and authenticity of the data, the data in this study were taken from public data sources such as websites, yearbooks, and bulletins from various levels of the Chinese government. Among them, the data on aging came from the China Population and Employment Statistics Yearbook, data on the circulation of rural land came from the China Rural Management and Operation Statistics Annual Report, and other raw data were all from the China Statistical Yearbook and China Rural Statistical Yearbook. Among them, some missing data were filled by interpolation.

The data on farmland transfer only included 2005–2021. To improve the data’s stability, the effects of inflation on GDP, primary industry output value, rural per capita disposable income, and other data were removed. In order to alleviate the impact of extremum values on the study, the relevant continuous variables were subjected to a shrinkage treatment in the tail at the 1% and 99% levels. In order to largely alleviate the heteroscedasticity, logarithmic processing was conducted on the data on rural per capita disposable income, area of crop disasters, area of farmland transfer, and scientific research and innovation. Details of the variables are provided in Appendix A. The descriptive statistics of each variable are shown in

Table 2. Statistics of each variable.

Variable	Sample Size	Mean Value	Variance	Minimum	Maximum
$ntfp$	682	1.106	0.214	0.518	2.602
$nold$	713	0.110	0.044	0.046	0.255
$ppi$	713	0.336	0.141	0.042	0.652
$inc$	713	8.559	0.657	7.280	9.974
$caa$	713	6.165	1.726	0.000	8.406
$gi$	713	0.238	0.181	0.072	1.256
$ope$	713	0.476	0.566	0.056	3.742
$lp$	713	2.025	1.747	0.300	9.970
$ft$	527	4.750	1.791	0.427	8.290
$sri$	712	6.978	2.024	1.946	11.051

.

5. Empirical Test and Analysis of the Results

5.1. Analysis of the Calculated Results of AGTFP

On the basis of the input, output, and undesired output indicators shown in

Table 3. AGTFP index of 31 regions in China.

Region	2001	2004	2007	2010	2013	2016	2019	2022
Beijing	1.210	1.084	1.273	1.049	1.201	1.034	1.069	1.313
Tianjin	1.129	1.025	1.075	1.174	1.152	0.881	1.096	2.602
Hebei	1.061	1.185	1.239	1.176	1.094	0.944	1.108	1.103
Shanxi	0.922	1.138	1.106	1.125	1.096	1.038	1.129	1.033
Inner Mongolia	0.977	1.172	1.145	1.065	1.072	2.602	1.107	1.115
Liaoning	1.048	0.984	1.205	1.081	1.092	0.833	1.094	1.043
Jilin	0.955	1.121	1.174	1.058	1.084	0.753	1.141	1.075
Heilongjiang	0.897	1.140	1.104	1.059	1.122	1.021	1.127	1.054
Shanghai	1.096	1.120	1.111	1.073	1.036	1.042	1.032	1.108
Jiangsu	1.018	1.240	1.129	1.264	1.070	1.036	1.068	1.081
Zhejiang	1.046	1.129	1.138	1.179	1.095	1.144	1.146	1.082
Anhui	0.999	1.219	1.168	1.118	1.048	1.023	1.119	1.053
Fujian	1.009	1.130	1.157	1.154	1.077	1.186	1.140	1.109
Jiangxi	1.022	1.108	1.128	1.065	2.602	1.080	1.165	1.058
Shandong	1.016	1.204	1.182	0.888	1.127	0.986	1.071	1.079
Henan	1.037	1.320	1.125	1.188	1.077	1.007	1.275	1.028
Hubei	0.999	1.252	2.602	1.141	1.084	1.060	1.120	1.074
Hunan	1.023	1.228	1.299	1.155	1.000	0.903	1.237	1.115
Guangdong	1.137	1.109	1.161	1.270	1.073	1.129	1.167	1.099
Guangxi	1.006	1.225	1.198	1.095	1.055	1.083	1.140	1.087
Hainan	0.926	2.102	1.112	1.141	1.048	1.142	1.135	1.136
Chongqing	1.023	1.232	1.214	1.106	0.933	1.093	1.165	1.039
Sichuan	1.047	1.228	1.310	1.092	1.031	1.085	1.140	1.053
Guizhou	1.002	1.081	1.113	1.125	1.103	1.175	1.410	1.000
Yunnan	0.794	1.156	1.125	1.000	1.123	1.093	0.518	1.039
Tibet	0.680	0.920	1.156	0.828	0.982	1.159	1.270	2.333
Shaanxi	1.031	1.201	1.164	1.258	1.243	1.087	1.112	1.271
Gansu	1.098	1.074	1.019	1.154	1.095	0.927	1.150	1.092
Qinghai	1.119	1.129	1.317	1.160	1.142	1.085	1.325	2.105
Ningxia	1.036	1.153	1.102	1.133	1.201	1.083	1.031	1.314
Xinjiang	1.027	1.053	1.273	1.310	1.152	1.284	1.093	1.040
Mean value	1.013	1.176	1.214	1.119	1.139	1.097	1.126	1.217

, AGTFP for the 31 Chinese provinces from 2000 to 2022 was calculated using MAXDEA 8.0 software. The AGTFP index for some years is shown in Table 3.

It can be seen from Table 3 that AGTFP in various regions of China showed a fluctuating upward trend from 2000 to 2022, which reflected how China has achieved certain results in promoting sustainable agricultural development, such as improving resource utilization efficiency and reducing environmental pollution. Specifically, from 2000 to 2007, AGTFP in 31 regions continued to rise and grew faster. Since 2010, the AGTFP index in different regions fluctuated greatly and then gradually recovered to stabilize, showing the resilience and adaptability of Chinese agriculture.

At the same time, we noted that the AGTFP index of most provinces declined to a certain extent around 2012. The reason may be that the Chinese economy was in a critical period of transition from high-speed growth to medium-high-speed growth around 2012. Different industries are facing economic restructuring and industrial upgrading. In the process of economic transformation, the production mode and resource allocation of traditional agriculture may face greater challenges, resulting in a decline in China’s overall AGTFP index. In 2022, the average value of AGTFP index in 31 regions of China reached 1.217, which was an increased compared with 2000, but the growth rate was limited. This shows that although China’s agriculture has made some progress in sustainable development, it still faces some challenges and constraints. Resource constraints, environmental pressures, bottlenecks in technological innovation, and the traditional concept of agricultural producers have hindered China’s further improvement of its AGTFP.

In terms of regional differences, the AGTFP index was relatively high in developed coastal areas such as Beijing, Tianjin, Shanghai, and Hainan. This may be because more economically developed regions tend to have higher levels of scientific and technological innovation and more adequate R&D funding, enabling these regions to adopt and apply advanced agricultural technologies earlier, such as precision agriculture, smart agriculture, etc. At the same time, agricultural producers in economically developed regions also have higher environmental awareness and better capabilities for resource management, which will help improve the efficiency of agricultural production and resource utilization efficiency, thereby continuously improving the level of sustainable agricultural development. The AGTFP index in Heilongjiang, Jilin, Inner Mongolia, Tibet, and other regions was relatively low. These areas may face resource constraints and problems of environmental pressure, such as water shortages, soil degradation, and fragile ecological environments, which limit the sustainable development of agricultural production in these areas. At the same time, problems such as relatively backward agricultural production techniques may also lead to low resource utilization efficiency and increased environmental pollution in these regions, hindering the sustainable development of agriculture. The differences in regional dimensions partly reflect China’s uneven regional development.

5.2. Empirical Analysis of the Impact of the Aging of the Rural Population on AGTFP

In the study, there may be a high degree of correlation between independent variables, which would have an impact on the stability, interpretability, and predictive ability of the regression model. Therefore, firstly, the VIF test was used to study the problem of multicollinearity of the independent variables in the model. The results showed that the VIF values of each variable were less than 2, indicating that there was no serious problem of multicollinearity in the model. To explore the impact of the aging of the rural population on AGTFP, the panel fixed-effect model was used to estimate the results, as shown in

Table 4. Results of the regression models.

Variable	Model 1	Model 2	Model 3	Model 4
$nold$	−0.759 *** (−3.10)	−1.315 *** (−2.97)	−0.653 ** (−2.18)	−1.334 *** (−3.00)
$ppi$			0.165 *** (2.03)	0.557 *** (3.24)
$inc$			0.061 * (1.79)	0.097 * (1.75)
$caa$			−0.014 ** (−1.98)	−0.018 * (−1.72)
$gi$			0.010 (0.16)	0.041 (0.28)
$ope$			−0.046 ** (−2.45)	−0.049 * (−1.86)
$\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{s}\mathrm{t}\mathrm{a}\mathrm{n}\mathrm{t}$	1.070 *** (26.15)	1.100 *** (16.45)	0.659 ** (2.32)	0.398 (0.86)
$\text{Time-fixed}$	$\mathrm{Y}\mathrm{e}\mathrm{s}$	$\mathrm{Y}\mathrm{e}\mathrm{s}$	$\mathrm{Y}\mathrm{e}\mathrm{s}$	$\mathrm{Y}\mathrm{e}\mathrm{s}$
$\text{Province-fixed}$	$\mathrm{N}\mathrm{o}$	$\mathrm{Y}\mathrm{e}\mathrm{s}$	$\mathrm{N}\mathrm{o}$	$\mathrm{Y}\mathrm{e}\mathrm{s}$
$N$	682	682	682	682
$R^2$	0.124	0.150	0.138	0.174

Note: ***, **, and *, respectively, indicate that the coefficients are significant at the levels of 1%, 5%, and 10%; the values in parentheses are t values.

below.

From the regression results of the model in

Table 5. Regression results of the models in the robustness test.

Variable	Model 5	Model 6	Model 7
$nold$		−2.514 *** (−3.70)	−6.307 *** (−3.41)
$ndep$	−0.011 *** (−3.85)
$ppi$	0.583 *** (3.40)	1.113 *** (4.72)	0.720 *** (3.79)
$inc$	0.091 * (1.65)	0.056 (0.88)	0.077 (1.31)
$caa$	−0.020 * (−1.86)	−0.005 (−0.32)	−0.016 (−0.41)
$gi$	0.043 (0.29)	−0.395 * (−1.88)	−0.188 (−1.06)
$ope$	−0.047 * (−1.80)	−0.042 (−1.37)	−0.035 (−1.26)
$\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{s}\mathrm{t}\mathrm{a}\mathrm{n}\mathrm{t}$	0.450 (0.97)	1.065 * (1.89)	0.981 * (1.85)
$\text{Time-fixed}$	$\mathrm{Y}\mathrm{e}\mathrm{s}$	$\mathrm{Y}\mathrm{e}\mathrm{s}$	$\mathrm{Y}\mathrm{e}\mathrm{s}$
$\text{Province-fixed}$	$\mathrm{Y}\mathrm{e}\mathrm{s}$	$\mathrm{Y}\mathrm{e}\mathrm{s}$	$\mathrm{Y}\mathrm{e}\mathrm{s}$
$\mathrm{A}\mathrm{n}\mathrm{d}\mathrm{e}\mathrm{r}\mathrm{s}\mathrm{o}\mathrm{n}’\mathrm{s} \mathrm{L}\mathrm{M}$			43.325
$CD-WF$			$42.328$
$N$	682	495	682
$R^2$	0.182	0.209

Note: ***, and *, respectively, indicate that the coefficients are significant at the levels of 1%, and 10%.

, whether it was time-fixed, or time-and province-fixed, aging of the rural population had a significant negative impact on AGTFP, and the regression’s results were significant at the 1% significance level. Among them, Model 1 only controlled for time, and the result showed that the coefficient of $nold$ was 0.401. After time and province were controlled for, the coefficient of $nold$ decreased slightly to 0.399. Model 3 and Model 4 were made time-fixed and doubly time- and province- fixed by adding certain control variables. The results showed that the regression coefficient of $nold$ was still significant and the effect was still negative.

According to the regression results of the four models, aging of the rural population had a significant negative impact on AGTFP, that is, aging of the rural population helped to promote the improvement in AGTFP.

5.3. Robustness Test

This study conducted a shrinkage treatment in the tail before using the data, and used the doubly time- and province-fixed model to effectively reduce the possibility of endogeneity, but there may have been omitted variables that reduced the robustness of the results. Therefore, methods such as substitution variables, instrumental variables, and adjusted sample periods were used in this study for further robustness testing.

5.3.1. Replacing the Explanatory Variables

The rural elderly dependency ratio ($ndep$) refers to the ratio of the rural elderly population to the rural working-age population, reflecting how many elderly people each 100 members of the rural working-age population should bear. As an economic indicator, the calculation method and data sources for the rural elderly dependency ratio are relatively fixed. With the increased aging of the rural population, the elderly dependency ratio will also rise accordingly. Therefore, the rural elderly dependency ratio can be used to replace the aging of the rural population for a robustness test. Before the regression of the model, the data of the rural elderly dependency ratio were processed logarithmically and by a 1% shrinkage treatment in the tail.

According to the regression results of Model 5 in Table 5, the rural elderly dependency ratio ($ndep$) had a positive impact on AGTFP at the significance level of 10%, which is consistent with the robustness and direction of the impact of aging rural populations on AGTFP. It showed that the results are relatively robust and the main regression results have high credibility.

5.3.2. Shortening the Sample Period

Reducing the sample period is a common approach in robustness tests. If the output of the model does not change significantly after a reduction in the sample period, the model can be considered to be robust. In 2006, China began to cancel the agricultural tax. This policy change had a profound impact on China’s agriculture. To remove the effect of changes in the policy environment, only the 2007–2022 period was retained for analysis.

According to the regression results of Model 6 in Table 5, $nold$ had a negative effect on AGTFP at the 1% significance level. The results were consistent with the direction and significance level of the benchmark regression, indicating that the results of the benchmark regression have high credibility.

5.3.3. Instrumental Variable Method

To prevent bidirectional causality or missing variables between aging rural populations and AGTFP, which could bias the results of the estimation, government social security spending was chosen as the instrumental variable. Aging of the rural population will inevitably increase the government’s social security expenditure, but the government’s social security expenditure is not affected by the green factor productivity of agriculture. Therefore, the government’s social security expenditure satisfies the requirement of an instrumental variable.

As shown in Table 5, Model 7 used the least squares method (2SLS) to estimate the impact of the aging of the rural population on AGTFP. The results showed that the coefficient of $\mathrm{n}\mathrm{o}\mathrm{l}\mathrm{d}$ was significantly negative, which was consistent with the results of the benchmark regression. Anderson’s LM statistic was 43.325 and the p-value was less than 0.01, indicating that the original hypothesis of insufficient identification of the instrumental variables was significantly rejected at the 1% level, that is, there was no unidentifiable problem in the instrumental variables. The CD-WF statistic was 42.328, and the null hypothesis of the selected variable being a weak instrumental variable was rejected at the 1% level, indicating that the selected variable was not a weak instrumental variable, which verified that the selection of instrumental variables in this study was reasonable. The analysis above showed that the results of the benchmark regression have high credibility.

5.4. Analysis of Heterogeneity

There are great differences in the level of economic development, resource endowment, industrial structure, and environmental regulations in different regions of China, which makes the impact of aging rural populations on AGTFP show different characteristics in different regions. The eastern region of China enjoys a relatively high level of economic development, abundant resources, and a comparatively high level of aging of the population. Due to its high economic development, environmental regulations in this region are relatively stringent, and the government invests and monitors environmental protection with greater intensity. The level of economic development in the central and western regions is relatively poor, and their conditions of resource endowment are also relatively inferior. Although environmental regulations in these regions are relatively lenient, their agricultural production may face greater environmental pressure. Therefore, it is necessary to understand the specific situation of this impact in different regions more accurately through an analysis of regional heterogeneity to provide a scientific basis for formulating targeted policies and measures. This study conducted a consistency analysis based on different regions and different environmental regulations, and the results are shown in

Table 6. Regression results of the heterogeneity analysis.

Variable	Regional Heterogeneity			Heterogeneity of Environmental Regulation
	Eastern China	Central China	Western China	Low	High
$nold$	0.339 (0.66)	1.321 (−1.19)	−4.356 *** (−4.62)	−1.663 * (−1.79)	−4.345 *** (−3.42)
$ppi$	−0.060 (−0.16)	0.567 (1.37)	0.474 (1.50)	1.024 *** (3.76)	1.021 ** (2.47)
$inc$	0.002 (0.03)	0.049 (0.23)	0.176 (1.52)	0.059 (0.86)	0.164 (1.08)
$caa$	−0.007 (0.60)	−0.028 (−0.71)	−0.054 ** (−2.50)	−0.032 * (−2.04)	−0.029 (−1.11)
$gi$	0.120 (0.61)	−0.573 (−0.73)	−0.101 (−0.41)	0.229 (0.72)	0.560 (1.31)
$ope$	−0.009 (0.41)	−0.058 (−0.30)	−0.143 * (−1.88)	−0.104 ** (−2.44)	−0.055 (−1.12)
$\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{s}\mathrm{t}\mathrm{a}\mathrm{n}\mathrm{t}$	1.032 ** (2.06)	0.778 (0.47)	0.280 (0.31)	0.924 (1.58)	0.245 (0.20)
$\text{Time-fixed}$	$\mathrm{Y}\mathrm{e}\mathrm{s}$	$\mathrm{Y}\mathrm{e}\mathrm{s}$	$\mathrm{Y}\mathrm{e}\mathrm{s}$	$\mathrm{Y}\mathrm{e}\mathrm{s}$	$\mathrm{Y}\mathrm{e}\mathrm{s}$
$\text{Province-fixed}$	$\mathrm{Y}\mathrm{e}\mathrm{s}$	$\mathrm{Y}\mathrm{e}\mathrm{s}$	$\mathrm{Y}\mathrm{e}\mathrm{s}$	$\mathrm{Y}\mathrm{e}\mathrm{s}$	$\mathrm{Y}\mathrm{e}\mathrm{s}$
$N$	239	178	264	238	237
$R^2$	0.231	0.238	0.302	0.429	0.281

Note: ***, **, and *, respectively, indicate that the coefficients are significant at the levels of 1%, 5%, and 10%.

.

According to China’s official classification criteria, the eastern region includes 11 provinces, the central region includes 8 provinces, and the western region includes 12 provinces. According to the results of the analysis of regional heterogeneity in Table 6, at the significance level of 1%, the aging of the rural population in the western region had a negative inhibitory effect on AGTFP, while the aging of the rural population in the eastern and central regions had no significant impact on AGTFP. The reasons for these results are as follows. Firstly, as aging of the rural population intensifies, elderly people gradually withdraw from agricultural production activities due to age and physical reasons, resulting in a decrease in the number of agricultural laborers, which directly affects the scale and efficiency of agricultural production. At the same time, the elderly labor force may be inefficient in managing and utilizing agricultural resources, and they may fail to fully utilize land resources, water resources, and other agricultural production factors, leading to a decline in resource utilization efficiency and negatively impacting the agricultural ecological environment. The age structure of agricultural labor is changing. Secondly, due to the relatively backward economy in the western region, young and middle-aged laborers are more likely to migrate to the economically more developed eastern and central regions to seek better employment opportunities and living conditions. This phenomenon of labor loss has exacerbated the aging of the rural population in the western region, further weakening the labor base of agricultural production. The labor reduction brought by aging, especially the loss of young laborers, may hinder the inheritance and update of agricultural technology. Elderly farmers may prefer traditional agricultural production methods and be unwilling or unable to master new agricultural technologies. This will lead to stagnation in the technological level of agricultural production, making it difficult to adapt to the needs of modern agricultural development, and posing greater challenges for the western region in agricultural technological innovation and green development. Thirdly, with the intensification of aging of the rural population, the western region has needed to invest more resources into social security such as elderly care and medical care for the elderly. To cope with the pressure of social security brought about by aging, the government may adjust its policies to prioritize meeting the basic needs of the elderly, which may crowd out funds originally used for agricultural research and development, such as research and development into environmental protection technologies and the construction of green agricultural infrastructure, squeezing investment and support for agricultural green development, thereby having a negative impact on AGTFP.

Aging of the rural population may have different effects on AGTFP under different environmental regulations. In order to analyze the impact of aging of the rural population on AGTFP under different environmental regulations, this study used the government’s environmental expenditure as the basis for measuring the intensity of the government’s environmental regulation, and divided the intensity of the government’s environmental regulation into low-intensity environmental regulation and high-intensity environmental regulation according to the median of the government’s environmental expenditure. According to the results of the regression in Table 6, under high- and low-intensity environmental regulation, aging of the rural population had a negative impact on AGTFP, and under high-intensity environmental regulation, aging of the rural population had a stronger inhibitory effect on AGTFP. The following are the reasons for these findings of the analysis. First, in the context of stringent environmental regulations, enterprises need to invest more resources and costs to meet the government’s requirements for environmental protection, such as adopting environmentally friendly materials, updating environmentally friendly equipment, and implementing environmental protection treatments. These increased costs will indirectly affect agricultural producers, forcing them to pay higher production costs. For households with a high degree of aging, these additional economic burdens may be difficult to bear, leading to reduced agricultural production inputs or a choice to withdraw from agricultural production, ultimately resulting in a decline in AGTFP. Second, elderly workers have relatively limited capabilities to accept and apply new technologies. Especially in the context of stringent environmental regulations, environmental protection technologies often require higher technical levels and financial support, which may be difficult for older farmers to adapt to and master. Therefore, they are more likely to continue using traditional agricultural production methods, which are not conducive to improving AGTFP. Finally, stringent environmental regulations impose restrictions or prohibitions on agricultural production methods with high pollution and high energy consumption, which increases the demand for labor in agricultural production. However, with the intensification of aging of the rural population, the problem of insufficient labor supply has become more prominent, further exacerbating the problem of labor shortage, adversely affecting the normal conduct of agricultural production and the improvement of AGTFP.

6. Analysis of the Mechanisms

6.1. Analysis of Mediating Effects

In the analysis above, the impact of aging of the rural population on AGTFP had a mechanism effect, and labor productivity ($lp$), innovation in scientific research ($sri$), and farmland transfer ($ft$) had a mediating effect between the two. To verify the mechanism of the impact of aging of the rural population on AGTFP, a model for testing mediation effects was used, which was fixed for both time and province. The results of this regression model are shown in

Table 7. The results of the regression of the mechanisms.

Variable	Model 8 $\mathit{l}\mathit{p}$	Model 9 $\mathit{A}\mathit{G}\mathit{T}\mathit{F}\mathit{P}$	Model 10 $\mathit{s}\mathit{r}\mathit{i}$	Model 11 $\mathit{A}\mathit{G}\mathit{T}\mathit{F}\mathit{P}$	Model 12 $\mathit{f}\mathit{t}$	Model 13 $\mathit{A}\mathit{G}\mathit{T}\mathit{F}\mathit{P}$	Model 14 $\mathit{A}\mathit{G}\mathit{T}\mathit{F}\mathit{P}$
$lp$		0.019 * (1.86)					0.022 * (1.78)
$sri$				0.367 *** (2.82)			−0.344 (−1.10)
$ft$						0.175 *** (2.62)	0.303 ** (2.56)
$nold$	−1.786 (1.06)	−1.305 *** (−2.94)	−0.415 *** (−3.03)	−1.160 *** (−2.60)	−3.355 *** (−7.55)	−2.010 *** (2.95)	−1.920 *** (−2.81)
$ppi$	0.325 (0.51)	0.548 *** (3.19)	−0.163 *** (−3.16)	0.617 *** (3.57)	0.027 (0.19)	1.055 *** (5.12)	0.994 *** (4.79)
$inc$	−1.070 *** (−5.05)	0.117 ** (2.07)	0.047 *** (2.70)	0.079 (1.41)	0.121 *** (3.08)	0.035 (0.62)	0.046 (0.80)
$caa$	−0.094 ** (−2.33)	−0.048 * (−1.85)	−0.003 (−0.77)	−0.017 (−1.60)	0.002 (0.19)	−0.013 (−1.14)	−0.010 (−0.82)
$gi$	−2.786 *** (−5.19)	0.096 (−0.63)	0.319 *** (7.23)	−0.098 (−0.61)	0.894 *** (7.24)	−0.537 *** (−2.85)	−0.474 ** (−2.49)
$ope$	0.001 (0.01)	−0.042 (−1.37)	−0.026 *** (−3.33)	−0.039 (−1.49)	−0.092 *** (−4.51)	0.006 (0.19)	0.004 (0.13)
$\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{s}\mathrm{t}\mathrm{a}\mathrm{n}\mathrm{t}$	13.824 *** (7.83)	0.136 (0.28)	1.505 *** (10.50)	−0.126 (−0.25)	0.679 ** (1.99)	0.783 (1.58)	1.074 (1.51)
$\text{Time-fixed}$	$\mathrm{Y}\mathrm{e}\mathrm{s}$	$\mathrm{Y}\mathrm{e}\mathrm{s}$	$\mathrm{Y}\mathrm{e}\mathrm{s}$	$\mathrm{Y}\mathrm{e}\mathrm{s}$	$\mathrm{Y}\mathrm{e}\mathrm{s}$	$\mathrm{Y}\mathrm{e}\mathrm{s}$	$\mathrm{Y}\mathrm{e}\mathrm{s}$
$\text{Province-fixed}$	$\mathrm{Y}\mathrm{e}\mathrm{s}$	$\mathrm{Y}\mathrm{e}\mathrm{s}$	$\mathrm{Y}\mathrm{e}\mathrm{s}$	$\mathrm{Y}\mathrm{e}\mathrm{s}$	$\mathrm{Y}\mathrm{e}\mathrm{s}$	$\mathrm{Y}\mathrm{e}\mathrm{s}$	$\mathrm{Y}\mathrm{e}\mathrm{s}$
$N$	712	681	711	680	525	525	525
$R^2$	0.815	0.179	0.966	0.184	0.942	0.224	0.231

Note: ***, **, and *, respectively, indicate that the coefficients are significant at the levels of 1%, 5%, and 10%.

.

As shown in Table 7, Model 8 is the regression of aging of the rural population on labor productivity ($lp$). The coefficient for the aging of the of rural population as negative but not significant. When only the provinces were fixed and the model was regressed again, the coefficient for aging of the rural population was negative and significant, indicating that aging of the rural population had some negative impact on labor productivity. Model 9 was based on the base model and added a regression for the intermediary variables of labor productivity. According to the results of the regression of Model 9, the coefficient of labor productivity was positive at the significance level of 10%, and the coefficient of aging of the rural population was negative at the significance level of 1%, indicating that aging of the rural population had an inhibitory effect on AGTFP by reducing labor productivity; that is, Hypothesis 1 was verified.

Model 10 is the regression of aging of the rural population on innovation in scientific research ($sri$). The coefficient for aging of the rural population was negative at the significance level of 1%, indicating that the aging of the rural population had an inhibitory effect on innovation in scientific research. Model 11 is the result obtained by adding the mediating variable of innovation in scientific research to the base model. According to the results of the regression in Model 11, the coefficient of innovation in scientific research was positive at the significance level of 1%, and the coefficient of aging of the rural population was negative at the significance level of 1%, indicating that the aging of the rural population had an inhibitory effect on AGTFP by reducing the level of innovation in scientific research; that is, Hypothesis 3b was verified.

Model 12 is the regression of aging of the rural population on farmland transfer ($ft$). The coefficient for aging of the rural population was negative at the significance level of 1%, indicating that the aging of the rural population had a significant inhibitory effect on farmland transfer. Model 13 is the result for the regression of the intermediary variable of farmland transfer based on the fiducial model. According to the results of Model 13, the coefficient of farmland transfer was positive at the significance level of 1%, and the coefficient of aging of the rural population was negative at the significance level of 1%, indicating that the aging of the rural population had a negative impact on AGTFP by reducing the level of farmland transfer; that is, Hypothesis 2 has not been verified.

The reasons for the results above are, first, that in rural areas of China, land often carries the emotional and cultural values of farmers. Elder farmers may have a deep emotional connection to the land, seeing it as an asset left to them by their ancestors. This complex attitude to land may make them reluctant to move from their land, thus inhibiting the activity of farmland transfer. Second, in rural areas of China, the land transfer market may have problems such as asymmetric information, high transaction costs, and imperfections. These issues may make older farmers wary of land transfers, as they fear losing control of their land or future sources of livelihood after the transfer. Therefore, the aging of the rural population may inhibit the transfer of agricultural land and thus negatively affect AGTFP.

6.2. Robustness Test

To obtain robust research results, the effects of the mechanism were tested using the methods of replacing variables and adjusting the sample period in the main regression for robustness checks. The results are presented in

Table 8. Results of the robustness test of the regression model.

Variable	Model 15 $\mathit{A}\mathit{G}\mathit{T}\mathit{F}\mathit{P}$	Model 16 $\mathit{A}\mathit{G}\mathit{T}\mathit{F}\mathit{P}$	Model 17 $\mathit{A}\mathit{G}\mathit{T}\mathit{F}\mathit{P}$	Model 18 $\mathit{A}\mathit{G}\mathit{T}\mathit{F}\mathit{P}$	Model 19 $\mathit{A}\mathit{G}\mathit{T}\mathit{F}\mathit{P}$	Model 20 $\mathit{A}\mathit{G}\mathit{T}\mathit{F}\mathit{P}$
$lp$	0.195 * (1.89)			0.025 * (1.95)
$sri$		0.345 *** (2.65)			0.328 (1.58)
$ft$			0.181 *** (2.72)			0.183 ** (2.40)
$nold$				−2.475 *** (−3.66)	−2.136 *** (−2.97)	−1.600 ** (−2.16)
$ndep$	−0.011 *** (−3.82)	−0.010 *** (−3.41)	−0.011 *** (−2.81)
$\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{s}\mathrm{t}\mathrm{a}\mathrm{n}\mathrm{t}$	$\mathrm{Y}\mathrm{e}\mathrm{s}$	$\mathrm{Y}\mathrm{e}\mathrm{s}$	$\mathrm{Y}\mathrm{e}\mathrm{s}$	$\mathrm{Y}\mathrm{e}\mathrm{s}$	$\mathrm{Y}\mathrm{e}\mathrm{s}$	$\mathrm{Y}\mathrm{e}\mathrm{s}$
Time-fixed	$\mathrm{Y}\mathrm{e}\mathrm{s}$	$\mathrm{Y}\mathrm{e}\mathrm{s}$	$\mathrm{Y}\mathrm{e}\mathrm{s}$	$\mathrm{Y}\mathrm{e}\mathrm{s}$	$\mathrm{Y}\mathrm{e}\mathrm{s}$	$\mathrm{Y}\mathrm{e}\mathrm{s}$
Province-fixed	$\mathrm{Y}\mathrm{e}\mathrm{s}$	$\mathrm{Y}\mathrm{e}\mathrm{s}$	$\mathrm{Y}\mathrm{e}\mathrm{s}$	$\mathrm{Y}\mathrm{e}\mathrm{s}$	$\mathrm{Y}\mathrm{e}\mathrm{s}$	$\mathrm{Y}\mathrm{e}\mathrm{s}$
$N$	681	681	525	495	495	495
$R^2$	0.164	0.168	0.192	0.142	0.139	0.224

Note: ***, **, and *, respectively, indicate that the coefficients are significant at the levels of 1%, 5%, and 10%.

.

Models 15, 16, and 17 present the results of the regression after replacing the core explanatory variables. The significance and direction of the results of the regression for labor productivity, scientific research and innovation, farmland transfer, and the mediating effects showed strong consistency, indicating a certain degree of reliability in the results of the mechanism. Models 18, 19, and 20 are the results of the regression after shortening the sample period. The results of the regression using data from 2007 to 2022 remain significant and consistent with the regression of the mechanisms, indicating the high reliability of the mechanisms’ effects. Specifically, aging of the rural population had a negative impact on AGTFP by inhibiting farmland transfer, labor productivity, and scientific research and innovation.

7. Discussion

The influence of aging of the rural population on AGTFP has been confirmed by many scholars, but relatively few studies have focused on the analysis of the mechanisms and heterogeneity between them. This study analyzed the impact of aging of the rural population on AGTFP in different regions and under different environmental regulations, and further introduced three mechanism variables: farmland transfer, labor productivity, and innovation in scientific research. It constructed and tested the impact and mechanisms of the aging of the rural population on AGTFP.

First, the study found that the impact of aging of the rural population in the western region on AGTFP was more significant. After entering the aging stage, due to limitations in their own abilities, people tend to adopt new technologies at a relatively low level, thus having a negative impact on AGTFP. In the western region, due to the constraints of its own development and resource endowments, young laborers are more likely to flow to the relatively developed regions in the east and central regions, further increasing the rate of rural aging in the western region. According to the theory of the technological diffusion gradient, the application of a new technology often starts in developed regions and then gradually spreads to less developed regions, making it difficult for the western region to access new agricultural technologies. As a result, agricultural production in the western region is more affected by aging of the rural population. Therefore, in the future development of agriculture in China, it is necessary to reduce regional differences, increase the construction of infrastructure and investment into education in the western region, and improve local farmers’ knowledge and ability to use technology.

Second, the negative impact of the aging of the rural population on AGTFP was more significant in areas with high-intensity environmental regulations. As the largest developing country, China actively promotes green development and the construction of ecological civilization. Facing environmental pollution issues, the government has intensified environmental regulations, aiming to promote environmental protection, green development, and achieve its sustainable development goals. For farmers, the requirements for environmental protection in agricultural production in high-regulation areas are higher, requiring farmers to invest more capital and human resources to meet the environmental requirements, which increases the cost of agricultural production and increases farmers’ burden of production. For enterprises, environmental regulations increase the production costs, reducing the market competitiveness of their products and further affecting their income and enthusiasm for production. Therefore, when implementing environmental regulations, the government needs to comprehensively consider both the producers and consumers in the enterprise sector, providing some policy support from the supply or demand side. On the one hand, it helps enterprises reduce production costs; on the other hand, it increases farmers’ purchasing demand.

Thirdly, the aging of the rural population has had a negative impact on farmland circulation, labor productivity, and innovation in scientific research. In China, although farmland is collectively owned, farmers generally consider farmland as their personal property that can produce unlimitedly. Elderly farmers are afraid that they will not be able to reclaim the land after leasing it, so they are more inclined to retain their own land rather than transferring it. Due to age, physical strength, and other limitations, elderly farmers may find it difficult to adapt to high-intensity agricultural production activities and are unwilling to lease out their land, leading to a decline in labor productivity. As elderly farmers have a low acceptance of new technologies and methods, this limits the speed of promoting agricultural technology. Meanwhile, the increasing demand for elderly care also squeezes the development and application of national agricultural scientific research and innovation. Therefore, the government needs to properly address the negative impact of aging on agriculture and mitigate the challenges brought by aging to agriculture by establishing and improving the farmland circulation system and attracting the return of the young labor force.

Of course, this study still has some limitations. Firstly, there may be a certain degree of endogeneity in the selection of the instrumental variables in the study. Given the limitation of resources, more suitable instrumental variables have not been found. Subsequent scholars with relevant capabilities can identify more appropriate instrumental variables for verification. Secondly, the study used a double fixed-effects model and did not adopt other methods for verification. Future scholars may adopt more appropriate methods or compare multiple methods for analysis.

8. Conclusions and Policy Implications

With the improvement of the “double carbon” goal and requirements for environmental protection faced by China’s agriculture, how to give full play to the role of rural elderly people in the green development of agriculture, reduce the use of chemical inputs such as fertilizers and pesticides, and improve the sustainability of agricultural production has become an urgent problem to be solved. Based on panel data from China’s 31 provinces from 2000 to 2022, this study examines the impact of aging rural populations on AGTFP and the transmission mechanisms between them. It was found that, first, the aging of the rural population had a negative inhibitory effect on AGTFP, and the results of multiple robustness tests remained significant. This indicated that as the aging of the rural population intensifies, its negative impact on AGTFP will become increasingly significant. Therefore, the government must fully consider the context of the aging population in planning and policy-making regarding agricultural development in the future and take corresponding measures to address it. Second, from the point of view of the analysis of heterogeneity, there have been significant differences in the impact of aging rural populations on AGTFP in different regions and at different levels of environmental regulation. To be specific, aging of the rural population in western regions had a significant negative effect on AGTFP, while in the eastern and central regions, it was not significant. From the point of view of the strength of environmental regulations, the negative impact of aging rural populations on AGTFP was greater in regions with high-intensity environmental regulation than in regions with low-intensity environmental regulation. Third, from the point of view of the intermediary mechanisms, the intermediary roles of labor productivity, innovation in scientific research, and farmland transfer were all present, and the aging of the rural population negatively affected all three, which, in turn, had an inhibitory effect on AGTFP. The research conclusions are consistent with the life cycle theory and labor supply theory.

According to the life cycle theory, with increasing age, the physical strength and cognitive ability of the labor force may decline, which, in turn, affects the production efficiency. In the labor supply theory, the elderly labor force may withdraw from the labor market due to physical reasons or retirement, resulting in a decrease in the labor supply and a negative impact on AGTFP. Innovation in scientific research and farmland transfer are important means to optimize resource allocation and improve land use efficiency. However, the results showed that the aging of the rural population may inhibit scientific research and innovation, resulting in increased difficulty in farmland transfer, which is inconsistent with the expected results, but this result also revealed the deep-seated impact of aging on rural society and agricultural production.

For Chinese farmers, there is an inseparable emotion connection between farmers and the land, which hinders the activity of farmland transfer. At the same time, elderly farmers may be more accustomed to traditional agricultural production methods, and their acceptance of new technologies is low, which, to some extent, inhibits the transformation and application of the achievements of scientific research and innovation.

From the findings above, four policy recommendations have been made. First, the government should strengthen training the skills of the rural elderly labor force. Regarding the rural elderly labor force, targeted training of skills should be carried out to improve their agricultural production skills and environmental awareness, so that they can better adapt to the needs of the green development of agriculture. At the same time, green agricultural technologies should be promoted, and new agricultural business entities, such as family farmers and members of agricultural cooperatives, should be developed so that they can use modern agricultural technology and management models. The second is to promote the orderly transfer of agricultural land and the socialization of agricultural services. This will encourage and support the development of agricultural social service organizations to ensure the circulation of rural land, operation of agricultural machinery, pest control, and other comprehensive services for elderly rural people to reduce their labor burden and increase the efficiency of agricultural production. Establishing and improving information platforms and intermediary institutions for the circulation of rural land, providing information on the circulation of rural land, consulting services, evaluation and trading services, reducing the cost of circulating rural land, and improving the efficiency of circulating rural land. This will strengthen agricultural social service organizations, improve service networks, and provide agricultural technology, market information, sales of agricultural products, and other comprehensive services to meet the diverse and individualized needs of farmers.

Third, the government should strengthen the construction of a rural social security system; improve rural old-age insurance, medical insurance, and other social security systems; reduce the living pressure of elderly farmers; and improve their enthusiasm for agricultural production. It could explore the establishment of rural old-age service systems, such as rural mutual pensions, day care centers, etc., to provide life care and spiritual comfort for elderly farmers. It can also encourage social forces to participate in rural pension services and supply diversified pension services. Fourth, it should further optimize the structure of the agricultural labor force, and encourage and attract the return of the young rural labor force by providing preferential policies and entrepreneurial support. On the one hand, in policy on the circulation of rural land, those of prime working age should be given more benefits and convenience, such as reducing the cost of circulating rural land and extending the transfer period, so that they are more likely to obtain land management rights. On the other hand, it should provide entrepreneurship training services and venture capital support for the prime-working-age labor force returning to rural areas, helps them master modern agricultural technology and knowledge of management, and improves the success rate of entrepreneurship. By effectively attracting the return of the young rural labor force and engaging in agricultural production activities, it will help alleviate the challenges brought by the aging of the rural population and inject new vitality into rural economic development. Fifth, the government should formulate policies based on local conditions. On the one hand, it is necessary to continue to strengthen environmental protection policies and raise the standards of environmental regulation. Research has shown the impact of environmental regulations on AGTFP; thus the government needs to increase the intensity of environmental protection policies while providing sufficient funds to help agricultural producers adapt to the requirements of environmental protection and reduce production costs. On the other hand, the government should formulate regionally differentiated policies and take corresponding measures based on the actual situation of different regions to better address the challenges brought by aging of the rural population.