How Can State-Owned Forest Farms Promote Sustainable Forest–Village Cooperation? A Configuration Analysis Based on the Resource Orchestration Perspective

Abstract

Cooperative afforestation, reforestation, and forest management initiatives between state-owned forest farms and village collectives serve as pivotal strategies for restoring degraded ecosystems, establishing new forested areas, and revitalizing collective forestland resources. These collaborations offer a practical pathway to enhance forest resource utilization while contributing to rural revitalization in forest-dominated regions. Despite their significance, achieving the sustainability of Forest–Village Cooperation through efficient resource allocation remains a critical challenge. This study investigates Forest–Village Cooperation cases in Fujian Province, employing resource orchestration theory to develop an analytical framework for sustainable resource allocation in these partnerships. By integrating Data Envelopment Analysis (DEA), Necessary Condition Analysis (NCA), and Fuzzy-Set Qualitative Comparative Analysis (fsQCA), the research examines how policy resources, human resources, natural resources, economic resources, grassroots connectivity capability, and technological innovation capability collectively influence sustainability. The findings reveal that no single resource factor is necessary for Forest–Village Cooperation Sustainability (FVCS). However, economic resources, human resources, and technological innovation capability emerge as key drivers of high sustainability. State-owned forest farms with weaker grassroots connectivity capability can offset this limitation through natural resource advantages, while those with stronger connectivity achieve cooperation upgrades via efficient economic resource allocation. Furthermore, this study identifies three pathways for FVCS: “Resource Integration-Driven”, “Technology Innovation-Enabled”, and “Capability–Resource Synergy”, each tailored to specific resource endowment contexts. This research not only extends the application of resource orchestration theory in the forestry cooperation domain but also provides actionable policy recommendations for optimizing collaborations between state-owned forest farms and village collectives.

1. Introduction

Forest–Village Cooperation refers to collaborative forestry operations conducted jointly by state-owned forest farms and village collectives, encompassing afforestation, reforestation, forest management, and other activities aimed at the sustainable utilization and restoration of collective forestlands [1]. This model seeks to revitalize collective forestland resources through resource sharing and mutual benefits, providing a strategic pathway for state-owned forest farms to fulfill their social responsibilities, stimulate rural economic development, and advance the intensification and scaling-up of forestry operations. In September 2023, the Central Office and State Council of China jointly issued the “Deepening the Reform of the Collective Forest Rights System Plan”, which clearly proposed key tasks such as “developing appropriately scaled forestry management”, providing policy support for improving the efficiency and economic benefits of collective forest land utilization. Earlier, in July 2022, the Fujian Provincial Forestry Bureau launched the “Fujian Province State-owned Forest Farm ‘Hundred Forest Farms Supporting Thousand Villages’ Initiative”. This initiative aimed to leverage the strengths of state-owned forest farms in technology, resources, and management to revitalize collective forestland resources, expand afforestation and reforestation activities, and explore innovative forestry cooperation models. The initiative strives to achieve an organic integration of forestry reform, sustainable development, and rural revitalization, providing both a policy framework and practical guidance for advancing Forest–Village Cooperation.

Despite these efforts, Forest–Village Cooperation in Fujian Province faces significant challenges. Insufficient funding, unclear property rights, and the absence of robust resource distribution mechanisms impede the deepening and expansion of cooperative projects [2,3]. Such limitations hinder the ability to sustain and enhance the long-term benefits of Forest–Village Cooperation, reducing its developmental potential. Addressing these challenges is essential to mobilize the sustained cooperation of both parties and to ensure the sustainable development of this model. Exploring pathways to enhance Forest–Village Cooperation Sustainability (FVCS) holds both theoretical and practical significance. It contributes to refining forestry cooperation models, improving resource utilization efficiency, and fostering shared prosperity in forest-dependent regions.

Cooperative management has emerged as a significant development trend and an essential model for achieving the integrated advancement of social, ecological, and economic benefits on collective forestlands [4,5]. Fujian Province, recognized as a pioneering region in China’s collective forest tenure reform, has garnered widespread attention for its innovative Forest–Village Cooperation practices. As the collective forest tenure reform continues to deepen, the collaboration between state-owned forest farms and village collectives has increasingly become a focal point in academic research, highlighting its potential to address critical challenges in forestry management and rural development.

Research indicates that state-owned forest farms possess significant potential in resource integration, optimizing benefit distribution, and enhancing ecological, social, and economic outcomes [6]. Consequently, exploring how state-owned forest farms can promote FVCS is not only of considerable theoretical significance but also offers valuable practical insights for advancing forestry development and rural revitalization.

In recent years, research on state-owned forest farms has primarily centered around three key themes. First, in terms of management systems and reform paths, scholars have explored how to optimize the management mechanisms of state-owned forest farms to meet the needs of modern forestry development, arguing that deepening the reform of state-owned forest farms requires breakthroughs in policy support and institutional innovation to achieve a dual improvement in operational and ecological benefits [7,8]. Second, in the context of ecological protection and green development, state-owned forest farms are regarded as pivotal actors in attaining green development objectives. Researchers have analyzed their contributions to enhancing both operational and ecological performance, underscoring the critical role of policy resources in supporting forest farm reforms [9,10]. Third, concerning local economic development and ecological coordination, studies have explored the interactions between state-owned forest farms and local economies. Particular attention has been paid to the mechanisms through which resource allocation fosters rural revitalization [11].

Research on forestry cooperative management has predominantly focused on farmers and forestry cooperative organizations as the primary actors [4,12], with relatively little attention given to the relationship between forestry cooperative organizations and collective actions [13,14]. Existing studies primarily explore forestry carbon sequestration projects [15,16], comparisons of cooperation models [17], and optimization of cooperation modes [18,19]. Methodologically, the field is dominated by quantitative approaches, including binary logistic regression, multinomial logit models, and game-theoretic frameworks, which focus on cooperative strategies [20,21,22].

As forestry policies deepen and ecological construction progresses, Forest–Village Cooperation has emerged as an innovative forestry management model, attracting growing academic attention [1]. Studies show that cooperation between state-owned forest farms and village collectives can lead to mutually beneficial outcomes in ecological conservation and economic development through resource sharing and joint management [20,21]. However, much of the existing research remains focused on descriptive analyses of cooperation models [17], preliminary evaluations of cooperative outcomes [23], and the influence of farmers’ willingness to participate on cooperative results [4,12]. Systematic investigations into FVCS remain limited, particularly studies that delve into the underlying mechanisms of sustainability from the perspectives of resource integration and dynamic configuration. Addressing this gap is crucial to advancing theoretical understanding and providing actionable insights for optimizing cooperative forestry practices.

Although existing research has explored the mechanism of forestry cooperative management models and the factors influencing Forest–Village Cooperation from multiple dimensions, several critical shortcomings remain: First, the lack of a systematic analysis framework. Research on resource allocation and dynamic integration is relatively fragmented, failing to establish a cohesive framework to elucidate how resources can effectively promote FVCS through optimal orchestration. Second, the limitations of research methods. Most studies rely on qualitative or quantitative approaches, often focusing on individual state-owned forest farms or isolated variables. Consequently, there is insufficient empirical evidence regarding the impact of resource integration within state-owned forest farms on FVCS. Finally, the limitations of the research perspective. Few studies position state-owned forest farms as the central focus, while simultaneously employing configurational analysis to uncover how the synergistic interaction of multiple resource elements enhances social, ecological, and economic benefits. Some existing studies have explored similar aspects [24], but the research gap remains significant in fully addressing the role of state-owned forest farms in resource orchestration for Forest–Village Cooperation.

To address these gaps, this paper investigates how state-owned forest farms can promote FVCS through dynamic resource allocation and collaborative optimization, guided by the perspective of resource orchestration. The contributions of this study are reflected in three innovative aspects: First, the paper introduces the theory of resource orchestration to analyze the synergistic effects of diverse resource elements such as policy resources, human resources, and natural resources on FVCS. This provides a robust theoretical foundation for optimizing resource allocation in state-owned forest farms. Second, employing a mixed-methods approach, this study integrates DEA (Data Envelopment Analysis), NCA (Necessary Condition Analysis) and fsQCA (Fuzzy-Set Qualitative Comparative Analysis). This combination enables a comprehensive evaluation of both quantitative and qualitative aspects, identifying the necessary and sufficient conditions for sustainable resource allocation in state-owned forest farms. Furthermore, it constructs configurational paths for resource integration, addressing methodological gaps in current research. Third, by centering the analysis on state-owned forest farms, this paper provides a multi-dimensional exploration of resource optimization pathways. Supported by empirical evidence, it proposes actionable policy recommendations for enhancing FVCS, bridging theoretical insights with practical applications.

2. Theoretical Foundation

2.1. Resource Orchestration Theory

Resource orchestration theory, initially proposed by Sirmon, Hitt, and Ireland in 2007, was originally applied within the field of strategic management. The theory focuses on how organizations can enhance performance and create value by acquiring, integrating, and optimizing resources in rapidly changing external environments [25]. Subsequent research has expanded its applicability to a variety of fields. For instance, in innovation management, it has provided a theoretical framework for examining how firms orchestrate resources to accelerate the transition to a circular economy [26]. And in inter-organizational cooperation, it has been employed to analyze collaborative mechanisms for achieving shared goals [27].

Currently, research has shifted from optimizing internal resources within individual organizations to integrating resources across multiple organizations in collaborative settings [28]. Additionally, the theory has expanded beyond its initial emphasis on economic performance to encompass a more multi-dimensional approach to resource utilization, one that also emphasizes the balance of social and ecological benefits [29]. This evolution has not only enriched the range of scenarios in which resource orchestration theory can be applied but has also enhanced its explanatory power in understanding cross-sector collaboration and resource management in complex, dynamic environments.

While these studies provide valuable insights, they primarily focus on organizational or firm-level contexts. In contrast, this study uniquely applies resource orchestration theory to the Forest–Village Cooperation context, emphasizing the orchestration of policy, human, and natural resources to achieve sustainable forestland management and revitalize rural economies. Simultaneously, resource orchestration theory emphasizes the achievement of resource synergies through the dynamic acquisition, integration, and utilization of resources in multi-stakeholder collaborations [25]. This aligns closely with the central role of state-owned forest farms in managing resources within Forest–Village Cooperation. State-owned forest farms contribute essential resources, such as human and technical expertise, to these partnerships, while also enhancing cooperation by integrating and optimizing the social networks and labor resources of village collectives, thereby maximizing cooperative benefits.

In addition, recent developments in resource orchestration theory have increasingly highlighted the importance of social and ecological benefits. This focus aligns well with the objectives of Forest–Village Cooperation, offering valuable theoretical support for investigating how state-owned forest farms contribute to rural revitalization and sustainable development in forested areas. Therefore, resource orchestration theory provides a robust and insightful analytical framework for exploring the resource organization and utilization mechanisms within state-owned forest farms in the context of Forest–Village Cooperation.

2.2. Theoretical Analytical Framework

Based on resource orchestration theory, this section constructs a theoretical analytical framework for examining how state-owned forest farms can promote FVCS. The framework focuses on three key processes: resource acquisition, resource integration, and resource utilization [25]. It systematically explores the mechanisms of resource optimization and collaboration within this cooperation, identifying the critical pathways through which state-owned forest farms contribute to FVCS.

2.2.1. Resource Acquisition: Efficient Identification and Introduction of Diverse Resources

Resource acquisition serves as the initial stage in cooperation, reflecting the ability of state-owned forest farms to identify and obtain the essential resources from both internal and external environments. In the context of Forest–Village Cooperation, resource acquisition is achieved through four primary pathways:

(1)

Policy Resource Acquisition: State-owned forest farms leverage their institutional advantages to secure policy support, higher-level subsidies, and legal protections, ensuring the smooth advancement of cooperative projects [30]. Financial subsidies, tax incentives, and favorable forestry policies provided by local governments create a stable policy environment and funding sources for the cooperation.

(2)

Economic Resource Acquisition: State-owned forest farms establish strong links with the local economy, attracting social capital and market resources. This enhances the financial foundation and economic vitality needed to implement Forest–Village Cooperation projects [31]. This integration not only highlights the pivotal role of state-owned forest farms in regional economies but also broadens the financing channels for cooperative initiatives.

(3)

Natural Resources Acquisition: As entities endowed with valuable natural resources, state-owned forest farms integrate forest resources, ecological services, and related production materials to provide essential support for cooperation [32]. These natural resources form the basis for value creation within the cooperation, ensuring both ecological and social benefits.

(4)

Human Resource Acquisition: State-owned forest farms must cultivate high-quality internal human resources while attracting external professionals through institutional improvements. Skilled human resources are critical for accurate resource identification, effective integration, and enhanced cooperation efficiency and innovation [33]. Acting as a bridge between policy, economic, and natural resources, human resources play a central role in mobilizing other resource types.

2.2.2. Resource Integration: Collaborative Operation of Heterogeneous Resources

Resource integration is the core link for realizing the benefits of Forest–Village Cooperation, reflecting the ability of state-owned forest farms to coordinate and collaborate in combining diverse resources from multiple stakeholders. The key to successful integration lies in constructing a collaborative resource network that fosters multi-dimensional organic connections and synergistic effects [34]:

(1)

Complementary Resource Integration: State-owned forest farms integrate their natural resources, human resources, and technological innovation capabilities with the land, labor, and social networks of village collectives to achieve complementarity. Policy resources provide institutional guarantees and financial support, while natural and economic resources are further integrated to amplify the cooperation’s potential benefits.

(2)

Integration of Technology and Grassroots Connections: By combining technological R&D investments with the introduction of new technologies and the production factors of village collectives, state-owned forest farms can promote the high-quality implementation of afforestation, reforestation, and other projects [35]. Meanwhile, grassroots connectivity capability plays a stabilizing role in the integration process. State-owned forest farms enhance trust and foster cooperation by addressing forest rights disputes, signing co-construction agreements, and organizing joint activities, which in turn improves the stability and durability of the cooperative relationship [36].

(3)

Dynamic Adjustment and Feedback Optimization: Resource integration is a dynamic process. State-owned forest farms adjust resource allocation through monitoring and feedback mechanisms, based on evolving needs and changes in the external environment during cooperation. This flexibility ensures the adaptability and continued effectiveness of resource integration [2]. Policy and economic resources underpin system adjustments, while human resources and technological innovation enhance integration through feedback optimization. The complementary nature of these resources fosters a self-optimizing network, ensuring long-term stability and FVCS.

2.2.3. Resource Utilization: Transformation from Resources to Value

Resource utilization is the final stage of resource orchestration, focusing on how integrated resources can be efficiently transformed into tangible economic, ecological, and social benefits. In Forest–Village Cooperation, state-owned forest farms drive optimal resource allocation and value creation through scientific management and technological innovation [37], manifesting in three key aspects:

(1)

Efficient Management Mechanism: State-owned forest farms optimize management models to achieve the efficient allocation and utilization of resources. Refined management and scientific decision-making ensure that various resources are maximally applied in Forest–Village Cooperation, providing a solid foundation for its long-term sustainability.

(2)

Technology-Driven Resource Transformation: By introducing advanced forestry technologies and innovative methods, state-owned forest farms not only optimize resource allocation but also improve forest management outcomes. The integration of technology and management ensures the full utilization of resources, further enhancing the cooperation benefits.

(3)

Multi-Dimensional Benefits Realization: The benefits of resource utilization are reflected in the simultaneous enhancement of economic, ecological, and social outcomes. At the same time, by providing employment opportunities, it supports the achievement of rural revitalization goals. These multi-dimensional benefits lay the foundation for long-term FVCS.

In summary, as the core entity in resource orchestration, state-owned forest farms can maximize resource advantages through scientific resource management, driving the enhancement of cooperation sustainability. This study, grounded in resource orchestration theory, explores how state-owned forest farms can foster the long-term development of Forest–Village Cooperation through resource configuration and optimization. The theoretical analysis framework is presented in Figure 1.

3. Materials and Methods

3.1. Overview of the Study Area

As one of China’s provinces with the richest forest resources, Fujian stands out as a national model for forestry ecological conservation and development, boasting the highest forest coverage rate in the country, which reached 65.12% in 2023. Provincial state-owned forest farms play a pivotal role in Fujian’s forestry development, contributing significantly to ecological protection, forest management, and industrial growth. By undertaking national key projects such as natural forest protection and public welfare forest construction, and actively promoting scaled operations, these state-owned forest farms have not only achieved synergistic improvement in ecological and social benefits but also provided robust support for regional economic growth and environmental enhancement.

Fujian has taken the lead in exploring cooperation models between state-owned forest farms and village collectives, exemplified by the 2022 launch of the “Hundred Forest Farms Supporting Thousand Villages” Initiative, aiming to promote rural economic development and shared prosperity in forest regions through the leadership of state-owned forest farms. Fujian’s successful experience in collective forest tenure reform has been widely recognized and replicated across the country, offering a valuable practical model for studying Forest–Village Cooperation.

Currently, Fujian Province hosts 84 provincial state-owned forest farms, managing a total operating area exceeding 408,700 hectares, which accounts for approximately 3.30% of the province’s total land area. These farms possess abundant resource endowments and perform vital ecological functions, serving not only as the backbone of the province’s forestry development but also as key practitioners in Forest–Village Cooperation. For this study, the state-owned forest farms in Fujian Province were chosen as the research subjects.

Table 1. Basic geographical and demographic information of Fujian Province (2023).

Item	Value	Description
Permanent Population	41.83 million	Total population residing in Fujian Province
Population Density	337 people/km2	Average population per square kilometer
Total Land Area	124,000 km2	Total land area of Fujian Province
Mountain and Hill Area	105,400 km2	Area covered by mountains and hills in Fujian Province
Forest Area	80,773.33 km2	Total area covered by forests in Fujian Province
Agricultural Land Area	9320 km2	Land used for farming and cultivation in Fujian Province
Urban Area	4377.55 km2	Area occupied by urban environments in Fujian Province

outlines Fujian’s demographic data, such as population density and land-use distribution. Figure 2 illustrates Fujian’s location in China and shows the distribution of provincial state-owned forest farms across the region, along with the province’s land-use types, providing enriched contextual information. This study aims to offer actionable insights for advancing sustainable forestry cooperation globally.

3.2. Data Sources

The data for this study primarily come from three sources: First, data collected and organized from government department websites, including data on the number of policy support documents from local government websites, and data on the number of forestry industry-leading enterprises from the official website of the Fujian Provincial Forestry Bureau. Second, statistical yearbook data, including the total output value of agriculture, forestry, animal husbandry, and fishery, as well as GDP figures for the counties where the state-owned forest farms are located, which were sourced from the Fujian Statistical Yearbook. Third, data collected through a field survey conducted by the Collective Forest Rights Reform Research Group of Fujian Agricultural and Forestry University in July and August of 2024. In addition to the aforementioned indicators, other data used in this study were obtained through questionnaires and structured interviews. The questionnaires covered topics such as (1) basic resources (e.g., operating area), (2) human resources, and (3) financial status. The interviews focused on (1) policy support, (2) social relations, and (3) research and development. A total of 84 valid questionnaires were obtained, and after removing samples with missing data, 83 questionnaires were selected for analysis.

3.3. Research Methods

This study employs a combination of NCA, fsQCA, and DEA methods for a multi-dimensional analysis of FVCS.

First, the NCA method is utilized to identify the necessary conditions for FVCS, pinpointing the indispensable resources or factors essential to its success [38]. This quantitative analysis provides scientific evidence for policy-making.

Second, fsQCA is applied to examine the combinatory effects of multiple antecedent conditions and their influence on the sustainability of the cooperation. The focus is on revealing the complexity and asymmetry of causal relationships. By considering the interactions between conditions, fsQCA uncovers multiple paths to achieving cooperation sustainability under different resource configurations, offering a multi-dimensional perspective for policy optimization [39].

Additionally, DEA is used to quantitatively assess the efficiency of Forest–Village Cooperation. DEA, with its ability to handle multiple inputs and outputs, constructs the optimal production frontier through linear programming, making it highly suitable for efficiency analysis in complex settings. As an extension of DEA, the BCC model accounts for scale returns, making it especially relevant for scenarios with varying scale benefits. This feature enables a more accurate representation of the complexity and diversity of Forest–Village Cooperation [40].

The integration of these three methods achieves a fusion of qualitative and quantitative analysis: NCA reveals the necessary conditions, fsQCA explores the diverse configurational paths of sufficient conditions, and DEA evaluates efficiency of the outcome variable. By synthesizing these methods, this study not only provides an in-depth analysis of the mechanisms underlying FVCS but also lays a solid theoretical foundation and empirical support for optimizing cooperation models and informing policy improvements.

3.4. Variable Measurement

3.4.1. Measurement of Forest–Village Cooperation Sustainability (FVCS)

The DEA-BCC model is utilized to measure the scale efficiency of sustainability in Forest–Village Cooperation, serving as the outcome variable. In the context of Forest–Village Cooperation, scale efficiency reflects how increased resource inputs drive the incremental effects of cooperation outcomes, thereby indicating the efficiency and sustainability of the cooperation. The model’s input and output indicators are as follows:

• Input indicators include capital input, labor input, and production factor input [41]. Specifically: (1) Capital input is measured by the total amount of funds invested by the state-owned forest farm in the Forest–Village Cooperation projects, reflecting the financial support for the projects. (2) Labor input is measured by the number of employees directly involved in the Forest–Village Cooperation projects from the state-owned forest farm, representing the level of labor resource input. (3) Production factor input uses the total area managed by the state-owned forest farm as an indicator, reflecting the scale of its resource base.
• Output indicators focus on the social benefits generated by the Forest–Village Cooperation, specifically: (1) Forest–Village Cooperation area, which reflects the scale and coverage of the cooperation. (2) Number of co-built projects in Forest–Village Cooperation, assessing the effectiveness of the cooperation in promoting project development and deepening collaboration. (3) Number of employment opportunities, which measures the role of cooperation in stimulating local employment and supporting socio-economic development.

These indicators comprehensively measure the scale efficiency of Forest–Village Cooperation from both resource input and cooperation output dimensions, thereby assessing its level of sustainability. Based on this, an input–output efficiency evaluation system is constructed, as shown in

Table 2. The evaluation index system for the input–output efficiency of FVCS.

Indicator Type	Indicator Content
Input indicators	Total investment of state-owned forest farms in 2023 for cooperative projects between farms and villages (in thousand CNY)
	Number of employees directly involved in village cooperation projects in state-owned forest farms in 2023 (person)
	Total operating area of state-owned forest farms (km2)
Output indicators	As of 2023, the cooperative area between villages and towns (km2)
	As of 2023, the number of cooperative construction projects between villages and towns (units)
	State-owned forest farms provide employment opportunities in 2023 (person-times)

.

Under the assumption of variable returns to scale (VRS), the DEA-BCC model uses linear programming technology to calculate the relative efficiency of decision-making units (DMUs), which helps identify the strengths and weaknesses of resource utilization, providing a scientific basis for optimizing resource allocation and improving cooperation benefits. The specific model expression is as follows:

$$\mathrm{M}\mathrm{i}\mathrm{n}\mathrm{i}\mathrm{m}\mathrm{i}\mathrm{z}\mathrm{e}:\theta$$

(1)

$${\sum }_{j=1}^n{\lambda }_j{x}_{ij}\le \theta x_{i0},i=\mathrm{1,2},\dots ,m.$$

(2)

$${\sum }_{j=1}^n{\lambda }_j{y}_{rj}\ge y_{r0},r=\mathrm{1,2},\dots ,s.$$

(3)

$${\sum }_{j=1}^n{\lambda }_j=1$$

(4)

$${\lambda }_j\ge 0,j=\mathrm{1,2},\dots ,n$$

(5)

The objective function, as represented in Equation (1), is employed to evaluate the efficiency value ($\theta$) of the target DMU. The value of $\theta$ ranges from [0, 1], reflecting the scale efficiency of Forest–Village Cooperation. When $\theta =1$, the target DMU lies on the production frontier, achieving optimal scale efficiency. When $\theta <1$, the target DMU fails to reach optimal scale efficiency, indicating resource inefficiencies or insufficient performance. This necessitates improving the input–output structure to enhance efficiency. The variable ${\lambda }_j$ denotes the weight assigned to other DMUs to construct a linear combination relative to the target DMU. Variables $x_{ij}$ and $y_{rj}$ represent the $i$-th input and $r$-th output values for the $j$-th state-owned forest farm, respectively.

Equation (2) represents the input constraints, which adjust the input proportions of the target state-owned forest farm to maximize Forest–Village Cooperation efficiency under optimal scale conditions. This constraint minimizes resource wastage by optimizing input resource utilization. Equation (3) addresses the output constraints, ensuring that the actual output level of Forest–Village Cooperation for the target state-owned forest farm is not less than its current level. This guarantees that the practical benefits of Forest–Village Cooperation are not compromised while maintaining the effectiveness of resource allocation and the stability of cooperative outcomes.

Equation (4) introduces the returns-to-scale constraint under the assumption of VRS, enabling fair comparisons of Forest–Village Cooperation efficiency across state-owned forest farms. This avoids bias in evaluation caused by scale differences. Lastly, Equation (5) imposes non-negativity constraints, requiring all variables to be non-negative to align with practical scenarios, ensuring the scientific validity and rationality of the model’s computations.

3.4.2. Antecedent Conditions

Building upon the theoretical framework, this section provides a detailed description of the antecedent conditions, focusing on their operational definitions, key indicators, and specific roles in influencing FVCS outcomes. These antecedent conditions are categorized into six dimensions—policy resources, human resources, natural resources, economic resources, grassroots connectivity capability, and technological innovation capability—each measured by distinct indicators to capture their contribution to FVCS.

• Policy Resources

Policy resources provide the institutional foundation for FVCS, directly influencing the initiation and sustainability of cooperative projects. Key indicators include the number of policy documents supporting cooperation and the total financial subsidies received. These indicators reflect the extent of governmental support and financial security available for cooperative initiatives.

2.

Human Resources

Human resources are measured through indicators such as employee training frequency, training expenditure as a percentage of annual budgets, and average annual income levels. These indicators capture the quality of personnel and the effectiveness of human resource management in facilitating cooperation efficiency.

3.

Natural Resources

Natural resources are evaluated based on forest coverage rate, per-hectare forest stock volume, and the proportion of commercial forest area. These metrics reflect the availability and quality of material resources essential for achieving cooperation goals.

4.

Economic Resources

Economic resources encompass the gross output value of agriculture, forestry, animal husbandry, and fisheries; GDP; fixed assets; and the number of leading forestry enterprises. These indicators demonstrate the economic strength and market potential in regions hosting state-owned forest farms.

5.

Grassroots Connectivity Capability

Grassroots connectivity capability is assessed through the number of co-construction agreements and the resolution status of forest tenure disputes. These indicators highlight the strength of collaboration and trust between state-owned forest farms and village collectives.

6.

Technological Innovation Capability

Technological innovation capability is captured through R&D investment ratios, the number of new technologies introduced, and their application in cooperative projects. These indicators reflect the ability of state-owned forest farms to leverage innovation for sustainable cooperation.

To ensure the scientific rigor and comparability of variable measurement, this study standardizes the data and applies the entropy weighting method. This approach assigns objective weights to each indicator based on their inherent variability, thus quantitatively reflecting the relative contribution of each dimension to cooperative benefits.

Table 3. Antecedent conditions setting.

Antecedents and Notations	Secondary Indicators	Variable Declaration	Weight
Policy Resources (PRs)	Policy Support	As of 2023, the number of policies related to supporting forestry cooperative operation in prefecture-level cities where state-owned forest farms are located	0.0356
	Subsidy Funds	Total amount of superior subsidy funds for state-owned forest farms in 2023	0.0313
Human Resources (HRs)	Training Expenses	The proportion of personnel training expenses in state-owned forest farms in 2023 to the total annual expenditure	0.1302
	Staff Training	Number of employee training sessions organized by state-owned forest farms in 2023	0.0869
	Employee income	Annual average income of employees in state-owned forest farms in 2023	0.0071
Natural Resources (NRs)	Forest Coverage	Forest coverage rate of state-owned forest farms in 2023	0.0046
	Forest Stock	Average forest stock per hectare in state-owned forest farms in 2023	0.0130
	Commercial Forest	The proportion of commercial forest area to the total forest area of state-owned forest farms in 2023	0.0635
Economic Resources (ERs)	Total Output Value	The total output value of agriculture, forestry, animal husbandry and fishery in the county or district where the state-owned forest farm is located, in 2023	0.0204
	GDP	The GDP of the county or district where the state-owned forest farm is located, in 2023	0.0461
	Fixed Assets	Total fixed assets of state-owned forest farms in 2023	0.1040
	Forestry Enterprise	Number of leading forestry industrialization enterprises in counties and districts where state-owned forest farms are located, in 2023	0.0558
	Operational Income	Total operating income of state-owned forest farms in 2023	0.0359
Grassroots Connectivity Capability (GCC)	Forest Rights Dispute	Is there a forest rights dispute between state-owned forest farms and surrounding village collectives in 2023?	0.0519
	Jointly Build Village Collectives	As of 2023, the number of village collectives that have signed joint construction agreements and carried out activities in state-owned forest farms	0.0501
Technological Innovation Capability (TIC)	R&D Investment	Proportion of technology R&D investment in state-owned forest farms to total annual expenditure in 2023	0.1052
	Technology Import	The number of new technologies introduced by state-owned forest farms in 2023	0.1585

provides a detailed summary of the specific variables and their respective weight assignments.

3.5. Data Calibration

Before conducting the analysis, it is necessary to calibrate the antecedent conditions and the outcome. This study adopts the direct calibration method, setting the thresholds of 95%, 50%, and 5% as the anchors for full membership, crossover point, and full non-membership, respectively [42,43]. To avoid ambiguity in configuration assignment for cases with a membership score of 0.5, the score is adjusted to 0.499 [44].

The calibration anchors and descriptive statistics are detailed in

Table 4. Calibration and descriptive statistics.

Variables	Calibration			Descriptive Statistics
	Full Non-Membership	Crossover Point	Full Membership	Mean	Std. Dev	Max.	Min.
FVCS	0.2618	0.7940	1	0.7411	0.2458	1	0.1810
PRs	0.0523	0.2620	0.6846	0.3375	0.2098	1	0.0101
HRs	0.0190	0.0530	0.2070	0.0746	0.0903	0.6367	0.0120
NRs	0.0775	0.1607	0.2354	0.1644	0.0946	0.8773	0.0200
ERs	0.0506	0.1213	0.2736	0.1412	0.0850	0.5263	0.0333
GCC	0.0001	0.6249	0.7678	0.4321	0.3144	1.1649	0.0001
TIC	0.0001	0.0168	0.5061	0.1136	0.1765	0.6661	0.0001

.

4. Results

4.1. Necessity Analysis

The necessity analysis was conducted using the CR (ceiling regression) and CE (ceiling envelopment) estimation methods in NCA [38]. A condition is considered necessary if its necessity effect size reaches 0.1 or above, and the effect size is statistically significant (p ≤ 0.01) as verified by Monte Carlo permutation tests [38].

The results presented in

Table 5. Analysis results of necessary conditions for NCA.

Conditions 1	Method	Accuracy	Ceiling Zone	Scope	Effect Size (d)	p-Value 2
PRs	CR	100.00%	0	0.88	0	0.576
	CE	100.00%	0	0.88	0	0.576
HRs	CR	100.00%	0	0.89	0	1
	CE	100.00%	0	0.89	0	1
NRs	CR	98.80%	0.009	0.91	0.010	0.007
	CE	100.00%	0.011	0.91	0.013	0.170
ERs	CR	100.00%	0	0.90	0	1
	CE	100.00%	0	0.90	0	1
GCC	CR	100.00%	0	0.87	0	1
	CE	100.00%	0	0.87	0	1
TIC	CR	100.00%	0	0.86	0	1
	CE	100.00%	0	0.86	0	1

¹ Using calibrated membership scores; ² Using permutation test with 10,000 resampling iterations.

indicate that none of the antecedent conditions—policy resources, human resources, natural resources, economic resources, grassroots connectivity capability, or technological innovation capability—met the criteria for necessity. This implies that none of these conditions alone constitute a necessary condition for achieving high FVCS.

The bottleneck analysis also reveals that among all antecedent conditions, natural resources represent the first bottleneck, with a Forest–Village Cooperation Sustainability (FVCS) of 30%, requiring a 0.1% level. To achieve 100% FVCS, the required levels are 2.1% for policy resources and 2.7% for natural resources. No bottleneck levels are observed for the remaining conditions.

Furthermore, the necessity of individual antecedent conditions was rigorously tested using necessity analysis in fsQCA. As shown in

Table 6. The necessity test of single conditions in QCA.

Antecedent Conditions	High FVCS		Non-High FVCS
	Consistency	Coverage	Consistency	Coverage
PRs	0.6728	0.6990	0.6330	0.5371
~PRs	0.5544	0.6491	0.6452	0.6169
HRs	0.5548	0.7263	0.5401	0.5774
~HRs	0.6772	0.6432	0.7440	0.5771
NRs	0.6367	0.7102	0.5971	0.5440
~NRs	0.5912	0.6424	0.6819	0.6052
ERs	0.6157	0.7143	0.5489	0.5201
~ERs	0.5863	0.6141	0.6985	0.5975
GCC	0.5373	0.6938	0.5313	0.5603
~GCC	0.6595	0.6327	0.7097	0.5561
TIC	0.5248	0.6810	0.5416	0.5741
~TIC	0.6718	0.6422	0.6990	0.5457

, the necessity consistency values of individual antecedent conditions for the outcome were generally low (all below 0.9). This confirms that none of the antecedent conditions constitute a necessary condition for high FVCS, consistent with the results of the NCA.

4.2. Configuration Analysis

Referring to prior research [45], this study set the frequency threshold at 1, the raw consistency threshold at 0.80, and the PRI consistency threshold at 0.75, resulting in configurations that lead to high FVCS, as detailed in

Table 7. Configurations leading to high FVCS.

Antecedent Conditions	Configuration 1	Configuration 2	Configuration 3
	S1	S2	S3a	S3b
PRs				●
HRs	◉		◉	◉
NRs	◉	◉	◯
ERs	◉	◉	◉	◉
GCC	◎	◎	◉	◉
TIC		◉	◉	◉
Consistency	0.8953	0.9223	0.9439	0.9286
Raw Coverage	0.2662	0.2524	0.1806	0.2101
Unique Coverage	0.0521	0.0383	0.0188	0.0212
Overall Consistency	0.9029
Overall Solution Coverage	0.3769

Note: ◉ and ● represent the presence of core conditions and the presence of peripheral conditions, respectively; ◎ and ◯ represent the absence of core conditions and the absence of peripheral conditions, respectively. A blank space indicates that it is irrelevant to the outcome.

.

4.2.1. Resource Integration-Driven Model

Configuration 1 identifies high levels of human, natural, and economic resources as key drivers for achieving high FVCS. While grassroots connectivity capability may not be highly developed, integrating these internal and external resources can effectively drive FVCS. This model highlights the significant role of resource integration, even in the absence of strong local linkages, and is referred to as the “Resource Integration-Driven Model”.

This configuration accounts for approximately 26.62% of the sample cases, with about 5.21% of the cases being explained exclusively by this configuration. Typical cases of this configuration include the Guangzhuang State-owned Forest Farm, Shuinan State-owned Forest Farm in Sha County, Mingxi State-owned Forest Farm, Taoyuan State-owned Forest Farm in Datian, and Shuixi State-owned Forest Farm in Jianou.

For instance, the Guangzhuang State-owned Forest Farm in Sha County, Fujian Province, leverages its advantages in capital, technology, management, and human resources to innovate a “Forest Farm-Driven Village Linkage Connecting to Households” cooperation mechanism. This has resulted in seven cooperative models for forest resource management, including cooperative afforestation, reforestation, shareholding cooperative management, and entrusted management. Guangzhuang State-owned Forest Farm has signed cooperation agreements with 19 administrative villages in four towns (or streets), covering an area of 9733.33 hectares, increasing forest stock volume by 277,000 m³, and generating an average per-hectare income increase of CNY 234. Moreover, over 10,000 labor employment opportunities are provided annually to the villagers, contributing over CNY 3 million in additional income.

Additionally, the forest land and timber assets under Forest–Village Cooperation are legally quantified and converted into forest certificates, a model that has been piloted in several villages. To date, forest certificates valued at CNY 27.75 million have been issued. Furthermore, the forest farm continues to explore mechanisms for realizing the value of ecological products, having issued 1320 tons of carbon certificates and completed transactions of 418 tons, contributing to the achievement of dual carbon goals and promoting the revitalization of the forest area.

4.2.2. Technology Innovation Empowerment Model

Configuration 2 emphasizes the importance of technological innovation, along with abundant natural resources and a solid economic base, in driving high FVCS. This model indicates that state-owned forest farms can rely less on grassroots connectivity capability by leveraging technology, which empowers cooperation and enhances operational efficiency. Thus, it is termed the “Technology Innovation Empowerment Model”.

This configuration explains approximately 25.24% of the sample cases, indicating that it is one of the sufficient condition combinations for state-owned forest farms to achieve high operational performance. Additionally, about 3.83% of the cases are exclusively explained by this configuration, underscoring its uniqueness and irreplaceability in certain contexts. Typical cases include Yangkou State-owned Forest Farm, Shaowu Guxian State-owned Forest Farm, Jiangle State-owned Forest Farm, Shaowu Weimin State-owned Forest Farm, and Pinghe Tianma State-owned Forest Farm, among others.

For example, the Yangkou State-owned Forest Farm in Fujian Province, the only National Cunninghamia Lanceolata Germplasm Resource Bank in China, has been dedicated to transforming scientific research into practical applications since its establishment, achieving continuous breakthroughs in Cunninghamia research. By 2020, Yangkou Forest Farm had provided over 7200 forestry-related labor jobs to surrounding communities, generating an additional income of CNY 120 million. With the “Yangkou Forests Spirit”, the forest farm has continuously promoted common prosperity and rural revitalization in the surrounding forest area.

4.2.3. Capability–Resource Synergy Model

Configuration 3 highlights the interplay of high human, economic, grassroots connectivity capability, and technological capabilities as essential for high FVCS. This configuration includes two sub-configurations—S3a and S3b—distinguished by their marginal conditions: S3a relies on lower levels of natural resources, while S3b is supported by high policy resources. These findings suggest that while FVCS is primarily driven by internal capabilities, natural and policy resources can act as supplementary elements that support sustainability. Hence, this model is referred to as the “Capability–Resource Synergy Model”.

This configuration explains approximately 39.07% of the sample cases, suggesting that it is one of the sufficient condition combinations for state-owned forest farms to achieve high operational performance. Additionally, around 4% of the cases are exclusively explained by this configuration, highlighting its significance and irreplaceability in certain contexts. Typical cases include Zhangping Wuyi State-owned Forest Farm, Nanan Luoshan State-owned Forest Farm, Liancheng Qiujia Mountain State-owned Forest Farm, and Shunchang Puchang State-owned Forest Farm, among others.

For instance, under the dual objectives of the “Hundred Forest Farms Supporting Thousand Villages” initiative and the “Double Growth” requirements (growth in both forest area and timber stock volume) of the state-owned forest farm reform pilot program, the Zhangping Wuyi State-Owned Forest Farm in Fujian Province overcame challenges related to insufficient funding and technology among local farmers. The farm collaborated with eight local administrative villages by signing Afforestation and Reforestation Cooperation Contracts, agreeing on a 60%–40% equity split. As of now, the afforestation area has reached 580 hectares, providing at least 3500 employment opportunities annually for the partner villages. In 2023, Wuyi Forest Farm held 86 technical training sessions, equipping employees and local farmers with skills in afforestation, reforestation, and forest management. Moreover, the forest farm undertook and completed 11 research projects, earning numerous provincial and national awards, which effectively demonstrate the remarkable impact of the “Capability–Resource Synergy Model” in promoting FVCS.

4.3. Robustness Test

First, the PRI consistency threshold was adjusted, loosening the original setting of 0.75 to 0.70. The results show that, after the adjustment, the overall distribution of configurations, as well as the combination of core and marginal conditions, still covers all configurations in Table 7.

Second, the calibration anchor points were modified, with the full membership point set at 85%, the crossover point at 50%, and the full non-membership point at 15%. The results indicate that the adjusted configurations closely align with the original configurations, reinforcing the robustness of the findings.

Lastly, recognizing the potential influence of different resource endowments across cities, particularly for provincial capitals like Fuzhou and separately listed municipalities such as Xiamen, 12 cases from these cities were removed from the analysis. These cities are expected to have resource stocks that differ from other cities in the sample. Despite this adjustment, the analysis yielded consistent configurations, further confirming the robustness of the configuration analysis results.

5. Discussion, Limitations and Prospects

5.1. Discussion

This study identifies three distinct configuration models that drive FVCS in state-owned forest farms in Fujian Province (see

Table 8. Distribution and characteristics of the configurations.

Configuration Type	Number of State-Owned Forest Farms	Main Features
Resource Integration-Driven Model	22	High resources in human, natural, and economic factors but limited grassroots connectivity capability.
Technology Innovation Empowerment Model	21	Strong in technological innovation and economic resources, but with lower grassroots connectivity.
Capability–Resource Synergy Model	32	Balanced resources in human, economic, and natural factors, with high grassroots connectivity.

). Each configuration reveals a unique pathway through which state-owned forest farms can achieve sustainable cooperation with surrounding village collectives, depending on the interplay of resources, capabilities, and linkages. The three configurations—Resource Integration-Driven Model, Technology Innovation Empowerment Model, and Capability–Resource Synergy Model—highlight the diverse strategies that these forest farms adopt to foster FVCS.

The Resource Integration-Driven Model underscores the importance of integrating human, natural, and economic resources for fostering sustainable Forest–Village Cooperation, even in the absence of strong grassroots linkages. This approach builds upon the existing literature [46,47], suggesting that resource-rich conditions can offset weaker local ties, offering key insights for policy and resource distribution. Guangzhuang State-owned Forest Farm is a notable example where a well-integrated approach has enabled effective forest resource management, even without robust grassroots connectivity.

The Technology Innovation Empowerment Model underscores the transformative power of technological innovation. By leveraging strong natural resources and economic foundations, this model reduces reliance on grassroots linkages. Yangkou State-owned Forest Farm exemplifies how technological innovation can enhance operational efficiency and sustain cooperation, providing a scalable solution for regions with similar resource conditions. This model’s substantial explanatory power (25.24% of cases) highlights technology as a critical enabler for cooperation [48].

The Capability–Resource Synergy Model adopts an integrative approach by combining human, economic, grassroots connectivity, and technological capabilities, supplemented by natural and policy resources in specific contexts. The Zhangping Wuyi State-owned Forest Farm demonstrates how this model enables sustainable cooperation by overcoming financial and technological barriers through strong local partnerships and human capital [46,49]. With 39.07% of cases fitting this model, it offers significant insights into how state-owned forest farms can align resource integration with capability development for effective FVCS.

This study contributes to the expanding research on community-based forestry, particularly in the context of China. While previous studies have primarily focused on individual factors such as resources, governance, and innovation [50], this research distinguishes itself by adopting a configurational approach that captures the complex combinations of factors necessary for FVCS. Unlike traditional models that examine individual factors (such as economic resources or technology), this study demonstrates how synergies created by multiple factors contribute to sustainability. The emphasis on technological innovation in Configuration 2 aligns with existing findings [51], regarding its role in improving operational efficiency in forest farms.

Furthermore, this study challenges the conventional view that grassroots connectivity is always essential for success [52]. While some models (e.g., Capability–Resource Synergy Model) highlight the importance of strong grassroots linkages, others (e.g., Resource Integration-Driven Model and Technology Innovation Empowerment Model) suggest that resource endowments and technological capabilities can compensate for weaker local connections [53]. This insight contributes to ongoing discussions on the role of community engagement in forest governance and cooperation.

5.2. Limitations and Prospects

This study has several limitations. First, the sample is limited to state-owned forest farms in Fujian Province, and the findings may not be fully generalizable to other regions in China or abroad. Future research could expand the geographical scope and explore the applicability of these models in different ecological and socio-economic contexts. Second, while this study focuses on the conditions for achieving sustainable Forest–Village Cooperation, future studies could examine the long-term impacts of these models on local communities, forest health, and biodiversity conservation.

6. Conclusions and Policy Implications

6.1. Conclusions

Building on resource orchestration theory and utilizing fsQCA, NCA, and DEA, this study explores the driving pathways for sustainable Forest–Village Cooperation facilitated by state-owned forest farms. The key findings are as follows:

First, economic resources, human resources, and technological innovation capability are identified as core drivers of FVCS. Economic resources provide material support, human resources enhance resource integration and utilization, and technological innovation fosters efficient cooperation. These three factors consistently emerge as central across all configurations, highlighting that sustainable FVCS relies on the synergy of multiple resources, with no single factor alone sufficient to achieve the desired outcomes.

Second, for state-owned forest farms with weak grassroots connectivity, abundant natural resources can compensate for this limitation by leveraging resource advantages to foster cooperation. When natural resources are less favorable, sustainable cooperation can still be achieved through the integration of human resources, economic resources, and advanced resource utilization capabilities, illustrating the adaptability of these farms under varying resource conditions.

Third, three distinct pathways driving sustainable FVCS have been identified: (1) Resource Integration-Driven Pathway, which emphasizes the comprehensive use of human, natural, and economic resources; (2) Technological Innovation Empowerment Pathway, which underscores the value added by technological innovation in the context of natural and economic resources; and (3) Capability–Resource Synergy Pathway, which highlights the collaborative synergy of diverse resources and capabilities. These findings demonstrate how state-owned forest farms adapt to their specific conditions, offering varied approaches to fostering FVCS.

These insights contribute to ongoing discussions on forest governance and cooperation at multiple levels. At the international level, this study advances the global research on cooperative forestry by integrating resource orchestration theory into the discourse on sustainable forestry cooperation, providing a configurational perspective that emphasizes the synergy of multiple resources. This research enriches the theoretical framework of international forest resource management by broadening the scope of cooperative forestry studies globally. At the Asian regional level, the findings complement existing studies in countries like Thailand and Indonesia [54], showcasing the contextual adaptability of resource orchestration strategies to diverse socio-economic and ecological landscapes. At the Chinese national level, this study deepens the understanding of how resource-based synergies and government support foster multi-stakeholder cooperation, offering a roadmap for achieving the dual goals of ecological conservation and rural development.

By demonstrating how state-owned forest farms adapt to varying conditions through diverse pathways, this study provides new perspectives for policymakers and practitioners engaged in global forest resource management. The emphasis on context-specific strategies not only enriches the theoretical discourse but also offers actionable implications for enhancing Forest–Village Cooperation Sustainability in different geographic and institutional settings.

6.2. Policy Implications

The findings from this study offer several policy implications for the sustainable development of Forest–Village Cooperation. First, it is evident that resource-rich forest farms can achieve sustainability without relying heavily on grassroots connectivity, suggesting that policymakers should focus on enhancing resource mobilization, particularly in areas with limited local engagement. For example, supporting technological innovation and infrastructure development in state-owned forest farms could help them achieve sustainable outcomes even in low-connection contexts.

Second, the importance of technology innovation cannot be overstated. Government agencies should prioritize investments in forest technology research and development, as well as create incentives for state-owned forest farms to integrate new technologies into their operations. As seen in the case of Yangkou Forest Farm, technological advancements can significantly increase operational efficiency and reduce reliance on local communities.

Finally, the synergy of resources and capabilities identified in the Capability–Resource Synergy Model suggests that future policies should focus on fostering integrated development strategies that combine human resources, economic resources, and technological innovation with strong local partnerships. This approach will not only enhance the sustainability of Forest–Village Cooperation but also contribute to broader rural revitalization and economic development goals.