Exploring the influence of cognitive differences on farmers’ participation in forestry carbon sequestration projects: evidence from China

Enhancing farmers’ cognition of forest carbon sequestration management and strengthening their agency in project participation are crucial strategies for addressing global climate change. Drawing on empirical evidence from bamboo industry clusters in Zhejiang Province, China, this study integrates cognitive behavioral theory with the Unified Theory of Acceptance and Use of Technology (UTAUT) framework. Using bivariate probit models and moderated mediation analysis, we examine how multidimensional cognitive factors shape farmers’ participation intentions and actual engagement in forest carbon sequestration projects. Results show that economic, ecological, and social cognition significantly increase participation probability by 1.6%, 2.8%, and 3.0%, respectively, whereas risk cognition reduces it by 2.7%. Policy cognition exerts the strongest effect, raising participation likelihood by about 10%. Land transfer significantly moderates the relationship between policy cognition and participation, enhancing farmers’ ability to act on policy awareness. Heterogeneity analysis indicates that cognitive effects vary across age groups and village leadership status, with elderly farmers and village cadres exhibiting distinct participation mechanisms. The study concludes with targeted policy recommendations to promote smallholder engagement in forest carbon sequestration, contributing to sustainable agroforestry governance and regional carbon sequestration goals.

1 Introduction

Forests play a critical role in mitigating climate change by sequestering carbon and supporting emission reduction targets (Phan et al., 2014; Briones et al., 2014). International agreements, including the 2015 Paris Agreement and earlier UNFCCC accords, recognize forests as essential for limiting global temperature increases to below 2°C above pre-industrial levels (UNFCCC, 2015). Maintaining ecosystem integrity and enhancing vegetation carbon stocks are therefore central strategies for climate mitigation (Watson et al., 2018; Babbar et al., 2021). Afforestation and forest management represent cost-effective, nature-based solutions (NBS) that can significantly contribute to climate change mitigation (Cai et al., 2022; Cammarata et al., 2025).

China has actively promoted forest carbon markets as part of its nationwide carbon trading system, issuing policy frameworks and methodological guidelines to incentivize forest and grassland carbon projects (Ke et al., 2023; DFZP, 2015). Bamboo-based carbon projects, in particular, offer high carbon sequestration efficiency, economic and ecological co-benefits, and suitability for ecologically fragile areas, making them a promising forestry carbon option. Globally, bamboo forests cover over 30 million hectares, primarily in Asia, with rapid growth rates that enable efficient CO2 absorption and biomass carbon storage (FAO, 2010; Yuan et al., 2020; Yen and Lee, 2011). Moso bamboo (Phyllostachys edulis), the dominant species in China, accounting for more than 70% of the country’s total bamboo forest area. Due to its fast growth rate and high capacity for biomass accumulation, Moso bamboo exhibits superior carbon accumulation potential (SFAPRC, 2015). Zhejiang Province is one of China’s major Moso bamboo producing regions, accounting for approximately 14.05% of the national Moso bamboo forest area, and ranks third nationally in Moso bamboo industry output value (DFZP, 2015; Perez et al., 1999). Therefore, Zhejiang Province holds substantial resources suitable for carbon project development and can be regarded as a representative study area (Chen et al., 2009; Song et al., 2011; Xu et al., 2018).

Despite this potential, bamboo-based carbon projects remain underdeveloped, largely due to methodological gaps, complex monitoring requirements, management challenges, and low farmer awareness. Following collective forest tenure reforms, farmers are primary forestry operators, yet most projects are government-driven, and autonomous participation is limited (Zeng et al., 2017; Hu and Zeng, 2020). Public cognition and perception play a pivotal role in shaping participation decisions, particularly under conditions of market uncertainty and ecological risk (Pratt, 1964; Arrow, 1971). Farmers play a central role in forest carbon sink projects, as their management practices directly determine the sustainability of these initiatives. Their engagement is shaped not only by economic incentives but also by external factors, including risk perceptions and policy frameworks, which influence how farmers perceive and evaluate the projects. Essentially, variations in management behaviors largely reflect differences in farmers’ cognitive understanding. Although prior studies have examined farmers’ willingness to participate, there remains a gap in understanding how these cognitive differences translate into actual operational behaviors (Slovic, 1987; Tam and McDaniels, 2013).

To address these knowledge gaps, this study applies an “environment–agent–behavior” framework to examine how cognitive differences influences farmers’ intentions and actual participation in bamboo-based forest carbon projects. Drawing on the research paradigm of the Unified Theory of Acceptance and Use of Technology (UTAUT) proposed by Venkatesh et al. (2003), this study incorporates farmers’ psychological perceptions of external factors into the formation of their participation intentions, thereby opening the “black box” of the drivers behind farmers’ engagement in forestry carbon sink projects. By integrating environmental context, individual cognition, and behavioral outcomes, the research investigates both direct effects and underlying mechanisms, while exploring heterogeneity across village- and individual-level characteristics. This approach provides empirical insights for designing policies and interventions to enhance smallholder engagement in carbon sequestration initiatives.

The remainder of this paper is organized as follows. Section 2 presents the theoretical framework and research hypotheses. Section 3 details the study design, including research area, data sources, variable definitions, and model construction. Section 4 reports the regression results. Section 5 discusses conclusions and policy implications.

2 Theoretical framework and hypotheses

2.1 Theoretical framework

Cognition is the foundation of behavior, as individuals’ observations and understanding of objects form their cognitive systems, which in turn guide actions (Ajzen and Fishbein, 1975; Fishbein, 1975). According to Ajzen’s Theory of Reasoned Action, behavior arises from attitudes formed through cognitive evaluation, based on the assumption that humans are rational (Ajzen, 1991). The “knowledge–affect–behavior” theory posits that behavioral change is a sequential process: individuals first acquire knowledge (“cognition”), develop emotions (“affect”), and ultimately act under the combined influence of both (Westbrook and Oliver, 1991; Frijda, 1993). Based on these theoretical foundations, Davis proposed the Technology Acceptance Model (TAM) in the field of information technology to explain and predict individuals’ adoption of new technologies, defining usage decisions in terms of behavioral intention and actual use (Davis et al., 1989). Later TAM extended into the UTAUT. UTAUT includes four core dimensions: performance expectancy (PE), effort expectancy (EE), social influence (SI), and facilitating conditions (FC), which respectively reflect perceptions of system utility, ease of use, social pressure, and organizational support (Venkatesh et al., 2003). This framework has been widely applied to study technology adoption and decision-making, and it can be extended by incorporating context-specific variables. For most farmers, participation in forestry carbon sink projects involves adopting entirely new management practices and carbon monitoring technologies. Performance expectancy refers to the degree to which an individual perceives that adopting carbon sink technologies will be beneficial to their work. Effort expectancy denotes the amount of effort perceived to be required to use these technologies. Social influence reflects the extent to which an individual feels affected by the surrounding group, while facilitating conditions indicate the degree of organizational support perceived by the individual regarding relevant technologies and equipment for carbon sink implementation. Drawing on the UTAUT framework, this study provides a detailed depiction of farmers’ perceptions of carbon sink projects and systematically examines how different cognitive dimensions influence both participation intentions and actual management behaviors.

Forest carbon projects provide ecological, social, and economic benefits. Farmers’ decisions are influenced not only by expected returns and policy support but also by cognitive perceptions. Existing research often emphasizes cost–benefit analysis, overlooking psychological factors. Farmers, as the primary implementers of carbon projects, face decisions shaped by risk perceptions, policy regulation, and potential economic and ecological outcomes. From a cognitive perspective, variations in participation behavior reflect differences in farmers’ understanding of carbon project benefits and risks. This study investigates how different cognitive dimensions influence farmers’ participation in bamboo forest carbon projects in Zhejiang Province, aiming to identify pathways to enhance engagement and inform policy and incentive design.

2.2 Research hypotheses

Behavioral intention is a prerequisite for action, as the likelihood of behavior depends on individual perception (Dodds et al., 1991). For most farmers, forest carbon projects represent new technologies; therefore, participation intention is a critical driver of actual behavior (Davis, 1989). Certain cognitive factors influence behavior indirectly by shaping intention, reflecting bounded rationality in farmers’decision-making (Venkatesh et al., 2003). Based on this, we propose an extended UTAUT framework for farmer participation (Figure 1).

FIGURE 1

Figure 1. Extended theoretical framework based on the UTAUT model.

2.2.1 Performance expectancy: economic and ecological cognition

According to the rational peasant theory, farmers’ decisions are driven by profit maximization (Schultz, 1987). Farmers’ decisions to participate in forestry carbon sequestration projects are predicated on the belief that engagement in such projects can yield returns exceeding their investments of time, effort, and resources (Frøbert et al., 1998). These performance expectations generally arise from two dimensions of farmers’ perceptions of forestry carbon sequestration projects: economic perceptions and ecological perceptions. Economic cognition refers to the perception of potential financial gains from carbon markets, timber sales, and subsidies, while ecological cognition reflects expected environmental benefits, such as improved forest quality, soil enhancement, and climate mitigation (Zhang et al., 2022).

H1: Economic cognition positively affects farmers’ intention and behavior to participate in forest carbon projects.

H2: Ecological cognition positively affects farmers’ intention and behavior to participate in forest carbon projects.

2.2.2 Effort expectancy: risk cognition

Agriculture is an inherently risky sector, and farmers routinely incorporate risk considerations into their management decisions (Timpanaro et al., 2023). Forest carbon projects involve long development cycles, high initial investments, delayed returns, and multiple risks (Galik and Jackson, 2009). High-risk perception discourages participation and fosters conservative decision-making (Li et al., 2006).

H3: Risk cognition negatively affects farmers’ intention and behavior to participate in forest carbon projects.

2.2.3 Social influence: social cognition

Farmers’ decisions are influenced by social networks and peer behaviors (Winston and Zimmerman, 2003; Miao et al., 2015). Forestry carbon sequestration projects are inherently pro-environmental and involve strong social interaction. Even when farmers do not regularly engage in environmental protection activities, community influences such as social norms and peer behavior may still affect their participation decisions, leading them to make choices that differ from their usual behavior (de Krom, 2017). Social cognition refers to perceived social benefits or costs associated with participation, motivating farmers to act in accordance with group expectations (Li and Dong, 2021; Thomas et al., 2018).

H4: Social cognition positively affects farmers’ intention and behavior to participate in forest carbon projects.

2.2.4 Facilitating conditions: policy cognition

According to the Theory of Planned Behavior, individual intention influences behavior, and stronger intention facilitates behavioral implementation (Savalia et al., 2016). However, intention alone is insufficient, as actual behavior also depends on whether individuals possess the necessary conditions and technical knowledge (Baum and Gross, 2017). The Porter–Lawler Motivation Model further indicates that effort is shaped by the value of expected rewards and the perceived likelihood of obtaining them (Lawler and Porter, 1967). Existing studies have shown that institutional conditions strongly influence responses to climate change (Stadelmann-Steffen, 2011). Effective policies cannot only alter public perceptions of climate change but also significantly increase the likelihood of behavioral change (Leiserowitz, 2006; Baum and Gross, 2017). Accordingly, farmers’ participation in carbon sequestration projects within a given period largely depends on policy support and their perceptions of expected benefits (Han et al., 2017). Policy cognition refers to farmers’ understanding of carbon project policies and benefits. Awareness of policy support can strengthen actual behavior (Tang, 2007; Shen and Liang, 2018).

H5: Policy cognition positively affects farmers’ behavior in forest carbon project participation.

2.2.5 Moderating effect: land transfer

Land transfer moderates the relationship between policy cognition and participation. Larger landholdings reduce per-unit costs, enhance risk tolerance, and encourage long-term investment, promoting participation in high-investment, long-return carbon projects (Tanaka et al., 2010). Farmers without transferred land may face size constraints, limiting participation despite high policy awareness.

H6: Land transfer positively moderates the effect of policy cognition on farmers’ participation behavior.

3 Materials and methods

3.1 Study area and data collection

This study was conducted in Zhejiang Province, located on the southeastern coast of China (118°01’–123°10’ E, 27°02’–31°11’ N), covering an area of 10.55 million hectares and comprising 11 prefecture-level cities. According to the third national land resources survey of Zhejiang Province, the forest area reached 6.0936 million hectares (91.4036 million mu) in 2021, with a forest coverage rate of 61.36%. Nearly 70% of the forests are classified as young and middle-aged, indicating considerable potential for forest quality improvement. Bamboo forests account for 906,300 hectares (13.595 million mu), representing 14.87% of the province’s total forest area and 11.99% of China’s total bamboo forest area.

Data for this study were collected through a household survey conducted by the research team in Zhejiang Province, a region with abundant bamboo resources. The survey focused on Anji, Longyou, Suichang, Kaihua, and Lin’an, areas with high forest coverage and rich bamboo plantations, making them priority sites for bamboo forest carbon sequestration projects. Fieldwork was carried out from June 2022 to August 2023 using one-on-one, face-to-face interviews. The questionnaire was developed through expert consultation and team discussion, and enumerators received comprehensive training. Respondents were contacted in advance with the support of local forestry bureaus and received participation compensation. A stratified random sampling method was used: two townships were selected per project, 2–3 villages per township, and 35 households with moso bamboo plantations per village, resulting in 850 households surveyed. After removing invalid responses, 811 valid questionnaires were retained.

3.2 Model selection

3.2.1 Bivariate probit modeling

Following Tang (2007), individual intention influences behavior, and stronger intention facilitates its execution. In the context of bamboo forest carbon projects, a household’s intention to participate can promote the actual adoption of management practices. Since intention and behavior are not fully independent, estimating them separately using standard Probit models may lead to efficiency loss. To account for the correlation between the two decision errors, this study employs a bivariate Probit model, which simultaneously estimates both equations (Greene, 1979) as specified in Equation (1).

$P(Y1,Y2)={\int }_{-\mathrm{\infty }}^{{\mathrm{\beta }}^{\prime }X}\mathrm{\phi }(t)\mathit{\hspace{1em}}d\text{t}=\frac{{\text{e}}^{{\mathrm{\beta }}^{\prime }X}}{1+{\text{e}}^{{\mathrm{\beta }}^{\prime }X}}(1)$

The farmers’ decisions regarding bamboo forest carbon management can be represented by two binary variables: Y1 for participation intention and Y2 for actual participation behavior. Specifically, Y1 = 1 indicates that the farmer has the intention to manage bamboo carbon forests, while Y1 = 0 denotes no such intention. Similarly, Y2 = 1 indicates actual participation in bamboo forest management, and Y2 = 0 indicates non-participation. Combining these two binary variables yields four possible observable outcomes: (1,1) farmers with intention and actual participation, (1,0) farmers with intention but no participation, (0,1) farmers without intention but with participation, and (0,0) farmers with neither intention nor participation. Accordingly, the bivariate Probit model can be specified as follows:

$\left\{\begin{array}{c}\hfill Y_1^{*}={\mathrm{\beta }}_1{x}_1{}^{\prime }+{\mathrm{\epsilon }}_1\hfill \\ \hfill Y_2^{*}={\mathrm{\beta }}_2{x}_2{}^{\prime }+{\mathrm{\epsilon }}_2\hfill \end{array}\right\}(2)$

Here, Y₁* and Y₂* are unobservable latent Variables, x₁’ and x₂’ are vectors of factors influencing farmers’ intention to operate carbon sink forests and management behavior of carbon sink forests, respectively, β1 and β2 are vectors of coefficients to be estimated, and ε1 and ε2 are stochastic perturbation terms and obey the two-dimensional joint normal distribution with correlation coefficients ρ that is,

$\left(\frac{{\mathrm{\epsilon }}_1}{{\mathrm{\epsilon }}_2}\right)\sim N\left\{\left(\frac{0}{0}\right)\left[\begin{array}{cc}\hfill 1\mathrm{\rho }\hfill & \\ \hfill \mathrm{\rho }\mathrm{\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}1}\hfill & \end{array}\right]\right\}(3)$

Y₁* > 0 indicates that farmers’ intention to operate carbon sink forests is positive, i.e., they are willing to operate; similarly, Y₂* > 0 indicates that farmers’ behavior of participating in carbon sink forests is positive, i.e., they are involved in the operation. Therefore, the relationship between Y₁* and Y₁ and Y₂* and Y₂ can be established by the following equation:

$Y_1=\left\{\begin{array}{c}\hfill 1{\mathrm{Y}}_1^{*}>0\hfill \\ \hfill 0{\text{Y}}_1^{*}\le 0\hfill \end{array}\right\}.(4)$

$Y_2=\left\{\begin{array}{c}\hfill 1{\text{Y}}_2^{*}>0\hfill \\ \hfill 0{\text{Y}}_2^{*}\le 0\hfill \end{array}\right\}.(5)$

The only link between the two equations of Eqs. (4) and (5) is the correlation of the perturbation terms ε₁ and ε₂. If ρ = 0, the two equations Eqs. (4) and (5) are equivalent to two separate models. If ρ≠0, there is a correlation between Y1* and Y2*, and the maximum likelihood estimation of the probabilities of the values of Y1* and Y2* can be performed using the bivariate probit model. If ρ > 0, there is a complementary effect between Y1* and Y2*; if ρ < 0, there is a substitution effect between Y1* and Y2*. According to the research object of this paper, taking ρ₁₁ as an example, the specific model calculation process is as follows:

$\begin{array}{c}\hfill {\mathrm{\rho }}_{11}=P(Y_1=1,Y_2=1)=P(Y_1^{*}>0,Y_2^{*}>0)\hfill \\ \hfill =P({\mathrm{\epsilon }}_1>-X_1^{*}{\mathrm{\beta }}_1,{\mathrm{\epsilon }}_2>-X_2^{*}{\mathrm{\beta }}_2)\hfill \\ \hfill =P({\mathrm{\epsilon }}_1<-X_1^{*}{\mathrm{\beta }}_1,{\mathrm{\epsilon }}_2<-X_2^{*}{\mathrm{\beta }}_2)\hfill \\ \hfill ={\int }_{-\mathrm{\infty }}^{X_1^{*}{\mathrm{\beta }}_1}{\int }_{-\mathrm{\infty }}^{X_2^{*}{\mathrm{\beta }}_2}\mathrm{\varphi }(Z_1,Z_2,\mathrm{\rho })\mathit{d}{z}_1\mathit{d}{z}_2\hfill \\ \hfill =\mathrm{\Phi }(X_1^{*}{\mathrm{\beta }}_1,X_2^{*}{\mathrm{\beta }}_2,\mathrm{\rho })\hfill \end{array}(6)$

where φ(Z₁, Z₂, ρ) and Φ(Z₁, Z₂, ρ) are the probability density function and the cumulative distribution function of the standardized two-dimensional normal distribution, respectively, with an expectation of 0, variance of 1, and a correlation coefficient of ρ. This is done by testing the original hypothesis, “H0: ρ = 0,” to determine whether to use two separate Probit models or a bivariate Probit model. If the test result rejects the original hypothesis, it is necessary to use the bivariate Probit model (Chen, 2014).

3.2.2 Binary probit model construction

To assess the impact of farmers’ policy cognitive differences on the business behavior of farmers’ FCSPs, an empirical analysis of hypothesis H5 is planned to be conducted using a binary probit regression model. The model is as follows:

$P(Y)=\mathrm{\theta }({\mathrm{\alpha }}_i+{\mathrm{\beta }}_3{\text{cog}}_{\text{i}}+{\mathrm{\gamma }}_i{C}_i+{\mathrm{\epsilon }}_i)(7)$

In Eq. (1): P is the explanatory variable, the probability that the farmer i has the behavior of participating in carbon sink forest management; cog is the cognitive variable of the farmer, which here stands for the policy cognition; C_i is the control variable, which includes the personal characteristics of the farmer, the family characteristics, and the village characteristics, α_i is the constant term, β₃ and γ_i represent the above explanatory Variables, respectively, regression coefficients, and ε_i is the random disturbance term.

3.2.3 Moderating effect test

Based on the benchmark regression, the moderating variable land transfer and its interaction term with farmers’ policy perceptions are introduced, and the specific model is constructed as follows:

$P(Y)={\mathrm{\alpha }}_i+{\mathrm{\beta }}_4{\text{cog}}_{\text{i}}+{\mathrm{\beta }}_{5}M+\mathrm{\gamma }{C}_i+{\mathrm{\epsilon }}_i(8)$

$P(Y)={\mathrm{\alpha }}_i+{\mathrm{\beta }}_6{\text{cog}}_{\text{i}}+{\mathrm{\beta }}_{7}M+{\mathrm{\beta }}_8{\text{cog}}_{\text{i}}\times M+{\mathrm{\gamma }}_i{C}_i+{\mathrm{\epsilon }}_i(9)$

Here, M is the moderating variable (land transfer); Y is the explanatory variable (farmers’ FCSPs business behavior); cog is farmers’ policy cognition. The analysis of the moderating effect in the model is mainly to estimate and test whether β₈ is significant, if β₈ is significant, it means that M has a moderating effect.

3.3 Variable selection

This study focuses on the effects of differences in economic cognition, ecological cognition, risk perception, and social cognition on farmers’ intentions and behaviors in carbon forest management, and examines the influence of policy cognition on participation behavior. Economic cognition reflects farmers’ perceived value of participating in forestry carbon sequestration projects. Ecological cognition captures farmers’ assessments of the environmental benefits of carbon sequestration forest management. Risk cognition refers to farmers’ perceptions of project-related risks and their tolerance for such risks. Social cognition represents farmers’ perceptions of the social benefits generated by these projects. Policy cognition indicates farmers’ understanding of policies related to carbon sequestration forest management. All cognition dimensions were measured using a 5-point Likert scale, with higher scores indicating stronger cognition. Based on prior studies and data availability, the key explanatory variables are defined as Table 1 (Cammarata et al., 2024; Block et al., 2024; Wu et al., 2025). According to the literature, farmers’ personal characteristics, household attributes, and regional resource endowments play important roles in their decision to engage in carbon forest management. Following Wang et al. (2018), control variables related to carbon forest management decisions were selected from personal and household characteristics; the specific variable definitions and descriptive statistics are presented in Table 1. Considering that some policies are effective only under certain scales, and that larger-scale households often participate in land transfer, land transfer was included as a moderating variable. In the survey, farmers were asked whether they had transferred land, with “yes” coded as 1 and “no” as 0.

TABLE 1

Table 1. Descriptive statistics.

4 Results

4.1 Descriptive analysis

Before the empirical analysis, variance inflation factors (VIF) were used to check for multicollinearity. All VIF values were below 5, indicating no multicollinearity. The Probit model’s marginal effects were also calculated to assess the impact of each variable (Table 2). Results from Model (1) indicate that economic and ecological cognition both have significant positive marginal effects on farmers’ willingness and actual participation in carbon forest management at the 1% level. Specifically, a one–unit increase in economic cognition raises the probability of participation by 1.6%, while a similar increase in ecological cognition increases participation probability by 2.8%. Social cognition also shows a significant positive marginal effect at the 1% level, increasing the likelihood of participation by 3.0%. In contrast, risk cognition exhibits a significant negative marginal effect at the 1% level, with higher risk awareness reducing participation probability by 2.7%, suggesting that perceived uncertainty discourages engagement in carbon forest management. From Model (2) show that policy cognition has a strong and statistically significant positive marginal effect on farmers’ participation behavior at the 1% level. Farmers with higher awareness of carbon afforestation policies are approximately 10.0% more likely to participate in carbon forest management. Overall, these results underscore the role of different forms of cognition in shaping farmers’ participation decisions, with policy cognition showing the largest marginal effect among the examined factors.

TABLE 2

Table 2. Benchmark regression results.

The control variables reveal distinct patterns in farmers’ decision processes. Age has a significant negative effect on participation intentions, indicating that older farmers exhibit lower willingness to adopt novel practices. In contrast, serving as a village cadre, holding a forest tenure certificate, and being a technical demonstration household all significantly enhance participation intentions, reflecting both stronger capacities to engage with new initiatives and greater institutional or tenure security. For actual participation behavior, age, forest tenure certificates, and forestry subsidies exert significant positive effects. Apart from the consistent influence of tenure certificates across both models, the divergence between intention and behavior highlights the practical constraints farmers face when translating willingness into action. Older farmers’ accumulated managerial experience may facilitate their final decision to engage in carbon forestry, while financial and technical barriers make subsidies particularly effective in enabling participation. Overall, these results corroborate Hypotheses 1-5.

4.2 Moderating effect analysis

Results in Table 3 indicate that the interaction between land transfer and policy cognition is positive and significant at the 10% level, suggesting that land transfer significantly moderates the effect of policy cognition on farmers’ participation in forestry carbon projects. Farmers with land transfer are more likely to act on their policy awareness and participate in carbon forestry projects, supporting hypothesis H6. Given the scale requirements of forestry carbon projects, simply improving policy awareness may be insufficient to increase participation. Clearly defining operational standards, scale requirements, or providing dedicated land transfer policies for carbon forestry development may be more effective in promoting engagement.

TABLE 3

Table 3. Regression results of the moderating effect of farmers’ land transfer behavior.

4.3 Heterogeneity analysis

To further explore the sources of cognitive differences, the sample was divided by age and village leadership status. Considering that age and village cadre status significantly influenced the results in previous regressions, farmers were grouped into older adults ( ≥ 60 years, n = 411) and middle-young adults ( < 60 years, n = 400), and into village cadres (n = 274) and non-cadres (n = 537) for heterogeneity analysis.

As shown in Table 4, Model (5) indicates that all five cognition variables are statistically significant at the 1% level for the senior group. For the younger group, ecological, social, risk, and policy cognition remain significant. While economic cognition is not. The coefficient signs are consistent with earlier results. This suggests that elderly farmers place greater weight on expected economic returns when deciding whether to participate in forestry carbon sink projects, whereas younger farmers focus more on non-economic values, making economic cognition insignificant in their decisions. Model (6) shows that, among village cadres, economic cognition has no significant marginal effect, while the other four cognition variables significantly influence participation decisions with consistent directions. For non-cadre farmers, only social cognition is insignificant, and all other cognition variables remain significant. This difference may reflect the fact that village cadres have better access to policy information and a stronger recognition of the social and public value of carbon sink projects, which weakens the role of economic cognition. In contrast, non-cadre farmers are more sensitive to short-term economic returns, increasing the importance of economic cognition.

TABLE 4

Table 4. Subgroup estimation results.

4.4 Endogeneity test

To address potential endogeneity between cognitive differences and farmers’ participation in carbon forestry management, this study employs the widely recognized instrumental variable Probit (IV-Probit) model (Rivers and Vuong, 1988). Careful logic and creativity are required in selecting instruments. Based on the peer effect, individual socioeconomic outcomes are often influenced by characteristics of the collective to which they belong (Chen, 2012). Following Liu et al. (2023), we consider that farmers’ cognitions are strongly shaped by other residents within the same village. We use village-level averages excluding the individual as instrumental variables for re-estimation (Shen et al., 2025). Specifically, the average education level of other villagers represents economic cognition, average adoption of ecological technologies represents ecological cognition, average out-migration ratio represents social cognition, average financial literacy represents risk cognition, and the average proportion of villagers holding forest property certificates represents policy cognition. These instruments allow us to account for peer-driven variation in cognition when conducting the regression analysis.

The results are reported in Table 5. First, the first-stage regressions indicate that all instruments significantly influence the endogenous explanatory variables. Second, after accounting for endogeneity, farmers’ cognition still significantly affects participation in carbon forestry, suggesting that ignoring endogeneity would overestimate the impact of cognition on participation. Third, weak instrument tests (AR and Wald) are both significant, confirming the validity of the instruments. Overall, these results reinforce the robustness of the baseline regressions.

TABLE 5

Table 5. Results of IV-probit model test using instrumental variables.

4.5 Robustness test

To ensure the reliability of the empirical results, this study follows Liu et al. (2021) and conducts robustness checks by employing alternative model specifications. Table 6 reports regressions of farmers’ willingness and behavior to participate in carbon forestry using a binary Logit model. The significance and direction of the estimated coefficients remain consistent with the baseline results, indicating robust findings.

TABLE 6

Table 6. Robustness test of the replacement logit model.

Considering that farmers over 70 years old may differ significantly from younger farmers in physical capacity, cognitive ability, and knowledge, and that carbon forestry management requires long-term input-to-output investment, older farmers may be less able to implement such practices. Following Ren and Guo (2023), regressions were re-estimated after excluding farmers aged over 70. As shown in Table 7, the results remain largely consistent with the baseline model, further confirming the robustness of the estimates.

TABLE 7

Table 7. Robustness test of bivariate probit model with restricted sample.

5 Discussion

Based on the survey data, current bamboo carbon forestry projects fall short of expectations in both number and scale. Although over 50% of surveyed farmers expressed willingness to participate, fewer than one-fifth had actually engaged in project activities. This indicates that farmers’ intentions alone are insufficient for effective implementation; a shared understanding, recognition of project benefits, and the ability to act on intentions are all necessary. This gap between intention and behavior may reflect bounded rationality, liquidity constraints, or institutional barriers, as farmers’ capacity to convert favorable perceptions into concrete action is often limited by financial, informational, and procedural constraints.

When farmers perceive potential economic returns from bamboo carbon projects, the probability of participation increases by 1.6% compared to those without such economic awareness. This suggests that while economic benefits are considered, they are not the primary driver of engagement. Economic cognition appears to strongly influence stated intentions but has a more limited effect on behavior, likely reflecting uncertainty or delays in realizing financial returns and the constraints that restrict immediate action. Interestingly, ecological and social cognition exert a stronger influence on actual participation, suggesting that farmers who perceive tangible environmental outcomes and social benefits are more likely to translate awareness into concrete engagement.

Conversely, risk cognition exerts a strong negative influence on both intention and behavior, highlighting that concerns over project feasibility, high initial costs, and long project cycles lead farmers to make rational, cautious decisions. The amplification of the negative effect on behavior relative to intention underscores how perceived risks can constrain the translation of willingness into action.

Among all dimensions of cognition, policy awareness exerts the strongest influence. Favorable policies are often the decisive factor in farmers’ decisions to participate, and deeper knowledge of carbon forestry policies raises participation probability by 10%, emphasizing the importance of policy-driven promotion. Furthermore, given the evident scale effects of forestry carbon projects, land transfer positively moderates the influence of policy awareness, facilitating broader participation.

Heterogeneity across demographic and social subgroups further reveals that the influence of different cognitive dimensions is context-dependent: for example, older farmers may prioritize economic considerations, younger or middle-aged farmers are more responsive to ecological motivations, and social learning opportunities primarily benefit those with prior leadership experience. These findings suggest that interventions targeting intention–behavior gaps should combine tailored informational, social, and institutional strategies to effectively mobilize participation in forestry carbon projects.

6 Policy implications

Based on our findings, several targeted policy measures can enhance participation in bamboo forest carbon projects. Strengthening ecological compensation mechanisms through employment opportunities, technical training, and transparent revenue-sharing can reinforce economic incentives, particularly for farmers influenced by ecological and social cognition. Expanding project publicity and policy dissemination via media and digital platforms can improve understanding of the economic, ecological, and social benefits, helping to bridge the gap between intention and behavior. The negative effect of risk perception highlights the need for risk mitigation measures, including risk-sharing schemes, insurance mechanisms, and financial support such as low-interest loans or subsidies. Finally, conducting pilot projects in regions with rich carbon ecological resources, combined with mid- to long-term planning and multi-dimensional performance evaluation systems, can facilitate adaptive management and ensure sustainable implementation of carbon projects.

Data availability statement

The original contributions presented in this study are included in this article/supplementary material, further inquiries can be directed to the corresponding author.

Author contributions

SZ: Writing – original draft, Writing – review & editing, Conceptualization. YS: Funding acquisition, Project administration, Writing – review & editing. ZZ: Writing – review & editing, Formal Analysis, Data curation.

Funding

The author(s) declared that financial support was received for this work and/or its publication. This study was supported by the Humanities and Social Science Project of the Ministry of Education of China (24YJCZH477), a Collaborative Research Project between the Chinese Academy of Forestry and the Zhejiang Government (2023SY02), and the Special Project of the Hangzhou Municipal Social Science Planning Program (25SWQH06).

Acknowledgments

The author thanks the government departments of Anji, Longyou, Suichang, Kaihua, and Lin’an for their valuable collaboration and support in the data collection process.

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that generative AI was not used in the creation of this manuscript.

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