Effects of Knowledge Transfer on Integrated Forest Management in China: A Social–Ecological System Framework Analysis

Abstract

Against the backdrop of global biodiversity crises and climate change, integrated forest management (IFM) has emerged as a critical pathway for sustainable forest development. Grounded in the social–ecological system (SES) framework, we examine the mechanisms and pathways through which knowledge transfer influences IFM, with a focus on residents in China’s state-owned forest regions in the Northeast. By constructing an IFM-SES theoretical framework and utilizing survey data, we employ OLS regression and mediation effect models to empirically assess the driving effects of knowledge transfer on IFM and its heterogeneous impacts. We show that: (a) community-based knowledge transfer significantly enhances IFM; (b) knowledge transfer indirectly promotes IFM by fostering collective action efficacy, strengthening institutional rule compliance, and optimizing conflict resolution mechanisms; and (c) heterogeneity analysis indicates that the impact of knowledge transfer varies across governance models, with stronger effects observed among local residents compared to migrants. This study provides theoretical insights for integrating traditional ecological knowledge with modern scientific management and offers empirical support for global forest sustainability policy design.

1. Introduction

The United Nations’ 2030 Agenda for Sustainable Development (2015) explicitly set the global goal to “protect, restore, and promote sustainable use of terrestrial ecosystems” (SDG 15). Driven by both sustainable development objectives and innovations in governance models, global forest governance has shifted from a purely economic orientation toward a socio-ecological synergistic approach. Integrated Forest Management (IFM), characterized by its core features of “multi-objective synergy, multi-scale interaction, and adaptive regulation,” has emerged as a critical pathway for achieving sustainable forest management [1]. Originating in Europe, IFM has been systematically defined as the integration of biodiversity conservation and diverse ecosystem services into sustainable forest management through scientific approaches, thereby optimizing multi-objective outcomes and maximizing comprehensive benefits [2]. Its target system encompasses maintaining ecological integrity, promoting close-to-nature forestry [3] and non-timber forest products for economic benefits [4,5], as well as engaging stakeholders to address social dimensions [6]. However, despite its well-established conceptual framework, the implementation of IFM varies significantly across regions, largely due to ineffective knowledge transfer and localization.

The effective implementation of IFM depends on specific foundational conditions and inherent mechanisms. External factors influence its feasibility, while endogenous foundations establish the concrete spatio-temporal context for its execution. IFM operates within distinct human and social environments, which comprise three key dimensions: human conditions, referring to the perceptions, beliefs, knowledge, and preferences of forest-dependent communities [7]; material conditions, which encompass forest resource endowments, infrastructure, and technological capabilities [8]; and socio-cultural conditions, involving community cultural foundations, traditional norms, and capacities for collective action [9].

The intrinsic mechanisms of IFM reflect its internal operational logic, evident in two main aspects: the processes and outcomes of top-down policy implementation within forest communities [10], and the endogenous governance systems developed from local knowledge, customs, and community-based models [11]. Forest-dependent and Indigenous communities have developed rich Traditional Ecological Knowledge (TEK) through prolonged interaction with forests, encompassing species identification, ecological processes, resource use practices, and disaster response strategies [11]. Such knowledge is traditionally transmitted orally, through rituals and lived experience, forming a place-based wisdom embedded in social structures and cultural beliefs [12].

However, under contemporary pressures of globalization and modernization, the endogenous foundation of IFM is increasingly threatened. Out-migration of youth, the marginalization of traditional knowledge in formal education, and the expansion of commercial forestry are disrupting the intergenerational transmission of TEK [13]. As elders pass away, orally transmitted wisdom is being lost. Moreover, standardized, technical approaches in modern forestry often overlook or devalue local knowledge systems, leading to a decline in cultural identity and self-recognition within communities [14]. This erosion does not weaken local capacity for participatory forest governance but also compromises the adaptive resilience of socio-ecological systems, thereby hindering the effective implementation of IFM.

China, as a critical region for global biodiversity conservation, has developed place-based forest-dependent knowledge systems through long-term community adaptation [15]. The Northeast State-Owned Forest Region (NSOFR) (The key state-owned forest regions, located in Heilongjiang, Jilin, and Inner Mongolia, encompass the Greater and Lesser Khingan Mountains, the Changbai Mountain state-owned forest areas, among others. These regions represent the largest and most concentrated areas of natural forests in China, serving as critical ecological security barriers and strategic bases for reserve forest resource cultivation. They are managed by five forestry groups—Longjiang Forest Industry, Heilongjiang Greater Khingan Mountains Forestry Group, Inner Mongolia Forest Industry, Jilin Changbai Mountain Forest Industry, and Yichun Forest Industry—along with their 87 subordinate forestry enterprises.) represents a paradigmatic case, serving both as China’s essential ecological barrier and a key timber reserve, while also providing a significant research context for human-environment interactions [16]. Encompassing 32.74 million hectares, which accounts for 67% of China’s state-owned forest area, this region sustains biodiversity, contributes to climate regulation, and supports numerous livelihoods. Local communities have developed a distinctive a long-standing “forest-edge cultural” tradition [17]. However, rapid urbanization and modernization are eroding these traditional knowledge systems [13]. Intergenerational transmission has declined as younger generations show diminished interest in traditional forestry skills, and place-based knowledge for emerging industries remains underdeveloped [18]. Institutional marginalization has further weakened the recognition of Indigenous Ecological Knowledge (IEK) and collective action capacity, while economic transitions degrade cultural transmission mechanisms such as folk customs and local memories [19]. Additionally, value conflicts between traditional practices and modern conservation paradigms reduce community participation and create cultural adaptation dilemmas [20].

In response, innovative policies have been implemented to revitalize traditional knowledge within modern governance frameworks. The 2023 National Pilot Implementation Plan for Sustainable Forest Management (2023–2025), serving as China’s localized IFM framework, emphasizes a government-led, multi-stakeholder co-governance approach. The effectiveness of this approach hinges on the integration of community IEK, which represents not only cultural heritage but also a critical resource for socio-ecological sustainability [21].

Current research on Integrated Forest Management (IFM) exhibits three critical limitations: First, existing literature predominantly focuses on policy instrument design [22] or technical standard optimization [4], while neglecting the coupled mechanisms between social and ecological systems. Second, most national IFM implementations rely on government- or expert-led top-down approaches, overlooking the role of local communities’ indigenous knowledge. Third, there is a paucity of empirical studies, with current IFM research primarily limited to theoretical discussions or short-term case descriptions [23]. The Social–Ecological System (SES) framework emphasizes the interdependencies and feedback mechanisms between social and ecological systems [23]. It has been widely applied in environmental management [24], sustainable development [25], and resource governance [26]. This framework not only provides a critical lens for analyzing the complexity of human-environment interactions in forest regions but also offers novel analytical perspectives for examining the dynamic interplay between knowledge transfer and IFM effectiveness.

Building upon this analytical foundation, we establish an IFM-SES theoretical framework to examine China’s Northeast State-Owned Forest Region (NSOFR). Utilizing cross-sectional survey data from NSOFR communities and employing both OLS and mediation effect models, we systematically investigate the mechanisms and pathways through which community knowledge transfer influences IFM implementation. The purpose of this study is to empirically examine the critical role of knowledge transmission in IFM, elucidate its underlying pathways and context-dependent characteristics, provide new evidence from Chinese forest governance for advancing social–ecological system theory, and contribute to the achievement of sustainable forest governance objectives. The research specifically addresses three core questions: (1) Does community knowledge transfer significantly affect IFM effectiveness? (2) Through what mechanisms does such transmission operate? (3) How do driving effects vary across different knowledge transfer contexts?

This study makes three key contributions to the field. Theoretically, it advances the application of a social ecological systems framework for analyzing Integrated Forest Management. The framework emphasizes multi-scalar and multi-actor dynamic feedback mechanisms, thereby extending the current theoretical discourse on forest governance. Methodologically, the research develops a novel indicator system to quantify the influence of community knowledge transfer on IFM effectiveness. This system integrates both traditional and scientific knowledge pathways, and its application in China’s Northeast State-Owned Forest Region provides a transferable model for similar socio-ecological contexts globally. From a policy perspective, the study disentangles the distinct effects of formal institutions and informal knowledge channels on IFM outcomes. It also identifies critical moderating factors, such as policy interventions, community agency, and cultural identity, which are essential for designing context specific and participatory governance strategies.

2. Integrated Forest Management Practices

2.1. Integrated Forest Management Practices in China

Since the 1992 United Nations Conference on Environment and Development, China has actively responded to global initiatives. It progressively localized sustainable forest management (SFM) through policy guidance. Pilot demonstrations and international cooperation also played key roles [27]. Yet global ecological challenges have grown increasingly complex. Traditional SFM now faces new constraints. These include climate change mitigation, biodiversity conservation, and efficient resource use. In response, China has advanced both conceptual and practical innovations. It is shifting toward more systematic and synergistic IFM. This evolution consists of three distinct phases (Figure 1).

Phase I (2002–2011): Theoretical framework establishment. This phase began with China’s Criteria and Indicators for Forest Conservation and Sustainable Management (2002), followed by seven regional pilot programs and policy documents including the National Guidelines for Zoned Forest Resource Management (2004), which initially established the institutional framework for sustainable forest management. From 2006 to 2009, China further improved the system by issuing the Compilation and Implementation Framework for Forest Management Plans and County-Level Sustainable Forest Management Planning Guidelines, making forest management plans a core tool. In 2009, the implementation of the Central Fiscal Forest Tending Subsidy Program and the issuance of Guidelines for Establishing Model Forest Management Demonstration Bases further promoted multifunctional close-to-nature forest management. This period was characterized by “single-element management,” primarily focusing on individual measures like harvest quotas and tending subsidies [28].

Phase II (2011–2019): Systemic integration period. Following 2011, China’s sustainable forest management adopted harvest management reform as a breakthrough point, initiating pilot programs across 200 units nationwide. These efforts expanded to include implementation of the UN Forest Instrument demonstration projects (2012) and the National Sustainable Forest Management pilot program (2015) [29]. Post-2013, China progressively enhanced international cooperation through active participation in the Montreal Process, the establishment of Sino-Finnish Sustainable Forest Management demonstration bases, and the adoption of advanced international forest management concepts. Notably, China championed and promulgated the Yanji Declaration, while compiling and publishing the National Report on Sustainable Forest Management in China and Status of Sustainable Forest Management in China (2016). The introduction of the *National Forest Management Plan (2016–2050) in 2016 marked China’s transition toward integrating ecological, economic, and social benefits within a unified framework, although synergistic mechanisms among these systems remained incompletely established [30].

Phase III (2019–present): Comprehensive IFM implementation. Since 2019, China has entered a genuine IFM phase. The Guidelines on Strengthening Forest Management (2019) reinforced the forest management plan system. They established four core principles: ecological prioritization, classified management, government leadership, and planning guidance. This laid the institutional foundation for IFM. In 2023, the National Forestry and Grassland Administration issued the National Pilot Implementation Plan for Sustainable Forest Management (2023–2025). This marked a shift from policy design to nationwide IFM practice. It initiated an era of quality-driven forest management. Based on sustainable development principles, the plan integrates ecological conservation with economic growth [31,32]. It advances sustainable practices to improve woodland productivity. The plan also innovates resource management mechanisms. These optimize timber supply and foster new carbon sink economies. Additionally, it encourages diversified understory economies. This expands local employment opportunities effectively.

Notably, the plan adopts region-specific strategies. It selects 310 pilot units across 28 provinces. These demonstrate a synergistic model enhancing ecological, economic, and social benefits together. This offers a distinctive Chinese solution to global forest governance.

China’s distinctive IFM model shows three defining features. First, it has an integrated governance structure. This structure overcomes departmental fragmentation through cross-sector coordination. It involves forestry, ecology, agriculture, and related fields [33]. Second, it employs adaptive management strategies. These implement differentiated measures based on ecological function zoning [28]. Third, it achieves synergistic benefits through institutional innovation. Examples include understory economies and ecological compensation systems. These turn ecological gains into economic returns [34]. This shift from traditional SFM to integrated governance solves goal fragmentation. It also offers the global community a practical transition path. This pathway enables comprehensive forest governance.

2.2. International Practices in Integrated Forest Management

At the international level, the exploration of IFM practices exhibits diverse and region-specific characteristics. As influenced by their political systems, resource endowments, and socio-economic development stages, countries have developed distinct IFM models.

European IFM practices, characterized by their early inception and mature systems, reflect a profound transition from single-objective timber production to multifunctional forest management [35]. The concept of Integrated Forest Management began to emerge and take shape in Europe during the 1990s. This period marked a paradigm shift in European forest management philosophy, moving from a focus on singular resource utilization toward ecological conservation. In terms of policy, there was a growing emphasis on the ecological service value of forests. The Habitats Directive strengthened biodiversity conservation by establishing the Natura 2000 network of protected areas, closely integrating sustainable forest management with the construction of regional ecological security patterns [36]. Entering the 21st century, the EU Forest Strategy and its subsequent revisions promoted the systematic development of IFM policies through cross-member state coordination mechanisms. In 2022, the European Commission formally adopted the proposal for a Nature Restoration Law, establishing a specific legal framework for restoring natural ecosystems across the EU [37]. In practical terms, Central European countries, exemplified by Germany, adhere to the concept of “close-to-nature forestry” [38]. This approach emphasizes mimicking the dynamic processes of primeval forests through the cultivation of mixed species stands and the application of selective logging and Continuous Cover Forestry techniques. It aims to simultaneously achieve multiple objectives—timber production, biodiversity conservation, and recreational services—within individual forest stands [39]. Nordic countries, while building upon their tradition of sustainable management, have developed a coordinated model of “zoned management strategies.” At the macro level, legislation designates large-scale natural reserves for strict protection. At the management level, leveraging robust forest certification systems, ecological considerations (such as retaining key habitat elements) are embedded into market-oriented forestry practices. This has enabled the coexistence of high timber yields and high ecological value [40]. Meanwhile, these countries place particular importance on integrating the grazing culture of the indigenous Sámi people and traditional burning practices with modern ecological conservation principles, forming unique mechanisms for the preservation of cultural landscapes [41].

As a multidimensional paradigm, the implementation of IFM in Latin America is constrained by multiple structural barriers, including weak governance, unclear land tenure, and insufficient cross-sectoral coordination [42]. In the ecological dimension, although policy interventions have achieved partial success in curbing deforestation, persistent forest degradation remains inadequately addressed. This governance challenge is closely linked to the varying levels of recognition among different stakeholders regarding the severity of forest degradation [43]. Meanwhile, some studies highlight that the gendered division of labor and resource-use rules embedded in traditional knowledge systems, if formally incorporated into decision-making processes, could not only promote gender equality but also significantly enhance the effectiveness of management measures [44]. However, the intergenerational transmission of traditional knowledge is facing a crisis, largely due to insecure land tenure and a generational knowledge gap among the youth [45]. In countries such as Costa Rica and Nicaragua, notable conceptual progress has been made in national forest management standards, yet their practical application in adaptive management remains hampered by challenges such as insufficient local capacity for contextualized implementation. Looking ahead, for Latin America to advance toward genuinely inclusive and sustainable IFM, it is essential to develop certification systems that integrate traditional knowledge, strengthen the central role of communities in cross-sectoral coordination, and formally recognize and protect traditional ecological knowledge within governance frameworks.

Facing the compounded pressures of deforestation, forest degradation, and climate change, IFM in Africa emphasizes safeguarding the long-term benefits of forest ecosystems through diverse pathways. Its core objective is to harmonize ecological conservation with social development, thereby maintaining critical ecosystem services. At the practical level, community-based forest management (CBFM) has demonstrated significant potential. By empowering local communities and integrating traditional knowledge, livelihood needs, and conservation goals, CBFM enhances both forest diversity and community resilience [46]. Landscape-scale approaches address habitat fragmentation through the establishment of ecological corridors, whose successful implementation depends on effective knowledge transfer and management consensus among diverse landowners [47]. This facilitates species migration and genetic exchange, thereby strengthening ecosystem stability. Meanwhile, decentralized forest management (DFM), serving as a complement to state-led models, offers decentralized institutional potential for resource conservation, despite limitations in conceptual understanding and practical implementation [48]. Furthermore, African IFM is increasingly recognizing and integrating the unique knowledge of women in areas such as non-timber forest product (NTFP) utilization and germplasm resource conservation. This necessitates breaking down the institutional barriers and social norms that constrain women’s effective participation, thereby enhancing the inclusiveness and equity of management [49]. The successful advancement of IFM in Africa depends on the meaningful participation of communities throughout the management process, the optimization of cross-scale governance structures, and the establishment of a systematic governance framework that promotes knowledge sharing and gender inclusion.

In summary, the global exploration of Integrated Forest Management demonstrates distinct regional characteristics alongside shared evolutionary trends, yet consistently converges on a core proposition: successful IFM must establish a multi-dimensional governance network integrating policy, economic, and social supports. This approach should be grounded in respect for local cultural and ecological contexts, ultimately achieving the organic integration of ecological conservation, economic development, and social equity.

3. Theoretical Analysis and Research Hypotheses

3.1. Social–Ecological Systems Theoretical Framework

With intensifying human interventions in natural environments, the study of collective action increasingly requires integrating both social contexts and natural ecological systems as critical factors into collaborative governance frameworks [50]. In this context, Elinor Ostrom proposed the SES theoretical framework [51]. Rooted in the Institutional Analysis and Development (IAD) framework, this approach incorporates key social, economic, political, and ecological factors into diagnostic analyses of collective action, providing multidimensional strategies for examining collective action problems from diverse perspectives [51]. Forests, as quintessential SES, fulfill dual functions: supporting economic activities (e.g., timber production) while maintaining biodiversity, regulating regional climates, and preserving local knowledge systems [52]. The SES framework achieves multifunctional forest conservation and sustainable development by analyzing interactions between social and ecological subsystems, embodying the principle of harmonious coexistence between humans and nature [53]. Applying the SES framework to analyze how community knowledge transfer influences IFM offers: A novel analytical perspective for global sustainability challenges; Robust theoretical explanatory power regarding human-environment interactions; Practical pathways for integrating local and scientific knowledge systems.

The SES theoretical framework is a generalized analytical framework constructed through multi-level variables. The first level of the SES framework consists of four subsystems: the resource system (RS), resource units (RU), governance system (GS), and actors (A), which collectively influence the interaction processes (I) and outcomes (O) in action situations. Simultaneously, all interaction processes and outcomes between variables are also affected by two subsystems representing the overall environment: the socio-economic-political settings (S) and related ecological factors (ECO) [23].

3.2. SES Theoretical Framework of Knowledge Transfer’s Impact on IFM

We develop an SES-based theoretical framework for IFM (designated as the IFM-SES framework). As detailed in

Table 1. Framework of IFM-SES.

Tier 1 Variables	Tier 2 Variables	Tier 3 Variables
Governance system (GS)	GS6: Collective-choice rules	GS6-a: Community knowledge transfer
Actors (A)	A2: Socioeconomic attributes	A2-a: Household income level A2-b: Gender A2-c: Age A2-d: Health status
	A3: Resource use history	A3-a: Collective action efficacy
	A4: Spatial relationships	A4-a: Workplace location A4-b: Residential permanence
	A6: Social norms/capital	A6-a: Rule identification A6-b: Conflict resolution mechanisms
Action situation: I→O	O1: Social performance metrics	O1-a: IFM
Social, economic, and political context (S), resource units (RU), resource systems (RS), ecological factors (ECO)	Control variables

, the original SES framework has been systematically adapted to three hierarchical levels specifically for IFM contexts. The framework’s scientific validity and applicability are demonstrated through three key aspects: Collective-choice rules (GS6) serve as a critical component of governance systems (GS), with their essence lying in collective participation and decision-making that enables community members to jointly determine resource management approaches [23]. As a social interaction process, knowledge transfer provides community members with essential information, thereby facilitating their equitable participation in collective decision-making while simultaneously enhancing both the acceptability and enforceability of these rules [54]. Furthermore, knowledge transfer represents both a process of accumulating and sharing social experience developed through long-term productive activities among community residents, and a reflexive practice that reciprocally shapes their daily production and livelihood practices [55]. This dual dynamic enhances community learning capacity and adaptive capabilities, thereby facilitating dynamic optimization of collective-choice rules to effectively address challenges arising from internal and external changes within the SES. Therefore, incorporating knowledge transfer into collective-choice rules demonstrates its critical role in governance systems by supporting both democratic decision-making and sustainable management [56]. The diversity of resource users significantly influences collective action. Key characteristics of community residents—including income level (A2-a), gender (A2-b), age (A2-c), and health status (A2-d)—collectively depict the heterogeneous nature of the community population [57]. Spatial variables such as workplace location (A4-a) and residential permanence (A4-b) precisely map the ecological functional zoning and the degree of local embeddedness. Furthermore, three critical dimensions—collective action efficacy (A3-a), rule identification (A6-a), and conflict resolution mechanisms (A6-b)—systematically analyze the pathways through which community knowledge transfer behaviors (GS6-a) affect IFM (O1-a) outcomes. Finally, interaction outcomes (O) must directly reflect the social performance of collective action [58]. Our framework operationalizes social performance (O1) as “IFM (O1-a)”, transcending the limitations of singular ecological or economic performance metrics. As a globally recognized approach to sustainable forest development and biodiversity conservation, the successful implementation of IFM fundamentally depends on establishing virtuous interaction mechanisms within SES.

Consequently, the process through which community residents’ knowledge transfer influences IFM—by shaping forest management perceptions, collective action efficacy, social identity formation, and through heterogeneous pathways including dominant knowledge transfer modes and occupational identities—constitutes the foundational scenario of the IFM-SES theoretical framework.

3.3. Research Hypotheses on Knowledge Transfer’s Impact on IFM

Integrating IFM into the SES framework enables a systematic analysis of how community knowledge transfer drives IFM implementation through empirically observable mechanisms. Within the IFM-SES framework, this study focuses on two core aspects: the pathways through which knowledge transfer influences IFM outcomes, and how rapid socioeconomic-political (S) changes affect governance results (O) by reshaping actor (A) dynamics and modifying governance systems (GS).

From the perspective of the Institutional Analysis and Development (IAD) framework, formal institutions require broad comprehension and acceptance among users to function effectively [59]. Each component of IFM relies on accurate cognition of forest ecosystems, species interactions, and environmental changes. When communities share such knowledge through daily communication, experiential learning, and collective deliberation, they effectively disseminate operational guidance and decision-making rationales in real time, ensuring management practices are continuously informed by reliable information. Moreover, institutional implementation depends on cultural foundations. Knowledge transfer—through rituals, oral traditions, and collective memory—helps internalize forest conservation norms as part of cultural identity, thereby reducing resistance to institutional compliance and facilitating IFM adoption.

Drawing on information economics, management failures often arise from information asymmetry [60,61]. Knowledge transfer lowers cognitive barriers to understanding IFM techniques, allowing management strategies to adapt dynamically and avoid decision-making delays. TEK transmitted intergenerationally through narrative and cultural practices, offers time-tested adaptive strategies that—when integrated with modern science—enhance the adaptability and sustainability of management interventions. Crucially, knowledge transfer not only clarifies “why to act” but also provides practical guidance on “how to act,” transforming institutional designs into actionable community practices. This mechanism operates through both technical standardization and cultural internalization, establishing a reinforcing cycle between cognition, behavior, and institutions.

Based on the above analysis, we propose the first research hypothesis:

H1: 

Community knowledge transfer has a significant positive effect on IFM.

Guided by the previously established SES theoretical framework within the IFM context, this study conducts a longitudinal decomposition of its subsystems and associated variables to examine the theoretical linkages between knowledge transfer and IFM implementation through three analytical dimensions: collective action efficacy, rule identification and conflict resolution mechanisms (Figure 2).

Collective action efficacy reflects a group’s shared belief in its capacity to accomplish goals via coordinated efforts, playing a crucial role in effective team performance [60]. Effective knowledge transfer facilitates community collective action by building shared cognitive frameworks and networks of social trust, which provide a foundation for cooperation and lower transaction costs. Furthermore, it improves residents’ technical skills and boosts their confidence in participating, thereby enhancing their proactive engagement in community practices. Research by Elms et al. (2022) [62] has shown that collective action efficacy is positively associated with performance outcomes, as strengthened collective efficacy reinforces self-efficacy and promotes the intention to implement behaviors. When individuals believe their collective actions can lead to positive environmental outcomes, they are more likely to adopt and sustain those behaviors. As a complex systems engineering endeavor requiring multi-objective synergy, IFM achieves its effectiveness through a critical reliance on community collective action efficacy [63]. Communities can effectively organize resource monitoring and conservation activities, often yielding outcomes superior to those achieved by external actors alone, owing to residents’ familiarity with local environments and lower operational costs [64]. Furthermore, community-based collective action supports the development and enforcement of locally adapted sustainable use rules, which help maintain resource use within ecological carrying capacities, facilitate coordinated resource allocation, reduce conflicts from resource competition, and promote equitable benefit distribution among households and groups [65]. Additionally, communities with robust collective action capacity can respond more swiftly to uncertainties—such as climate change, market fluctuations, and policy shifts—by efficiently sharing information, analyzing risks, making joint decisions, and implementing adaptive measures. This enhances their ability to integrate resources in response to sudden disturbances [56]. Rooted in trust, shared cognition, and strengthened capabilities, such collective action exhibits improved resilience and sustainability, thereby contributing to the long-term maintenance of forest management outcomes.

Rule identification refers to the degree to which individuals recognize, accept, and comply with social rules and legal norms. This sense of identification serves as a critical foundation for social order and stability, directly influencing individual behavioral choices and social participation [66]. Knowledge transfer transforms community residents’ ecological knowledge, traditional practical wisdom, and the scientific basis of regulations into comprehensible localized narratives [67]. This enables residents to comprehend the ecological rationale and long-term benefits underlying the rules, transcending superficial compliance to establish cognitive legitimacy. Simultaneously, knowledge transfer integrates externally introduced rules with the community’s existing cultural values, reconstructing the meaning system of the rules. This process establishes a cultural identity foundation, fosters emotional belonging [68], and embeds the “forest guardian” role into residents’ self-concepts, thereby shifting rule compliance from external constraints to value-based consciousness. As a policy instrument, IFM translates abstract policy objectives into concrete, localized action frameworks through rule-based constraints across ecological, economic, and community dimensions. The internalization of these rules enhances management efficacy and reduces IFM implementation costs. When rules are perceived as “our agreements” rather than “top-down mandates”, communities autonomously enforce them through endogenous monitoring mechanisms, significantly decreasing governmental enforcement expenditures.

Conflict resolution mechanisms refer to the processes or methods employed to address conflicts, aiming to facilitate consensus among involved parties or, at minimum, enable peaceful coexistence through various means. Forest resource conflicts fundamentally arise from a complex interplay of cognitive disparities, competing interests, and institutional failures. Knowledge transfer reshapes conflict resolution through several interconnected pathways. By fostering shared knowledge systems, it bridges cognitive gaps between traditional ecological knowledge and modern scientific approaches. Inter-generational and cross-group communication within communities helps integrate these knowledge bases, reducing conflicts stemming from information asymmetry or ideological differences [69,70]. Furthermore, knowledge transfer contributes to balancing stakeholder interests. Culturally transmitted community norms help constrain purely profit-driven behaviors, encouraging a shift from zero-sum competition toward cooperative and mutually beneficial outcomes [71]. Additionally, when formal institutions—such as national forestry regulations—become disconnected from local realities, culturally embedded informal rules, including customary laws and moral sanctions, can effectively fill governance voids. This enhances institutional continuity and improves governance resilience [72]. Effective conflict resolution mechanisms directly improve management efficacy and advance integrated forest management (IFM). Since IFM inherently involves multiple stakeholders with divergent interests, well-designed conflict resolution platforms enable residents to engage in constructive communication, negotiation, and compromise. This facilitates solutions that simultaneously reconcile ecological, economic, and social objectives, without allowing any single priority to dominate at the expense of overall sustainability [73]. Moreover, these mechanisms help consolidate community needs and strengthen the capacity of local groups to articulate coherent positions during external negotiations with government agencies, corporations, or NGOs [74]. This unified voice enhances their ability to secure favorable policies and external support, thereby contributing to more sustainable and equitable IFM outcomes. Therefore:

H2: 

Knowledge transfer drives IFM by enhancing collective action efficacy, strengthening rule identification, and establishing conflict resolution mechanisms.

4. Methodology

4.1. Method

4.1.1. Ordinary Least Squares (OLS) Model

To empirically examine the impact of knowledge transfer on IFM, we employ an OLS model for analysis. The specific model specification is as follows:

$$Y_i={\beta }_0+{\beta }_1{x}_i+{\beta }_2{x}_m+\cdots +{ϵ}_i$$

(1)

Here, $Y_i$ represents the perceived level of IFM by residents in the $i$ district; $x_i$ is the core explanatory variable quantifying knowledge transfer among community residents. The coefficient ${\beta }_1$ denotes the regression coefficient for the key independent variable $x_i$ related to IFM, reflecting its marginal effect on China’s IFM. $x_m$ represents the $m$ control variables used to account for other confounding factors. ${\beta }_0$ is the constant term, and ${ϵ}_i$ is the random error term.

4.1.2. Mediation Effect Model

To further examine the mechanism through which knowledge transfer affects IFM, we incorporate mechanism variables and establish regression models to assess the impact of knowledge transfer on these mediating variables. The specific model specifications are as follows:

$$M_i={\alpha }_0+{\alpha }_1{x}_i+{\alpha }_{2n}{x}_m+{\mu }_i$$

(2)

$$Y_i=b_0+c^{\prime }{x}_1+b_1{M}_i+b_{2n}{x}_m+{\delta }_i$$

(3)

In Equations (2) and (3), $M_i$ represents the mediator variables, including collective action efficacy, rule acceptance, and conflict resolution mechanisms; ${\alpha }_0$ and $b_0$ are constant terms; ${\alpha }_1$, ${\alpha }_{2n}$, $c^{\prime }$, $b_1$ and $b_{2n}$ are parameters to be estimated; ${\mu }_i$ and ${\delta }_i$ denote the random error terms.

4.1.3. Heterogeneous Effects Mode

To investigate the heterogeneous effects of knowledge transfer on integrated forest management, specifically investigating whether its impact varies systematically across different subpopulations, this study employs a subsample analysis. We focus on two key dimensions: the primary mode of knowledge transfer and staff roles. The empirical models are specified as follows:

$$Y_{i,g}={\beta }_{0,g}+{\beta }_{1,g}{x}_{i,g}+\sum _{m=1}^M{\beta }_{m,g}{x}_{m,g}+\cdots +{ϵ}_{i,g}$$

(4)

In the model, the subscript $g$ signifies distinct subsample groups. $Y_{i,g}$ denotes the level of integrated forest management perceived by the $i$-th household in the $g$-th group. $x_{i,g}$ represents the quantified value of knowledge transfer for the corresponding household. The coefficient ${\beta }_{1,g}$, which quantifies the marginal effect of knowledge transfer on integrated forest management within this specific subsample, constitutes the key parameter for comparison in the heterogeneity analysis. $x_{m,g}$ refers to the $m$-th control variable in the $g$-th group, ${\beta }_{0,g}$ is the intercept term for the group, and ${ϵ}_{i,g}$ denotes the stochastic error term.

4.2. Variables

The specific definitions and descriptive statistics of each variable in this paper are shown in

Table 2. Variable definitions and descriptive statistics.

Variable Category	Variable Name	Dimension	Variable Definition	Mean	Std. Dev.
Dependent variable	IFM (Y)	Ecological (Y1)	In forest management activities, I adopt close-to-nature practices (e.g., retaining snags) to enhance biodiversity.	3.8160	1.0892
			I believe the ecological health of our community-managed forest (e.g., tree growth, biodiversity, water/soil conservation) is excellent.
		Economic (Y2)	In forest operations (timber and non-timber products), I employ diversified approaches to increase income or reduce costs (e.g., understory economies, eco-tourism, processing innovations).
			Our community has developed long-term forest management plans to ensure sustainable benefits.
		Social (Y3)	I actively participate in discussions and decision-making on forest rule formulation, resource allocation, or project implementation.
			I perceive our community’s forest management rules (e.g., harvesting permits, benefit sharing, conflict resolution) as fair and transparent.
Core independent variable	Knowledge transfer (X)	Breadth (X1)	Community members frequently exchange work- and life-related ecological knowledge and methods.	3.7337	1.0826
			I can access traditional forest resource utilization knowledge through multiple channels (e.g., oral communication, group activities).
		Depth (X2)	Traditional knowledge circulating in the community provides detailed guidance for addressing practical forest issues (e.g., fire prevention, pest control).
			The younger generation can proficiently master and apply forest management knowledge transmitted by elders.
		Sustainability (X3)	The community regularly organizes collective activities (e.g., training sessions, council meetings) to discuss forest management experiences.
			The introduction of external technologies does not diminish the practical value of our community’s traditional forest knowledge.
		Innovation (X4)	Community members actively integrate traditional knowledge with modern forestry techniques to solve emerging problems.
			The community holds regular discussion sessions to update its forest management knowledge base.
Mediating variables	Collective action efficacy (M1)		Our community is united and fully capable of resolving forest management issues in our village.	3.7819	1.1118
			If a new forest management plan is introduced, our community can mobilize sufficient personnel to implement it.
			During collective actions, our community effectively integrates resources (labor, funds, tools, etc.) to accomplish forest conservation tasks.
			When forests face threats (e.g., fires, illegal logging), our community members can quickly organize and take action.
			Over the past year, our community’s collective actions (e.g., joint fire prevention, anti-poaching) have achieved significant results.
	Rule identification (M2)		The forest management rules established by our community are fair and reasonable, aligning with residents’ interests.	3.8082	1.0975
			I voluntarily comply with our village’s forest management rules even without supervision.
			I actively participate in discussions to revise our community’s forest management rules, believing they should reflect collective opinions.
			Residents who violate forest management rules should be penalized to maintain rule authority.
			I endorse the sustainable forest use objectives in the rules (e.g., water source protection, biodiversity conservation).
	Conflict resolution (M3)		Our community has established clear procedures (e.g., villager mediation panels, elder councils) to resolve forest management conflicts.	0.9787	0.1443
			When forest land disputes occur, residents know which formal channels to access for resolution (e.g., community committees, forestry stations).
			Traditional mediation approaches (e.g., clan leader arbitration, community assemblies) effectively settle forest resource disputes.
			Conflict outcomes generally balance interests fairly and gain villager acceptance.
			Over the past three years, all forest management conflicts were ultimately resolved through negotiation without escalation.
Control variables	Gender		0 = Female; 1 = Male	0.7694	0.4214
	Age		1 = 18–25; 2 = 26–35; 3 = 36–45; 4 = 46–59; 5 = 60+	3.3066	0.8222
	Health status		1 = Very poor; 2 = Poor; 3 = Fair; 4 = Good; 5 = Excellent	3.4677	0.9316
	Workplace		0 = Forest farm; 1 = Administrative office	0.3459	0.4758
	Position level		1 = Frontline; 2 = Middle management; 3 = Senior leadership	1.2249	0.4780
	Monthly income		1 ≤ 3000¥; 2 = 3001–5000¥; 3 = 5001–8000¥; 4 = 8001–10,000¥; 5 ≥ 10,001¥	1.9305	0.4762
	Resident status		0 = Non-resident; 1 = Permanent resident	0.9787	0.1443

. All variables were measured using 5-point Likert scales, with final values calculated as the mean scores of their respective dimensional items, as detailed below.

4.2.1. Dependent Variable

We select IFM as the dependent variable. Its conceptual framework is based on the policy brief Advancing Biodiversity Conservation Through Integrated Forest Management in Europe published by Integrate Network, with adjustments made to align with the context of integrated forest management practices in China. The core objective of IFM is to achieve synergistic improvements across three dimensions: ecological conservation, economic development, and social governance. These three dimensions are assigned equal weight, such that the overall IFM score is calculated as the arithmetic mean of the scores for each dimension. Specifically, ecological conservation focuses on biodiversity and ecosystem health, as well as the implementation of close-to-nature forestry measures [75]; Economic development examines diversified management and long-term economic sustainability [4]; Social governance evaluates the degree of community participation in decision-making and the fairness of rules [76].

4.2.2. Core Independent Variable

We select knowledge transfer as the core independent variable, with a specific focus on the processes of knowledge flow and sharing emerging from the production and livelihood practices of forest-dwelling communities. This study operationalizes the variable through four dimensions: (1) Breadth of knowledge transfer: Measured by the frequency of exchanges and diversity of channels through which community residents share experiences and methods related to forest resource utilization, reflecting the coverage scope of knowledge dissemination [77]. (2) Depth of knowledge transfer: Examined through the guiding role of traditional knowledge in solving practical forest management problems, along with the completeness of intergenerational transmission and practical application capabilities [78]. (3) Sustainability of knowledge transfer: Evaluated based on whether communities maintain knowledge transfer through institutionalized activities, and the adaptability of traditional knowledge when confronted with external technological influences [79]. (4) Innovativeness of knowledge transfer: Reflected in community members’ proactive integration of traditional knowledge with modern technologies, and their capacity to dynamically update knowledge systems to address emerging challenges. These four dimensions collectively constitute the core conceptualization of knowledge transfer, providing a systematic measurement framework for analyzing its impact on IFM.

4.2.3. Mechanism Variables

We select the efficacy of community collective action, institutional rule acceptance, and conflict resolution mechanisms as key mechanism variables to reveal the endogenous pathways through which knowledge transfer affects IFM. In the measurement design, each mechanism variable is assessed through five specific measurement items: (1) For collective action efficacy, the focus lies on examining the community’s capacity to organize forest management activities, specifically evaluating both the mobilization efficiency and implementation effectiveness of community collective actions [80]; (2) The rule acceptance dimension primarily measures residents’ internalization level of management norms and their willingness to participate [74]; (3) The conflict resolution mechanism evaluates the institutionalization level of the community’s approaches to handling resource disputes [81].

4.2.4. Control Variables

According to the SES theoretical framework, the formation of collective action capacity is influenced by changes across various subsystems. Therefore, the selection of control variables must closely follow the core logic of “human-environment” interactions. Based on this, the present study selected gender, age, and health as individual-level control variables; work location and position level as organizational-level control variables [82]; and monthly income and permanent resident status as societal-level control variables [83]. These three dimensions collectively form the “individual-organization-economic environment” nested structure within the socio-ecological systems theory. This tri-level structure embodies the “individual-organization-economic environment” nested hierarchy central to SES framework.

4.3. Data and Sample

The data utilized in this study were derived from the “IFM Survey in Northeast State-Owned Forest Regions” conducted by Northeast Forestry University in 2024. The fieldwork was conducted primarily during the summer and autumn of 2024, a period selected due to higher levels of forest-related activity, which facilitated more effective household visits. The survey employed a three-stage stratified systematic sampling approach to ensure representativeness across the Northeast State-Owned Forest Region. In the first stage, all 87 state-owned forest enterprises were included as primary sampling units. Subsequently, two communities were systematically selected from each enterprise based on size and geographic criteria. In the final stage, eight employee households were randomly chosen from each community for face-to-face questionnaire administration. The survey targeted a diverse group of respondents, including community residents, frontline forestry workers, and forest management personnel, to comprehensively assess basic demographic characteristics, IFM practices, and knowledge-transfer behaviors. Out of 1392 distributed questionnaires, 1223 valid responses were obtained, resulting in a high validity rate of 87.9%. The rigorous sampling design and high response rate ensure the robustness and reliability of the collected data for subsequent socio-ecological analysis.

5. Results and Analysis

5.1. Reliability and Validity Tests

Reliability reflects the consistency and stability of measurement results, while validity ensures that the measurement tool accurately captures the research objectives. The quality of survey questionnaires directly impacts the credibility of empirical findings and the explanatory power of theoretical models. To validate the research hypotheses and ensure the robustness of model construction, this study conducted reliability and validity tests on the collected questionnaires using SPSS 15.0 software prior to formal analysis.

Table 3. Results of reliability and validity test.

Variable Name	Dimension	Operational Definition	Factor Loading	CR	$\mathbf{Cronbach}\text{’}\mathbf{s}\mathsf{\alpha }$	AVE	KMO
Knowledge transfer (X)	X1	X1-a	0.8795	0.9670	0.9566	0.7869	0.9374
		X1-b	0.9094
	X2	X2-a	0.9112
		X2-b	0.9009
	X3	X3-a	0.8686
		X3-b	0.7543
	X4	X4-a	0.8834
		X4-b	0.9219
IFM (Y)	Y1	Y1-a	0.9266	0.9568	0.9567	0.7870	0.9127
		Y1-b	0.9204
	Y2	Y2-a	0.8801
		Y2-b	0.8982
	Y3	Y3-a	0.8615
		Y3-b	0.8323
Mediating variables (M)	M1	M1-a	0.9107	0.9369	0.9365	0.7484	0.8520
		M1-b	0.8081
		M1-c	0.9076
		M1-d	0.8214
		M1e	0.8727
	M2	M2-a	0.9472	0.9669	0.9673	0.8538	0.9140
		M2-b	0.9503
		M2-c	0.9541
		M2-d	0.8341
		M2-e	0.9296
	M3	M3-a	0.9285	0.9554	0.9547	0.8104	0.8663
		M3-b	0.9289
		M3-c	0.8972
		M3-d	0.9183
		M3-e	0.8238

summarizes the reliability and validity test results for the measurement models of two key variables, namely “knowledge transfer” and “IFM,” as well as their associated mediator variables. The results indicate excellent internal consistency, with all Cronbach’s alpha and composite reliability (CR) values exceeding 0.9, reflecting high scale reliability. In terms of validity, all factor loadings were above 0.7, and the average variance extracted (AVE) for each construct surpassed 0.7, which is well above the recommended threshold of 0.5, thus supporting strong convergent validity. Additionally, Bartlett’s test of sphericity was significant (p < 0.01), and all Kaiser–Meyer–Olkin (KMO) values exceeded 0.8, confirming the suitability of the data for factor analysis and reinforcing the structural validity of the measurement model.

Additionally, all scales employed in this study were based on self-reported measures, which may introduce common method bias. To assess this potential bias, Harman’s single-factor test was conducted. The results of exploratory factor analysis indicated that the first common factor accounted for 38.063% (<40%) of the variance, suggesting that common method bias is not a significant concern in this study. Therefore, the impact of common method bias on the research findings remains within an acceptable range.

5.2. Baseline Regression

Table 4. Results of baseline regression.

Variable	Model (1)	Model (2)
Knowledge transfer	0.9654 ***	0.9689 ***
	(0.0081)	(0.0084)
Gender		−0.0272
		(0.0213)
Age		0.0192 *
		(0.0110)
Health status		−0.0133
		(0.0101)
Workplace		0.0143
		(0.0190)
Position level		0.0014
		(0.0192)
Monthly income		−0.0061
		(0.0190)
Resident status		0.0148
		(0.0612)
Constant	0.2114 ***	0.1927 **
	(0.0315)	(0.0879)
VIF	1.0	1.07
N	1223	1223
R2	0.0927	0.9212

Notes: ① ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively. ② Robust standard errors are reported in parentheses.

presents the regression results analyzing the impact of knowledge transfer on IFM among Chinese forest-dwelling communities. The specifications are as follows: Model (1), which includes only the core explanatory and dependent variables, reveals that knowledge transfer has a statistically significant positive effect (coefficient = 0.9654, p < 0.01). This relationship remains robust and significant in Model (2) after incorporating control variables, thereby confirming the driving role of knowledge transfer in promoting IFM. These results provide strong empirical support for H1.

5.3. Robustness Checks

To ensure the reliability of the regression results, we conduct robustness checks through the following approaches, and the results are shown in

Table 5. Results of robustness checks.

Variable	Alternative DV (PCA)	Quantile Regression		Winsorization	Clustered Robust Standard Errors
		0.2	0.8
	Model (3)	Model (4)	Model (5)	Model (6)	Model (7)
Knowledge transfer	1.4748 ***	1.0323 ***	0.8738 ***	0.9179 ***	0.9688 ***
	(0.0126)	(0.0114)	(0.0247)	(0.0082)	(0.0151)
Controls	YES	YES	YES	YES	YES
Constant	−5.5169	−0.2796 **	0.7496	0.3538	0.1927
	(0.1326)	(0.1052)	(0.1924)	(0.0863)	(0.0978)
N	1223	1223	1223	1223	1223
R2	0.9225	0.7903	0.6240	0.9160	0.9212

Notes: ① *** and ** indicate statistical significance at the 1%, 5%, and 10% levels, respectively. ② Robust standard errors are reported in parentheses.

. First, we replaced the dependent variable by reconstructing a composite index for IFM through principal component analysis (PCA), which reweighted the three original dimensions. As shown in Model (3), knowledge transfer continues to exhibit a statistically significant positive effect on the PCA-constructed IFM index (β = 1.4748, p < 0.01); Second, we conducted quantile regression analyses at the 0.2 and 0.8 percentiles, with results presented in Models (4) and (5), respectively. The findings demonstrate that knowledge transfer maintains a statistically significant positive effect on IFM across both lower (20th percentile: β = 1.0323, p < 0.01) and upper (80th percentile: β = 0.8738, p < 0.01) quantiles, confirming the robustness of this relationship throughout the conditional distribution; Third, to mitigate potential distortions from outliers, we applied 5% winsorization to all variables in Model (2) at both tails of their distributions. After reconstructing the winsorized variables, the re-estimated results (Model 6) show that knowledge transfer remains statistically significant at the 1% level (β = 0.9179, p < 0.01) with consistent coefficient directionality, confirming the robustness of our baseline regression results against extreme value influences. Collectively, these approaches provide robust evidence supporting the validity of our conclusions; Fourth, clustered robust standard errors were employed. Given that operational units within the same forest industry group may exhibit within-group correlations in management systems, resource endowments, and policy environments, the models were re-estimated using clustered robust standard errors at the forest industry group level. As shown in Model (7), after controlling for cluster effects at the group level, the coefficient of knowledge transmission remains statistically significant and consistent in direction with the benchmark regression, indicating that the findings are robust to within-group correlation.

5.4. Mechanism Analysis

The aforementioned analysis demonstrates that knowledge transfer among community residents significantly drives IFM. Nevertheless, it remains necessary to conduct mechanism analysis on the three mediating mechanisms proposed in the theoretical framework. The mediation model aims to elucidate the process through which community residents’ knowledge transfer influences IFM via three key mechanisms: collective action efficacy, rule acceptance, and conflict resolution mechanisms. This analytical approach provides in-depth understanding of the specific pathways underlying these effects, thereby offering evidence-based support for formulating effective management strategies.

Based on the social–ecological system framework, this study further examines the pathways through which knowledge transmission among community residents influences IFM. Regression models were constructed with collective action efficacy, rule identification, and conflict resolution mechanisms as mediating variables, aiming to reveal the channels through which collective choice rules drive IFM. Models (8), (10), and (12) test the direct effects of knowledge transmission on the three mediating variables—collective action efficacy, rule identification, and conflict resolution mechanisms, respectively. Models (9), (11), and (13) further examine the mediating effects of these variables on IFM. The results, as shown in

Table 6. Results of mediation analysis.

Variable	Collective Action Efficacy		Rule Acceptance		Conflict Resolution
	Model (8)	Model (9)	Model (10)	Model (11)	Model (12)	Model (13)
Knowledge transfer	0.9455 ***	0.8782 ***	0.9508 ***	0.9123 ***	0.9717 ***	0.3266 ***
	(0.0118)	(0.0209)	(0.0098)	(0.0248)	(0.0085)	(0.0212)
Collective action efficacy		0.0959 ***
		(0.0202)
Rule acceptance				0.0595 **
				(0.0246)
Conflict resolution						0.6610 ***
						(0.0209)
Controls	YES	YES	YES	YES	YES	YES
Constant	−0.0485	0.1974 **	0.0889	0.1874 **	0.1455	0.0966
	(0.1236)	(0.0872)	(0.1025)	(0.0878)	(0.0894)	(0.0652)
N	1223	1223	1223	1223	1223	1223
R2	0.8506	0.9226	0.8927	0.9216	0.9197	0.9568

Notes: ① *** and ** indicate statistical significance at the 1%, 5%, and 10% levels, respectively. ② Robust standard errors are reported in parentheses.

, indicate that knowledge transmission has a statistically significant positive impact on collective action efficacy, rule identification, and conflict resolution mechanisms at the 1% level, with regression coefficients of 0.9455, 0.9508, and 0.9717, respectively. This suggests that knowledge transmission among community residents effectively enhances their collective action capacity, strengthens their identification with forest management rules, and improves the effectiveness of conflict resolution mechanisms within the community. Further analysis reveals that collective action efficacy, rule identification, and conflict resolution mechanisms, as mediating variables, all exhibit significant positive effects on IFM, validating the multidimensional transmission mechanism of knowledge transmission on IFM within the social–ecological system framework. Accordingly, H2 is supported.

Furthermore, to quantify the relative importance of each mechanism, this study calculated the indirect effects and their proportional contributions mediated through the three pathways. The results indicate that knowledge transmission exerts an indirect effect of 0.0907 via collective action efficacy, accounting for 9.35% of the total indirect effect. Through rule recognition, the indirect effect is 0.0566, representing 5.84% of the total. In contrast, the conflict resolution mechanism shows an indirect effect of 0.6423, constituting 66.30% of the total. The combined indirect effect of all three pathways amounts to 0.7896, indicating that the influence of knowledge transmission on integrated forest management is largely mediated through these mechanisms. Among them, the conflict resolution pathway plays a central mediating role, while collective action efficacy and rule recognition also provide stable, though supplementary, explanatory power.

5.5. Heterogeneity Analysis

5.5.1. Governance Mode-Based Heterogeneity Analysis

According to knowledge ecosystems theory, knowledge acts as an “ecological resource”. It involves complex interactions among knowledge agents, knowledge resources, and their environments [84]. Different knowledge agents significantly shape knowledge transfer pathways and outcomes. They achieve this influence through specific transmission mechanisms. China’s forest community governance showcases four institutional patterns of knowledge transfer. The government-led model institutionalizes knowledge integration via structured policy frameworks. This is evident in regulatory systems and resource allocation mechanisms. The enterprise-led model optimizes knowledge diffusion through market-driven processes. Technical standardization and scale effects exemplify this approach. The community-led model fosters localized knowledge co-creation. It relies on tacit experience sharing and social capital accumulation. The NGO-led model promotes public-good knowledge empowerment. It achieves this through environmental technology dissemination and community education programs. Building upon the aforementioned analysis regarding the impact of community residents’ knowledge transfer on IFM, we will further examine the heterogeneity of IFM under different dominant knowledge transfer models by integrating knowledge transfer mechanisms with the role positioning of governance entities. We operationalized typologies of knowledge transfer based on survey responses to the question: “What is the primary driving force behind knowledge transfer activities in your community?” Respondents selected from four options: government departments, forestry enterprises, community organizations, and NGOs. These responses were classified into four distinct models: policy-driven, enterprise-led, community-endogenous, and public-empowerment knowledge transfer.

The results presented in

Table 7. Heterogeneity analysis results of knowledge transfer across governance models.

Variable	Policy-Driven	Enterprise-Led	Community-Endogenous	Public- Empowerment
	Model (14)	Model (15)	Model (16)	Model (17)
Knowledge transfer	0.9590 ***	0.9150 ***	0.9920 ***	0.9873 ***
	(0.0202)	(0.0169)	(0.0188)	(0.0150)
Controls	YES	YES	YES	YES
Constant	0.6847 *	0.2518 *	0.3571 **	−0.0059
	(0.3099)	(0.1381)	(0.1839)	(0.1913)
N	240	313	275	395
R2	0.9125	0.9168	0.9167	0.9245
p	0.013 ***

Notes: ① ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively. ② Robust standard errors are reported in parentheses.

demonstrate that all four knowledge transfer models, namely policy-driven, enterprise-led, community-endogenous, and public-empowerment, which exhibit statistically significant effects at the 1% level. Furthermore, the between-group coefficient similarity test yields a p-value of 0.013, indicating significant heterogeneity in the regression coefficients measuring the impact of knowledge transfer on IFM across different governance models. Notably, the community-endogenous knowledge transfer model demonstrates the highest coefficient (β = 0.9873), which primarily stems from three distinctive advantages: Community-led knowledge transfer effectively integrates TEK accumulated by local residents over generations. For instance, the Oroqen people’s hunting taboo of “harvesting mature animals while sparing juveniles” has been empirically validated through long-term practice. This knowledge demonstrates exceptional compatibility with local ecosystems. Additionally, strong social networks formed through kinship and geographic ties offer natural channels for rapid knowledge diffusion. These networks further facilitate deep internalization of such knowledge. Moreover, by embedding knowledge transfer within traditional organizational structures, this model seamlessly links it with daily productive activities. As a result, both the practicality and acceptance of the transmitted knowledge are significantly enhanced. The public-empowerment model ranks second with a coefficient of 0.9572, demonstrating its dual strengths: NGOs effectively translate specialized modern forestry techniques into formats that are both locally accessible and scientifically rigorous. Through long-term community residency programs, they establish trust-based relationships that balance technological validity with cultural sensitivity. In comparison, the policy-driven model (β = 0.9590) and the enterprise-led model (β = 0.9150) demonstrate relatively weaker effects. This difference may be attributed to several underlying factors. Regulations and policies facilitate rapid standardization yet often fail to account for local variations, resulting in implementation gaps. Meanwhile, government-led training programs achieve broad coverage but frequently become formalistic, with limited active participation from residents. Moreover, corporate objectives sometimes conflict with community-based sustainable management goals, leading to selective knowledge transfer. Despite these limitations, enterprise-driven approaches offer unique advantages in standardization and scalability. Their methods remain highly valuable for efficient policy execution and rapid technological dissemination.

5.5.2. Identity-Based Heterogeneity Analysis

Within forestry communities, there exist both native residents with multi-generational ties to the area and migrant residents who relocated due to policy-driven resettlement, employment opportunities, or ecological migration programs. These two distinct groups demonstrate significant differences in knowledge transfer patterns, resource utilization approaches, and social participation levels, which collectively lead to heterogeneous impacts on IFM implementation outcomes. Native residents have developed highly contextualized TEK systems through intergenerational transmission of oral histories, ecological taboos, and productive practices. Their knowledge transfer exhibits distinct cultural embeddedness and has been internalized as community-shared behavioral norms. In contrast, migrant residents arriving through policy relocation or employment opportunities primarily acquire knowledge through external institutional inputs, such as technical training programs and policy documents. This pattern reflects an instrumental rationality orientation, which may lead to cognitive disembedding from local ecological ethics. Based on the strength of weak ties theory [85], differences in social network density further explain the divergence in core sociological phenomena, including information flow patterns, trust mechanisms, and resource acquisition efficiency. Building upon this foundation, the present study further elucidates the heterogeneous effects of knowledge transfer on IFM across distinct identity-based groups within forest communities.

The results presented in

Table 8. Heterogeneity analysis results by resident identity.

Variable	Native Residents	Migrant Residents
	Model (18)	Model (19)
Knowledge transfer	0.9802 ***	0.8716 ***
	(0.0084)	(0.0372)
Controls	YES	YES
Constant	0.1585	0.1100
	(0.1119)	(0.2820)
N	1135	88
R2	0.9277	0.8834
p	0.001 ***

Notes: ① *** indicate statistical significance at the 1%, 5%, and 10% levels, respectively. ② Robust standard errors are reported in parentheses.

demonstrate that knowledge transfer from both native and migrant community residents positively influences IFM, with native residents exhibiting a significantly stronger coefficient. The between-group coefficient similarity test yields a p-value of 0.001, confirming statistically significant differences in the impact of knowledge transfer on IFM across resident identity groups. The observed differences can be attributed to three key factors: The TEK of local residents exhibits distinctive “community of practice” characteristics, with knowledge encoding marked by high tacitness and strong context-dependence. This knowledge system is transmitted across generations through various channels, including daily labor, such as the experience gained in species selection during selective logging, and ritual activities, such as the worship of sacred trees. It has developed a co-evolutionary relationship with the feedback mechanisms of the forest ecosystem. In contrast, migrant residents rely primarily on explicit knowledge. While such knowledge offers advantages in standardization, it often lacks sufficient adaptability when confronting complex local ecological disturbances. Social network analysis further reveals that local residents possess significantly higher bonding social capital density compared to migrant residents. This structural difference enables local communities to perform real-time experiential calibration through dense network interactions. Migrant groups, however, depend more on unidirectional information transmission via official channels. Additionally, in the process of knowledge transmission, local residents integrate management norms with symbolic systems intrinsic to “forest-edge culture.” Examples include the Ewenki narrative that “the forest is mother.” This integration fosters affective commitment, embedding management norms into cultural identity and resulting in highly stable institutional compliance. By comparison, migrant residents tend to exhibit instrumental compliance, driven mainly by cost–benefit calculations.

5.6. Integrated Path Analysis

To present the core findings and internal logic of this study more clearly and systematically, this section will integrate the results from the aforementioned regression analysis, mediation effect tests, and heterogeneity analysis. Based on the theoretical framework and empirical testing, this paper constructs a visual path diagram (Figure 3). This diagram comprehensively illustrates the direct effect of knowledge transfer on integrated forest management, the indirect effects through mediating variables, and the heterogeneous effects present under specific conditions. This path diagram aims to provide readers with a holistic perspective on all key tested relationships in this study, thereby facilitating a more intuitive understanding of the complex mechanisms among variables and the overall theoretical model [86].

6. Discussion

Against the backdrop of global biodiversity crisis and climate change, IFM has emerged as a critical pathway for achieving forest sustainability, with its effectiveness heavily dependent on the synergistic governance of SES. Grounded in the SES theoretical framework and focusing on China’s northeastern state-owned forest regions, this study systematically investigates the mechanisms and pathways through which community residents’ knowledge transfer influences IFM. By developing an IFM-SES theoretical-analytical framework and employing empirical data, we validate both the direct effects of knowledge transfer on IFM and its indirect mediation pathways through collective action efficacy, rule acceptance, and conflict resolution mechanisms. These findings provide significant theoretical foundations and a Chinese case study for global sustainable forest governance. The following sections will synthesize our key discoveries, followed by discussions on research implications, limitations, and future directions.

6.1. Key Findings

This study’s hypothesis regarding the direct influence of knowledge transmission on IFM resonates with and extends existing theoretical foundations. Ruiz-Mallén and Corbera (2013) suggest that informal knowledge exchanges, such as experience sharing and collective deliberation, provide contextualized decision-making support for resource management [87]. Our quantitative analysis corroborates this mechanism and further reveals how knowledge transmission enhances management effectiveness by establishing a shared cognitive framework. In terms of institutional internalization, Herrmann and Torri (2009) demonstrated through studies of forest governance in developing countries that cultural practices, such as traditional rituals and oral histories, help transform conservation norms into community identity, thereby reducing resistance to institutional implementation [88]. This research validates their findings and additionally identifies that knowledge transmission significantly improves the adaptability and timeliness of management measures [89]. Li et al. (2016) emphasized that intergenerational transmission of traditional ecological knowledge through narratives and rituals, along with the integration of its adaptive strategies with scientific knowledge, enhances the sustainability of management interventions [90]. The present study not only supports this view but also quantifies the positive impact of knowledge transmission on integrated forest management, thereby substantiating a theoretical “cognition–behavior–institution” virtuous cycle.

Furthermore, this study elucidates three indirect pathways through which knowledge transmission exerts its influence: by enhancing collective action efficacy, strengthening rule recognition, and optimizing conflict resolution mechanisms. Specifically, in the dimension of collective action efficacy, knowledge transfer reduces the transaction costs of collective action by building trust networks [91]. This mechanism is widely supported by social capital theory and collective action research [92,93], validating the causal chain of “knowledge transfer → collective action efficacy → comprehensive forest management.” Regarding rule recognition, studies by Sinthumule and Mashau (2020) on cultural internalization mechanisms [94], as well as Palaschuk et al. (2024) on value integration [95], align with the theoretical proposition advanced in this study that knowledge transmission fosters the internalization of rules by strengthening identification with them. Furthermore, the socio-ecological knowledge diversity framework proposed by Schwermer et al. (2021) [96], along with Boix-Fayos et al.’s (2023) analysis of stakeholder values, provides cross-cultural theoretical support for the role of knowledge transmission in facilitating conflict resolution [97]. This indicates that knowledge transmission can enhance management effectiveness by harmonizing the interests and perspectives of multiple stakeholders.

Finally, this study enhances the understanding of boundary conditions for knowledge transmission by comparing four dominant governance models—policy-driven, enterprise-led, community-based, and philanthropy-enabled—as well as two types of resident identities (local vs. non-local). The analysis reveals that the governance effectiveness of the community-based model significantly surpasses that of the policy-driven model. This finding not only challenges the “local government advantage” pathway proposed by Sun et al. (2023) [98] but also underscores the central role of traditional social capital in the co-production of knowledge, aligning with De Iuliis et al.’s (2022) emphasis on the critical importance of community cohesion [99]. Moreover, knowledge transmission proves more effective among local residents compared to non-local residents. This outcome offers empirical support at the micro-behavioral level for Bayrak and Marafa’s (2020) community characteristics theory [100], indicating that local knowledge and social identity exert considerable influence during knowledge diffusion processes. Methodologically, this study adopts a between-group coefficient difference test to quantitatively assess the heterogeneity of knowledge transmission effects across different contexts. This approach moves beyond the limitations of qualitative comparisons commonly found in existing literature and provides a more precise analytical framework for examining the context-dependent nature of knowledge transmission.

6.2. Research Implications

The findings of this study carry substantial theoretical and practical significance. Theoretically, it quantifies the impact of knowledge transmission on IFM through empirical analysis and identifies three key mediating pathways, thereby deepening the understanding of the underlying mechanisms through which knowledge transmission influences integrated forest management. These findings provide new mechanistic evidence for the application of social–ecological system theory in the forest management context. Furthermore, the study verifies the heterogeneous effects of knowledge transmission, enriching the conceptual framework of knowledge ecosystem theory. Practically, the research offers a Chinese case study for advancing integrated forest management globally. The empirical findings provide concrete and actionable guidance for policymakers and forest managers in designing and implementing context-appropriate management strategies.

6.3. Research Limitations and Future Directions

This study has several methodological limitations that also delineate specific avenues for future research. The cross-sectional design constrains causal inference between knowledge transmission and IFM and limits insight into potential bidirectional effects or long-term dynamics. Longitudinal or multi-wave panel designs are needed to capture temporal evolution and clarify how socio-ecological drivers such as urbanization and climate change moderate these interactions over time. Particularly within a social–ecological systems framework, future studies should analyze how dynamic attributes of resource systems, including forest ecosystem scale and vulnerability, and resource units, such as tree species composition and regenerative capacity, shape the process and effectiveness of knowledge transmission.

Furthermore, since the sample was drawn exclusively from state-owned forest areas in northeastern China, the generalizability of the findings is limited. Subsequent research should incorporate comparisons across different tenure regimes, such as community forests in Africa or private woodlands in Europe, to test the boundary conditions and institutional specificity of the conclusions presented here. A systematic examination of core SES variables like resource system boundary clarity and resource unit mobility under varying tenure contexts would substantially extend the explanatory potential of the SES framework.

Although the knowledge transmission scale exhibited sound psychometric properties, the primary reliance on self-reported data may introduce social desirability bias. Future studies could strengthen validity by integrating behavioral data, for instance through participatory observation or social network analysis, to more objectively quantify the frequency, depth, and structural patterns of knowledge-sharing activities. Explicitly linking analytical dimensions from such methods to SES components, such as resource units represented by species-specific management knowledge and resource systems reflected at the forest stand level, could more precisely uncover connections between knowledge transmission and natural resource governance.

7. Conclusions

Grounded in social–ecological system theory, this study develops an IFM-SES framework to examine the multilevel mechanisms and heterogeneous effects of knowledge transmission on integrated forest management. The main findings are summarized as follows: First, knowledge transmission among community residents exerts a significant positive effect on integrated forest management. Second, knowledge transmission influences forest management through three mediating pathways: by enhancing collective action efficacy, strengthening rule recognition, and establishing conflict resolution mechanisms. Among these, the conflict resolution mechanism emerges as the most prominent pathway, indicating that effective conflict coordination is central to how knowledge transmission improves management outcomes. Meanwhile, collective action efficacy and rule recognition provide essential organizational and institutional foundations, respectively. Third, the impact of knowledge transmission varies significantly across governance models. Community-based knowledge transmission, deeply embedded in local culture and social networks, demonstrates the strongest effect, followed by philanthropy-enabled approaches. Both models improve management effectiveness by integrating traditional and modern knowledge. In contrast, policy-driven transmission is constrained by standardized implementation, while enterprise-led transmission performs weakest due to tensions between commercial objectives and ecological conservation. Finally, knowledge transmission has a stronger influence on forest management practices among local residents than non-local residents. This finding underscores the distinctive value of local knowledge in forest management and highlights how the place-based and culturally adapted nature of knowledge transmission plays a decisive role in shaping management effectiveness.

Based on the research findings, the following policy optimization pathways are proposed: First, community-based knowledge transmission systems should be established to strengthen endogenous drivers of IFM. Building on existing social networks, mechanisms for integrating traditional ecological knowledge with modern management techniques should be developed. This can be achieved through activities such as elder experience sharing, local expert training, and intergenerational collaborative practices, thereby facilitating the systematic documentation and dynamic transmission of local knowledge. Second, policy design should precisely target the core transmission pathways by enhancing collective action efficacy, rule recognition, and conflict resolution mechanisms. Specifically, community forest management funds and improved incentive structures for collective action can strengthen organizational capacity. Participatory planning and collaborative rule-making can increase residents’ identification with management norms. Emphasis should be placed on establishing multi-level conflict resolution mechanisms, including community mediation committees and standardized dispute resolution procedures, to fully leverage the central mediating role of conflict resolution between knowledge transmission and IFM. Third, tailored strategies should be adopted for different governance models and resident groups to optimize context-dependent effects. For community-based transmission, ensure resource support and institutional space; for philanthropy-enabled models, provide technical assistance; for policy-driven approaches, enhance flexibility and adaptation; and for enterprise-led initiatives, strengthen ecological accountability and community oversight. Regarding resident differences, incentive mechanisms should be designed to encourage knowledge transmission among local residents, while knowledge integration programs, such as mentorship partnerships and cultural adaptation training, should be developed for non-local residents to improve overall effectiveness.

In summary, by developing and validating the IFM-SES framework, this study emphasizes that successful integrated forest management depends not merely on isolated ecological or economic interventions, but on the systematic cultivation of the social foundations that sustain knowledge ecosystems. The theoretical contribution of this research lies in conceptualizing knowledge transmission as a multidimensional driver of social–ecological resilience, effectively bridging theoretical constructs with practical applications. The findings demonstrate that the IFM-SES framework, as a transferable analytical tool, can provide systematic solutions for community-based forest governance across diverse regions. It offers a practical roadmap for aligning global sustainable development goals with local wisdom, which constitutes a synergy essential for addressing the dual crises of biodiversity loss and climate change. Future research should strengthen interdisciplinary collaboration among ecology, sociology, and management science, with a focus on refining scalable models of knowledge co-production in forest governance. Such efforts will support the transition of community forest management from experience-based approaches toward scientifically informed and systematically implemented practices.