IntroductionTo assess forest ecosystem service value (FESV) in China and support ecological conservation and high-quality forest ecosystem development, this study examined FESV from 2000 to 2023 from a geographical perspective.MethodsThe equivalent factor method was applied with coefficient adjustments at the provincial and county levels. County-level FESV estimates were aggregated to obtain provincial and national estimates for each year, and the center-of-gravity model, standard deviational ellipse, and Geodetector were used for analysis.ResultsThe results showed that: (1) FESV in China followed an overall upward trend with fluctuations, increasing by 12% from 2000 to 2023. Regulating services accounted for the largest share, exceeding 66% in every year. (2) Total FESV was highest in western China, which accounted for more than 46% of the national total on average over the study period. Central and northeastern China occupied intermediate positions, while eastern China had the lowest total FESV. FESV per unit forest ecosystem area was highest in eastern China, while central China generally ranked second, with relatively lower values in northeastern and western China. Provinces in the highest FESV class (Level 5) were mainly located in western and northeastern China, and their number increased from two to four. The distribution of low-value provinces remained relatively stable. (3) The spatial distribution of FESV exhibited an overall northeast–southwest orientation, with a relatively stable distributional shape. The center of gravity remained within Henan Province and shifted predominantly northward, with a slight eastward component. Its locations in 2000 and 2023 were 88.42 km apart. (4) Among the natural factors selected in this study, slope and elevation had the greatest explanatory power for the spatial variation in FESV within the Geodetector framework. All pairwise factor interactions exhibited either bivariate enhancement or nonlinear enhancement in each representative year.DiscussionThese findings can inform the spatial allocation of forest resources and the formulation of ecological conservation and management policies.
Water scarcity poses a growing challenge to sustainable forest and agricultural policy in arid regions, where wood production increasingly depends on alternative water sources. In Iran, growing competition for limited freshwater resources threatens the sustainability of tree farming, highlighting the need for alternative water management strategies. Despite their potential to reduce pressure on conventional freshwater supplies, unconventional water resources remain underutilized. This study examines of the policy, institutional, and behavioral factors influencing farmers’ willingness to use unconventional water for tree farming. An extended Technology Acceptance Model (TAM) was developed by integrating institutional support, trust in water quality, perceived economic benefits, self-efficacy, and environmental concern. Survey data from multiple provinces were analyzed using partial least squares structural equation modeling, confirming strong reliability and validity. Perceived ease of use (total effect = 0.371) and institutional supports (0.323) were the most influential predictors, based on their total effects. Perceived ease of use exerted the strongest direct effect (p < 0.01), followed by trust (p < 0.01) and perceived usefulness (p = 0.01). Economic benefits (total effect = 0.139) also have significant influence on intention. Self-efficacy and environmental concern had no significant direct effects on intention. The findings highlight how institutional credibility, economic incentives, and psychological assurance jointly shape water reuse adoption. This transferable framework provides evidence for developing policies that promote sustainable resource use and enhance resilience in water-scarce forestry systems.
Extreme summer heat poses a growing threat to terrestrial carbon sequestration, particularly in the forest and mountainous ecosystems of rapidly urbanizing subtropical regions, yet the spatial variability of heat-induced carbon-cycle responses and associated thermal threshold-like behavior remain insufficiently quantified. This study examined the effects of extreme summer heat on carbon cycling in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) during summer 2023 using multi-source remote sensing and ERA5 reanalysis data processed through Google Earth Engine. A total of 4,158 spatially representative samples covering urban cores, suburban transition zones, and peripheral forested ecological areas were analyzed using an integrated framework combining geographically weighted regression (GWR), GW-XGBoost nonlinear prediction, SHAP explainability, and Q10-based carbon budget accounting. Land surface temperature (LST) was identified as one of the key factors associated with reduced net primary productivity (NPP), showing a strong negative correlation with NPP (r = −0.68, p < 0.001), whereas NDVI exhibited a strong positive association (r = 0.72). The LST–NPP relationship was nonlinear, with a SHAP-identified transition interval near 27 °C –28 °C marking the onset of accelerated NPP decline, which progresses to substantially larger reductions at higher temperatures. Q10-based carbon budget estimates indicate that NPP falls below soil respiration—an indicator of source-like behavior—as surface temperature approaches 32 °C (estimated switch temperature, Bswitch = 32.05 °C). Across the sampled locations, 93.6% were estimated to be carbon sinks and 6.4% carbon sources, with the latter concentrated primarily in urban heat island areas along the Pearl River estuary. Strong sinks were concentrated in the northern mountainous forest belt, indicating that forest carbon-sink stability along urban–forest gradients is a key determinant of regional carbon balance. These findings highlight spatially uneven carbon vulnerability and support targeted heat mitigation, forest conservation, and adaptive-management strategies in subtropical megacity regions.
