Policy Mix, Property Rights, and Market Incentives: Enhancing Farmers’ Bamboo Forest Management Efficiency and Productivity

Abstract

Enhancing forestry management efficiency is critical for global sustainable development goals, yet how institutional arrangements can effectively incentivize farmers’ performance requires deeper investigation. This study constructs an integrated framework to examine the effects of well-defined property rights and market certification on the output and technical efficiency of household bamboo management. Utilizing survey data from 1090 households in China, we employ stochastic frontier analysis (SFA), propensity score matching (PSM), and mediation models. The findings reveal a key divergence: (1) Forest tenure certificates significantly increased bamboo output but not technical efficiency. This “quantity-driven” effect stemmed from increased capital and land inputs. (2) Market certification enhanced both output and technical efficiency, operating via a “quality-driven” mechanism of standardized management. (3) Significant technical efficiency losses persist, indicating substantial potential for productivity gains through optimized practices. This study concludes that singular property rights institutions are insufficient to overcome the “output-without-efficiency” bottleneck. Complementary, market-based mechanisms are essential for a dual-pillar policy system. This research offers theoretical support for optimizing forestry policies and provides insights for other developing countries seeking sustainable resource management.

1. Introduction

Forests, as the core component of terrestrial ecosystems, hold a pivotal position in global sustainable development and climate change response [1]. Enhancing forestry management efficiency is not only directly linked to the stable supply of forest products and the livelihood well-being of forest farmers, but also holds profound strategic significance for maintaining regional ecological balance and achieving global “carbon neutrality” goals. China possesses one of the world’s richest bamboo resources [2]. Due to its rapid growth, versatility, and high ecological value, bamboo has become a vital source of rural economic income in southern China [3]. However, extensive traditional management practices over the long term have resulted in low resource utilization efficiency and poor economic returns in bamboo forests, severely constraining the healthy and sustainable development of the bamboo industry [4].

To address this predicament, the Chinese government has vigorously promoted the Collective Forest Tenure Reform, which focuses on clarifying property rights to grant forest farmers fuller management autonomy and income security, aiming to stimulate their enthusiasm for long-term investment and intensive management [5]. Concurrently, driven by rising global demand for sustainable products, market-based environmental governance tools, represented by forest certification, have been introduced into forestry practice. These tools incentivize farmers to adopt more sustainable management practices through mechanisms like price premiums and market access [6,7]. Against this backdrop, a core scientific question emerges: How do these two institutional arrangements, clear property rights and market certification, jointly or independently influence farmers’ bamboo forest management decisions, ultimately impacting management efficiency and output performance? Addressing this question holds significant theoretical and practical value for optimizing forestry policies and promoting the transformation and upgrading of the bamboo industry.

While existing literature has separately explored the singular impacts of secure property rights or market certification on forestry operations, notable research gaps persist. First, few studies integrate property rights and market certification within a unified analytical framework to systematically examine their complementary effects in the context of specific forest types in China. Second, there is a lack of in-depth empirical analysis on the internal transmission mechanisms through which they affect management performance, particularly the mediating role of farmers’ production inputs. This study aims to fill these gaps by constructing an integrated theoretical framework incorporating property rights, market incentives, and farmer input behaviors. The primary innovations are threefold:

First, this study constructs a comprehensive theoretical framework integrating clear property rights and market certification. It systematically explains how these factors jointly influence bamboo forest management efficiency and output by affecting farmers’ capital and land investments. Unlike most existing studies focusing on the analysis of single factors on farmer behavior or performance, this research places two core drivers, land property rights and market certification, within a unified framework. It aims to reveal their potential interactions and complementary effects in enhancing farmers’ bamboo forest management performance, with particular emphasis on the central mediating role of capital and land inputs.

Second, on the empirical methodology front, this study innovatively adopts a multi-model validation strategy, significantly enhancing the robustness and reliability of the findings. Specifically: (1) The application of a stochastic frontier production function model more accurately measures the impact of key factor inputs on bamboo forest management efficiency, effectively overcoming the inherent limitations of traditional methods in handling technical inefficiency; (2) The introduction of a counterfactual inference model aims to effectively mitigate selection bias caused by sample “self-selection,” enabling a more precise assessment of the direct effects of policy combinations on efficiency; (3) The use of Bootstrap sampling provides robust testing for the mediating effects of capital and land inputs in the pathways linking policy combinations to management efficiency. This cross-validation design using multiple models and methods offers a more comprehensive and accurate revelation of the mechanisms through which policy and market factors affect farmer performance compared to traditional single-model analyses. It effectively avoids potential estimation biases inherent in single models, thereby demonstrably enhancing the credibility and persuasiveness of the conclusions.

Finally, regarding findings and policy implications, this study confirms the critical mediating role of farmer capital and land inputs in the process through which forest tenure certificates and market certification affect bamboo output. It finds that merely issuing forest tenure certificates increases output but has limited impact on promoting technical efficiency, whereas bamboo forest certification significantly enhances technical efficiency. This conclusion deepens our understanding of how policy and market incentives translate into farmer management performance. It provides a robust theoretical foundation and practical guidance for formulating future targeted land policies that combine “property rights incentives” and “market guidance.”

2. Literature Review and Theoretical Analysis

2.1. The Impact of Institutional Arrangements on Forestry Management Performance: An Integrated Perspective

Enhancing forestry management performance is a core objective for achieving sustainable forestry development. The academic community widely recognizes that effective institutional arrangements are crucial for guiding forest farmers’ behavior and optimizing resource allocation. This section systematically reviews relevant literature from the dimensions of property rights theory and market incentive mechanisms, proposing an integrated analytical perspective.

Property rights economics theory, particularly the Coase Theorem [8], provides the theoretical cornerstone for understanding the central role of property rights in resource allocation. This theory posits that under ideal conditions of zero transaction costs, clearly defined property rights are a prerequisite for optimal resource allocation. In the forestry sector, vague or unstable property rights often lead to the “tragedy of the commons,” manifesting as over-harvesting, under-investment, and management deficiencies [9]. Conversely, stable and legally protected forest rights provide farmers with secure income expectations, thereby incentivizing long-term investments, the adoption of advanced technologies, and intensive management practices [10].

China’s Collective Forest Tenure Reform practice provides rich empirical evidence for testing this theory. Numerous studies confirm that clarifying forestland contractual management rights significantly boosts farmers’ production incentives. For instance, Yi found that after the devolution of forestland management rights to households, labor and capital inputs per unit area significantly increased, indicating that “clearly defined and effectively protected household property rights” have invigorated China’s small-scale forestry management system [5]. A series of empirical studies using econometric models further substantiate that enhanced property rights stability has a significant positive impact on forestry management efficiency and output [11,12]. However, these studies also suggest that the incentive effect of property rights is not instantaneous; its ultimate performance may be moderated by various factors such as farmers’ own capabilities and market conditions.

Building upon property rights security, market-based incentive mechanisms, particularly internationally aligned forest certification (e.g., FSC, PEFC), are becoming another crucial force driving forestry towards sustainability and efficiency. Certification systems like the Forest Stewardship Council (FSC) set stringent environmental, social, and economic standards, utilizing market access and product premiums as incentives to guide farmers and forestry enterprises towards adopting environmentally friendly and more efficient management models [7,13]. Furthermore, numerous empirical studies on the consumer side have verified the significant effect of forest certification on timber price premiums [14,15], providing assurance for the continued implementation of certification.

Although the aforementioned literature separately confirms the positive effects of property rights and market certification, real-world farmer decision-making is often the result of the combined action of both forces. The performance of market incentives exhibits significant heterogeneity across contexts, and their effectiveness frequently depends on the existing property rights foundation [16,17]. For example, without stable forest rights, farmers’ willingness to participate in certification and make long-term investments diminishes significantly. Conversely, having property rights alone without market access and technical guidance makes it difficult for farmers to achieve fundamental improvements in management efficiency. However, current academia lacks systematic theoretical explanation and empirical testing regarding how this “property rights + market” complementary incentive mechanism operates, particularly the internal pathways through which it affects management performance by influencing farmers’ specific input behaviors. Based on this research gap, this study aims to reveal the complementary effects and transmission mechanisms of property rights policies and market incentives in bamboo forest management.

2.2. Methods for Measuring Forestry Management Efficiency

Research Scientifically assessing forestry management efficiency is fundamental for understanding its influencing factors and formulating effective policies. In economic research, efficiency typically refers to the ratio of actual output to potential maximum output given the level of inputs and technology. Academia has developed various methods for measuring production efficiency, broadly categorized into parametric and non-parametric approaches.

Non-parametric methods, represented by Data Envelopment Analysis (DEA), have the advantage of not requiring a pre-specified production function form and can handle complex scenarios with multiple inputs and outputs [18,19,20]. However, DEA’s deterministic nature prevents it from distinguishing between technical inefficiency and random error. This sensitivity to measurement errors and random noise may lead to biased efficiency estimates [21].

In contrast, parametric methods, primarily Stochastic Frontier Analysis (SFA), are mainstream. Their core advantage lies in the ability to decompose deviations from the production frontier into a technical inefficiency component and a purely random disturbance component [22,23]. This characteristic makes SFA widely applicable in sectors like agriculture and forestry, which are susceptible to natural and market uncertainties [24,25,26]. SFA models can adopt various functional forms such as Cobb–Douglas or the more flexible translog form. The latter is widely used due to its capacity to capture substitution relationships between factors and non-linear influences [27,28,29].

In summary, while the DEA method offers structural convenience, SFA provides a more realistic analytical framework for assessing the forestry sector, which is prone to external environmental influences, by decomposing error into technical inefficiency and random noise. Therefore, a flexible translog SFA model is ultimately adopted to measure bamboo forest management efficiency.

2.3. Research Model and Hypothesis Development

In Under China’s Collective Forest Tenure Reform, most farmers have gained clearer forestland property rights, granting them greater management autonomy. Obtaining state-recognized forest tenure certificates significantly enhances farmers’ sense of security in forestland management, thereby incentivizing them to increase investments in forestry management [5]. Land output primarily depends on farmers’ inputs of various production factors. The impact of clear property rights on farmers’ land management efficiency can be decomposed into an investment incentive effect and a resource allocation effect. The investment incentive effect refers to how property rights security motivates farmer investment by guaranteeing expected forestry returns [17], while the resource allocation effect refers to farmers improving final management performance by adjusting inputs of resources like land area, labor, and fertilizer [30]. Overall, property rights clarification measures under the Collective Forest Tenure Reform policy will influence farmers’ bamboo forest management performance through the pathway: “Property Rights Security → Input Behavior → Management Performance”.

Driven by international market demand for bamboo forest certification, the growing demand for certified bamboo from Chinese bamboo processing enterprises is promoting the rollout of bamboo forest certification in China’s main bamboo-producing regions. Based on previous research, product quality certification may have a dual effect for farmers: negative aspects such as input restrictions (e.g., choice of fertilizers/pesticides), higher labor and capital input requirements, and potential yield loss; and positive aspects manifested as improved market efficiency through price premiums and expanded sales channels for certified products [31,32,33]. Furthermore, while product certification standardizes farmers’ production behavior and improves product quality, it also affects farmers’ resource allocation and consequently their management efficiency [34]. Therefore, in the context of market demand, bamboo forest certification will influence farmers’ bamboo forest management performance through the pathway: “Product Certification → Input Behavior → Management Performance”.

In summary, the theoretical model is constructed as shown in Figure 1, leading to the following research hypotheses:

H1: 

Land property rights enhance management efficiency and output through farmers’ capital inputs, with a mediating effect.

H2: 

Land property rights enhance management efficiency and output through farmers’ labor inputs, with a mediating effect.

H3: 

Land property rights enhance management efficiency and output through farmers’ land inputs, with a mediating effect.

H4: 

Market certification enhances management efficiency and output through farmers’ capital inputs, with a mediating effect.

H5: 

Market certification enhances management efficiency and output through farmers’ labor inputs, with a mediating effect.

H6: 

Market certification enhances management efficiency and output through farmers’ land inputs, with a mediating effect.

3. Methods and Data

3.1. Study Area and Survey Design

This study selects Sanming City, Fujian Province, China, as the empirical research area (Figure 2). Sanming boasts a forest coverage rate of 78.73% and ranks among China’s top regions in bamboo forest area and industry output. With 4.61 million mu of bamboo forests (90% being Phyllostachys pubescens forests), it is a critical bamboo resource hub. The bamboo industry supports local economies and farmer livelihoods, employing approximately 410,000 people. As a national pilot zone for collective forestry reform, Sanming’s experience in forest tenure institutional innovation provides an ideal case for examining property rights impacts.

Questionnaires were designed for comprehensiveness and efficiency, refined through pilot surveys. The final instrument covered: (1) household head and family demographics, (2) agricultural/forestry production, (3) household assets, (4) individual perceptions/preferences, and (5) participation in forestry programs.

Field surveys were conducted via face-to-face interviews in March and June-July 2021 by trained forestry economics graduate researchers. A multistage stratified random sampling method was employed to ensure the representativeness of the sample. First, based on the distribution of bamboo resources in Sanming City, six counties with the highest Phyllostachys pubescens coverage (Yong’an, Youxi, Jiangle, Shaxian, Taining, and Mingxi) were selected as the primary sampling units. Second, within each selected county, 3 townships were randomly selected based on their economic development levels and bamboo forest area. Third, 2 administrative villages were randomly chosen from each township. Finally, 15–20 households were randomly selected from the village roster for face-to-face interviews. This process yielded a total of 1090 valid questionnaires.

The survey instrument was structured into five core modules corresponding to the variable categories listed in

Table 1. Variable definitions for Propensity Score Matching.

Variable Type	Variable Name	Description	Mean	Standard Deviation
Outcome Variables	Bamboo forest output	tons/mu	2.254	0.898
	Technical efficiency	0–1	0.557	0.165
Treatment Variables	Participation in bamboo forest certification	1 = Yes; 0 = No	0.129	0.336
	Holding a forest tenure certificate	1 = Yes; 0 = No	0.606	0.489
Mediating Variables	Capital input	100 CNY/mu	4.690	2.872
	Land input	mu	39.83	20.271
Household Characteristics	Gender of household head	1 = Male; 0 = Female	0.931	0.259
	Age of household head	Years old	60.049	10.982
	Education level of household head	Years	6.495	3.668
	Whether the household head is a village cadre	1 = Yes; 0 = No	0.085	0.279
	Membership in a forestry cooperative	1 = Yes; 0 = No	0.172	0.377
	Number of household laborers	People	3.406	1.339
	Annual per capita income of the household	10,000 CNY	2.909	4.766
	Proportion of forestry income to total household income	%	15.141	27.414
Forestland Attributes	Forestland area	mu	35.682	146.962
	Average slope of forestland	1 = Gentle slope; 2 = Relatively steep; 3 = Very steep	1.819	0.804
	Average distance of forestland from home	km	5.040	21.100
	Average distance of forestland from the main road	km	1.395	4.653
Social Network Features	Whether relatives or friends are government employees	1 = Yes; 0 = No	0.186	0.384
	Whether relatives or friends manage a forestry enterprise	1 = Yes; 0 = No	0.102	0.302
Regional Characteristics	Whether bamboo forest certification is available/conducted in the county	1 = Yes; 0 = No	0.333	0.475

: (1) Household demographics (e.g., age, education); (2) Bamboo production inputs and outputs (e.g., labor, capital, yield); (3) Forestland characteristics; (4) Policy participation; and (5) Social capital. The questions primarily consisted of closed-ended quantitative inquiries to ensure data consistency.

3.2. Variable Selection and Descriptive Statistics

This study employs bamboo forest output (tonnes/mu) and technical efficiency as outcome variables in the PSM analysis, with forest tenure certificate ownership and bamboo certification participation as treatment variables. Based on field surveys, covariates are selected from household characteristics, forestland attributes, social networks, and regional factors. Specific indicators are detailed in Table 1.

(1)

Management Performance: Bamboo output (aggregated peak/off-peak cycle yields, standardized to tonnes/mu) and technical efficiency (estimated via translog SFA).

(2)

Policy & Market Indicators: Binary variables for tenure certificate ownership and certification participation. Crucially, to satisfy the assumption of temporal precedence for causal inference, these variables define the household’s status held prior to or throughout the entire observed production cycle, ensuring that the institutional arrangements influenced the subsequent input and output decisions.

(3)

Mediators: Initially labor/capital/land inputs; subsequently refined to capital input (100 CNY/mu) and land input (mu) based on decision equation estimates.

(4)

Household Traits: Head’s gender/age/education; cadre/cooperative status; labor size; per capita income; monoculture management intensity.

(5)

Land Characteristics: Total area (mu), average slope (°), distance to residence (km), distance to nearest road (km).

(6)

Social Capital: Government connections (dummy), forestry enterprise ties (dummy).

(7)

Regional Controls: County-level certification availability (dummy), reflecting implementation scope.

3.3. Estimation of Management Efficiency and Input-Output Elasticities

The Stochastic Frontier Production Function (SFA), also known as the stochastic boundary production function, posits that no economic agent can exceed the production frontier. Deviations from this frontier represent inefficiency. SFA is relatively flexible, requiring specification of a functional form tailored to the research object. It accommodates half-normal and truncated normal distributions while capturing time-varying efficiency errors, making it suitable for assessing forestry production efficiency [35,36,37]. Consequently, this study employs SFA to estimate household bamboo forest management efficiency.

SFA commonly adopts either the translog or Cobb–Douglas (C-D) functional form. The translog production function effectively handles unbalanced data and accounts for substitution effects among production factors, enabling evaluation of the impact of explanatory variables (e.g., land, capital, labor) on the dependent variable. Therefore, the translog form is selected. The model specification is as follows:

$$y_i=f\left(x_i;\beta \right)\mathrm{e}\mathrm{x}\mathrm{p}\left(v_i-u_i\right)$$

(1)

$$\begin{gathered} ln{y}_i={\beta }_0+{\beta }_{1}ln{L}_i+{\beta }_{2}ln{K}_i+{\beta }_{3}ln{F}_i+{\displaystyle \frac{1}{2}}\left[{\beta }_4{\left(ln{L}_i\right)}^2+{\beta }_5{\left(ln{K}_i\right)}^2+{\beta }_6{\left(ln{F}_i\right)}^2\right]+{\beta }_{7}ln{L}_{i}ln{k}_i+{\beta }_{8}ln{L}_{i}ln{F}_i \\ +{\beta }_{9}ln{K}_{i}ln{F}_i+v_i-u_i\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em} \end{gathered}$$

(2)

where $y_i$ represents bamboo forest output in tonnes, encompassing both bamboo shoots and culms. To mitigate efficiency distortions from annual yield fluctuations, all inputs and outputs are aggregated over a full management cycle (typically spanning peak and off-peak production years). Specifically, output comprises total yield across peak/off-peak years, with bamboo shoot and culm revenues standardized to tonnes using 2020 culm market prices. It should be noted that all monetary variables, including capital inputs and household income, are expressed in nominal values based on market prices during the 2019–2020 production cycle. Given the short duration of the study period, the fluctuation in general price levels was minimal, and thus no inflation adjustment was applied. Specifically, the average farm-gate price in the study area was approximately 700 CNY/ton for bamboo timber and 10,000 CNY/ton for dried bamboo shoots. Inputs include total labor (valued at the local average daily wage of approx. 150 CNY/day for reference) and capital, which aggregates expenditures on seedlings, transportation, fertilizers, pesticides, and utilities (averaging 469 CNY/mu). Inputs include total labor ($L_i$, person-days for family/hired labor), capital ($K_i$, CNY covering seedlings, transportation, fertilizers/pesticides, and utilities), and land ($F_i$, mu dedicated to bamboo management) over the cycle. This aggregation strategy collapses the biennial biological cycle into a single observation, producing a robust cross-sectional dataset that represents the average productivity capacity of each household, thereby ensuring the stability of the outcome variables required for the subsequent Propensity Score Matching analysis. Parameters ${\beta }_0$–${\beta }_9$ require estimation; $v_i$ denotes a stochastic disturbance term capturing measurement errors and random noise, following $N(0,{\sigma }^2)$ independently of $u_i$; $u_i$ signifies the non-negative technical inefficiency term, quantifying deviation from the production frontier, distributed as truncated normal $N^{+}(z_i\delta ,{\sigma }^2)$.

Battese and Coelli [38] model technical inefficiency as a function of exogenous determinants and stochastic noise:

$$u_i=z_i\delta +W_i$$

(3)

where $z_i$ denotes determinants of technical efficiency; $\delta$ represents parameters to be estimated; $W_i$ follows a truncated normal distribution with zero mean and variance ${\sigma }^2$, truncated at ${-z}_i\delta$. The total error variance is decomposed as ${\sigma }^2={\sigma }_v^2+{\sigma }_u^2$, with $\gamma ={\sigma }_u^2/({\sigma }_v^2+{\sigma }_u^2)$. The parameter $\gamma$ quantifies the proportion of total error variance attributable to technical inefficiency. When $\gamma$ approaches 1, it indicates that deviations from optimal output are primarily driven by technical efficiency losses, justifying the use of the stochastic frontier translog production function. Technical efficiency for observation $i$ is defined as ${TE}_i=\mathrm{e}\mathrm{x}\mathrm{p}\left(-u_i\right)$.

To examine the effects of land, capital, and labor inputs on technical efficiency, output elasticities for each factor are derived by partially differentiating the translog production function:

$$\begin{gathered} {\epsilon }_L={\beta }_1+{\beta }_{4}ln{L}_i+{\beta }_{7}ln{K}_i+{\beta }_{8}ln{F}_i \\ {\epsilon }_K={\beta }_2+{\beta }_{5}ln{L}_i+{\beta }_{7}ln{K}_i+{\beta }_{9}ln{F}_i \\ {\epsilon }_F={\beta }_3+{\beta }_{6}ln{L}_i+{\beta }_{8}ln{K}_i+{\beta }_{9}ln{F}_i \end{gathered}$$

(4)

3.4. Counterfactual Inference Model Specification

The Propensity Score Matching (PSM) method is employed to estimate the effects of property rights institutions and market incentives on household bamboo forest management performance. PSM offers key advantages: it mitigates selection bias and biased estimation arising from self-selection; and it relaxes stringent functional form assumptions, error distribution requirements, and instrumental variable constraints common in other methods addressing endogeneity [39]. PSM matches treated and control group subjects with similar characteristics based on propensity scores (the predicted probability of receiving treatment given observed covariates). This simulates counterfactual outcomes: what would have happened to the treated group if they had not received treatment, and what would have happened to the control group if they had received treatment. This allows identification of the average treatment effect on the treated (ATT), which is the average impact of participating in a policy/program for those who actually participated [40].

For analyzing the treatment effects of holding forest tenure certificates and participating in bamboo forest certification (both binary (0/1) variables) on management performance, binary Logit models estimate the propensity scores (PS). Subsequently, matching analysis is conducted to assess the impacts on bamboo forest efficiency and output. This study defines two distinct treatment groups: (1) households holding forest tenure certificates (Treatment Group 1, with non-holders as controls), and (2) households participating in bamboo forest certification (Treatment Group 2, with non-participants as controls); the conditional probability of participation (i.e., the propensity score) for each household is computed via binary Logit regression.

$${PS}_i=Pr\left[D_i=1|X_i\right]=E\left[D_i=0|X_i\right]$$

(5)

where $D_i$ denotes household i’s participation status in the policy, where $D_i$ = 1 signifies participation and $D_i$ = 0 indicates non-participation. $X_i$ represents the observable characteristics (covariates) of household i. Finally, the analysis estimates the average difference in impact performance between the treatment group and the control group, specifically the ATT, to ascertain the influence of policy participation on the household’s bamboo forest management performance.

$$ATT=E\left(y_{1i}|D_i=1\right)-E\left(y_{0i}|D_i=1\right)=E\left(y_{1i}-y_{0i}|D_i=1\right)$$

(6)

where $y_{1i}$ represents the bamboo management performance for farmer i who participated in the policy, while $y_{0i}$ represents the potential performance of the same participating farmer had they not participated. The term $E\left(y_{1i}|D_i=1\right)$, the average performance of the participants, is observable. However, $E\left(y_{0i}|D_i=1\right)$, representing what the participants’ performance would have been in the absence of the policy, is an unobservable counterfactual outcome. Therefore, Propensity Score Matching must be employed to construct a suitable proxy for $E\left(y_{0i}|D_i=1\right)$.

3.5. Mediating Effect Test of Factor Inputs

This study examines the mediating role of factor input behaviors within the impact pathways linking policy combinations to bamboo forest management efficiency. We employ the Bootstrap method, a widely adopted nonparametric statistical technique requiring no normality assumptions, to test mediation effects. Its principle involves repeated resampling with replacement from the original dataset to construct numerous Bootstrap samples for mediation assessment [37,41]. To enhance estimation accuracy, the bias-corrected nonparametric percentile Bootstrap approach is implemented: 5000 estimates of mediating effects are ordered, with the 2.5th and 97.5th percentiles defining the 95% confidence interval. Following the framework by Zhao et al. [42], we classify mediation types based on Bootstrap confidence intervals (CI): (1) Full Mediation occurs when the indirect effect is significant (CI excludes zero) while the direct effect remains insignificant (CI includes zero). (2) Partial Mediation is observed when both the indirect and direct effects are significant and their coefficients point in the same direction. (3) No Mediation applies when the indirect effect is insignificant.

4. Results

4.1. Sample Demographic Characteristics

Household Heads (Appendix A 

Table A1. Basic characteristics of the household head.

Variable	Variable Description	Proportion (%)	Variable	Variable Description	Proportion (%)
Gender	Male	93.35	Education level	Illiterate	10.33
	Female	6.65		Primary school	49.08
Age	Under 30	0.28		Junior high school	27.30
	30–40	3.25		Senior high school	10.18
	41–50	14.29		Bachelor’s degree or above	3.11
	51–60	37.63	Health status	Major illness	2.49
	Over 60	44.55		Minor illness	3.39
Party membership	Yes	17.54		Average	5.86
	No	82.46		Good	88.26
Whether a village cadre	Yes	19.24	Marital status	Married	93.21
	No	80.76		Unmarried	6.79
Employment type	Farming only	53.75	Employment location	This village	85.71
	Primarily farming	23.46		This town	5.86
	Primarily non-farming	8.49		This county	5.46
	Completely non-farming	9.62		This city	1.13
	Unemployed	4.68		Other cities	1.84

) were predominantly male (93.35%) with significant aging, 80% aged ≥50 years and nearly half over 60. Education levels were low, approximately 60% having primary education or less. Primary occupations included full-time farming (53.75%) and farming-dominant activities (23.46%), while 85.71% worked within their villages.

Household Economics (Appendix A 

Table A2. Characteristics of household assets and labor.

Variable	Variable Description	Proportion (%)	Variable	Variable Description	Proportion (%)
Annual per capita disposable income of the household (10,000 CNY)	Under 1	39.35	Household population size	Under 3	7.38
	1–2	26.17		3–5	46.58
	2–3	16.23		6–8	39.87
	3–4	6.69		Over 8	6.17
	Over 4	11.56	Number of household laborers	Under 3	40.65
Proportion of forestry income to household income	Under 10%	70.42		3–5	54.64
	10–50%	17.32		Over 5	4.71
	51–90%	7.52	Number of household members working away from home	1	64.74
	Over 90%	4.74		2–3	30.69
Household savings (10,000 CNY)	Under 1	20.34		4 or more	4.58
	1–5	39.31	Remaining household labor force (excluding migrant workers)	1	42.10
	5–10	22.07		2	31.20
	Over 10	18.28		3	12.80
				4 or more	13.90

) showed 34.48% with per capita disposable income exceeding ¥20,000, aligning with local rural levels. Forestry contributed minimally to household income, about 70% reporting ≤10% share, indicating low sector dependence and high livelihood diversification. Although 3–5 members was the typical household size, 73.3% had only 1–2 laborers available for forestry work, suggesting labor shortages requiring hired assistance.

Forestland Endowments (Appendix A 

Table A3. Characteristics of household forestland endowments.

Variable	Variable Description	Proportion (%)	Variable	Variable Description	Proportion (%)
Forestland area (mu)	Under 10	54.82	Average slope of forestland	Good	22.84
	10–20	16.96		Gentle	36.26
	21–30	7.16		Relatively Steep	36.58
	31–50	8.77		Very Steep	27.16
	Over 50	12.28	Average soil quality of forestland	Poor	30.35
Number of forest plots	1	67.74		Average	46.81
	2–3	29.17		Good	22.84
	4 or more	3.09	Average distance of forestland from the main road	Within 1000 m	61.66
Average distance of forestland from home	Within 1 km	20.13		1000–3000 m	31.79
	1–3 km	53.99		Over 3000 m	6.55
	Over 3 km	25.88

) revealed over half of households managing ≤10 mu, yet 12.28% held >50 mu with concentrated plots (67.74% single plot). Accessibility varied distance from residence (≈25% > 3000 m) contrasted with proximity to roads (93.45% within 3000 m). Land quality was generally moderate-to-poor, approximately one-third on steep slopes (>25°).

4.2. Bamboo Forest Management Efficiency and Input-Output Elasticities

4.2.1. Impact of Factor Inputs on Management Efficiency

Estimates of household bamboo forest management efficiency are presented in

Table 2. Stochastic frontier production function estimates.

Variable	Parameter Estimate	Std. Error	t-Value
Intercept	4.299 **	1.908	2.254
Labor input	0.089	0.385	0.231
Capital input	1.026 ***	0.389	2.637
Land input	1.031 **	0.437	2.362
Labor input squared	0.096	0.042	2.286
Capital input squared	0.077 ***	0.033	2.333
Land input squared	−0.075 *	0.064	−1.168
Labor input × Capital input	−0.065	0.048	−1.354
Labor input × Land input	0.036	0.088	0.417
Land input × Capital input	−0.042	0.070	−0.604
${\sigma }^2$	1.438 ***	0.444	3.238
$\gamma$	0.665 **	0.283	1.995
Log likelihood	−239.816
LR one-sided test	2.328 * > Critical value: 1.642 (10% significance level)
Mean technical efficiency	0.557

Note: ***, **, and * represent significance levels of 1%, 5%, and 10%, respectively.

. The model parameters ${\sigma }^2$ and $\gamma$ are statistically significant, with $\gamma$ = 0.665, indicating that 66.5% of the deviation between actual and optimal output stems from technical inefficiency. The significant LR one-sided test rejects the null hypothesis of no technical inefficiency, confirming the superiority of the translog production function over the Cobb–Douglas specification. The results show that the labor input exhibits no significant effect on output, suggesting limited marginal returns under current technologies, while capital and land inputs show positive and significant coefficients, confirming their productivity-enhancing roles. Furthermore, the squared capital term is positive and significant at the 1% level. Combined with the positive coefficient of the linear term, this reveals a relationship with increasing marginal returns. This indicates that the output elasticity of capital rises with investment intensity, suggesting that capital inputs exhibit a reinforcing effect where higher investment levels generate progressively larger productivity gains. Conversely, the squared land term is negative and significant at the 10% level, indicating diminishing returns to scale (an inverted U-curve). All interaction terms (labor × capital, labor × land, capital × land) are statistically insignificant. This indicates that the interaction effects between production factors are not evident, suggesting that the marginal productivity of one input is not significantly altered by the intensity of other inputs under the current technical conditions.

4.2.2. Output Elasticities of Factor Inputs

To further analyze the impact of labor, capital, and land inputs on technical efficiency, output elasticities for each factor were derived by differentiating the translog production function, with results stratified by technical efficiency levels presented in

Table 3. Output elasticities of factor inputs by technical efficiency group.

Technical Efficiency	Sample Proportion	Labor Elasticity	Capital Elasticity	Land Elasticity
[0–0.25)	6.90%	0.13	0.67	0.82
[0.25–0.50)	28.74%	0.14	0.69	0.81
[0.50–0.75)	56.32%	0.13	0.67	0.81
[0.75–1]	8.05%	0.09	0.67	0.86
Mean	100.00%	0.13	0.68	0.81

. The analysis of technical efficiency distribution shows that 56.32% of households fell within the 0.50–0.75 efficiency range (over half the sample), followed by 28.74% in the 0.25–0.50 range, and the mean technical efficiency was 55.68%, indicating substantial room for improvement. Key findings on elasticity reveal that labor elasticity decreased with rising efficiency (mean: 0.13), implying diminishing marginal returns, as a 1% labor increase raised output by only 0.13% on average. In contrast, capital elasticity remained stable across efficiency groups with a mean of 0.68, where a 1% capital increase consistently boosted output by 0.68%. Land elasticity increased with higher efficiency (mean: 0.81), with a 1% land expansion raising output by 0.81%. These results demonstrate that enhancing technical efficiency requires reducing reliance on labor due to its weak output response, prioritizing capital investment for stable productivity gains, and optimizing land expansion to leverage its scalability at higher efficiency levels.

4.3. Policy Impact Assessment on Bamboo Management Performance

4.3.1. Propensity Score Estimation Results

To evaluate the effects of forest tenure certificates and bamboo certification on management performance, logistic regression models first estimated conditional probabilities for treatment assignment, with key determinants summarized in

Table 4. Logistic regression estimates for treatment assignment.

Variable	Holding a Forest Tenure Certificate	Participation in Bamboo Forest Certification
Gender of household head	0.008 (0.772)	−2.163 *** (0.726)
Age of household head	0.023 (0.019)	−0.001 (0.019)
Education level of household head	0.030 (0.068)	0.047 (0.071)
Whether the household head is a village cadre	0.633 * (0.855)	1.228 * (0.732)
Membership in a forestry cooperative	0.772 (0.503)	0.205 (0.505)
Number of household laborers	0.164 (0.151)	0.264 (0.166)
Annual per capita income of the household	0.075 (0.721)	0.097 (0.074)
Proportion of forestry income to total household income	0.021 *** (0.007)	0.014 ** (0.007)
Forestland area	0.109 * (0.007)	0.014 ** (0.005)
Average slope of forestland	0.193 (0.271)	−0.107 (0.268)
Average distance of forestland from home	0.026 (0.018)	−0.002 (0.007)
Average distance of forestland from the main road	0.109 * (0.169)	−0.044 (0.131)
Whether relatives or friends are government employees	0.451 * (0.567)	0.535 (0.550)
Whether relatives or friends manage a forestry enterprise	0.678 (0.715)	1.491 * (0.848)
Whether bamboo forest certification is available in the county	—	0.602 (0.550)
LR statistic	39.900 ***	43.970 ***
Pseudo R2	0.193	0.214

Note: ***, **, and * represent significance levels of 1%, 5%, and 10%, respectively. The values in parentheses are the regression standard errors.

. For holding a forest tenure certificate, the significant positive predictors were household heads serving as village cadres, a higher forestry income share of total household income, larger forestland area, shorter distance to roads, and government connections among relatives or friends. For bamboo forest certification participation, the significant positive predictors included female household heads (vs. male), household heads serving as village cadres, a higher forestry income share, larger forestland area, and forestry enterprise ties among relatives or friends. Collectively, these results confirm the appropriate covariate selection for matching.

4.3.2. Common Support Domain Validation

Matching quality was ensured by examining the common support domain. Insufficient overlap between treatment and control groups would cause significant sample loss and biased estimates. Figure 3 and Figure 4 compare kernel density distributions before and matching for forest tenure certificates and bamboo forest certification, respectively. Post-matching distributions show substantial overlap, indicating valid common support with minimal sample attrition.

4.3.3. Balance Test

Post-matching balance tests, with results summarized in

Table 5. Balance test results.

Matching Method	Holding a Forest Tenure Certificate			Participation in Bamboo Forest Certification
	Pseudo R2	LR Statistic	MeanBias	Pseudo R2	LR Statistic	MeanBias
Unmatched	0.195	40.28 ***	20.50	0.218	44.63 ***	22.70
K-Nearest Neighbor Matching	0.093	6.12	8.20	0.084	6.44	7.20
K-Nearest Neighbor Matching within Caliper	0.058	3.53	4.80	0.053	6.74	9.30
Radius Matching	0.087	4.13	5.32	0.076	5.53	6.10
Kernel Matching	0.027	1.52	7.87	0.019	2.22	7.60

Note: *** represent significance levels of 1%.

, confirmed covariate comparability between the treatment and control groups. After matching, the Pseudo R² and LR statistics decreased significantly, and the post-matching LR statistics became insignificant (p > 0.1). Furthermore, all standardized biases were below the 10% threshold, with a maximum of 9.3% (participation in bamboo forest certification) and 6.12% (holding a forest tenure certificate). These results satisfy the criteria established by Rosenbaum and Rubin [43], confirming that the matching process effectively reduced selection bias.

4.3.4. PSM Treatment Effects

As shown in

Table 6. Estimated effects on bamboo forest management performance.

Management Performance Indicator	Matching Method	Forest Tenure Certificate (ATT)	Bamboo Forest Certification (ATT)
Bamboo forest output	K-Nearest Neighbor Matching	1.666 *** (0.353)	1.642 *** (0.507)
	K-Nearest Neighbor Matching within Caliper	1.650 *** (0.356)	1.513 *** (0.495)
	Radius Matching	1.800 *** (0.131)	1.873 *** (0.515)
	Kernel Matching	1.666 *** (0.343)	1.221 *** (0.470)
Mean (significant only)		1.696	1.562
Technical efficiency	K-Nearest Neighbor Matching	0.063 (0.057)	0.715 (0.045)
	K-Nearest Neighbor Matching within Caliper	0.050 (0.054)	0.069 *** (0.037)
	Radius Matching	0.025 (0.015)	0.044 *** (0.021)
	Kernel Matching	0.052 (0.055)	0.032 *** (0.033)
Mean (significant only)		-	0.048

Note: *** represent significance levels of 1%. The values in parentheses are the regression standard errors.

, the ATT across four different matching methods consistently demonstrate the impacts of both interventions. Holding a tenure certificate significantly increased bamboo forest output by an average of 1.696 t/mu (p < 0.01) but had no significant effect on technical efficiency. In contrast, participation in bamboo forest certification significantly increased output by 1.562 t/mu (p < 0.01) and also significantly improved technical efficiency by 0.048 (p < 0.01). This indicates that while securing property rights alone boosts output, it may not be sufficient to improve efficiency, whereas market-oriented mechanisms like bamboo forest certification enhance both output and efficiency.

As shown in

Table 7. The interaction between property rights and market incentives on the enhancement of bamboo forest management performance.

Sample Group	Bamboo Forest Output			Technical Efficiency
	Mean	Median	Standard Deviation	Mean	Median	Standard Deviation
Has tenure certificate and participates in bamboo certification	3.54	3.40	1.58	0.60	0.62	0.12
No tenure certificate and does not participate in bamboo certification	1.46	1.30	1.09	0.52	0.58	0.18
Absolute difference	2.08	2.10	-	0.08	0.04	-
Has tenure certificate	3.30	3.00	1.43	0.56	0.61	0.16
No tenure certificate	1.50	1.30	1.07	0.53	0.59	0.18
Absolute difference	1.80	1.70	-	0.03	0.20	-
ATT	1.70	-	-	-	-	-
Participates in bamboo certification	3.54	3.40	1.58	0.60	0.62	0.12
Does not participate in bamboo certification	1.76	1.88	1.55	0.54	0.60	0.17
Absolute difference	1.78	1.52	-	0.06	0.02	-
ATT	1.56	-	-	0.05	-	-

, the descriptive statistics reveal substantial differences in output between groups. The standard deviations and the proximity of means to medians suggest that the aggregated output data smoothed over the biennial production cycle does not suffer from severe outliers, justifying the robustness of the subsequent mean-based comparison. Further statistical comparisons divided samples into: (1) households without tenure certificates nor bamboo forest certification vs. those with both interventions, (2) tenure holders vs. non-holders, and (3) bamboo forest certification participants vs. non-participants (Table 7). All groups showed minimal mean-median discrepancies and low standard deviations, indicating uniform distributions without outliers and high result reliability. Key findings reveal: households with both interventions achieved +2.08 t/mu output and +0.08 technical efficiency versus baseline non-participants. Simple comparisons overestimated impacts—tenure holders showed +1.80 t/mu output (vs. non-holders), while PSM-adjusted ATT was lower at 1.70 t/mu; bamboo forest certification participants had +1.78 t/mu output and +0.06 efficiency, whereas PSM estimates were 1.56 t/mu and 0.05 respectively. This demonstrates that uncontrolled analyses overestimate policy-market impacts by 6–13% due to self-selection bias, confirming the necessity of PSM correction for causal inference.

4.3.5. Robustness Check

While propensity score matching controls for self-selection bias from observed covariates, residual bias from unobserved confounders may persist. To assess the robustness of our findings, we implement Rosenbaum bounds sensitivity analysis to evaluate the sensitivity of the ATT to hidden biases. In this analysis, Gamma (Γ) quantifies the odds ratio differential induced by unobserved confounders. If small increases in Γ (e.g., Γ > 1) cause the significance bounds to cross the α = 0.05 threshold (i.e., p+ and p− > 0.05), the estimates are considered sensitive. As demonstrated in

Table 8. Rosenbaum bounds sensitivity analysis.

Gamma (Γ)	Forest Tenure Certificate (Bamboo Forest Output)		Forest Tenure Certificate (Technical Efficiency)		Bamboo Certification (Bamboo Forest Output)		Bamboo Certification (Technical Efficiency)
	p+	p−	p+	p−	p+	p−	p+	p−
1	<0.001	<0.001	<0.001	<0.001	<0.001	<0.001	<0.001	<0.001
1.2	<0.001	<0.001	<0.001	<0.001	<0.001	<0.001	<0.001	<0.001
1.4	<0.001	<0.001	<0.001	<0.001	<0.001	<0.001	<0.001	<0.001
1.6	<0.001	<0.001	<0.001	0.001	<0.001	<0.001	<0.001	<0.001
1.8	<0.001	<0.001	<0.001	0.002	<0.001	<0.001	<0.001	<0.001
2.0	<0.001	<0.001	<0.001	0.014	<0.001	<0.001	<0.001	<0.001

, for the efficiency effect of tenure certificates, significance persists until Γ = 2.0 (p− = 0.014 < 0.05). Given that critical Γ values in social science studies typically range between 1.2 and 1.5, our finding that results remain robust up to Γ = 2.0 indicates that the estimated treatment effects are highly insensitive to potential hidden biases or measurement errors. All other ATTs, including the output effect of tenure certificates and the output and efficiency effects of certification, exhibit robustness with Γ < 2.0 and p+/p− < 0.001. These results confirm that our estimates have minimal sensitivity to unobserved confounders, thus supporting robust causal inferences.

4.4. Regression of Demographic Sociological Characteristics

Given that the PSM analysis revealed insignificant effects for labor inputs, leading to the rejection of hypotheses H2 and H5, the subsequent analysis focuses on the mediating roles of capital and land inputs. The results of the mediation tests, which examine these pathways for different policy combinations, are presented in

Table 9. Mediation effects of farmer inputs on management performance under policy context.

X	Mediator	Y	Direct Effect	90% CI	Indirect Effect	90% CI
Has tenure certificate	Capital input	Technical efficiency	0.020	−0.027, 0.068	0.005	−0.005, 0.020
	Land input		0.025	−0.023, 0.072	0.001	−0.008, 0.011
	Capital input	Bamboo output	0. 127	−0.182, 0.437	0.463	0.244, 0.708
	Land input		0.181	−0.117, 0.478	0.409	0.177, 0.668
Participates in bamboo certification	Capital input	Technical efficiency	0.048	−0.001, 0.097	0.004	−0.013, 0.020
	Land input		0.056	0.006, 0.106	0.004	−0.025, 0.012
	Capital input	Bamboo output	0.619	0.309, 0.928	0.559	0.355, 0.785
	Land input		0.493	0.183, 0.803	0.684	0.487, 0.901

.

For households holding forest tenure certificates, the analysis reveals no significant mediation from either capital or land inputs on technical efficiency. The indirect effects for capital (0.005) and land (0.001) were not statistically significant, as their 90% confidence intervals included zero. In contrast, a different pattern emerged for bamboo output, where both capital and land acted as full mediators. The indirect effect of capital was significant at 0.463 (90% CI: [0.244, 0.708]), while the direct effect of tenure certificates became insignificant. Similarly, the indirect effect of land was 0.409 (90% CI: [0.177, 0.668]), with an insignificant direct effect. These findings align with the PSM results, which showed that tenure boosts output but not efficiency, thereby offering partial support for hypotheses H1 and H3.

Regarding participation in bamboo forest certification, no significant mediation effect on technical efficiency was found for either capital or land, despite significant direct effects (0.048 and 0.056, respectively). For bamboo output, however, both capital and land were found to be partial mediators. The analysis identified a significant indirect effect for capital (0.559, 90% CI: [0.355, 0.785]) alongside a persistent and significant direct effect (0.619, 90% CI: [0.309, 0.928]). A parallel result was observed for land, with a significant indirect effect (0.684, 90% CI: [0.487, 0.901]) and a significant direct effect (0.493, 90% CI: [0.183, 0.803]). This suggests that certification fails to stimulate sufficient input adjustments for efficiency gains, which partially supports hypotheses H4 and H6.

A key contrast emerges from the analysis regarding the primary pathways through which each policy impacts output. The output-enhancing effect of forest tenure certificates appears to rely more on capital inputs, as its indirect effect was 13.2% larger than that of land. Conversely, the impact of bamboo certification is driven more significantly by land expansion, with its indirect effect being 38.7% larger than that of capital. This suggests a fundamental difference in their mechanisms: securing property rights primarily operates by encouraging capital intensification, whereas market-based certification leverages economies of scale achieved through land expansion.

5. Discussion

This study systematically dissects the independent and complementary impacts of well-defined property rights and market incentives on household bamboo forest management performance, with particular focus on the mediating roles of capital and land inputs. The findings not only confirm the positive effects of both institutional arrangements but also reveal their divergent pathways, offering novel insights into the mechanisms of policy combinations.

5.1. The “Output Growth Without Efficiency Gains” Dilemma of Property Rights

A key finding is that forest tenure certificates significantly increase bamboo output but fail to improve technical efficiency. While consistent with prior evidence that tenure security incentivizes investment [5,12], this reveals a deeper paradox: output growth and efficiency gains are asynchronous.

Mediation analysis elucidates this phenomenon. Capital and land inputs exhibit full mediation in the output-enhancing effect of tenure certificates, indicating that incentives operate almost exclusively through expanded management scale and capital intensification. Secured property rights motivate long-term investments (e.g., machinery, soil improvement) and land consolidation, directly boosting yield. However, they do not spur adoption of advanced management techniques or optimal resource allocation. Farmers appear to augment inputs under existing extensive practices rather than upgrading production quality, explaining the stagnant efficiency. Thus, property rights alone resolve “whether to invest” but not “how to invest efficiently.”

5.2. The Dual Advantage of Certification: Quality and Efficiency

Unlike tenure’s singular output effect, bamboo certification delivers dual benefits: simultaneous output growth and efficiency gains. This validates forest certification as an effective market-based instrument for sustainable management [7].

Notably, capital and land inputs show no significant mediation in certification’s efficiency pathway. Efficiency improvements stem instead from knowledge spillovers and management standardization embedded in certification processes [6,7,44]. Systems like FSC require adherence to science-based protocols, compelling farmers to adopt optimized techniques, a de facto technical training that directly elevates input-use efficiency. Certification thus transcends price premiums by fundamentally transforming production modes, achieving quality-driven growth.

5.3. Re-Evaluating the Roles of Factor Inputs

This study provides new insights into the functions of production factors in bamboo forest management. The results consistently demonstrate that labor inputs have an insignificant effect on output. This finding aligns with the reality of an aging farmer population and the outmigration of young laborers in the study area, suggesting that bamboo management has transcended the labor-intensive stage, where the marginal returns to additional labor time are negligible.

In contrast, capital and land inputs emerge as the core drivers of output. A key finding is the divergent roles these factors play as mediators for different policy interventions. The analysis reveals that capital inputs exhibit a stronger mediation effect on the output gains associated with the clarification of property rights. Conversely, land inputs show greater mediation in the output effects of market certification.

This divergence implies distinct underlying mechanisms for each policy. The security provided by property rights primarily activates farmers’ willingness to make long-term capital investments, providing them with the confidence needed for such commitments [5,17]. On the other hand, market certification incentivizes land expansion to achieve scale economies. This is because certified products often require minimum volumes to ensure a stable supply for processors and downstream markets, compelling farmers to increase their operational scale to meet these demands [45].

Beyond the significant determinants of productivity, the statistically non-significant findings in this study provide equally critical insights for forestry management. First, the insignificance of labor inputs (p > 0.1) challenges the traditional reliance on labor-intensive practices, indicating that manual labor is no longer the primary driver of output in this region. This trend reflects the severe constraints imposed by an aging rural workforce, considering that over 80% of household heads are aged 50 or older, and suggests that policy interventions should pivot from labor subsidies to promoting labor-saving machinery to facilitate capital substitution. Furthermore, the absence of significant interaction terms between land, labor, and capital points to weak factor substitutability under current technological conditions. For forest managers, this implies that a strategy of increasing a single input such as fertilizer without a proportional increase in complementary factors yields limited marginal gains, thereby necessitating a balanced and multi-factor input strategy. Finally, the null effect of forest tenure certificates on technical efficiency underscores a critical distinction. While institutional security effectively resolves the willingness to invest, it does not automatically enhance the capacity to manage efficiently. This highlights that property rights reform alone is insufficient and must be supplemented with technical extension services to bridge the gap between input accumulation and efficiency optimization.

5.4. Limitations and Future Research

Despite the rigorous analytical approach employed in this study, we acknowledge several limitations that should be considered when interpreting the results. First, the empirical analysis relies on cross-sectional data collected at a single point in time. This temporal constraint limits the ability to capture the dynamic evolution of bamboo forest management over the long term and prevents the observation of potential lagged effects of policy implementation. Future research would benefit from employing panel data to track changes over time and better establish causal relationships. Second, the data were strictly collected from Sanming City in Fujian Province. As Sanming is a pioneering pilot zone for China’s collective forest tenure reform, its institutional environment and the high level of market development may not perfectly reflect the conditions in other forestry regions. Consequently, the local specificities of the study area imply that caution should be exercised when generalizing these findings to the national level or to regions with different socioeconomic contexts. Finally, while PSM was utilized to mitigate selection bias based on observable covariates, potential unobserved confounding factors may still exist. Variables such as the intrinsic motivation of farmers, unrecorded soil micro-conditions, or informal social norms could influence both policy participation and management outcomes. Future studies could incorporate instrumental variable approaches or randomized controlled trials to further address these endogeneity issues and validate the robustness of the findings.

6. Conclusions

This study reveals significant divergences in how property rights clarification and market incentives enhance bamboo forest management performance among Chinese farmers. The core finding demonstrates that well-defined property rights (forest tenure certificates), as foundational institutional arrangements, primarily stimulate quantitative inputs of capital and land by securing investment expectations, thereby driving output growth without translating into technical efficiency gains, exhibiting an “investment-driven” production model. In contrast, market-based bamboo forest certification serves as a more refined governance instrument that not only increases output but crucially elevates technical efficiency through introducing advanced management standards, achieving “quality-driven” growth that simultaneously boosts both output and efficiency.

These insights offer profound guidance for developing countries facing similar challenges globally. While land tenure reform remains essential, it cannot automatically foster sustainable production; it must integrate complementary measures enhancing management efficiency and environmental performance to genuinely contribute to Sustainable Development Goals (SDGs). The validated “property rights security + market incentives” dual-pillar framework provides a scalable model. By bridging domestic institutional development with global market mechanisms (e.g., forest certification), it empowers nations to leverage external market demand to catalyze green and efficient transformations in local production systems.