Land use and land cover change (LUCC) exerts substantial influence on ecosystem service values (ESV). But conventional ESV valuation approaches frequently neglect temporal shifts in crop economic returns. Integrating high-resolution land use data (2000-2020) with the Patch-generating Land Use Simulation (PLUS) model-and augmenting it with spatial econometric analysis-we project 2030 land use configurations under four policy-relevant scenarios: Business-as-Usual (BAU), Economic Development Priority (EDP), Ecological Protection Priority (EPP), and Ecological Economic Balance (EEB). Under EDP, construction land expands by 5.52%, critically low-ESV areas increase by 476.83 km2 (a 2.7-fold surge relative to the 2010-2020 period), and net ESV declines by 1.04%. By contrast, EPP achieves a 10.19% reduction in urban land through targeted ecological restoration, effectively arresting ESV degradation. High-value ESV zones are concentrated in water bodies and forests, which dominate regional regulating services. The three-province in the Middle Reaches of the Yangtze River epitomizes the inherent tension between rapid urbanization and ESV conservation. These findings provide a basis for assessing the social, economic and environmental factors. Furthermore, the results provide a new solution approach for formulating differentiated ecological environment protection policies in the study area and addressing key technical challenges in land use planning for large-scale ecological functional area.

Northeast China is a vital ecological barrier in China, possessing abundant forest resources, mineral resources, and the largest grain production area in the country. However, the region has long been affected by human activities, resulting in a relatively fragile ecological environment. Clarifying the spatial-temporal evolution of the ecological environment quality and implementing zoning management are of great significance for promoting ecological governance and sustainable development in Northeast China. Utilizing the Google Earth Engine (GEE) cloud platform and combining the ecological characteristics of Northeast China, the modified remote sensing ecological index (MRSEI) based on five indicators of comprehensive greenness (mNDVI), humidity (WET), dryness (NDBSI), heat (LST), and air pollution (DI) was constructed. The MRSEI was employed to analyze the spatial-temporal evolution characteristics of the ecological environment quality in Northeast China from 2000 to 2024 from a multi-scale perspective (pixel, urban type, and resource type scales). The Sen+Mann-Kendall trend analysis method and Hurst index were used to investigate the change trends of MRSEI within the study period and in the future. Finally, a zoning management plan for the ecological environment in the study area was proposed based on the coupling of human activity intensity and ecological environment quality. The results show that: ① The assessment results of ecological environment quality by MRSEI and RSEI were consistent to a certain extent. However, MRSEI was more accurate than RSEI in identifying local ecological environment elements, especially in industrial and mining land, construction land, and areas with high vegetation cover, with evaluation results more closely matching the actual surface conditions. ② From 2000 to 2024, the ecological environment quality in Northeast China exhibited a trend of rapid improvement followed by slow degradation. The MRSEI increased by 16.65% from 2000 to 2014 and decreased by 1.75% from 2014 to 2024. Spatially, the study area generally showed a pattern of "high in the east and low in the west." The MRSEI values of growth-oriented and forestry-oriented resource-based cities decreased slowly from 2000 to 2024. Petroleum-oriented resource-based cities had a poor ecological base, with an average MRSEI value lower than that of other types of cities. ③ The change trends of ecological environment quality from 2000 to 2024 were mainly characterized by non-significant improvement, extremely significant improvement, and non-significant degradation. The Hurst index values ranged from 0.06 to 0.95. The area with 0<H<0.5 was 1 031 304.00 km2, accounting for 79.18%, among which the area with reverse persistent improvement (improvement turning into degradation) accounted for as high as 59.48%, indicating that most areas that had improved in the past will face the risk of degradation in the future. ④ Based on the coupling of ecological environment quality and human activity intensity, Northeast China was divided into coordinated development zones, protection zones, risk zones, and potential zones for the ecological environment, and differentiated zoning management strategies were proposed to provide references for the implementation of feasible ecological environment protection policies and governance plans in Northeast China.

A study on the impact of human activities on the ecosystem health of Three-River-source national park

The occurrence of zoonotic diseases on livestock farms, especially infectious diseases such as echinococcosis, poses a severe threat to surrounding ecosystems and the health of nearby residents. It is crucial to take proper measures to increase farmers' knowledge, awareness and practices regarding zoonoses. Cognitive interventions are widely used in the control of epidemics. Using Chinese livestock farming as a case study, this study examined the effect of cognitive intervention (i.e., training and dissemination) on farmers' pro-environmental behaviors, with the key dependent variable including both zoonosis-prevention behaviors (e.g., dog deworming and lamb vaccination) and daily management behaviors (e.g., household waste and sewage handling). Based on survey data collected from 496 livestock farmers over four years in Qinghai province, China, the study employed a two-way fixed-effects regression model to explore both the direct and indirect effects of zoonotic cognitive intervention on farmers' pro-environmental behaviors, and also explored the mediating effects of zoonotic knowledge and perceptions of environment. The findings reveal that: (1) Zoonotic cognitive interventions significantly improved farmers' behaviors in preventing and controlling zoonotic diseases. (2) The interventions generated positive indirect effects on other pro-environmental behaviors, particularly proper disposal of livestock waste and household garbage. (3) Increased knowledge on zoonosis, as well as pollution awareness were key mechanisms linking interventions to behavioral changes, and (4) the effects were heterogeneously stronger among less-educated farmers and in regions with lower epidemic prevalence and stricter ecological protection policies. These results highlight the policy relevance of incorporating cognitive interventions into rural public health and environmental management programs, providing new evidence to support sustainable livestock farming and inform integrated strategies for health and agricultural policy.

Vapor pressure deficit dominated the rate of greening and yellowing in the Northern Hemisphere

Investigating the influence of landscape evolution on river eutrophication is critical for optimizing spatial patterns to improve water quality. Machine learning (ML) models can capture the complex relationship between landscape metrics and water quality, but their black-box property restricts the interpretability of the underlying mechanisms and makes it difficult to forecast future trends in water quality. To address this, we developed a novel framework that, for the first time, couples an interpretable ML model with the Patch-generating Land Use Simulation (PLUS) model for eutrophication index (EI) prediction. This approach elucidates the response of river eutrophication to landscape dynamics and forecasts future river EI trends. The random forest regression (RFR) model outperformed other algorithms in quantifying these relationships (R2 = 0.934 for training, 0.711 for testing). SHAP analysis revealed that landscape metrics contributed 81.78% to the river EI, far exceeding climate factors (18.22%). Consequently, landscape evolution emerged as the dominant explanatory factor. Scenario simulations indicated that while the ecological protection (EP) scenario effectively mitigates river eutrophication, the urban development (UD) scenario significantly exacerbates it. Specifically, under the UD scenario, the average EI in urban sub-watersheds is projected to reach 60.78 by 2040, approaching heavy eutrophic levels. Our findings inform spatial optimization strategies for river eutrophication management and facilitate the design of targeted, localized water ecological protection policies in subtropical monsoonal basins.

Understanding the coordinated changes in soil carbon and nitrogen is essential for evaluating ecosystem responses to environmental change, particularly in ecologically fragile alpine regions such as the Qilian Mountains. In this study, the denitrification–decomposition (DNDC) model was used to assess the spatiotemporal dynamics of soil organic carbon density (SOCD) and total nitrogen density (STND) in the 0–30 cm soil layer from 1975 to 2024. The results revealed that SOCD and STND were higher in the northern and east-central grasslands and lower in the southwestern regions. Both stocks exhibited fluctuating but overall increasing trends, with notable increases aligned with major ecological protection policies in China. To better understand the coupling of soil carbon and nitrogen, we constructed a composite indicator called soil carbon and nitrogen density (SCND) using principal component analysis. This indicator captures the synergistic accumulation of organic carbon and total nitrogen driven by shared ecological processes and was further used to explore its associations with environmental factors, enabling an integrated assessment of soil carbon–nitrogen dynamics. The results revealed that elevation and soil bulk density were the main direct drivers of carbon and nitrogen accumulation, both of which exerted negative effects, whereas the other factors acted through indirect pathways. These findings underscore the importance of topography and soil structure in regulating carbon and nitrogen dynamics. It is recommended to plant deep-rooted grass species, limit heavy machinery, and maintain long-term ecological protection to prevent declines after initial gains from interventions. In addition, the carbon-to-nitrogen (C/N) ratio showed increasing spatial heterogeneity over time, with high values in the western and central regions, where nitrogen input can be enhanced by introducing legumes or applying organic fertilizers. In the northern and southeastern areas, grazing exclusion or low-intensity grazing is recommended to promote organic matter accumulation. Vertically, the C/N ratio decreased with soil depth, indicating strong carbon and nitrogen coupling within the soil profile. Overall, this study highlights the coordinated dynamics of soil carbon and nitrogen in the Qilian Mountain grasslands, providing valuable insights for the sustainable management and resilience improvement of grasslands in this region under changing environmental conditions.Supplementary InformationThe online version contains supplementary material available at 10.1186/s13021-026-00422-8.

Accurate estimation of forest aboveground carbon (AGC) is crucial for understanding the carbon cycle and formulating climate policies, yet it remains challenging in complex mountainous regions. This study used machine learning framework to estimate the spatiotemporal dynamics of AGC in the Three Parallel Rivers region of China from 2003 to 2024. By integrating China’s National Forest Continuous Inventory (NFCI) data with multispectral satellite imagery, we employed a two-stage feature selection strategy to identify key predictor variables. Among three ensemble algorithms tested, the Random Forest model achieved the optimal performance (R2 = 0.74). The results indicated a net increase of 67.05 Tg in total AGC over the two decades, with a spatial pattern characterized by higher densities in the west and north. Geographical Detector analysis revealed that the driving forces were synergistic, with the interaction between temperature and population density exhibiting the most prominent explanatory capacity. This study provides a high-resolution (30 m) benchmark for AGC in a global biodiversity hotspot and underscores the critical role of ecological protection policies in enhancing carbon sequestration, offering valuable insights for managing similar mountain ecosystems worldwide.

Achieving a balance between ecological conservation and economic development is essential for regional coordination and sustainable development. This study examines 56 national key ecological function counties （cities） in Sichuan Province， employing a coupling coordination model to assess the current state and dynamic evolution of ecological-economic coupling coordination. Temporal analysis， trend surface analysis， and spatial autocorrelation analysis are utilized to delineate spatiotemporal evolution patterns， while a geographically and temporally weighted regression （GTWR） model is applied to explore influencing factors and spatiotemporal heterogeneity. The results indicate that： ① The coupling coordination degree generally fell within the range of 0.4-0.5 （mild imbalance） and 0.5-0.6 （primary coordination）， exhibiting a U-shaped trend of initial decline followed by subsequent improvement. ② High coordination areas were mainly concentrated in the eastern and southern regions of Sichuan Province， with an expanding north-south disparity. ③ Spatial correlation of the coupling coordination degree weakened initially before strengthening， revealing significant spatial heterogeneity and regional clustering characteristics. ④ Key influencing factors， including river network density， road network density， government intervention， industrial structure， and population density， displayed pronounced spatiotemporal heterogeneity. These findings suggest that while the alignment between ecological protection policies and economic development strategies in Sichuan's national key ecological function areas is strengthening， regional disparities remain prominent. Therefore， targeted policies should be formulated based on local conditions to achieve a sustainable synergy between environmental conservation and economic growth.

Spatio-temporal evolution and driving mechanisms of rural resilience under ecological protection policies: A multi-scale analysis of technology-governance synergy in the Changsha-Zhuzhou-Xiangtan Green Heart, China

Accurate identification of the spatiotemporal characteristics and spatial matching of ecosystem services (ESs) supply and demand, as well as determination of the factors influencing the ESs supply-demand relationship, is of great significance for controlling the design of regional ecological protection policies and sustainable management. Unfortunately, the comprehensive characteristics of changes in ESs supply and demand, as well as their driving mechanisms, after large-scale vegetation restoration in the Shaanxi section of the Yellow River basin (SYRB) are still unclear. This study, conducted in the SYRB, employed specialized models to assess water yield, soil conservation, and carbon fixation on both the supply and demand sides after vegetation restoration in 2000 and 2023. Subsequently, the spatiotemporal heterogeneity of the supply-demand matching of ESs was explored by constructing the supply-demand ratio index. Finally, using the optimal parameter Geodetector, the influencing factors of the ESs supply-demand matching relationship in the SYRB were further identified. The results showed that the water yield in the SYRB was generally in a deficit, indicating insufficient supply throughout the study period, but this deficit state improved over time. In 2023, the counties with insufficient water yield supply were primarily located in the northern part of the SYRB and the Guanzhong Plain. Soil conservation reached a fundamental reversal from a “general deficit” to an “overall surplus.” In 2023, the counties with insufficient soil conservation supply were mainly located in the northern part of the SYRB. In contrast, the supply-demand relationship of carbon fixation in the vast majority of countries deteriorated. In 2023, the counties with insufficient carbon fixation supply were primarily located in the northern part of the SYRB and the Guanzhong Plain. Economic density, vegetation coverage, and population density were identified as the key factors in monitoring the water yield supply-demand matching relationship. Precipitation, slope, and population density were the main controlling factors for the soil conservation supply-demand matching relationship. Economic density, forest and grassland percentage, and population density were identified as the key factors shaping the carbon fixation supply-demand matching relationship. This study clarified the supply-demand relationship and driving mechanisms for key ESs in the SYRB, thereby providing a theoretical basis for the comprehensive management of regional ecosystems.

The coastal zone, as a typical land-sea interaction area, has experienced significant changes in habitat quality under the dual influence of climate change and human activities. Identifying the land-sea differences in the mechanisms influencing habitat quality in the coastal zone is essential for the development of targeted ecological protection strategies. Based on the integrated habitat quality assessment results for the Jiangsu coastal zone from 2010 to 2020, kernel density curves and Optimal Parameter Geographic Detector (OPGD) were employed to investigate the land-sea differences in the composition, spatiotemporal variations, and driving mechanisms of habitat quality. The results indicate that, in terms of composition, low-quality habitats are mainly distributed on land, while areas with medium to high habitat quality are concentrated in the sea; in terms of spatiotemporal changes, from 2010 to 2020, habitat quality degradation in the Jiangsu coastal zone is primarily manifested as the expansion of low-quality terrestrial habitats into natural areas, as well as the transition of high-quality marine habitats into suboptimal habitats; in terms of driving mechanisms, changes in terrestrial habitat quality are primarily driven by human activities, whereas marine changes are mainly influenced by natural factors such as topography and hydrodynamics, with indirect disturbances from human activities observed in specific years. These findings provide a scientific basis for the targeted formulation of coastal zone ecological protection policies.

The inclusion of esports in the Olympics presents new opportunities and challenges for event operations. While enhancing industry recognition and attracting policy support and capital investment, it also faces issues with traditional sports concepts and insufficient standardization of event rules. This study focuses on a SWOT analysis of esports event operations to identify competitive advantages and potential risks, aiming to provide strategic insights for sustainable development. This study employs the SWOT analysis model, integrating literature analysis, case studies, and data statistics to evaluate the internal and external environments of esports event operations. Data sources include industry reports, typical event cases (e.g., the Asian Games esports project), and policy texts. The analysis identifies strengths, weaknesses, opportunities, and threats to propose strategic combinations. The core strengths of esports event operations are a young audience base, capital-intensive investment, and digital platform advantages (e.g., live streaming and fan economy). Weaknesses include an imperfect rule system, low recognition of traditional sports culture, and insufficient professional training for athletes. Opportunities arise from policy support, cross-industry cooperation (e.g., sponsorships from new energy vehicle brands), and technological advancements (e.g., AI referee systems). Threats include public opinion disputes, intensified industry competition, and ecological protection policies. Based on these findings, the study proposes SO (using Olympic resources to promote event internationalization), ST (enhancing transparency to mitigate public opinion risks), WO (improving rule standardization and talent development), and WT (establishing industry alliances to resist external shocks) strategies. The entry of esports into the Olympics opens a new dimension for event operations, requiring dynamic strategic adjustments to balance opportunities and challenges. The study recommends prioritizing the SO strategy to integrate the Olympic brand with esports culture, strengthening commercial value and social responsibility. The WO strategy should focus on addressing rule and talent gaps to promote industry standardization. Future research exploring the integration of esports and traditional sports offers insights for the digital transformation of the global sports ecosystem.

The green plants around the world and in regions can change the climate and the transfer of energy on land. Climate is a very important element that plays a vital role in vegetation cover. This study aims to analyze the land cover changes in the urban context of the Imphal area, Manipur, India, using satellite images and vegetation indices generated from multi-spectral remote sensing data. Landsat 8 (OLI) images were processed by multi-source classification and vegetation index differencing techniques to discover changes over time. The study finds that there were large land use changes between 2018 and 2023 that revealed bare soil had varied the most, increasing from around 200 square kilometers in 2018 to 300 square kilometers in 2023, which reflects fast urbanization and possible loss of vegetated areas. These changes reflect the growing urbanization and its effect on the natural ecosystems of the region. The most important vegetation indices used in this study are NDVI (Normalized Difference Vegetation Index), GNDVI (Green Normalized Difference Vegetation Index), SAVI (Soil Adjusted Vegetation Index), and MSAVI2 (Modified Soil Adjusted Vegetation Index 2) for monitoring vegetation health and land cover change. The average values of the threshold of these indices in the area of Imphal were stated as NDVI (0.267), GNDVI (0.262), SAVI (0.157), and MSAVI2 (0.075), providing the necessary data on geographic and temporal variability of vegetation coverage that allow indicating the areas at risk of degradation and high productivity. The paper has highlighted the potential of remote sensing (RS) technology in detecting land cover change as well as vegetation variability that offers useful information in sustainable land use management and environmental planning. The conclusions will help policymakers in formulating sustainable land use and ecological protection policies.

The Hadamengou gold deposit, located on the northern margin of the North China Craton, represents one of the region‘s most significant gold mineralization clusters. However, exploration in its deeper and peripheral sectors is constrained by ecological protection policies and the structural complexity of the ore-forming systems. Multivariate analysis combined with multi-model integration provides an effective mathematical approach for interpretating geochemical datasets and guiding mineral exploration, yet, its application in the Hadamengou region has not been systematically investigated. To address this research gap, this study developed a pilot framework in the key Buerhantu area, on the periphery of the Hadamengou metallogenic cluster, applying and adapting a multivariate-multimodel methodology for mineral prediction. The goal is to improve exploration targeting, particularly for concealed and deep-seated mineralization, while addressing the methodological challenges of mathematical modeling in complex geological conditions. Using 1:10,000-scale lithogeochemical data, we implemented a three-step workflow. First, isometric log-ratio (ILR) and centered log-ratio (CLR) transformations were compared to optimize data preprocessing, with a reference column (YD) added to overcome ILR constraints. Second, principal component analysis (PCA) identified a metallogenic element association (Sb-As-Sn-Au-Ag-Cu-Pb-Mo-W-Bi) consistent with district-scale mineralization patterns. Third, S-A multifractal modeling of factor scores (F1–F4) effectively separated noise, background, and anomalies, producing refined geochemical maps. Compared with conventional inverse distance weighting (IDW), the S-A model enhanced anomaly delineation and exploration targeting. Five anomalous zones (AP01–AP05) were identified. Drilling at AP01 confirmed the presence of deep gold mineralization, and the remaining anomalies are recommended for surface verification. This study demonstrates the utility of S-A multifractal modeling for geochemical anomaly detection and its effectiveness in defining exploration targets and improving exploration efficiency in underexplored areas of the Hadamengou district.

Against the backdrop of global natural sand scarcity and stringent ecological protection policies, tuff mechanism sand has emerged as a promising alternative fine aggregate for concrete, especially in coastal infrastructure hubs like Ningbo, where abundant tuff resources coexist with acute natural sand shortages. However, existing research on TMS concrete lacks systematic multi-factor optimization, while the performance regulation mechanism of TMS remains unclear, hindering its application in large-scale engineering. To address this gap, this study employed a L16(45) orthogonal experimental design to systematically investigate the effects of five key factors, including fineness modulus, sand ratio, fly ash-to-ground granulated blast-furnace slag ratio, stone powder content, and water–binder ratio, on the 3 d, 7 d, and 28 d compressive, splitting tensile, and flexural strengths of TMS concrete from Ningbo. The results indicate that all three strengths exhibit rapid growth from 3 d to 7 d and stable growth from 7 d to 28 d, with the 3 d compressive strength accounting for 72.5% of the 28 d value, while flexural strength shows the lowest 3 d proportion (63.1%) and highest late-stage growth rate. Range analysis reveals that water–binder ratio is the dominant factor controlling compressive strength and splitting tensile strength, whereas fineness modulus dominates flexural strength. The optimal fineness modulus values for compressive, splitting tensile, and flexural strengths are 2.60, 2.90, and 2.30, respectively; a stone powder content of 0% optimizes compressive and flexural strengths, while 6% is optimal for splitting tensile strength. Notably, the interaction between fineness modulus and water–binder ratio exerts a statistically significant effect on compressive strength (p = 0.008), while the other interactions are negligible. This study fills the gap in research on multi-factor synergistic optimization of TMS concrete and provides targeted mix proportion designs for different engineering requirements. The findings not only enrich the theoretical system of manufactured-sand concrete but also offer practical technical support for the resource utilization of TMS in medium-to-high-strength concrete engineering, aligning with the sustainable development goals of the construction industry.

The Shaanxi section of the Yellow River Basin, located in the middle reaches of the basin, is a core area characterized by a fragile ecological environment. Since the implementation of the Grain for Green Program, the fractional vegetation cover （FVC） in this region has changed significantly, so it is of great significance for ecological conservation and sustainable development in the Yellow River Basin to study the spatiotemporal variations in FVC and its driving factors. Firstly, based on the Google earth engine （GEE） platform, the MODIS NDVI data and a dimidiate pixel model were used to estimate the FVC of the study area from 2000 to 2020. Secondly, the Theil-Sen median trend analysis, Mann-Kendall significance test, and Hurst index, were employed to analyze the spatio-temporal distribution, change trend, and future sustainability of FVC. Finally, combined with natural, social, and economic datasets, the Geodetector was used to clarify the primary driving factors influencing the variation in FVC. The results indicated that： ① The FVC in the Shaanxi section of the Yellow River Basin increased at a rate of 0.51%·a-1 from 2000 to 2020, exceeding the overall growth rate of 0.36%·a-1 for the entire Yellow River Basin, indicating a positive improvement in vegetation growth conditions. ② Approximately 75.17% of the region showed an increasing trend in FVC, with areas of significant improvement accounting for 54.1%, mainly distributed in the eastern part of Yulin City, the northern part of Yan'an City, and Baoji City. The Hurst index analysis showed a right-skewed unimodal distribution, indicating that the FVC in the study area might continue to improve in the future. ③ Precipitation and land use change were the main natural and anthropogenic drivers of FVC change, with q-values of 0.641 and 0.27, respectively. In particular, the cumulative afforestation area in Guanzhong and northern Shaanxi showed a strong correlation with FVC changes, with correlation coefficients of 0.84 and 0.89, respectively, indicating a positive impact of the Grain for Green Program on increasing vegetation cover. ④ The interactive effects of multiple factors had stronger explanatory power than individual factors, exhibiting either dual-factor enhancement or nonlinear enhancement relationships. The interaction between precipitation and temperature had the greatest influence on the spatial differentiation of FVC across the study area and northern Shaanxi. Meanwhile, the interaction between elevation and land use types dominated the spatial differentiation of FVC in the Guanzhong region. ⑤ Over time, the influence of natural factors on FVC in the study area showed a declining trend, while the influence of anthropogenic factors has gradually increased. Overall, these findings provide important insights for further formulation of ecological protection policies in the Yellow River Basin.

Balancing human development and environmental protection: Evidence from the Yangtze River Economic Belt ecological and environmental protection policy

Assessing the implementation effectiveness of ecological protection policy based on element-pattern-function dimensions: A case of Changzhutan ecological green heart, China

Background Protected areas like national parks play a pivotal role in carbon sequestration, a function essential for achieving global climate mitigation goals as climate change accelerates. However, a significant challenge lies in reconciling conservation mandates with pressures for economic growth within these regions. Methods The present study addresses this issue by investigating China’s Hainan Tropical Rainforest National Park (HNTRNP). By integrating 10 natural and socioeconomic variables, we applied the PLUS-InVEST model to quantify historical carbon stock dynamics from 1980 to 2020 and to project future storage capacities for 2035 under various development pathways. Results Our results demonstrate that: (1) In the last forty years, there has been a notable rise in forest area alongside a reduction in grassland and arable land. This shift has led to a pattern of carbon storage characterized by an initial decline of 0.65 Tg between 1980 and 2010, succeeded by a swift expansion during the period 2010–2020; (2) he geographic arrangement of carbon stocks has been largely stable, except for marked variations observed in the eastern high-altitude regions, namely Bawangling, Yinggeling, Wuzhishan, and Diaoluoshan; (3) Ecological protection policies effectively curb built land expansion and enhance carbon sequestration. By 2035, carbon storage under the ecological protection (EP) scenario is projected to reach 110.85 Tg, 1.28 Tg (1.17%) higher than the natural development (ND) scenario and 1.64 Tg (1.50%) higher than the tourism development (TD) scenario. Conclusion Ultimately, this study informs future land management and conservation efforts within HNTRNP by demonstrating that sustainable socioeconomic development must be synthesized with robust ecological protection.