Model specification

To identify optimal application scenarios for the two primary development approaches—government intervention and market demand—this study constructs a theoretical framework (Fig. 1). The theoretical framework adopts a directed acyclic graph structure to analyze three core components: CES origins, development approaches, and final outputs. This formalism is chosen because directed acyclic graphs effectively disentangle the complex causal relationships between government interventions, market forces, and CES outcomes across varying contexts in protected areas (Colombo et al. 2012).

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The final CES typology adopts the Millennium Ecosystem Assessment classification system, encompassing five service categories: spiritual, scientific, educational, psychological, and recreational services provided by ecosystems (Millennium Ecosystem Assessment 2005). Spiritual services are delivered through natural and cultural heritage assets (Ryfield et al. 2019). It provides indigenous communities with symbolic representations of local knowledge, traditional practices, and ecological beliefs (Chaudhary et al. 2019). Moreover, spiritual services progressively incorporate contemporary popular culture elements through temporal evolution (Coscieme 2015). Scientific services use ecosystems as natural laboratories and data sources, enhancing human benefits in scientific research, technological innovation, and ecological monitoring (Friess et al. 2020). Educational services employ ecosystems as knowledge platforms and living classrooms, fostering environmental literacy and ecological ethics through immersive landscape experiences, thereby bridging scientific knowledge and public understanding (Mocior et al., 2016). Psychological services originate from sensory stimulation by ecosystems. Proximity to or visits to ecosystems maintain mental well-being. A Beijing case study demonstrates dual mechanisms: urban green spaces directly provide psychological benefits for elderly residents, which indirectly enhance mental health by enabling physical exercise (Guo et al. 2023). Recreational services are supported by ecosystem landscapes, providing humans with leisure activities and aesthetic experiences. These services demonstrate significant development potential (Moseley et al. 2018). For instance, recreational services account for approximately one-third of the economic benefits generated by European forests; however, both their scale and return rates remain lower than timber provisioning services, thus indicating substantial potential for enhancement (Lovrić et al. 2025).

Despite their capacity to deliver multiple critical CESs, these services remain significantly understudied in academic research and largely neglected in management practices. Educational and scientific services are typically perceived as non-excludable public goods (Mocior et al., 2016; Friess et al. 2020). Spiritual and psychological services are systematically excluded from mainstream assessment frameworks in most studies (Garrett et al. 2023). Therefore, targeted development requires embedded government intervention or market regulation. Government intervention encompasses three approaches: formulating ecological policies and development plans; implementing ecological engineering projects with corresponding investments; and promoting public participation in CES conservation and use through awareness campaigns. Market regulation operates by aligning supply with demand for CESs. Specifically, recreation, scientific research, and educational services demonstrate clearly identifiable demand entities. Government approaches to CES development exhibit significant diversification. High-quality CES development necessitates synergistic integration of governmental and market forces. This rationale generates two testable research hypotheses.

Hypothesis 1: Government intervention exerts a positive effect on the CES value in protected areas.

Hypothesis 2: Market demand demonstrates a positive causal relationship with the CES valuation in protected areas.

The quantity and quality of CES are fundamentally determined by their originating sources. Ecological resources, geographical location, and social development constitute the three primary sources of CESs (Goodness et al. 2016). Among these, ecological resources serve as the direct provisioning source for CESs. Biodiversity provides essential study subjects for educational and scientific services (Schaich et al. 2010; Blake et al. 2021). Natural landscapes and cultural heritage constitute the direct value sources for recreational, spiritual, and psychological services (Plieninger et al. 2014). Geographical location fundamentally determines ecosystem dynamics and development potential.

When an ecosystem is situated in a location with favorable climatic conditions, a superior environmental baseline, and well-developed infrastructure, it not only exhibits strong ecological resilience but also ensures accessibility for human activities (Al-Assaf et al. 2016). The level of development enables the exploitation of CESs. A region with advanced economic development and high technological capacity can attract a significant influx of tourists and researchers, thereby enabling the substantial realization of CES value (Wang et al. 2019). This leads to the formulation of three research hypotheses:

Hypothesis 3: Geographic environment exerts a positive influence on the CES value of protected areas.

Hypothesis 4: Resource endowment has a positive effect on the CES value of protected areas.

Hypothesis 5: Development level positively affects the CES value of protected areas.

Moreover, a close relationship occurs between resource endowment and development level. Abundant natural resource endowment can provide raw materials for industrial production, reduce production costs, and promote industrial development. For instance, the Middle East has developed into a globally significant energy supply center thanks to its rich oil resources. The growth of the oil industry has driven rapid local economic expansion and enabled the region to attain a relatively high level of economic development in a short period. Similarly, the advantage of resource endowment often attracts the clustering of related industries, forming industrial clusters and generating scale economic effects, which further propel economic development (Raymond et al. 2009). Resource development and use can also create several job opportunities and boost residents’ income levels, thereby promoting consumption, stimulating domestic demand, and promoting a virtuous economic cycle (Chen et al., 2012).

Moreover, resource-abundant regions may overly depend on resource-based industries, neglecting the development of other industries. This leads to a monolithic industrial structure and unsustainable economic development. When resources are exhausted or resource prices fluctuate, the economy can easily fall into difficulty. Abundant resource endowment may cause enterprises and local governments to develop path dependence, lacking the motivation and pressure to innovate, which is against technological progress and industrial upgrading (Liu et al. 2008).

The degree of market mechanism perfection and quality of the institutional environment can affect the efficiency of transforming resource endowment into economic development advantages. In areas with well-developed market mechanisms and good institutional environments, resources can be allocated more reasonably and effectively, and resource development benefits can be used more for economic diversification and social development, achieving virtuous interaction between resource endowment and the level of economic development. Conversely, in areas with underdeveloped market mechanisms and poor institutional environments, the advantages of resource endowment are often not fully utilized and may even have a negative effect on economic development (Ma 2018).

These factors also influence the possibilities of government intervention and market regulation. Therefore, it is necessary to test these complex relationships.

Hypothesis 6: Government intervention demonstrates significant correlation with market demand.

Hypothesis 7: Resource endowment is significantly correlated with development level.

Hypothesis 8: Resource endowment exerts a positive influence on government intervention, which in turn affects the CES value of protected areas.

Hypothesis 9: Resource endowment positively affects market demand, thereby influencing the CES value of protected areas.

Hypothesis 10: Development level has a positive effect on policy intervention, consequently affecting the CES value of protected areas.

Hypothesis 11: Development level shows a positive association with market demand, ultimately influencing the CES value of protected areas.

Unit of analysis

This study selected a state-owned forest region in NE China as the study area for data collection. The research area encompasses the Greater Khingan Mountains, Lesser Khingan Mountains, and Changbai Mountain range (117°06′ to 135°05′E and 41°25′ to 53°23′N), administered by 87 state-owned forest enterprises. The research regions contain 27.2748 million hectares of forest cover, representing 12.64 percent of China’s total forested land. The standing timber volume reaches 3.007 billion cubic meters, constituting 17.55 percent of the nation’s total forest stock (Wang et al., 2021). The region encompasses several nationally and locally protected areas, including the NE China Tiger and Leopard National Park, Songnen Crane National Park (national-level protected areas), as well as the Zhalang Wetland Reserve and Jingpo Lake Wetland Reserve (local-level protected areas; Dai et al. 2025).

The study reveals that the valuation of CESs in this region reached a remarkable CNY388.11 billion (approximately US$53.25 billion) in 2020. However, only 1.35 percent of this value translated into tangible economic growth (Huang et al. 2024). This constitutes one rationale for selecting this study region—namely, its need to enhance the utilization efficiency of CESs. An additional selection criterion stems from the region’s representative development model.

The state-owned forest region in NE China has undergone development since the 1940s, historically operating under an exclusive government-enterprise integration management model. In 2019, institutional reforms restructured the governance system by separating enterprise operations from governmental administration, as well as liberalizing market capital participation in regional development (Zhao et al. 2023). Consequently, the region currently operates under a hybrid governance system combining governmental regulation and market mechanisms, wherein the ambiguous boundary between these dual governance modalities poses practical challenges for stakeholders (Chen et al. 2024). The selection of this study region effectively operationalizes the conceptual framework’s development paradigm, serving as an exemplary case for examining the interplay between institutional arrangements and ecosystem service utilization.

Data collection

The study employed a questionnaire-based survey approach guided by the conceptual framework. Data collection occurred from July to August 2021 through multiple channels, including email distribution, on-site interviews, and telephone consultations. A supplementary survey round was implemented from July to August 2023 to ensure adequate sample representation. Random sampling protocols were strictly maintained throughout both survey phases. The final dataset achieved a 95.5 percent response rate, with 405 valid questionnaires representing 93.75 percent of total responses. These metrics confirm robust data quality. Complete survey results appear in Table 1.

Table 1.Index design and Sample characteristics.

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Research methods

Structural equation modeling was selected as the analytical framework to examine the causal relationships between government intervention and market demand on service configurations and quality across differential contexts. The general model formulation is expressed as: (1) $$X=Ax\omega +\delta$$ (2) $$\text{Y}=Ay\eta +\epsilon$$ (3) $$\mathit{H}=\alpha \eta +\beta \omega +\gamma$$where

X = observed exogenous variables;

Y = observed endogenous variables;

Ax = factor loadings of X on ω;

Ay = factor loadings of Y on η;

δ, ε = measurement errors for X and Y, respectively;

η = endogenous latent variable;

ω = exogenous latent variable;

α = structural coefficient matrix of endogenous latent variables;

β = structural coefficient matrix of exogenous and endogenous latent variables; and

γ = structural equation residuals.

The selection of this analytical approach is primarily based on two critical considerations. First, it enables the comprehensive characterization of the complex composition of CESs, including their value sources and development patterns. Because core variables such as CES components, government interventions, and market demands exhibit inherently unobservable characteristics that cannot be adequately captured by conventional econometric models, this method employs factor loadings to effectively integrate multiple observed indicators. Second, the approach facilitates rigorous assessment of complex causal pathways, particularly in examining the differential effects of combined government-market policy instruments across various CES development scenarios. This essentially involves testing different combinations of causal pathways through the generation of structural coefficient matrices, which systematically elucidate intricate causal relationships and their integrated pathways. The methodology’s technical superiority lies in its unique capacity for latent variable modeling and comprehensive causal analysis, overcoming the limitations of standard analytical techniques while maintaining robust measurement-structure estimation capabilities. This makes it particularly suitable for investigating the complex institutional–ecological interactions central to CES research, as demonstrated in recent studies published in leading journals such as Ecological Economics and Ecosystem Services (Posthumus et al., 2010; Turkelboom et al., 2018).

Reliability test results

The reliability of the measurement scales corresponding to the six latent variables was assessed using Cronbach’s alpha coefficient (Table 2). The analysis yielded Cronbach’s alpha values of 0.843 for CESs, 0.906 for government intervention, 0.852 for market demand, 0.926 for resource endowment, 0.854 for development level, and 0.824 for geographical environment, all of which exceeded the recommended threshold of 0.8, indicating excellent internal consistency. Furthermore, all corrected item–total correlations were above 0.5, confirming that the observed variables met the required standards. Examination of the “Cronbach’s alpha if item deleted” values demonstrated that deleting any individual item would not lead to an increase in the overall reliability coefficients. These results demonstrate that the observed variables exhibit strong consistency and that the measurement outcomes possess satisfactory reliability.

Table 2.Reliability test results.

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Fitting degree results

The validity of the scale was examined using confirmatory factor analysis (Table 3). Regarding the overall model validity, the CMIN/DF (Chi-Square Divided by Degrees of Freedom) value was 1.935, which is below the threshold of 3, indicating a good fit between the model and the data. The GFI (Goodness-of-Fit Index) and AGFI (Adjusted Goodness-of-Fit Index) values were 0.874 and 0.865, respectively, both exceeding the recommended cutoff of 0.8, suggesting that the model adequately explains the observed data. The IFI (Incremental Fit Index), TLI (Tucker–Lewis Index), and CFI (Comparative Fit Index) values all surpassed 0.9, with these high values further confirming the strong fit between the model and the data. Additionally, the RMSEA (Root-Mean-Square Error of Approximation) was 0.043, well below the critical value of 0.08, demonstrating minimal model fit error and a high degree of consistency between the model and the data.

Table 3.Model fitting degree.

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Path test results

The direct causal paths outlined in research hypotheses 1 to 7 were empirically tested (Table 4). The results indicate that government intervention, market demand, resource endowment, development level, and geographical environment exert a statistically significant positive influence on the development of CES. However, the hypothesized statistical correlations between resource endowment and development level were not supported. Consequently, hypotheses 1 to 6 were validated, whereas hypothesis 7 was rejected.

Table 4.Direct path test results.

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The indirect effects theorized in hypotheses 8 to 11 were tested using the Bootstrap method (Table 5). Results revealed statistically significant mediation pathways: Government intervention exerted an indirect effect between resource endowment and service value (95% confidence interval [CI; 0.0152, 0.0957], mediation effect = 0.048) and between development level and service value (95% CI [0.0048, 0.0355], mediation effect = 0.019). Similarly, market demand demonstrated indirect effects between resource endowment and service value (95% CI [0.0008, 0.0457], mediation effect = 0.059) and between development level and service value (95% CI [0.0192, 0.0865], mediation effect = 0.034). These findings provide robust support for all proposed indirect relationships (hypotheses 8–11), confirming the mediating mechanisms in the conceptual model.

Table 5.Indirect path test results.

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In summary, the causal relationships within the CES development model can be systematically delineated, as presented in Table 6. The results confirm the validation of hypotheses 8 to 11, indicating that all proposed hypotheses remain substantiated with the exception of hypothesis 7, which was not supported by the empirical evidence.

Table 6.Decomposition of total effect, direct effect, and indirect effect of the model.

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Limitations and outlook future

The research concentrates on protected areas within the state-owned forest regions of NE China. The relatively narrow scope may hinder the direct extrapolation of findings to other parts of China or globally. Variations in other concerning regions, types and value of CESs and development boundaries between the government and the market may also occur. When quantifying the value of CESs, different stakeholders may have varying evaluation criteria and perceptions. This diversity could compromise the accuracy and objectivity of the data. Although a theoretical framework was built to analyze the relationship between government intervention, market demand, and the development of CES, real-world relationships are more complicated and dynamic. Other factors (e.g., policy shifts, socio-cultural changes, technological advances) might also play a role but were not fully captured in the research.

Future research could expand the geographical scope to cover protected areas across different regions of China or even globally. This would verify and refine the applicability and universality of the theoretical framework in diverse contexts and explore regional differences cultural in ecosystem services and development boundaries.

It is valuable to investigate the long-term dynamic trends of CESs in protected areas, as well as the effects of policy changes, technological progress, and shifting social demands. This could provide a scientific basis for adjusting relevant policies and management measures in a timely manner to meet the evolving development needs of protected areas.