1. Introduction
National parks refer to specific terrestrial or marine areas with clearly defined boundaries approved and centrally managed by the state whose primary purpose is to protect large-scale natural ecosystems of national significance [1]. They aim to achieve scientific conservation and the rational utilization of natural resources. As a type of global protected area, the fundamental attributes of national parks lie in their public welfare nature, state leadership, and scientific management [2]. As a proven and effective model for natural ecosystem conservation, national parks have now been widely adopted in the majority of countries and regions worldwide [3,4,5].
However, it is undeniable that, while the establishment of national parks represents a quintessential environmentally beneficial initiative, it inevitably entails certain ecological and social challenges [6]. Among these, the most prominent issue is residents’ livelihoods [7]. This arises because national parks are typically established subsequent to existing human activities and communities, generally triggering competing conflicts between the national park and local residents over the fundamental resource of land. Current research on this topic primarily focuses on national parks in regions such as Africa and Southeast Asia. The Awash National Park in Ethiopia [3], the Doi Inthanon National Park in Thailand [4], the Mount Elgon National Park in Uganda [5], and other national parks in different areas have attracted widespread attention on this topic. This concentration stems primarily from the underdeveloped socioeconomic conditions in these areas, resulting in more pronounced impacts of national parks on local communities’ livelihoods and, consequently, generating greater social risks [5,8].
At the inception of Yellowstone National Park—the world’s first national park—its establishment aimed solely at preventing human disturbance to protected wilderness [9]. In the 20th century, the exclusionary nature of national parks toward human activities garnered widespread attention. Park founders and administrators gradually recognized the problems arising from such exclusion, including resettlement conflicts [10], community disintegration, and livelihood deterioration [11,12]. Ironically, the emergence and escalation of these issues threatened the integrity of national parks themselves, while diminishing environmental benefits due to the resultant social problems. Consequently, numerous governments began safeguarding the rights of residents within national park boundaries during park establishment [13]. Driven by the diffusion and evolution of this humanistic philosophy, balancing environmental and social benefits became a global consensus in national park development [14]. Particularly noteworthy is the geographic overlap between proposed national park areas and regions characterized by traditional ethnic cultures or widespread livelihood poverty. This convergence necessitates that national park development advance not only ecological sustainability but also social sustainability [7].
When addressing this issue, protecting the livelihoods of resident indigenous communities at the community level becomes a crucial consideration in the establishment of ecological conservation areas represented by national parks [15]. Such livelihood protection must account for both preserving the current standard of living and enhancing future livelihood opportunities [16], thereby achieving sustainability over time. The assessment of livelihood sustainability does not hinge on whether relocation occurs but is measured by the degree of impairment to livelihood capitals, which determines the resilience of indigenous residents’ livelihoods [17].
Compared to other nation-states in the international community, China embarked on its national park development initiative at a comparatively later historical juncture [18]. Following international best practices, China has begun integrating previously fragmented and functionally limited protected areas through its national park initiative [19]. However, as a developing nation with a vast territory and extraordinary biodiversity, China faces heightened challenges in reconciling ecological conservation goals with local socioeconomic development. Furthermore, it is impractical to achieve the wholesale relocation of resident communities within designated protected areas due to competing considerations of investment requirements, economic repercussions, social consequences and risks, ethnocultural preservation needs, and natural resource utilization imperatives [20,21]. Consequently, achieving balanced sustainable development between national parks and resident livelihoods remains a critical challenge demanding urgent research [22]. These recurring scenarios across populous developing countries signify that China’s national park development approaches and insights will constitute a particularly valuable component of global conservation knowledge systems.
In 2017, China issued the “Master Plan for Establishing the National Park System”, then quickly established the first batch of five national parks: Sanjiangyuan, Giant Panda, Northeast Tiger and Leopard, Hainan Tropical Rainforest, and Mount Wuyi in 2021 [23]. In the development of China’s national parks, a similar logic existed as in other countries, positing mutual exclusivity between park establishment and the productive/livelihood activities of resident communities [24]. This is explicitly codified in the zoning provisions of the Interim Measures for National Park Management, which stipulate that core protection zones shall prohibit all human activities in principle, while general protection zones ban development-oriented production activities [25]. It is evident that such restrictive measures have imposed significant external social constraints on the local residents’ productive activities and daily lives, probably resulting in negative impacts. Furthermore, one of the stated objectives of national park construction—wildlife conservation—has paradoxically led to an increased frequency of human–wildlife conflicts. Consequently, the establishment of national parks not only affects the development and utilization of land and other resources but also perpetuates risks to personal safety and property security [26]. However, it is noteworthy that China has substantially drawn lessons from past international experiences during its national park development process. All five current national park master plans in China explicitly emphasize the imperative to safeguard local residents’ basic livelihood standards [27]. Through initiatives such as promoting tourism operations and forest-based agriculture, these plans aim to facilitate livelihood transformation for local communities.
Current discourse and research predominantly focus on analyzing the composition of residents’ livelihood capitals while rarely systematically investigating the impact pathways through which national parks influence these capital assets. This study aims to address the following questions: (1) Which elements within the livelihood capital system of residents in national parks determine the final livelihood outcomes? (2) Between the restrictive measures imposed by the establishment of national parks and the rise in wildlife–human conflicts caused by these protected areas, which one has a more profound impact on local residents’ livelihoods? (3) What are the specific pathways through which national parks influence the livelihood capital system of residents? To address these questions, this research selects the Northeast Tiger and Leopard National Park as a case study to analyze the specific mechanisms by which national parks affect the livelihood capital system of residents. Adopting a quantitative research paradigm, the study combines multiple variables to explore the driving forces behind the livelihood transformation of national parks residents. The primary contributions of this study include the following: (1) Integrating the structure of residents’ livelihood capitals with risk shocks under national park influences to establish an analytical framework for livelihood risk shocks; (2) Employing structural equation modeling (SEM) to quantitatively map the causal pathways through which national park development affects residents’ livelihoods; (3) Comparing the differential impact of national park establishment across various types of livelihood capital. Building on these findings could provide clarity to discuss evidence-based policies to foster the synergistic sustainability of both national parks and community livelihoods.
2. Review
2.1. Purpose and Policies of Chinese National Parks
Unlike nature reserves, which aim to protect single or a few species of wildlife or natural landscapes, national parks are characterized by their systematic protection and management of all wildlife, flora, and natural landscapes (sometimes also including cultural asset) within a designated geographical area. Therefore, the establishment of national parks signifies that China’s ecological and environmental conservation efforts have officially entered a new historical phase [28].
In 2013, the Chinese government first proposed the establishment of national parks. After years of practical exploration and experience accumulation, China officially inaugurated its first batch of national parks in 2021, including Sanjiangyuan, Giant Panda, Northeast China Tiger and Leopard, Hainan Tropical Rainforest, and Wuyishan [28]. These parks span a total protected area of 230,000 km2, covering nearly 30% of China’s nationally protected terrestrial wildlife species. Through the creation and refinement of the national park system, China has strengthened the conservation of rare and endangered wildlife and their habitats and ecosystems [29]. Numerous critically endangered species have achieved population recovery, with the national protection rate for key wildlife rising to 74% and biodiversity becoming significantly richer. According to the National Park Spatial Layout Plan, China aims to establish 49 national parks by 2035, forming the world’s largest national park system with a total area of approximately 1.1 million km2 [30].
While ecological and environmental conservation efforts continue to advance, it is crucial to recognize that the establishment of a national park fundamentally relies on restrictions and limitations on land use and the restriction of human activities [1,31]. Under current regulations, China’s national parks are divided into core conservation zones and general control zones based on their functional priorities [22]. These two categories differ in the intensity of restrictions imposed on human activities, with the general control zones typically having a lesser impact on local residents’ livelihoods and daily activities [19]. Nevertheless, due to variations in ecological functions and geographical contexts, the proportion of core conservation zones to general control zones differs across national parks. This discrepancy further manifests in the uneven demographic impact of different parks, as the number of residents affected by these zoning policies varies significantly depending on the park’s specific ecological mandates and regional characteristics.
Regulatory measures in core conservation zones and general control zones vary across different national parks. For instance, Northeast Tiger and Leopard National Park, Sanjiangyuan National Park, and Giant Panda National Park primarily emphasize “no expansion of existing productive and commercial activities” within these zones. Hainan Tropical Rainforest National Park and Wuyishan National Park, however, adopt stricter policies that explicitly require the phasing out of human activities, reflecting a more stringent approach. In general control zones, current policies largely acknowledge the impracticality of the immediate and complete cessation of local livelihoods [29,32]. Consequently, these areas are granted greater policy flexibility and operational leeway. A notable exception is Hainan Tropical Rainforest National Park, where the migratory and sedentary behaviors of its flagship protected species, the Hainan gibbon (Nomascus hainanus), have necessitated a dynamic management policy for its general control zones. This approach adapts to the primates’ ecological needs while balancing human activity regulation.
The five existing Chinese national parks currently adopt largely consistent management strategies for livelihood restoration and alternative livelihood development, primarily encouraging local communities to establish green, specialty businesses centered on unique local resources, particularly eco-tourism, leveraging ecological landscapes and cultural heritage [33]. Some parks have implemented innovative practices such as purchasing services from residents to employ them as forest rangers, which has already yielded measurable success [34]. Additionally, notable progress is being made in two key areas of ecological compensation: (1). Wildlife Conflict Compensation System: National parks are refining mechanisms to provide financial compensation for residents’ unexpected losses caused by wildlife, ensuring agricultural productivity and community engagement remain intact [35]. (2). Carbon Credit Market Participation: China’s national parks are exploring entry into carbon markets, capitalizing on their ecological assets to generate self-sustaining revenue. This initiative aims to reduce fiscal reliance on central and local governments while advancing climate goals [35].
Both approaches represent critical ecological compensation mechanisms, balancing conservation objectives with socio-economic sustainability.
2.2. Sustainable Livelihood Framework
Considering that national parks, as an external factor, have an impact on the livelihood base of residents, it is necessary to assess the livelihood status of residents within national park development areas. Following the widely adopted DFID Sustainable Livelihoods Framework, the livelihood capital system of residents can be categorized by elemental characteristics and logical relationships into human capital, social capital, natural capital, physical capital, and financial capital [36,37]. This allows the determination of each livelihood capital dimension through measurable variable combinations, thereby calculating the comprehensive level of livelihood capital and evaluating residents’ sustainable livelihood capacities [38]. Similarly, this framework can visualize the impacts of national park development on local residents’ livelihoods.
The DFID Sustainable Livelihoods Framework (SLF) is an assessment tool introduced by the UK Department for International Development (DFID) for evaluating the livelihood standards of residents [36]. As an analytical tool, it has been extensively employed to assess livelihood capabilities [39]. Its central tenet lies in revealing the structural characteristics formed through the differentiation of livelihood assets, thereby determining communities’ capacity to sustain production and daily life [40,41]. As an analytical instrument, the DFID framework predominantly focuses on statically presenting the livelihood assets portfolio [42]. By analyzing historical and current asset configurations while incorporating external risk shocks or enhancement opportunities [43], it evaluates potential impacts on future livelihood capacities and living standards [44].
The DFID Sustainable Livelihoods Framework (SLF) has been ubiquitously applied in current research to analyze livelihood disruptions caused by external natural or societal factors. Natural drivers predominantly encompass catastrophic events—such as earthquakes, typhoons, tsunamis, and floods—that induce the systemic degradation of livelihood assets. Societal drivers primarily involve large-scale infrastructure projects (e.g., reservoirs, nuclear power plants, industrial parks) leading to resettlement and asset depreciation [38,45,46,47]. The SLF demonstrates analytical clarity in delineating resilient versus vulnerable assets within livelihood portfolios, enabling the identification of which asset categories face heightened exposure to exogenous risks [48]. By integrating these two diagnostic procedures, researchers can map the causal chain of “External Stressors → Asset Erosion → Livelihood Capacity Deterioration”, ultimately formulating targeted policy interventions and community support mechanisms. Currently, there remains a lack of such research in the context of China’s national parks, with most existing studies confined to discussions on the structure of residents’ livelihood capital. As a hybrid external stressor, national parks exert dual impacts: ecologically through biodiversity conservation mandates and socially via governance structures requiring state–society synergies—a duality rooted in their dual mission of ecological enhancement and polycentric institutional implementation [3].
2.3. Livelihood Risk Generated by a National Park
Risk is characterized by the uncertainty between production objectives and labor outcomes [49]. This uncertainty can be classified into uncertainties in production returns and cost uncertainties, depending on the aspects affected. For most farmers in developing countries, land serves as the core productive asset in household-based economic activities [50]. However, since land is frequently a prerequisite for large-scale infrastructure projects, conflicts occasionally arise between residents and the state over land use rights [51].
Due to the heterogeneity in risk sources, mechanisms, and impacts, there is currently no unified analytical framework for risk assessment [52]. Researchers typically categorize external risks affecting residents’ productive and daily lives across dimensions such as personal health, environmental factors, employment opportunities, socioeconomic conditions, and infrastructure integrity [53,54,55]. These external risks often originate from two categories: (1). Mega-projects (e.g., reservoirs, industrial facilities, protected areas, ports, transportation infrastructure); (2). Unexpected incidents (e.g., floods, earthquakes, wildfires, nuclear accidents).
Building on the above context, this study categorizes risks by their specific forms into human capital risks, social capital risks, natural capital risks, physical capital risks, and financial capital risks, aligning with the Sustainable Livelihoods Framework (SLF) to ensure conceptual coherence and analytical consistency. When residents face such risks, their existing livelihood capital systems are negatively impacted [56]. To cope, they may either reduce expenditures, leading to a decline in living standards, or sell off personal assets to sustain their current lifestyle [57]. However, the latter strategy further depletes livelihood capital, ultimately trapping individuals in a cycle of poverty [58,59]. This situation establishes a foundational analytical framework: national parks, as external mega-projects, generate risks to local residents’ livelihood capital due to their development objectives and operational requirements. This framework applies equally to analyzing social impacts arising from other large-scale infrastructure initiatives.
However, it is crucial to recognize that this risk categorization operates within the context of specific livelihood capital systems. Fundamentally, the ontological origin of risks resides in the national park establishment itself—a dual process that simultaneously excludes human activities while generating derivative wildlife disturbance incidents [60,61]. These cascading effects perpetuate asset depreciation across all livelihood capital domains. This reveals national parks as the singular exogenous risk generator, with perceived risks to human, social, natural, physical, and financial capitals merely representing differentiated manifestations of park-induced vulnerabilities within discrete asset categories.
2.4. Research Hypotheses
Based on the above analysis, existing research on China’s national parks has produced numerous findings discussing residents’ livelihood risks and potential losses. However, there is still insufficient exploration of how national parks influence various forms of livelihood capital through specific pathways, ultimately affecting livelihood outcomes. Moreover, little attention has been paid to the dual nature—both natural and social—of national parks’ external impacts.
Therefore, this study attempts to formulate three hypotheses to investigate the specific pathways through which national parks affect residents’ livelihoods. It also aims to assess the risk levels faced by different types of livelihood capital, thereby proposing targeted strategies for the generation, maintenance, and improvement of livelihood capital.
Variations in livelihood capital systems constitute the fundamental determinant of changes in residents’ livelihood outcomes. Generally, higher robustness and structural balance within the livelihood asset portfolio correlate with elevated living standards and enhanced resilience to external shocks. While this causal relationship holds under controlled conditions devoid of exogenous disturbances, the intrinsic dynamics of capital transformation remain pivotal. Consequently, this study proposes the first hypothesis:
Each individual livelihood capital exerts a positive contributing effect on ultimate livelihood attainment.
Given that livelihood risks fundamentally originate from exogenous natural and societal stressors, national park establishment can be posited as the direct exogenous driver of livelihood capacity transformations in this study. Functioning as a polyvalent external shock encompassing both ecological and institutional dimensions, national parks disrupt residents’ productive and domestic systems. However, the magnitude of impact may exhibit capital-specific heterogeneity. Recognizing the causal mechanism whereby external perturbations operationalize through discrete capital erosion pathways to ultimately depress livelihood outcomes, we accordingly formulate the second hypothesis:
National parks exert negative impacts across all five core capital dimensions within the DFID framework.
Finally, to contrast the differential impact pathways through which national parks affect livelihood outcomes, we introduce the third hypothesis:
National parks, as an exogenous institutional intervention, induce systemic deterioration in residents’ livelihood standards.
The specific structure is illustrated in Figure 1.
3. Methods
3.1. Research Areas
We selected the Northeast Tiger and Leopard National Park as the study area for the following reasons: (1) This national park is located in the boundary between Heilongjiang Province and Jilin Province, which means the development of industries in different towns is different, and the laws operated by local governments would also be different. (2) The overall livelihood level of the farming community living in this area is relatively poor, and the local government, due to financial constraints, cannot provide sufficient funds to help them recover their livelihoods. (3) The wildlife protected in this national park is highly prone to causing accidents and inflicting harm, and this uncontrollable risk will continue to increase as their population grows.
The above situation indicates that the recovery of the livelihood level of local farmers faces significant risks. Therefore, we selected Northeast Tiger and Leopard National Park as a case study to analyze the internal driving mechanisms of the livelihood transformation of local farmers.
The Northeast Tiger and Leopard National Park covers a total area of 14,100 km2 [1]. Its eastern border is adjacent to Russia’s Land of the Leopard National Par”, and its southeastern part faces North Korea across the Tumen River, with a border line of 256 km [62]. The administrative regions involved include 18 townships in three counties (cities) of Hunchun, Wangqing, and Tumen in Jilin Province, and 10 townships in three counties (cities) of Dongning, Muleng, and Ning’an in Heilongjiang Province (Figure 2). Among them, the area in Jilin Province is 9557 km2, accounting for 68% of the total area of the Northeast Tiger and Leopard National Park, while the area in Heilongjiang Province is 4508 km2, accounting for 32% [63].
In the Northeast Tiger and Leopard National Park area, there are currently 8540 registered households with a population of 19,100 people. Among them, 691 households and 15,200 people are registered under forestry management, while 1629 households and 3900 people are registered in rural villages. The population is predominantly composed of Han and Korean ethnic groups. In addition, nearly 70,000 people live outside the park, but their arable land, forest land, and other means of production are located within the park [64].
The residential areas within the park are characterized by large concentrations and small dispersals, with 12 natural villages and 43 forestry station headquarters. The total land area within the park is 14,065 km2, of which 12,893 km2 are state-owned, accounting for 91.7%, and 1172 km2 are collectively owned, accounting for 8.3% [65]. The predominant land type is forest land, which makes up 97.6% of the total area, with a small amount of arable land, orchard land, water bodies, and construction land also present [66].
3.2. Data Source
In June 2024, this study distributed 642 questionnaires, with 614 returned and 567 validated. The research focused on rural community residents within the Northeast Tiger and Leopard National Park area, defining them as residents caused by national park development. The study aimed to measure the structure and intensity of livelihood capital among these residents. Data were collected using Likert scales and scoring methods to quantify livelihood status and alternative livelihood strategies. Following the Sustainable Livelihoods Framework, the livelihood levels of residents were categorized into five dimensions: human capital, social capital, natural capital, physical capital, and financial capital. The underlying logic of this analytical framework lies in treating the intensity and structure of various livelihood capitals as the root causes of changes in livelihood status, while positioning the impact of national parks as the external direct causes driving transformations across these livelihood capitals.
Since livelihood, state, human capital, social capital, natural capital, physical capital, and financial capital are abstract concepts that require further refinement into measurable indicators, this study breaks down and elaborates on these concepts [67]. The system of variables for evaluating the livelihood path is shown in Table 1 and Table 2.
3.3. Research Methodology
This study employs structural equation modeling (SEM) as the primary quantitative analysis tool. SEM enables the exploration of mathematical relationships between latent variables that cannot be directly measured, thereby simulating correlations among multiple factors [68]. However, it is critical to note that the causal relationships between latent variables and observed variables in SEM can be either reflective or formative:
Reflective models posit latent variables as the cause of observed variables.
Formative models posit observed variables as the cause of latent variables.
Errors in model specification (e.g., misclassifying reflective/formative relationships) may lead to unreliable estimates and inaccurate descriptions of relationships between latent variables, ultimately compromising the validity of the entire study. In this study, the defined relationships between measurement variables and latent variables are shown as formative measurement models [69]. The causal relationships of latent variables under this hybrid model structure are mathematically expressed as the following:
represents the latent variable;
denotes the n-th observed variable;
is the path coefficient of the n-th variable;
is the residual error term.
This equation indicates that a latent variable η is formed by the aggregation of n observed variables (X1, X2, … Xn). The explanatory power of each observed variable Xn on the latent variable η is quantified by , while δ accounts for unavoidable measurement error. Although the error term is typically not displayed, given that structural equation modeling (SEM) relies on subjective perceptions as research data, the inclusion of error terms can ensure the reliability of the study.
Integrating the Sustainable Livelihoods Framework (SLF) defined earlier, the research model can be formally operationalized within the SEM framework. Specifically, the hypothesized relationships among livelihood capitals (e.g., social, natural) and livelihood outcomes are mapped onto the structural and measurement components of the SEM equations, enabling a systematic test of the theoretical propositions.
Given that the measurement relationships between latent variables and observed variables in this study’s design are shown as formative indicators, the use of SmartPLS 3.0 software (a software developed by SmartPLS GmbH, working as a data analysis tool that uses Partial Least Squares (PLS) path modeling to estimate structural equation models) demonstrates high methodological suitability for data analysis in this study. SmartPLS 3.0 specializes in partial least squares structural equation modeling (PLS-SEM), a methodology particularly suitable for small sample sizes, non-normally distributed data, and complex models (e.g., formative measurement models) [70]. The key advantages of its algorithm include a high tolerance for multicollinearity and a focus on predictive power over model fit indices [71]. By enabling the deconstruction of structural equations built upon formative indicator systems, SmartPLS 3.0 is adept for exploratory research in social sciences [72]. After combining with the variables system, the SEM model is illustrated in Figure 3.
This study has designed the relationships among national park impacts, livelihood capitals, and livelihood state, but in the specific structural equation, the three hypotheses (H1, H2, H3) need to be refined. When using structural equation modeling (SEM) to investigate relationships between latent variables, it is necessary to formulate hypotheses regarding the relationships among these latent variables and evaluate whether these hypotheses are empirically supported through data analysis [73]. This process allows for a discussion of the model’s explanatory power. As this study fundamentally follows an exploratory research logic grounded in field survey data, it cannot pre-estimate the statistical significance of effects (i.e., the magnitude of path coefficients between variables). However, reasonable hypotheses about the directionality (positive/negative nature) of these relationships can be proposed, as follows:
Human capital exerts a positive influence on livelihood states;
Social capital exerts a positive influence on livelihood states;
Natural capital exerts a positive influence on livelihood states;
Physical capital exerts a positive influence on livelihood states;
Financial capital exerts a positive influence on livelihood states;
National park impact exerts a negative influence on human capital;
National park impact exerts a negative influence on social capital;
National park impact exerts a negative influence on natural capital;
National park impact exerts a negative influence on physical capital;
National park impact exerts a negative influence on financial capital;
National park impact exerts a negative influence on livelihood state.
4. Results
4.1. Model Validation
In the process of structural equation modeling (SEM) analysis, it is essential to validate the reliability and validity of field survey data. Given that all constructs in this study’s model were mostly designed as formative indicators, SmartPLS 3.0 could not perform conventional reliability and validity tests (e.g., Cronbach’s alpha, KMO measure) [74]. Therefore, the analysis shifted to SPSS 19.0 statistical software (a professional software developed by International Business Machines Corporation, which can be used to analyze the correlations between data.) to calculate Cronbach’s alpha coefficients and the Kaiser–Meyer–Olkin (KMO) measure, thereby assessing whether the data met required reliability and validity standards. It was also necessary to address potential multicollinearity issues; SmartPLS 3.0 was utilized to conduct collinearity diagnostics by computing variance inflation factors (VIFs).
This study assessed the reliability of the questionnaire data using Cronbach’s alpha coefficient. Generally, a Cronbach’s alpha coefficient above 0.7 is considered good, between 0.4 and 0.7 is moderate, and below 0.4 is poor [75]. The Cronbach’s alpha coefficients of the questionnaire items in this study met the basic threshold for exploratory research, indicating that the data exhibited adequate reliability (Table 3).
To validate the construct validity of the questionnaire data in this study, a factor analysis was conducted. The Kaiser–Meyer–Olkin (KMO) measure and Bartlett’s test of sphericity were employed to assess the structural validity. Generally, the KMO value should exceed 0.5 [76]; in this study, the KMO coefficient was 0.636, meeting the threshold. Bartlett’s test requires the data distribution to exhibit sphericity with a significance probability (p-value) below 0.05 [77]. The test results in this study also satisfied these criteria (p < 0.05). Therefore, the questionnaire data demonstrated adequate construct validity and was suitable for further in-depth analysis.
In SmartPLS analyses, formative indicators are not suitable for calculating composite reliability (CR) or average variance extracted (AVE). Instead, collinearity diagnostics must be conducted, typically using the variance inflation factor (VIF) as the criterion, with VIF values required to be below 3.3 [78]. Both the outer model (latent variable formative measurement model) and the inner model (latent variable structural relationship model) in this study satisfied this requirement (Table 4 and Table 5).
4.2. Hypothesis Testing
The significance of path relationships between variables was tested using the bootstrap method in SmartPLS3.0, with 5000 bootstrap resamples generated from the original sample size of 567. All path coefficients (Table 6 and Table 7) in both the structural model (inner model) and measurement model (outer model) were statistically significant (p < 0.05).
The findings demonstrate that the hypothesized pathway relationships proposed in this study exhibited exceptionally high statistical significance. The direction (positive/negative) and magnitude of the path coefficients accurately reflected the relationships between latent variables, lending themselves to robust further investigation and scholarly discourse.
4.3. Path Coefficient of the Livelihood
The path coefficients (Table 8) derived from the SmartPLS 3.0 analysis confirmed the hypothesized relationships among variables in the structural equation model, thereby validating the theoretical assumptions of this study.
In the structural model of the livelihood system, the strongest positive relationship was observed in the “NC → LS” path (natural capital → livelihood status), with a path coefficient of 0.351. Other livelihood capitals (human, social, physical, financial) also exhibited positive effects on livelihood status. These results imply that for the study’s target population, national park environmental resettlements, natural capital serves as the most critical core livelihood capital and the primary driver of household income within their livelihood system. The impact of different livelihood capitals on livelihood state varied significantly. Specifically, a one standard-deviation (SD) increase in human capital, social capital, natural capital, physical capital, and financial capital corresponded to increases of 0.120 SD, 0.225 SD, 0.351 SD, 0.164 SD, and 0.179 SD in livelihood status, respectively. The findings above validate Hypothesis H1, demonstrating that enhancements of livelihood capital in any form contribute to improved livelihood state.
In the structural model of national park impact, the path with the strongest negative impact relationship was “NPI → NC” (national park impact on natural capital), demonstrating a coefficient of −0.383. Furthermore, the national park’s effects on other livelihood capitals also exhibited negative tendencies, validating Hypothesis H2. These results indicate that the establishment of national parks, as an exogenous social event, not only diminishes residents’ livelihood capital through exclusionary policies but also generates persistent adverse impacts on human communities’ production and daily life by continuously expanding wildlife populations.
Within the formation structures of social capital, natural capital, physical capital, and financial capital, each contained a single dominant core indicator. Specifically, HC-3 demonstrated the strongest influence (β = 0.713) in determining human capital levels; SC-3 (β = 0.502) served as the primary determinant of social capital; NC-1 (β = 0.685) exerted the strongest effect on natural capital; PC-2 (β = 0.802) dominated physical capital formation; and FC-2 (β = 0.784) emerged as the key driver of financial capital. Notably, human capital exhibited the most balanced internal formation pattern. Overall, all measurement indicators in the five livelihood capital models demonstrated positive contributions, albeit with varying magnitudes of influence.
The effects of LS-1 and LS-2 on LS were 0.856 and 0.827, respectively, indicating strong positive influences. This finding suggests that higher levels of livelihood status intensity correlate with increased income levels and an enhanced ability to maintain balanced budgets among national park residents.
Based on the results of the structural model and measurement model, NC-3 (Cultivated Land Area) was the core indicator that determined livelihood status. Nature capital, which was mainly contributed to by NC-3, plays a central driving role in the development of the livelihood capacity of residents and is the main source that ultimately affects the income level and financial balance of these residents. Empirical analysis revealed that the pathway ‘NPI > NC > LS’ exhibited the strongest indirect effect (β = −0.134), indicating its dominant mediating role in the observed livelihood dynamics. Similarly, due to the most severe impact on natural capital resulting from the construction of national parks, the overall livelihood capacity of local residents will also be reduced.
Finally, as a comparative illustration, the total effect of national parks on residents’ livelihood state was quantified as β = −0.256. This indicates that for every 1-standard-deviation (SD) increase in policy exclusion and wildlife-induced disturbances originating from national parks, residents’ livelihood status decreased by 0.256 SD. These results validate Hypothesis H3, demonstrating that national parks—as external entities with dual natural and social attributes—exert a significant adverse impact on the maintenance and improvement of local livelihoods.
In this study, Figure 4 is specifically developed to provide a visual representation of the complex relationships among national park impacts, residents’ livelihood capital, and livelihood state.
5. Discussion
The research findings clearly demonstrate that the pathways through which national park establishment impacts local residents’ livelihoods manifest in two primary dimensions: restrictive development policies and expanding wildlife populations, which jointly erode the residents’ livelihood capital system. This cumulative effect ultimately diminishes overall livelihood capacity, shown as income reduction and fiscal imbalance. Quantitative analyses substantiate the hypotheses proposed in this exploratory study, revealing the precise mechanisms by which external social events disrupt livelihood systems. Consequently, it becomes imperative to systematically examine the impact mechanisms behind livelihood deterioration, assess emerging livelihood risks, and formulate viable improvement strategies for affected communities.
5.1. Impacts of National Parks on Residents’ Livelihood
The impacts of national park establishment on local residents’ production and living patterns can be systematically categorized into two dimensions: (1) land-use restrictions inherent to park development [31] and (2) the increased frequency of human–wildlife conflicts following wildlife population growth [79]. These dual pressures usually degrade various livelihood capitals, ultimately elevating systemic livelihood vulnerability [80]. The specific manifestations include the following:
The exclusionary nature of national park governance triggers the withdrawal of infrastructure and social services, resulting in constrained access to educational and medical resources [81]. This institutional marginalization leads to irreversible human capital depletion through deteriorating health conditions and educational attainment [82]. Concurrently, escalating wildlife encroachment poses direct threats to personal safety, reflecting an inherent ecological–social paradox between conservation objectives and human settlement needs.
Beyond individual hardships, national park implementation generates community disintegration risks [83]. As human communities fundamentally rely on shared production–living systems, the spatial overlap between protected areas and human communities creates inevitable tension. The dual pressures of regulatory constraints and ecological displacement progressively erode social capital: the vital bonding networks that sustain collective livelihood strategies.
The most acute manifestation of national park-induced exclusion lies in human–wildlife competition for land resources, rooted in the dual nature of land tenure [84]. Geospatial overlap renders land as both the foundational requirement for park establishment and the core productive asset for community livelihoods, necessitating stringent regulatory constraints on land use. Simultaneously, wildlife encroachment extends beyond physical threats to also systematically undermine agricultural productivity through crop destruction and asset damage. Thus, national parks operationalize natural capital depletion through twin mechanisms: policy-imposed land restrictions and ecological restoration-driven wildlife population growth [85].
National park establishment fundamentally disrupts the material basis of sustainable living [86]. Housing quality, productive equipment stocks, and durable household assets—key components of material capital—lose functional relevance under constrained livelihood systems. This systemic depreciation directly diminishes comprehensive livelihood capacity.
The exclusionary imperative of national park governance forces residents to deplete savings buffers to compensate for other capital losses. Concurrently, diminished livelihood capacity impairs creditworthiness for formal financing [87]. Coupled with inadequate fiscal transfer mechanisms in underdeveloped regions, these dynamics inevitably degrade financial capital robustness within livelihood portfolios.
Compared to the inherent exclusion of human activities and communities in national park establishment itself, the increased frequency and intensity of wildlife incidents resulting from park development have exerted greater and more persistent negative impacts on local residents’ livelihoods. Moreover, such impacts cause more extensive damage to various livelihood capitals. Previous studies predominantly focused on singular aspects of this issue and overlooked the dual social and natural attributes intrinsic to national parks. In reality, resolving conflicts between national park management and residents’ livelihood security necessitates heightened attention to wildlife-induced disturbances [79,82].
5.2. Risk Management
Based on the preceding analysis, national parks have jointly impaired residents’ livelihood capacity through two dimensions: policy restrictions and wildlife impacts. This indicates that risk management should be addressed from both the national park and livelihood capital perspectives.
Regarding national park construction, it is essential to reasonably control the exclusion of human activities within park boundaries. For residents who cannot relocate promptly, basic livelihood safeguards must be implemented, particularly concerning land as a critical means of production [88]. A portion of essential land should be retained for residents to sustain their fundamental productive and daily needs. Simultaneously, given the rising wildlife populations, it is imperative to refine compensation systems for wildlife-related incidents, appropriately increase compensation amounts, streamline administrative procedures, and integrate external commercial insurance to strengthen protections for residents’ personal and property safety [4,89,90].
Regarding risks to livelihood capital, the ecological compensation system must be enhanced. Comprehensive investigation and registration mechanisms should be established to accurately assess residents’ losses, with dedicated funding accounts created for financial redress. Additionally, livelihood recovery for residents should be prioritized and systematically integrated into the planning and implementation of national park development. This approach aims to achieve a dual sustainable development, balancing ecological conservation with the socioeconomic well-being of local communities [91,92,93].
Concurrently, it is noteworthy that the Northeast Tiger and Leopard National Park is situated within a transboundary region encompassing China, Russia, and North Korea. While China and Russia have established preliminary cooperation concerning the joint conservation of wildlife within their contiguous border areas, such collaboration remains predominantly research-oriented at present [1]. Notably, a unified fund for the compensation of affected residents has yet to be established and managed bilaterally; instead, respective national authorities provide compensatory relief independently to their impacted residents [11]. Effective communication channels with North Korea regarding collaborative biodiversity conservation and economic coordination remain undeveloped [19]. Domestically, within China, the current level of human–wildlife conflict and associated economic compensation remains manageable, largely attributable to the low population density in the park’s vicinity. However, anticipating future growth trends in wildlife populations, the establishment of a more efficient and responsive compensation system for wildlife-inflicted damage proves imperative.
5.3. Livelihood Improvement
The majority of residents within national park construction zones predominantly rely on land-based economic production as their primary source of household income, occasionally supplementing through migratory labor opportunities [35,94]. Given that land, as the most critical productive asset, is now subject to development restrictions, this livelihood model has demonstrably lost its sustainability [95]. Consequently, implementing targeted interventions to facilitate the transition of livelihood sustenance mechanisms has become imperative. Based on these situations, livelihood transition strategies should be implemented with two strategies:
Maintain existing livelihood frameworks while enhancing production technologies and augmenting reproduction funds to circumvent land scarcity constraints, thereby boosting agricultural economic returns. This strategy could be marked as an “Intensification Approach”.
Transform traditional livelihood systems by redirecting household labor and time allocations toward non-primary industrial activities, achieving capacity enhancement through structural transition. This strategy could be marked as a “Diversification Approach”.
Whether these livelihood enhancement strategies can be effectively implemented is contingent on both generating sufficient agency at the individual level of residents and the provision of essential support from the government, businesses, and other social organizations.
It should also be recognized that national park construction recruits a significant number of local residents as forest rangers. This provision of jobs can somewhat compensate for local residents’ economic losses and promote changes and improvements in their livelihood patterns. The government and scholars should pay attention to such positive opportunities.
6. Conclusions
This study constructed a livelihood capital composition system for residents in national parks and employed structural equation modeling as the primary analytical tool to examine the impact of various livelihood capitals on the ultimate livelihood levels of residents. Key findings revealed that natural capital exerted the most significant influence on the livelihood capital system of residents, followed by social capital, financial capital, physical capital, and human capital. Among these, cultivated land area emerged as the most impactful factor within the livelihood capital framework.
Aligning with these findings and the master plan of the Northeast Tiger and Leopard National Park, three critical insights emerge. First, maintaining essential land resources proves crucial, as agricultural production remains the primary livelihood source for residents. Land resources serve as irreplaceable production factors; the substantial loss of land would render additional labor and capital investments meaningless. Second, industrial transformation requires phased implementation. Gradual restrictions on land development could provide temporal flexibility for stable livelihood transition. Third, the master plan essentially seeks equilibrium between national park construction and local residents’ livelihoods while pursuing sustainable development. Although inherent contradictions exist (e.g., land use conflicts), targeted policies could enhance the feasibility of sustainable development.
It should be noted that national park construction can enhance ecological benefits and effectively protect cultural assets. In addition to addressing the impacts on local livelihoods, attention should also be paid to the development opportunities which could be achieved by local residents. A more rigorous method is also needed to quantify the gap between residents’ actual behavior and their stated expectations. However, due to the limitations of the length and subject of this study, this issue will be further discussed in the future.
Based on these conclusions, this study proposes the following policy recommendations to maintain basic livelihood security for residents: (1) Permit residents to retain a certain amount of arable land and appropriately extend policies aimed at safeguarding their livelihoods; (2) Provide residents with acceptable vocational skills training and promote the adoption of scientific cultivation techniques to enhance per-unit-area yield; (3) Offer residents access to necessary credit support, thereby ensuring financial conditions conducive to the stable advancement of agricultural reproduction; (4) Actively expand alternative employment opportunities, aiming to transition as many farmers as possible into wage laborers within a five-year period, so as to mitigate the constraints on agricultural production imposed by limited land availability through employment absorption in the secondary and tertiary sectors. (5) Improve the wildlife-caused damage compensation mechanism by moderately increasing compensation amounts and simplifying administrative procedures, thereby providing timely and adequate social compensation for potential losses of life or property suffered by residents.
Conceptualization, L.G. and G.S.; methodology, L.G.; software, Y.Z.; validation, L.G., H.L., and X.Y.; formal analysis, L.G.; investigation, L.G., Y.Z., H.L., and X.Y.; resources, G.S.; data curation, G.S.; writing—original draft preparation, L.G.; writing—review and editing, L.G. and G.S.; visualization, L.G.; supervision, G.S.; project administration, G.S.; funding acquisition, G.S. All authors have read and agreed to the published version of the manuscript.
The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.
During the investigation and writing of this study, assistance was received from the forestry departments of Jilin and Heilongjiang Provinces in China.
The authors declare no conflicts of interest.
Footnotes
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Figure 1 The structure of livelihood level.
Figure 2 The map of the research area (Northeast Tiger and Leopard National Park).
Figure 3 The structure of the SEM model for evaluating the livelihood path.
Figure 4 The effect path coefficient for evaluating the livelihood path.
Variables system of the SEM for evaluating the livelihood path.
| Categories | Variable | Measurement Method |
|---|---|---|
| National Park Impact (NPI) | Policy Impact | −2 = Significant Negative Impact; −1 = Moderate Negative Impact; 0 = No Impact; 1= Moderate Positive Impact; 2 = Significant Positive Impact |
| Wild Animal Impact | −2 = Significant Negative Impact; −1 = Moderate Negative Impact; 0 = No Impact; 1= Moderate Positive Impact; 2 = Significant Positive Impact | |
| Human Capital (HC) | Health Level | 0 = Has severe illness or disability, unable to work; 1 = Has some illness or injury, can engage in limited work; 2 = No severe illness or injury, can engage in most physical labor. |
| Education Level | 0 = Illiterate; 1 = Primary school; 2 = Junior high school; 3 = High school/Vocational school; 4 = Bachelor’s degree; 5 = Graduate degree or above. | |
| Vocational Skill level | 0 = None; 1 = Farming, animal husbandry, special industries, new media operation, etc. (Add 1 point for each skill or experience possessed). | |
| Social Capital (SC) | The Number of Local Relatives and Friends | 0 = None; 1 = 1 to 5 people; 2 = 6 to 15 people; 3 = 16 to 30 people; 4 = 31 to 50 people; 5 = More than 50 people. |
| The Relationship of Local Relatives and Friends | 0 = Poor or average relationship; 1 = Good relationship; 2 = Very good relationship. | |
| Local Employment Opportunities | 0 = None; 1 = Limited; 2 = Relatively Sufficient. | |
| Natural Capital (NC) | Livestock Rearing Area | Calculated in "mu". (1 mu = 666.67 m2.) |
| Forest Land Area | Calculated in "mu". (1 mu = 666.67 m2.) | |
| Cultivated Land Area | Calculated in "mu". (1 mu = 666.67 m2.) | |
| Physical Capital (PC) | The Quality of the House | 1 =Earth-Timber Structure; 2 =Brick-Timber Structure; 3 =Brick-Concrete Structure; 4 = Reinforced Concrete Structure. (If multiple houses are owned, select the structurally superior one) |
| Number of Production Tools | 0 = None; 1 = 1–10 items; 2 = More than 10 items. | |
| Number of Durable Consumer Goods | 0 = None; 1 = 1–5 items; 2 = More than 5 items. | |
| Financial Capital (FC) | Borrowing Capacity | 0 = None; 1 = Available but with a low amount; 2 = Available and with a high amount. |
| Savings Balance | 0 = None; 1 = Has savings but with a low amount; 2 = Has savings and with a high amount. | |
| Transfer Payment | 0 = None; 1 = Available but with a low amount; 2 = Available with a high amount. | |
| Livelihood State (LS) | Income | 1 = Less than 10,000 yuan; 2 = 10,000–30,000 yuan; 3 = 30,000–50,000 yuan; 4 = 50,000–100,000 yuan; 5 = More than 100,000 yuan. |
| Balance of Income and Expenditure | 0 = Cannot cover; 1 = Can cover but with little surplus; 2 = Can cover with a substantial surplus. |
Data information of the variables system in the SEM model.
| Categories | Variable | Maximum Value | Minimum Value | Average Value | Standard Deviation |
|---|---|---|---|---|---|
| NPI | NPI-1 | 2 | −2 | −0.934 | 0.678 |
| NPI-2 | 2 | −2 | −1.096 | 0.535 | |
| HC | HC-1 | 2 | 1 | 1.929 | 0.258 |
| HC-2 | 3 | 2 | 2.865 | 0.342 | |
| HC-3 | 4 | 1 | 2.291 | 0.516 | |
| SC | SC-1 | 4 | 1 | 2.079 | 0.426 |
| SC-2 | 2 | 0 | 1.295 | 0.518 | |
| SC-3 | 2 | 1 | 1.395 | 0.489 | |
| NC | NC-1 | 2.1 | 0.8 | 1.309 | 0.260 |
| NC-2 | 1.6 | 0.3 | 0.533 | 0.308 | |
| NC-3 | 0.6 | 0 | 0.342 | 0.074 | |
| PC | PC-1 | 4 | 1 | 3.081 | 0.433 |
| PC-2 | 2 | 0 | 0.267 | 0.528 | |
| PC-3 | 3 | 1 | 1.720 | 0.473 | |
| FC | FC-1 | 2 | 0 | 1.237 | 0.442 |
| FC-2 | 2 | 0 | 1.355 | 0.494 | |
| FC-3 | 2 | 0 | 1.107 | 0.380 | |
| LS | LS-1 | 4 | 2 | 2.226 | 0.436 |
| LS-2 | 2 | 1 | 1.263 | 0.440 |
Results of questionnaire reliability measurement in the SEM structure.
| Latent Variables | Cronbach’s Alpha | Items |
|---|---|---|
| NPI | 0.734 | 2 |
| HC | 0.705 | 3 |
| SC | 0.688 | 3 |
| NC | 0.739 | 3 |
| PC | 0.628 | 3 |
| FC | 0.501 | 3 |
| LS | 0.718 | 2 |
Multicollinearity test results (VIF values) for inner model in the SEM structure.
| Latent Variables | Observed Variables | VIF |
|---|---|---|
| NPI | NPI-1 | 1.148 |
| NPI-2 | 1.178 | |
| HC | HC-1 | 1.093 |
| HC-2 | 1.122 | |
| HC-3 | 1.063 | |
| SC | SC-1 | 1.013 |
| SC-2 | 1.002 | |
| SC-3 | 1.015 | |
| NC | NC-1 | 1.112 |
| NC-2 | 2.070 | |
| NC-3 | 2.229 | |
| PC | PC-1 | 1.170 |
| PC-2 | 1.206 | |
| PC-3 | 1.071 | |
| FC | FC-1 | 1.388 |
| FC-2 | 1.347 | |
| FC-3 | 1.046 | |
| LS | LS-1 | 1.215 |
| LS-2 | 1.233 |
Multicollinearity test results (VIF values) for outer model in the SEM structure.
| Dependent Variables | Independent Variables | VIF |
|---|---|---|
| HC | NPI | 1.203 |
| SC | 1.158 | |
| NC | 1.632 | |
| PC | 1.337 | |
| FC | 1.272 | |
| LS | HC | 1.094 |
| SC | 1.115 | |
| NC | 1.621 | |
| PC | 1.268 | |
| FC | 1.350 |
Hypothesis testing of the structural model in the SEM structure.
| Hypothesized Path | t-Statistics | p-Value | Decision |
|---|---|---|---|
| NPI > HC | 3.132 | 0.025 | Supported |
| NPI > SC | 3.268 | 0.024 | Supported |
| NPI > NC | 6.822 | 0.011 | Supported |
| NPI > PC | 4.384 | 0.013 | Supported |
| NPI > FC | 4.128 | 0.007 | Supported |
| HC > LS | 2.363 | 0.018 | Supported |
| SC > LS | 3.165 | 0.013 | Supported |
| NC > LS | 6.105 | 0.012 | Supported |
| PC > LS | 2.457 | 0.014 | Supported |
| FC > LS | 3.503 | 0.021 | Supported |
Hypothesis testing of the measurement model in the SEM structure.
| Hypothesized Path | t-Statistics | p-Value | Decision |
|---|---|---|---|
| NPI-1 > NPI | 2.532 | 0.011 | Supported |
| NPI-2 > NPI | 5.762 | 0.018 | Supported |
| HC-1 > HC | 2.895 | 0.013 | Supported |
| HC-2 > HC | 2.144 | 0.025 | Supported |
| HC-3 > HC | 9.747 | 0.012 | Supported |
| SC-1 > SC | 3.238 | 0.017 | Supported |
| SC-2 > SC | 2.069 | 0.039 | Supported |
| SC-3 > SC | 5.997 | 0.022 | Supported |
| NC-1 > NC | 6.083 | 0.017 | Supported |
| NC-2 > NC | 7.620 | 0.028 | Supported |
| NC-3 > NC | 22.701 | 0.033 | Supported |
| PC-1 > PC | 3.478 | 0.041 | Supported |
| PC-2 > PC | 25.083 | 0.012 | Supported |
| PC-3 > PC | 2.673 | 0.018 | Supported |
| FC-1 > FC | 3.272 | 0.031 | Supported |
| FC-2 > FC | 18.321 | 0.022 | Supported |
| FC-3 > FC | 3.462 | 0.029 | Supported |
| LS > LS-1 | 14.508 | 0.013 | Supported |
| LS > LS-2 | 11.071 | 0.021 | Supported |
Path coefficients of the SEM model for evaluating the livelihood path.
| Effect | Path | Value |
|---|---|---|
| Direct Effect | NPI > HC | −0.219 |
| NPI > SC | −0.217 | |
| NPI > NC | −0.383 | |
| NPI > PC | −0.298 | |
| NPI > FC | −0.258 | |
| HC > LS | 0.120 | |
| SC > LS | 0.225 | |
| NC > LS | 0.351 | |
| PC > LS | 0.164 | |
| FC > LS | 0.179 | |
| Indirect Effect | NPI > HC > LS | −0.026 |
| NPI > SC > LS | −0.049 | |
| NPI > NC > LS | −0.134 | |
| NPI > PC > LS | −0.049 | |
| NPI> FC > LS | −0.046 | |
| Total Effect | NPI > LS | −0.256 |
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; Shi, Guoqing 1
; Zhao Yuanke 1
; Liu Huicong 1 ; Ye Xinyu 1 1 National Research Center for Resettlement, Hohai University, Nanjing 211100, China; [email protected] (Y.Z.); [email protected] (H.L.); [email protected] (X.Y.), Institute of Social Development, Hohai University, Nanjing 211100, China, Asian Research Center, Hohai University, Nanjing 211100, China