The Impact of Granting of Forest Certificates on Farmers’ Income—Intermediation Effects Based on Forestland Lease

Abstract

This study examines the impact of granting forest certificates on farmer income. Linear regression and mediating effect models were used to analyze repeated survey data of 505 households in 50 villages in Jiangxi Province in 2017 and 2018. We examined the impacts of granting forest certificates on forestry income and the total income of rural households, taking into account forestland leases. We draw the following conclusions: first, granting forest certificates has a significant positive effect on total household income but not on forestry income. Second, farmers prefer forestland leasing in their behavior. Granting forest certificates can promote forestland lease out, but the effect on forestland lease in is not obvious. Third, granting forest certificates contributes to the increase in total household income through forestland lease out. Our analysis suggests that the government should increase the proportion of granted forest certificates and improve the policies related to the lease of forestland so as to realize an increase in farmer income.

1. Introduction

Climate change and the lack of material resources are causing tremendous problems for human development. Forests play an important role in mitigating climate change and providing renewable resources [1,2,3]. Forests can mitigate climate change by reducing global greenhouse gas emissions, but the problem of forest degradation in developing countries has led to an increase in global greenhouse gas emissions [4]. As a typical developing country, it is important for China to pay attention to forestland. The types of forestland in China are mainly divided into state-owned forests and collective forests [5,6]. The results of the Ninth National Forest Resources Inventory found that the area of collective forestland in China accounted for 61.34% of the country’s total forestland, but the average productivity level of collective forestland was only equivalent to 45.08% of the average productivity level of state-owned forestland [7]. The reason lies in the unclear property rights of collective forestland, which has led to the problems of low motivation of farmer investment and low management efficiency [8]. In response, the central government issued the “Decision of the Central Committee of the Communist Party of China and the State Council on Accelerating the Development of Forestry” in 2023, which opened a new round of collective forest tenure reforms (CFTRs). The CFTR proposes to clarify the property rights of forestland as the core and to incentivize farmers to operate forestland through measures such as “clarifying the property rights, revitalizing the right to operate, implementing the right to dispose, and guaranteeing the right to income”, and thus to realize the growth of the income of rural households [9,10]. With the continuous promotion of CFTR policies, China’s collective forestland resources have steadily increased. According to the Eighth National Forest Resources Inventory, the area of collective forestland and the volume of standing wood stock increased by 18.57% and 44.69%, respectively, compared with the Sixth [11]. The government attributes this result to the CFTR [12,13,14,15], and China is actively pushing forward to deepen the CFTR.

Despite the notable accomplishments in expanding forestland resources, the CFTR has also resulted in the issue of forestland fragmentation. The aforementioned issue has an important negative impact on the efficiency of forestland and overall economic growth [14]. Currently, in order to address the suboptimal issue of resource allocation, it is imperative to expedite the process of leasing forestland. The 2008 Opinions of the State Council of the Central Committee of the Communist Party of China on Comprehensively Pushing Forward the CFTR System emphasized that owners of contracted forestland titles have the flexibility to lease both the operational title and ownership of the forestland title through various means. This highlights the significance of leasing forestland in the ongoing process of the CFTR. How does grating forest certificates, which are at the center of the CFTR, affect farmer income? By what mechanism is this impact realized? Examining the aforementioned questions will contribute to the continual improvement of the CFTR as a whole. This study holds significant theoretical and practical value in directing the CFTR toward achieving its ultimate objective.

The academic community has not yet reached a unanimous conclusion on the impact of granting forest certificates on income. Some scholars hold a positive view that the granting of forest certificates results in a positive impact on the growth of farmer income from forestland [16,17,18]. This is mainly reflected in the following aspects: first, the income increase brought by the investment effect. Under the incentive of clear property rights and higher expected returns, farmer investment has increased, further increasing farmer income [5,16]. Second, the mortgage effect increases rural household income from property. After the CFTR, the possibility of farmers obtaining forest tenure mortgage loans has been improved [15]. Third, CFTR-supporting policies stimulate farmer forestry production enthusiasm and effectively increase farmer income. However, some scholars hold the opposite view that the granting of forest certificates does not have a significant impact on farmer income, or that it has a negative impact on farmer income [19]. There are several reasons for this including the following: first, labor migration can inhibit the income-increasing benefits of granting forest certificates. Labor migration has led to the aging of the rural labor force and the leasing of forestland to elderly farmers with poor management capabilities [20]. Second, granting forest certificates has brought about the problem of forestland fragmentation [21], and fragmented forestland will lead to increased production costs and lower efficiency for rural households, thus reducing their income [22]. Third, granting forest certificates reduces comparative income from forestry [23], and farmers will invest more in other sectors, leading to a decline in forestry income.

In exploring the perspective of forestland lease as a mediating variable, this paper’s review of the existing literature is analyzed at two levels. The first is whether granting forest certificates promotes forestland leasing. The existing studies have not reached a unanimous conclusion. On the one hand, most scholars believe that granting forest certificates has a positive impact on forestland leasing. The CFTR guarantees the security and stability of farmers’ forestland titles at the legal level, reduces the risk of farmers losing their land when they go to the city to work [24], helps to improve the perception of the security of farmers’ property titles [25,26], and reduces the cost of forestland transactions [27], which strengthens the willingness of farmers to lease forestland and promotes the occurrence of forestland lease behavior [28]. On the other hand, some scholars believe that granting forest certificates has a negative effect on forestland leasing. Granting forest certificates would increase the price that farmers expect to pay for forestland leases and strengthen the farmer endowment effect on forestland, thus inhibiting the forestland lease [29,30,31]. The second is whether forestland leases increase the income of rural households. The existing studies usually acknowledge that forestland leasing has a substantial influence on forester income. On the one hand, the lease of forestland can stimulate the forestland lease market, enhance the efficiency of resource allocation [13,32], and contribute to the growth of farmers’ income [33]. On the other hand, it can partially address the inequality in resource and income distribution and narrow the income gap [34,35]. Nevertheless, several academics argue that forestland leases could also exacerbate income inequality and widen the income difference among these households [36].

The above studies analyzed the relationship among granting forest certificates, the forestland lease, and farmer income from different aspects, which lays the theoretical foundation for the research of this study. But few studies have analyzed the mediating effect of the forestland lease on granting forest certificates and rural household income. Therefore, in order to further explore the impact of granting forest certificates on farmer income, this study used field research data from 505 farmers in the collective forest areas in Jiangxi Province, and linear regression and mediating effect models were used to conduct the analysis. Therefore, the contribution of this paper lies in the following three aspects: first, in terms of data selection, the robustness of the conclusions is provided by the repeated panel data from the same study in 2017–2018. Second, the division of the dependent variable, i.e., rural household income, into total household income and forestry income enables a more accurate exploration of the impact of forest titling at the level and structure of rural household income. Finally, this study incorporates the forestland lease as a mediating variable when exploring the impact of granting forest certificates on rural household income, which provides a new direction for exploring the mechanism of the impact of granting forest certificates on rural household income.

2. Analytical Framework and Assumptions

2.1. Granting Forest Certificates and Forestland Leases

Granting forest certificates is the key content in the CFTR, which has made clear provisions on the attribution of forestland at the institutional level. Following the granting of forest certificates, farmers’ sense of security is enhanced, leading to increased leasing of forestland [27] and facilitating the efficient allocation of labor and forestland resources [37,38]. The good impact of granting forest certificates on forestland leases is mostly achieved through the following methods: first, in terms of transaction costs, granting forest certificates has a clear land contracting relationship and strengthens the role of forestland management, which can provide institutional guarantees and improve the market for the lease of forestland, thus enhancing the stability of the property title of forestland, reducing the transaction cost of the lease of forestland, and promoting the lease of forestland [39]. Second, based on security perception, granting forest certificates reduces the transaction cost of forestland leases and promotes the lease of forestland by clarifying people’s knowledge of granting forest certificates and improving farmers’ security perception [40]. Third, granting forest certificates has a “collateral effect”, “guarantee effect”, and “realization effect” [5], which is conducive to the realization of property title transactions [41] and can promote the lease of forestland to a certain extent.

In summary, it can be seen that granting forest certificates, on the one hand, from the clear and enforceable title to the realization of the forestland lease of institutional security, can enhance the perception of security of farmers to reduce transaction costs, thereby promoting the lease of forestland. On the other hand, through the clarity of property title, farmers are able to obtain access to mortgage loans through forestland and promote forestland leases, thus lifting people out of poverty [42]. Therefore, this study tests the following hypotheses:

Hypothesis 1.Granting forest certificates will promote the lease of farmer forestland, including the lease of forestland in and out.

2.2. Granting Forest Certificates Contributes to Forestry Income through Forestland Lease In

The object of a forestland lease is defined as the right to utilize or manage forestland. Presently, scholars widely concur that the forestland lease has a positive impact on creating income. Regarding the income-generating method of the forestland lease, it mostly manifests in the following two aspects: On the one hand, a forestland lease can improve the efficiency of forestland resource allocation. Clear and secure tenure rights can promote more leasing of forestland to more productive farm households, thus improving resource allocation efficiency and agricultural productivity to increase forestry income [43]. Granting forest certificates will motivate farmers to maintain optimism about future expectations, which can stimulate the enthusiasm of families for forestland management and incentivize productive investment of farmers, thus expanding the area of forestland management [41]. On the other hand, forestland lease in can provide farmers with incentives to undertake profitable investments, resulting in improved efficiency and higher forestry income. When the scale of forestland is increased to a certain extent, farmers can implement intensive agricultural methods and lower the cost per unit, thus achieving economies of scale and increasing their income from forestry [44,45]. Furthermore, when forest farmers possess a specific amount of forestland, they have the opportunity to enhance forestry production and management techniques through mechanization. This, in turn, minimizes the potential hazards and unpredictability associated with forestry production while simultaneously boosting forestry income. Furthermore, the rise in the scale of forestland owned by farmers facilitates the development of new forestry management models such as corporations, bases, cooperatives, and so on. This, in turn, leads to a further boost in the income generated from forestry activities for farmers. In conjunction with hypothesis 1, we test the following hypotheses:

Hypothesis 2.Granting forest certificates increases the growth of forestry income by facilitating forestland lease in, thereby increasing forestry income.

2.3. Granting Forest Certificates Contributes to Total Household Income through Forestland Lease Out

Currently, scholars generally believe that forestland lease out can promote an increase in the total income of rural households, where this income increase is mainly divided into the increase in non-farm employment income and the increase in property income [46,47,48]. On the one hand, in terms of the increase in income from non-farm employment, some farmers are able to obtain more income-advantageous off-farm employment opportunities after leasing forestland out of the country. Transferring labor and resources originally used for forestry production to other industries, such as looking for a job in a city or operating other small businesses, gives full play to the contribution of human capital to income, thus increasing their own off-farm employment income [49,50]. On the other hand, in terms of enhancing property income, forestland lease out within the framework of the CFTR facilitates the utilization of rural forestland resources, particularly land that has been left uncultivated because of a shortage of labor in rural households. Simultaneously, forestland lease out can benefit rural households with a limited and insufficient labor force by providing them with forestland lease out fees that generate rental income that is slightly higher than the net income from “self-farming and self-cultivation”. This leads to a direct increase in property income [51]. In conjunction with hypothesis 1, we test the following hypothesis:

Hypothesis 3.Granting forest certificates increases the growth in total household income by facilitating the lease out of forestland, thereby increasing the growth in total household income.

Figure 1 summarizes the different aspects of income generation from granting forest certificates discussed above.

3. Research Design

3.1. Data Sources

Jiangxi Province is situated in the central and downstream regions of the Yangtze River, covering a land area of 166,900 square kilometers. Jiangxi Province is a significant forestry province located in the southern collective forest area. It is abundant in forest resources, with a forestland area of 10.720 million hectares and a forest coverage rate of 63.1%, ranking second in the country. The province has a total live wood reserve of 710 million cubic meters, of which 506,658,300 cubic meters are forest reserves, accounting for 88.01%. The collective forestland area in the province is 9.13 million hectares, which make up 85% of the total forestland area (data from http://tjj.jiangxi.gov.cn/col/col38742/index.html. Geographic resource. 1 December 2023). Jiangxi Province has been at the forefront of implementing the CFTR. The reform was initiated in September 2004, and the primary tasks of the reform were accomplished by 2009. Currently, the focus is on further improving and deepening the reform process. Therefore, the examination of Jiangxi Province holds immense importance in advancing the changes related to communal forest tenure. Additionally, it can serve as a valuable source of knowledge for developing targeted policies in similar locations.

The data utilized in this paper were obtained from the survey of rural households in collective forest areas of Jiangxi Province in 2018 and 2017. According to the distribution of forest resources and the level of economic development in Jiangxi Province, a stratified random sampling method was used to select 10 sample counties, in which the northern, central, and southern parts of the province accounted for the same number of counties (Figure 2). Within each sample county, a combination of typical sampling and random sampling was employed to select 2 townships based on the per capita net income of farmers and the development of forestry. Two or three sample villages were chosen in each township. From each sample village, 10 farmers were randomly selected. A total of 505 rural families were surveyed through face-to-face interviews. This resulted in the collection of 1020 data points (Figure 3). After eliminating invalid data, a total of 1010 valid data points were found, resulting in a probability of valid data of 99.02%.

Two distinct questionnaires were developed for this investigation. The first was a survey that collected information about the status of farm households, including individual characteristics, household characteristics, and forestland characteristics. It also obtained information about the level and structure of family income, ownership and lease of forestland, and the labor force. The second was a village-level questionnaire, which primarily examined the fundamental conditions of a selected village, the general status of granting forest certificates, utilization of the labor force, income level and composition, and other related factors.

3.2. Selection of the Model’s Variables

• (1). The dependent variable in this study is farm household income. It is important to mention that the research sample in this article comprises forest farmers, meaning that the overall income of farming households includes money from forestry activities as well as income from off-farm employment. The main sources of income from forestry were fuelwood extraction and the collection of non-timber forest products (NTFPs) [52]. In the model for the separate regression, the particular variables of total household income and forestry income were incorporated, as referenced in earlier works [17]. The term “total household income” is employed to capture the variations in overall income resulting from the increase in forestland lease and off-farm income generated by the leasing of farmers’ forestland. On the other hand, “forestry income” is utilized to represent the alterations in income derived from the management of forestland subsequent to the leasing of farmers’ land. To minimize the impact of outliers on the empirical results, the data were transformed using logarithms.

• (2). The core independent variable in this study is granting forest certificates. Granting forest certificates refers to the process of granting forest titles to farmers. Hence, it is logical to quantify the granting of forest certificates based on the possession of forest titles (data from http://theory.people.com.cn/n/2014/0402/c40531-24802635.html. China’s strategy of economic reform through a dual route. 2 April 2014). In this paper, granting forest certificates is defined as the possession of forestland certificates, as determined by a questionnaire. The questionnaire included a specific question asking whether the individual responding has forestland certificates. Granting forest certificates was treated as a dichotomous variable, with a value of 1 assigned to farmers who responded yes and a value of 0 assigned to those who responded no.

• (3). The different grouping variables tested are the area of forestland ownership and business mode. The varying scale and manner of forestland management would significantly influence the business conduct of farming households, resulting in notable disparities among households of different scales and forms of operation. This would also lead to a substantial income difference among farming households [53,54]. The titling of forestland can significantly affect the level of impact on the income of farm households. Thus, this paper categorized farm households based on the characteristics of forestland ownership and business style. The forestland region was classified into three groups based on a field study using the “50 mu” and “100 mu” standards for grouping. These groupings were labeled “1”, “2”, and “3”. The focal variable in this research is forestland lease, and the forestland lease variables encompass the magnitude of forestland leases in and out of the area. The implementation of granting forest certificates has the potential to influence farmers’ willingness to lease forestland. Leasing forestland can generate income for farmers. Therefore, this article incorporates the forestland lease in the regression model to investigate the mediating effect of forestland leases on farmer income when examining the impact of granting forest certificates.

• (4). In order to enhance the stability of the regression results and minimize the impact of omitted variables, this paper incorporates a comprehensive set of control variables that may influence the income of farm households. These control variables are classified into four dimensions as follows: the head of the household’s individual characteristics, family characteristics, forestland characteristics, and village-level characteristics. This approach is based on a relevant study [55]. The primary decision-maker for a household’s entire production and operations is typically the head of the household. To account for the influence of the head’s individual characteristics, such as gender, age, education level, occupation, and whether they hold a position as a village cadre, these factors are taken into consideration. On the other hand, the business activities of the farm household are influenced by the household’s characteristics. To control for this, factors such as the total population of the household, the number of laborers, the percentage of off-farm employment, and whether the household is a member of the farmers’ forestry professional cooperatives are considered. Forestland characteristics play a significant role in forestry management. Forestland fragmentation hinders large-scale management and increases transaction and management costs. This study focuses on controlling the impact of forestland fragmentation. Woodland fragmentation is quantified by dividing the total area of woodland owned by households by the number of individual woodland plots. A higher value indicates a lower degree of woodland fragmentation. Furthermore, village-level characteristics encompass the village-level labor force transfer ratio and the village-level forestry income ratio. The labor force transfer ratio at the village level contributes to a decrease in the rural labor force, thereby impacting the labor input of rural residents. Similarly, the village-level forestry income ratio influences the overall forestry income of farmers. Hence, this study selects these two variables to mitigate the effects of village-level characteristics.

3.3. Econometric Model

The purpose of this study was to investigate the income-increasing role of granting forest certificates; therefore, the income of rural households was categorized into total household income, forestry income, and off-farm income. In addition, in order to further explore the income-increasing mechanism of granting forest certificates, this study also included forestland lease as a mediating variable in the model. The specific models were constructed as follows:

(1)$Y_i={\beta }_0+{\partial }_{1}X+\sum {\beta }_i{R}_i+{\epsilon }_i$

(2)$W_i={\beta }_0+{\partial }_{2}X+{\epsilon }_i$

(3)$Y_i={\beta }_0+{\partial }_3{W}_i+{\epsilon }_i$

(4)$Y_i={\beta }_0+{\partial }_{4}X+{\partial }_5{W}_i+{\epsilon }_i$

In the above model, $Y_i$ is the dependent variable, i.e., the total income of farming households and forestry income. $X$ is the core independent variable of this paper, i.e., granting forest certificates, which was measured by a dummy variable set with the proportion of ownership of granted forest certificates. $R_i$ is the other control variable. In order to explore the impact of each control variable on the dependent variables and the intervening role of the dependent variable on the impact of the independent variable, the control variables were added one by one to the regression to create a stepwise regression. $W_i$ is forestland lease, which was categorized into forestland lease in and forestland lease out. In addition, the constant term is denoted by ${\beta }_0$, the coefficients by $\partial$, and the random error term by ${\epsilon }_i$.

4. Results

4.1. Descriptive Analysis

According to the statistical results in Table 1, it is found that the mean value of the proportion of forestland certificates owned was 0.951, and the mean value of the proportion of village forestland certificate issuance was 0.934, indicating that the current rate of granting forest certificates for the farmers is higher. The mean value of the area of granting forest certificates was 1.853, with a standard deviation, indicating that the area of the granting of forest certificates owned by forest farmers at present is middle level. The mean value of the dummy variable for forestland area was 1.853, with a small standard deviation, indicating that the area of forestland owned by current forest farmers is generally 50–100 acres. The mean value of the operation mode was 1.516, indicating that single-family and joint-family operation modes are dominant among farmers. The mean value of the scale of leasing in forestland was 28.297, while the scale of leasing out was only 1.428, i.e., the scale of leasing in forestland is much larger than the scale of leasing out forestland, indicating that the current farmers are more inclined to leasing forestland in to obtain income. Regarding the respondents of the survey on the level of personal characteristics, statistically, men were more at the level of personal characteristics, where their average age was 56 years old and general education level was junior high school, and most of them were mainly engaged in farming and sideline work. At the household level, the proportion of off-farm employment and the labor force was mostly about 50%. Farmers’ forestry cooperatives were available in various places, but the number of farmers joining them was very small. At the level of village characteristics, the population of out-of-home workers accounted for about 50% of the total population of the countryside, and the average per capita income from forestry accounted for 21.20% of the average per capita income in the village. The mean value was 0.212, indicating that forestry income is still low in the proportion of total income.

4.2. Benchmark Regression: Granting Forest Certificates and Total Rural Household Income

Table 2 shows the findings of the empirical analysis investigating how granting forest certificates affects the total income of rural households. Disregarding the control variables, specifically in Model 1, this study’s findings indicate a significant positive correlation between granting forest certificates and the total income of rural households at a significance level of 1%. However, when control variables were included, Models 1 to 13 demonstrate that granting forest certificates continues to have a noteworthy and beneficial impact on the total income of rural households. The increase in the total income of rural households can be attributed to the clarification of property titles after the CFTR, enabling farmers to acquire forest title mortgages. This has resulted in an increase in the total income of rural households. Furthermore, granting forest certificates can also contribute to an increase in income by simplifying the lease of forestland.

At the level of individual characteristics, Models 2 through 13 indicate that there was no significant effect of gender on the total household income of farm households. Models 3 to 13 show that age was negatively correlated with total farm household income. The reason for this may be that as the source of income continues to expand, the restrictions in terms of gender diminish and women are also able to earn income in various ways, so the differences brought about by gender are minimal. In addition, the older the average age of the household members, the less labor is invested in their work, and their total household income decreases. Models 4 to 13 indicate that there was a positive relationship between education level and total household income. Models 5 to 13 indicate that the lower the income from farming in the composition of the income structure, the higher the total household income. This result was significant at the 1% significance level, which may be due to the relatively low income from farming, indicating why more people choose to leave their hometowns and go out to work. Models 6 to 13 indicate that the total household income of village cadres was less than that of non-village cadres, and this result was significant at the 1% significance level.

Models 7 to 13 indicate that the total number of households had a significant positive impact on total rural household income, without considering the percent of off-farm employment. However, when off-farm employment was taken into account, this relationship became insignificant. Models 8 to 13 demonstrate that the number of laborers had a significant positive effect on total rural household income. Models 9 to 13 establish that the share of off-farm employment had a significant positive effect on the total household income of rural households. Lastly, Models 10 to 13 reveal that membership in farmers’ forestry cooperatives had a significant positive effect on the total household income of rural households.

Models 11 to 13 indicate that the degree of forestland fragmentation had a positive effect on the total household income of rural households at the 5% significance level when village-level characteristics were not taken into account, while the relationship between the two was not significant when village-level characteristics were taken into account. The possible reasons for this are that about 50 percent of the rural labor force chose to work outside the village, and the average per capita forestry income in a village accounts for a small proportion of the average per capita total income in a village, so the effect of forestland fragmentation on the total income of the farming household is not significant.

4.3. Impact of Granting Forest Certificates on Farmer Forestry Income

The impact of forestland leases on farmer income is shown in Table 3. Models 14 to 26 indicate that granting forest certificates did not have a substantial impact on forestry income. One possible reason for this is that the CFTR emphasized the importance of clarifying the granting of forest certificates as a key aspect of the reforms. This led to a high rate of granting forest certificates in rural areas. However, when examining the relationship between granting forest certificates and forestry income, it was found that the proportion of granted forest certificates did not have a significant impact on the regression results. Another potential reason could be the extended intervals between forestry operations and the gradual return of income. Additionally, since this study only considered two years of data, the effect of granting forest certificates on forestry income may not have been accurately captured, resulting in a certain degree of underestimating forestry income.

Models 15 to 26 indicate that gender had a positive effect on farmer forestry income at the level of personal characteristics, but the relationship between the two became insignificant with the addition of the forestland fragmentation variable. The addition of age to Model 16 indicates that there was a significant negative correlation between age and farmer forestry income. After adding farmer education level to Model 17, it was found that the influence of education level and occupation on farmer forestry income was not significant. Models 18 to 26 confirm that the forestry income of village cadres was less than that of non-village cadres, and this result was significant at the 1% significance level. Forestry income was not significant, which may be due to the fact that forestry labor does not require a high level of education.

Models 19 to 26 indicate that at the level of household characteristics, membership in farmers’ forestry cooperatives had a positive effect on the forestry income of farmers at the 1% significance level. This may be attributed to the fact that after joining farmers’ forestry cooperatives, cash and labor inputs on forestland become more specialized through cooperation and exchange, which will increase the level of outputs and thus increase forestry income.

According to Models 24 to 26, the degree of forestland fragmentation had a negative effect on farmer forestry income at the 1% significance level, that is, because of the existence of management costs and other factors, forestland fragmentation leads to a decline in farmer forestry income. Model 25 and Model 26 confirm that the share of village forestry income had a positive effect on farm household forestry income at the 1% significance level. The reason for this may be that when the share of village forestry income increases, other farmers increase their forestry operation behavior when they consider forestry operation profitable, thus increasing household forestry income.

4.4. Mediating Effects of Forestland Lease

The regression results with forestland lease out as a mediating variable are presented in Table 4. Model 27 explores the effect of granting forest certificates on forestland lease out, and the results indicate that the granting of forest certificates had a positive effect on forestland lease out. A possible reason for this is that the forestland titling program has increased the security of forestland ownership for farmers. There is also some legal protection for the forestland that has been leased out. Model 28 explores the effect of forestland lease out on the total household income of farmers, and the results indicate that there was a positive relationship between the two at the 1% significance level. In model 29, granting forest certificates and forestland lease out were both included to explore their impact on the total household income of farm households, and the results show that there was a positive relationship between granting forest certificates and leasing out forestland on the total household income of rural households at the 1% significance level. That is, granting forest certificates can positively affect the total household income of rural households, and the lease out of forestland has a positive effect on the income of rural households. Forestland lease out has a mediating effect on the impact mechanism. All in all, granting forest certificates contributes to the increase in total household income through forestland lease out. Granting forest certificates can facilitate the lease of forestland by increasing farmers’ perceptions of security. As a direct result of the lease of forestland, farmers are able to earn income from forestland leases, which in turn increases their total household income. In terms of indirect effects, farmers are able to transfer more labor resources to non-farm industries. Because of the higher comparative returns from non-farm employment, an increase in total household income will be realized.

4.5. Grouped Regression Results at the Forestland Scale

According to the above, granting forest certificates has a significant impact on the total household income of farmers, while the impact on forestry income is not significant. Therefore, the dependent variable of the group regression was selected as the total household income. In order to make the result more significant, the independent variable of forestland size was assigned the value of “1” for more than 100 acres, “2” for less than 50 acres, “3” for 50–100 acres, and “4” for less than 50 acres. The regression results are shown in Table 5.

For farmers with a forestland area of less than 50 acres, their total household income was influenced by whether the head of household is a village cadre or not, the number of people in the household labor force, the percentage of off-farm employment, and the degree of forestland fragmentation. The total household income of farmers whose head of household is a village cadre was higher than the total household income of those whose head of household is not a village cadre. This result was significant at the 10% level of significance, which may be caused by the fact that when the head of the household is a village cadre, the head of the household’s salary is relatively fixed, and those who are not village cadres have more ways to obtain income. The household labor force population was positively related to the total income of the farming household at the 5% level of significance. The share of off-farm employment had a positive effect on total farm household income at the 1% significance level. The degree of forestland fragmentation had a negative effect on total farm household income at the 10% level of significance, which is different from the ungrouped regression results, indicating that the degree of forestland fragmentation can affect total farm household income only when the forestland owned by a farmer is small.

For farmers who own forestland between 50 and 100 acres, their total household income was influenced by the following factors: occupation and the number of people in the household labor force. Within the occupational structure, there was a negative correlation between the proportion of individuals engaged in farming and their total household income. This correlation was shown to be statistically significant at the 5% level of significance. The share of off-farm employment had a positive effect on the total household income of the farm household at the 1% level of significance. The combination of the two results suggests that income from labor is higher than income from farming for farmers with this level of forestland area.

For farm households granted forest certificates of more than 100 acres, their total household income was influenced by the following factors: granting forest certificates, age, gender, education, occupation, whether they are village cadres, the number of people in the household labor force, and the percentage of off-farm employment. Granting forest certificates had a significant positive effect on the total household income of farmers at the 5% level of significance. Age had a significant negative effect on total farm household income at the 10% significance level. The total household income of farm households with a high level of education was more than that of farm households with a low level of education, and this result was significant at the 10% level of significance. In the occupational structure, the higher the share of farming, the lower the total household income, and this result was significant at the 5% level of significance. The total household income of farming households whose head is a village cadre was higher than that of households whose head is not a village cadre, and this result was significant at the 5% level of significance. The household labor force size was positively related to the total household income of farm households at the 1% level of significance. The share of off-farm employment had a positive effect on total farm household income at the 1% level of significance.

5. Discussion

The following section examines both the advantages and disadvantages of this study, along with the unexpected discoveries. Initially, in terms of data selection, our study includes multiple instances of panel data from the same study conducted between 2017 and 2018, which enhances the reliability of our findings. However, a mere two years of replicated data is insufficient to accurately represent the long-term dynamics of results, particularly in the forestry business. Rather, a systematic analysis is required over an extended period of time when the data may be organized in a systematic manner. Simultaneously, significant disparities exist in the abundance of forestry resources and the level of economic development across various locations, resulting in potential biases in the conclusions drawn. The scope of data collection for this study was limited to Jiangxi Province, so the findings of this study may not be generalizable to other places. In light of this, our team will augment the quantity of data in the future in order to investigate the long-term and universal implications of the findings.

Furthermore, while examining the income of agricultural households, many scholars have not thoroughly analyzed the many components of income. Some have solely focused on forestry income [18] or total household income [8] as the dependent variable, without considering other variables. Unlike other approaches, this study categorizes the income of rural households into total household income and forestry income. By combining these two sources, this study aimed to conduct a more precise analysis to examine the influence of granting forest certificates on the level and composition of rural household income. Our research indicates that issuing titles for forestland leads to an increase in the total income of rural households. This finding aligns with previous studies [17]. Nevertheless, our research findings indicate that granting forest certificates has no major impact on the forestry income of farmers, thus contradicting our first hypothesis. One possible cause for this could be the limited duration of data collection, which may not accurately capture fluctuations in forestry income. However, this study’s conclusion is not significant because of the high rate of granting forest certificates in rural areas and the tiny interval of change in statistical time.

Regarding research perspectives, this paper examined the influence of granting forest certificates on rural household income. It included forestland lease as a mediating variable and categorized it into lease in and lease out. This allowed for the creation of two theoretical frameworks including the following: “granting of forest certificates–forestland lease out–total household income” and “granting of forest certificates–forestland lease in–forestry income”. Based on empirical evidence, issuing titles to forestland can encourage the leasing of forestland, leading to an increase in total household income. This supports hypotheses 1 and 3; however, the impact on hypothesis 2 is not clearly evident. Additionally, hypothesis 1 supported and enhanced the discoveries made in reference [56]. This offers an interesting method for investigating the influence of granting of forest certificates on farmer income.

6. Conclusions and Policy Implications

This study examines the impact of granting of forest certificates on farmer income in the context of the CFTR. Using panel research data from farmers in Jiangxi Province between 2017 and 2018, a linear regression model was used to analyze the relationship between granting forest certificates and farmer income. This study incorporated forestland leasing as a mediating variable in the model to gain a deeper understanding of the mechanism of action between the two. This study arrived at the following conclusions: first, the rate of granting forest certificates to farmers is high. From the viewpoint of farmer business behavior, farmers prefer forestland lease in rather than forestland lease out behavior. In terms of business mode, farmers are mainly in single-family and joint-family business mode. Second, granting forest certificates has a significant positive effect on total household income but not on forestry income. Granting forest certificates has a direct effect on the total household income of farmers through the supporting policies of the CFTR. It also indirectly affects total household income through the investment effect and the mortgage loans effect. However, since forestry is a relatively long periodic industry, the impact of granting forest certificates on farmer forestry income is not significant. Third, granting forest certificates can promote the lease out of forestland, but the effect on the lease in of forestland is not obvious. Granting forest certificates can improve people’s knowledge of their own rights, reduce disputes over forestland, and provide institutional safeguards for the forestland lease market, thus reducing the cost of transaction fees for leasing out forestland. However, people prefer granted forest certificates, resulting in a smaller scale of forestland lease, which leads to a non-significant impact of granting forest certificates on forestry leases. Fourth, granting forest certificates contributes to the increase in total household income through forestland lease out. Farmer income is mainly divided into forestry income and off-farm employment income. Compared with off-farm employment income, it usually takes a longer time to obtain forestry income through forestry products, while off-farm labor is rewarded more quickly. In a certain period of time, this is reflected in more off-farm income than forestry income. Therefore, when forestland is leased out of the forest and farmers transfer the human investment originally used in forestry to off-farm employment, the substitution effect occurs, resulting in an increase in total household income.

This study also provides policy advice on how to achieve the income-generating impacts of the CFTR effectively. Initially, it is imperative to advocate for the CFTR, thus enhancing the forest title system and augmenting the issuance of forest titles. The forest title, functioning as a legally binding instrument, enhances farmer awareness of the legal security of forestland. Furthermore, the issue of forestland certificates can aid in clarifying farmer property titles and diminishing forestland conflicts. Simultaneously, the granting of forest certificates stimulates farmers to diversify their management methods, thus achieving the desired outcome of income growth. Ultimately, the granting of forest certificates is a crucial element in advancing the CFTR, as it plays a vital role in facilitating the seamless implementation of the CFTR and attaining increased income for farmers. Furthermore, it actively advances the development of the forestland lease market and enhances the legal framework for forestland lease. Leasing forestland can effectively generate money as a result of the CFTR. Currently, China is facing the challenges of an aging population and the division of forestland, which significantly impede the lease of forestland. To achieve the goal of increasing farmer income through forestland lease, the government should actively facilitate the development of the forestland lease market, incentivize farmers to lease unproductive forestland, and enhance the total effectiveness of forestland management. Simultaneously, the government should enhance the legal framework for the lease of forestland to ensure that farmers can lawfully acquire money from such leases. This will boost farmers’ motivation to engage in forestland leases and consequently lead to an increase in their income. Finally, the government should aim to enhance the oversight of forestry, implement adaptable transformations to forestland management, and elevate the effectiveness of farmer management practices. The government should enhance oversight to prevent the illicit lease of forestland. Simultaneously, farmers should be granted the autonomy to select their own management methods, enabling them to opt for more efficient approaches based on their own circumstances. Simultaneously, the limited technical expertise of farmers and the absence of viable financing options will impede the successful implementation of efficient farmer management. The government can address this by offering specific forestry subsidies, broadening farmers’ access to financing, promoting rural migration among young individuals, and encouraging enterprises to provide communal services.

Author Contributions

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

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Acknowledgments

The authors appreciate the editors and anonymous reviewers for their valuable comments and suggestions.

Conflicts of Interest

The authors declare no conflicts of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures and Tables

Figure 1. Mechanisms underlying the effect of granting forest certificates on household income.

Figure 2. Locations of sample villages.

Figure 3. Flowchart of Research.

Table 1

Definition of the variables involved in the model.

Variable		Variable Symbol	Definition	Expected Impact	Mean	SD
Dependent variable	Total household income	lnincome	Total household income (CNY) taken in logarithms	——	11.067	1.127
	Forestry income	lnfincome	Total household forestry income/household forestland area (CNY/hectare)	——	8.260	1.940
Independent variable	Proportion of forestland certificates owned	certificate	Whether the forestland certificates is in your possession (No = 0; Yes = 1)	Positive relationship	0.951	0.217
Grouping variable	Forestland area classification	areatype	Area codes: 1 = less than 50 acres, 2 = 50–100 acres, 3 = more than 100 acres	Positive relationship	1.853	0.908
	Business model	mtype	Business model: 1—single-family, 2—joint-family, 3—family forestry, 4—company forestry, 5—joint-stock cooperation	Positive relationship	1.516	1.167
Intermediary variable	Scale of forestland lease in	rin	The area of lease in	Positive relationship	28.297	228.934
	Scale of forestland lease out	nout	The area of lease out	Positive relationship	1.428	1.318
Instrumental variable	Proportion of village forestland certificates issued	VRcer	Number of village forestland certificates actually issued/due for issuance	Positive relationship	0.934	0.307
Personal characteristics of the head of household	Gender	gender	Sex of head of household (M = 1; F = 0)	Positive relationship	0.943	0.231
	Age	age	Age of head of household in year of survey (actual years)	Negative relationship	56.414	10.253
	Educational level	edu	Educational level of the head of household (elementary school and below = 1; middle school = 2; middle or high school = 3; college or bachelor’s degree or higher = 4	Positive relationship	1.892	0.826
	Occupation	ocp	Farming = 1; farming and part-time work = 2; farming and part-time work = 3; permanent work outside the home = 4; regular wage income = 5; other = 6	Positive relationship	2.800	1.905
	Whether village cadre	cadre	Whether the head of household is a village cadre (Yes = 1; No = 0)	Positive relationship	0.389	0.494
Family characteristics	Total household population	num	Total household population (persons)	Positive relationship	4.821	2.166
	Number of laborers	numlabor	Number of family laborers (persons)	Positive relationship	2.800	1.429
	Percentage of off-farm payrolls	Rout	Household off-farm employment/total household labor force	Positive relationship	0.506	0.511
	Membership in farmer forestry cooperatives	wcooperation	Are you a member of a farmers’ forestry cooperative? (no local cooperative = 0; yes, but not joined = 1; joined = 2)	Positive relationship	0.215	0.411
Woodland characteristics	Woodland fragmentation	frafor	Household woodland area/number of woodland plots	Negative relationship	25.529	48.369
Village characteristics	Percentage of labor transfer at the village level	Vmig	Number of permanent migrant workers in the village/total population of the village	Positiverelationship	0.528	0.242
	Percentage of village forestry revenue	Vfincome	Average per capita income from forestry in the village/average total per capita income	Positive relationship	0.212	0.206

Table 2

Regression results for the effect of granting forest certificates on total rural household income.

Variables	Dependent Variable: Lnincome
	Model 1	Model 2	Model 3	Model 4	Model 5		Model 6
certificate	0.003 *** (0.001)	0.003 *** (0.001)	0.002 ***(0.001)	0.002 **(0.001)	0.002 **(0.001)		0.002 **(0.001)
gender		0.079(0.154)	0.122(0.153)	0.121(0.152)	0.114(0.152)		0.107(0.155)
age			−0.016 ***(0.004)	−0.010 ***(0.003)	−0.010 ***(0.004)		−0.010 **(0.004)
edu				0.208 ***(0.044)	0.174 ***(0.047)		0.167 ***(0.047)
ocp					0.057 ***(0.020)		0.062 ***(0.020)
carde							−0.188 ***(0.071)
constant	11.048 ***(0.038)	10.972 ***(0.149)	11.840 ***(0.267)	11.132 ***(0.277)	11.044 ***(0.278)		11.049 ***(0.280)
R2	0.007	0.008	0.028	0.049	0.057		0.061
Variables	Dependent Variable: Lnincome
	Model 7	Model 8	Model 9	Model 10	Model 11	Model 12		Model 13
certificate	0.002 **(0.001)	0.002 ***(0.001)	0.002 **(0.001)	0.001 **(0.001)	0.001 ***(0.001)	0.001 **(0.001)		0.001 **(0.001)
gender	0.071(0.144)	0.092(0.144)	0.062(0.130)	0.058(0.129)	0.038(0.130)	0.056(0.143)		0.055(0.143)
age	−0.011 ***(0.004)	−0.008 **(0.004)	−0.010 *** (0.004)	−0.010 **(0.004)	−0.009 **(0.004)	−0.009 **(0.004)		−0.009 **(0.004)
edu	0.160 ***(0.047)	0.145 ***(0.046)	0.148 ***(0.044)	0.141 ***(0.044)	0.129 ***(0.043)	0.099 **(0.045)		0.010 **(0.045)
ocp	0.058 ***(0.019)	0.060 ***(0.019)	0.059 ***(0.018)	0.058 ***(0.019)	0.054 ***(0.018)	0.055 ***(0.020)		0.055 ***(0.020)
carde	−0.191 ***(0.069)	−0.205 ***(0.068)	−0.262 ***(0.066)	−0.256 ***(0.066)	−0.256 ***(0.067)	−0.221 ***(0.070)		−0.222 ***(0.071)
num	0.126 ***(0.016)	0.053 **(0.022)	0.026(0.022)	0.026(0.023)	0.017(0.022)	0.006(0.025)		0.006(0.025)
numlabor		0.155 ***(0.032)	0.206 ***(0.033)	0.204 ***(0.033)	0.214 ***(0.032)	0.210 ***(0.034)		0.210 ***(0.034)
rout			0.631 ***(0.071)	0.622 ***(0.070)	0.631 ***(0.070)	0.639 ***(0.074)		0.638 ***(0.074)
wcooperation				0.121 **(0.054)	0.116 **(0.053)	0.129 **(0.060)		0.130 **(0.061)
frafor					0.002 ***(0.001)	0.001(0.001)		0.001(0.001)
vmig						0.115(0.138)		0.112(0.138)
vfincome								−0.028(0.169)
constant	10.624 ***(0.263)	10.355 ***(0.264)	10.187 ***(0.260)	10.162 ***(0.260)	10.152 ***(0.261)	10.127 ***(0.291)		10.135 ***(0.301)
R2	0.121	0.139	0.209	0.210	0.216	0.195		0.195

Note: *** p < 0.01, ** p < 0.05. Robust standard errors are presented in parentheses.

Table 3

Regression results for the effect of granting forest certificates on farmer forestry income.

Variables	Dependent Variable: lnfincome
	Model 14	Model 15	Model 16	Model 17	Model 18		Model 19
certificate	0.000 (0.001)	−0.000 (0.001)	−0.001 (0.001)	−0.001 (0.001)	−0.001 (0.001)		−0.001 (0.001)
gender		0.693 **(0.298)	0.750 ** (0.292)	0.749 ** (0.292)	0.752 ** (0.289)		0.725 ** (0.295)
age			−0.024 ***(0.009)	−0.023 ***(0.009)	−0.023 **(0.009)		−0.020 **(0.009)
edu				0.018(0.107)	0.036(0.111)		0.078(0.110)
ocp					−0.026 ***(0.047)		−0.009(0.046)
carde							−0.736 ***(0.157)
constant	8.254 ***(0.088)	7.608 ***(0.287)	8.888 ***(0.551)	8.827 ***(0.639)	8.840 ***(0.641)		8.780 ***(0.635)
R2	0.000	0.007	0.022	0.022	0.022		0.053
Variables	Dependent Variable: Lnfincome
	Model 20	Model 21	Model 22	Model 23	Model 24	Model 25		Model 26
certificate	−0.001(0.001)	−0.001 (0.001)	−0.001 (0.002)	−0.002 (0.001)	−0.001 (0.001)	−0.001 (0.001)		−0.001 (0.001)
gender	0.723 ***(0.295)	0.727 ** (0.296)	0.667 ** (0.315)	0.585 ** (0.295)	0.461 (0.296)	0.443 (0.322)		0.476 (0.331)
age	−0.020 **(0.009)	−0.019 **(0.009)	−0.020 **(0.009)	−0.020 **(0.009)	−0.018 **(0.008)	−0.019 **(0.009)		−0.019 **(0.009)
edu	0.076(0.110)	0.075(0.111)	0.063(0.111)	0.022(0.109)	0.050(0.104)	0.099(0.120)		0.084(0.119)
ocp	−0.010(0.046)	−0.009(0.046)	−0.006(0.047)	−0.025(0.047)	−0.037(0.042)	−0.019(0.048)		−0.016(0.049)
carde	−0.736 ***(0.157)	−0.736 ***(0.157)	−0.708 ***(0.160)	−0.643 ***(0.161)	−0.675 ***(0.157)	−0.495 ***(0.175)		−0.460 ***(0.175)
num	−0.006(0.036)	−0.004(0.046)	−0.008(0.048)	−0.017(0.048)	−0.007(0.046)	−0.016(0.053)		−0.024(0.052)
numlabor		0.022(0.073)	0.012(0.076)	0.017(0.048)	0.050(0.071)	0.078(0.077)		0.086(0.077)
rout			−0.142(0.154)	−0.222(0.149)	−0.189(0.149)	−0.192(0.150)		−0.165(0.152)
wcooperation				0.659 *** (0.133)	0.627 *** (0.128)	0.677 *** (0.143)		0.650 *** (0.141)
frafor					0.010 ***(0.002)	0.010 ***(0.003)		0.010 ***(0.003)
vmig						0.049(0.365)		0.136(0.366)
vfincome								0.993 ***(0.358)
constant	8.762 ***(0.651)	8.717 ***(0.663)	8.883 ***(0.673)	8.784 ***(0.649)	8.691 ***(0.622)	8.722 ***(0.710)		8.356 ***(0.725)
R2	0.054	0.054	0.053	0.110	0.172	0.148		0.160

Note: *** p < 0.01, ** p < 0.05. Robust standard errors are presented in parentheses.

Table 4

Regression results of the mediating effect of forestland lease out.

	Model 27	Model 28	Model 29
Variables	Nout	Lnincome	Lnincome
certificate	0.0018		0.0020 ***
	(1.5963)		(2.7842)
nout		0.3438 ***	0.3413 ***
		(13.0132)	(13.0118)
Constant	1.4159 ***	10.5858 ***	10.5747 ***
	(30.6321)	(179.8228)	(178.5880)
Observations	991	983	983
R-squared		0.1614	0.1658
Number of id	530

Note: *** p < 0.01. Robust standard errors are presented in parentheses.

Table 5

Grouped regression results.

Dependent Variable: Lnincome
Variables	Areatype = 3	Areatype = 2	Areatype = 1
certificate	−0.001(0.003)	0.017(0.013)	0.002 **(0.001)
gender	−0.197(0.454)	−0.040(0.399)	0.161(0.161)
age	−0.015(0.012)	−0.005(0.014)	−0.009 *(0.005)
edu	0.096(0.139)	0.190(0.141)	0.090 *(0.052)
ocp	0.077(0.075)	0.127 **(0.062)	0.043 **(0.022)
carde	−0.436 *(0.226)	−0.370(0.276)	−0.179 **(0.082)
num	−0.105(0.118)	0.126(0.078)	0.001(0.027)
numlabor	0.417 **(0.165)	0.184(0.121)	0.195 ***(0.037)
rout	1.016 ***(0.269)	0.544 ***(0.178)	0.628 ***(0.088)
wcooperation	0.183(0.265)	−0.018(0.677)	0.053(0.125)
frafor	0.008 *(0.004)	0.003(0.003)	0.001(0.001)
vmig	0.514(0.436)	−0.657(0.490)	0.131(0.143)
vfincome	−0.243(0.449)	−0.221(0.561)	0.010(0.200)
constant	10.206 ***(0.869)	9.590 ***(1.153)	10.111 ***(0.345)
R2	0.300	0.322	0.182

Note: *** p < 0.01, ** p < 0.05, and * p < 0.1. Robust standard errors are presented in parentheses.

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