Atmospheric and Oceanic Sciences

Continental shelves along tropical semi-arid margins remain sparsely mapped at high resolution, limiting process-based interpretations of bedforms dynamics and reducing quality of baseline information required for marine spatial planning and offshore development. In this study we present an integrated geomorphological and sedimentological baseline for two middle- to outer-shelf sectors of the Ceará Basin, on Northeast Brazil. We combined multibeam echosounder bathymetry and backscatter, side-scan sonar imagery, and sediment sampling to map bedform morphology and sediment textures in detail across more than 12 km 2 in total. High-resolution mapping reveals strong spatial contrasts in seabed morphology and acoustic texture across the two areas. The Western Area comprises a heterogeneous mosaic of geomorphic elements, including a channel, clustered circular depressions, and a terrace-like bench, associated with marked variability in backscatter and grain size, from granule-grade sediment on the bench to sandier substrates in depressions and channel-floor domains. The Eastern Area is dominated by sand-wave and organized into a crest–trough system, with geomorphometric patterns and side-scan sonar textures pointing to spatial variability in seabed roughness and bedform expression. Together, these results highlight how inherited shelf morphology and present-day hydrodynamic reworking interact on a sediment-limited shelf to produce contrasting bedform–sediment domains over short along-shelf distances. The mapped bedform-sediment domains provide a robust physical baseline to guide future process studies and to inform monitoring and risk-aware planning for infrastructure and marine spatial management on the Brazilian semi-arid continental shelf.

Analyzing the impervious surface area (ISA) of China’s three major urban agglomerations and expansion pattern of representative cities is of great significance for formulating context-specific urban planning policies and enhancing the resilience of urban development. This research employs impervious surface expansion indicators to reveal the spatiotemporal dynamics of impervious surface area, and further applies the location centrality index (LCI) to investigate the expansion structures and patterns of five megacities. The results indicate that the ISA of China’s three major urban agglomerations expanded from 22,544.10 km2 to 69,348.59 km2 from 1985 to 2024; both the expansion rate and intensity have exhibited a distinct decelerating trend. However, significant regional variation exists in the peak expansion phases: the Guangdong–Hong Kong–Macao Greater Bay Area Urban Agglomeration (GBA) peaked during the Initial Exploratory Phase (1985–1995), whereas the Yangtze River Delta Urban Agglomeration (YRD) and Beijing–Tianjin–Hebei Urban Agglomeration (BTH) reached their peaks during the High-speed Development Phase (2005–2015). At the urban scale, LCI-based analysis of urban expansion across five megacities reveals that adjacency expansion type has become the predominant form of growth, exceeding 50% in most cities. These findings enhance our understanding of evolutionary trajectories of ISA within urban agglomerations and the expansion patterns of five megacities, providing a scientific basis for sustainable urban planning and urban resilience.

Changes in urban form strongly affect surface thermal conditions, yet long-term quantitative assessments of this relationship, particularly the role of ventilation resistance, remain limited. To address this gap, this study integrates XGBoost, SHapley Additive explanations (SHAP), and multi-scale geographically weighted regression (MGWR) to examine how six morphological, ecological, and human-activity factors influence land surface temperature (LST) in Kaifeng City. The results indicate three main findings. First, LST increased significantly from 1986 to 2024, while interannual variability declined, indicating a gradual reduction in regional thermal fluctuations. Second, NTL was consistently the dominant indicator across the five representative years, while BF and NTL together captured the effects of urban expansion and intensified human activity. Third, FAD coefficients were more spatially heterogeneous in urban fringe areas than in the urban core. In 2020, the dispersion of FAD coefficients in fringe areas was 2.74 times greater than that in the central area, indicating stronger spatial differentiation in ventilation-related morphological constraints during urban expansion. Although FAD made only a modest contribution to overall predictive accuracy, it provided supplementary diagnostic information not captured by conventional density indicators and showed nonlinear, directional, and spatially heterogeneous responses. Compared with previous studies that mainly examined short-term or single-dimensional relationships between urban morphology and LST, this study integrates building densification, ventilation-related morphological resistance, ecological conditions, and human activity intensity into a long-term LST-driver framework, providing evidence to support heat-risk management during urban regeneration and outward expansion.

Accurate estimation of rice chlorophyll status is essential for precision nitrogen management. Although multispectral and hyperspectral sensors are effective, their high cost and data-processing complexity limit widespread adoption. This study investigated the efficacy of low-cost UAV-based RGB imagery for estimating rice chlorophyll status, represented by SPAD measurements, focusing on the effects of image resolution, feature integration of vegetation indices (VI) and texture features (TF), and TF parameterization. UAV RGB imagery with three image resolutions (3.8, 9.6, and 25 mm) was analysed, and six regression algorithms, including multiple linear regression (MLR), support vector machine (SVM), random forest (RF), Gaussian process (GP), light gradient boosting machine (LGBM), and categorical boosting (CAT), were used to establish estimation models. Results demonstrated that 1) the integration of VIs and TFs improved model performance compared with VIs alone, and the best results were obtained at the 25 mm resolution; 2) TF parameterization also affected model performance, and the best results were obtained with a window size of 11 × 11 and a direction of 90°. Validation on an independent test dataset confirmed the model’s reliability (R2 = 0.84, RMSE = 2.35). Overall, these findings provide a high-efficiency, cost-effective framework for rice chlorophyll status monitoring and RGB-based decision making in precision nutrient management.

Using accurate land cover data is essential to monitor land use and cover changes and assess the effectiveness of various environmental policies. This study evaluates the accuracy of contemporary global land cover products with 10 m spatial resolution, including Google’s Dynamic World (GDW), European Space Agency’s World Cover (ESA WC) and Esri Land Cover (ELC) in mapping forested areas in Poland, aiming to test an assumption if the combination of these products may improve forest mapping accuracy compared to any individual product. Three global datasets and their combinations were assessed with the 2022 EU Land Use/Cover Area Frame Survey (LUCAS). A land cover map of Poland (S2GLC PL) for 2021 served as an auxiliary reference data set. Forest cover classification accuracy was evaluated using precision, recall, and F1-score metrics, and spatial agreement of binary forest maps in the thematic global products was measured with the Intersection over Union (IoU) at two various scale levels (country and province). Our results showed that forest mapping accuracy of three global products varies for Poland, with F1-score equal to 72.2% for ELC, 76.9% for ESA WC, and 68.8% for GDW. IoU against S2GLC PL was equal to 82.6%, 82.3% and 75.2%, for ELC, ESA WC and GDW, respectively, and slightly exceeded 70.5% for three global products. A specific combination of binary forest maps from global products, where the output forest area consisted of forests mapped at the same time by all three products and forests mapped at the same time only by GDW and ESA WC yielded better accuracy indicators than any single product and other tested combinations (F1-score equal to 80.4%, and IoU against S2GLC PL equal to 87.1%).

Accurately assessing the spatiotemporal evolution of ecological environment quality (EEQ) on the Loess Plateau of Northern Shaanxi is of great significance for consolidating the ecological security barrier of the Yellow River Basin. Most of the existing research focuses on a single ecological theme, which does not reflect the overall ecological status of the region. In this study, a remote sensing ecological index (RSEI) model was constructed to systematically assess the EEQ from 2000 to 2024. The Theil–Sen estimator, Mann–Kendall test, and Hurst exponent were jointly employed to detect change significance and predict future trends, while the Geodetector model was applied to explore driving factors. The results were as follows: (1) EEQ exhibited a fluctuating but overall upward trend, with the mean RSEI rising from 0.376 in 2000 to 0.545 in 2024—an average annual increase of approximately 0.00569. (2) Spatially, a distinct pattern of “higher in the south, lower in the north and the lowest in the northwest” was observed. Over the 25-year period, the combined proportion of “excellent” and “good” grades increased by roughly 20 percentage points, and the “moderate” grade expanded from 13.61% to 47.12%. (3) Areas showing an improving trend accounted for 91.21% of the total area and highly overlapped with those projected to improve in the future. (4) Single-factor detection revealed that geomorphological type exerted the greatest influence on the spatial heterogeneity of EEQ, with a multi-year mean q-value of 0.701. Interaction detection further indicates that the geomorphology–land use interaction may continue to shape the regional EEQ’s spatial distribution. These findings provide a scientific basis for precise ecological restoration planning and spatial optimization on the Loess Plateau of Northern Shaanxi.

Introduction Lead (Pb 2+ ) contamination in acid mine drainage (AMD) poses a significant environmental challenge due to its persistence, bioaccumulation potential, and severe toxicity to living organisms. This study evaluated the performance of a Fenton-modified pine cone-derived poly (hydroxamic acid) (PHA) ligand for the selective removal and recovery of Pb 2+ from acidic mine drainage systems. Methods The PHA adsorbent was synthesized through Fenton-assisted oxidation and applied in batch adsorption experiments. The effects of contact time, solution pH, temperature, and adsorbent dosage on Pb 2+ removal were systematically investigated. Adsorption kinetics, equilibrium isotherms, thermodynamic parameters, selectivity, and regeneration performance were evaluated to elucidate the adsorption mechanism and practical applicability of the material. Results Maximum Pb 2+ adsorption was achieved at pH 6, with equilibrium attained after 180 min and an adsorption capacity of 86 mg g -1 under optimal conditions. The adsorption process was best described by the pseudo-second-order kinetic model (R 2 = 1.000), indicating that chemisorption governed the rate-limiting step. Equilibrium data exhibited excellent agreement with the Langmuir isotherm, suggesting predominantly monolayer adsorption. However, the experimentally observed adsorption capacity exceeded the Langmuir-predicted q max , indicating the contribution of strong coordination-driven Pb–O interactions beyond ideal monolayer assumptions. Thermodynamic analysis revealed positive enthalpy values (ΔH° > 0) and negative Gibbs free energy values (ΔG° < 0), confirming that adsorption was endothermic and spontaneous. The adsorbent maintained high Pb 2+ selectivity in complex polymetallic AMD matrices and retained more than 80% of its initial removal efficiency after five adsorption–desorption cycles. Discussion The superior adsorption performance of PHA was attributed to the abundance of hydroxamic acid functional groups capable of forming strong coordination complexes with Pb 2+ ions. The combined selectivity, high adsorption capacity, favorable thermodynamics, and excellent reusability demonstrate that Fenton-modified PHA is a cost-effective and sustainable adsorbent for the selective recovery of Pb 2+ from acid mine drainage and other metal-contaminated wastewaters.

The contamination of microplastics (MPs) and heavy metals (HMs) in water has caused widespread concern, while their effects on submerged macrophytes have rarely been reported. Experiments were carried out to investigate the toxic effects of polyamide microplastics (PAMPs; 0.1%, 0.3%, and 1.0% w/w) and cadmium (Cd; 0.3 and 1.0 mg/L), alone or in combination, on the submerged macrophyte Vallisneria natans (V. natans). The results showed that PAMPs significantly reduced Cd accumulation in leaves (decrease of 2.38%~26.12%) but elevated Cd accumulation in roots. Both Cd exposure and high PAMP exposure alone inhibited plant growth. The combined stress showed concentration-dependent effects: the low Cd concentration (0.3 mg/L) and PAMPs synergistically exacerbated toxicity (synergism, MDR > 1.3), as PAMPs disrupted the sediment structure and enhanced the bioavailability of Cd, whereas when V. natans was co-exposed to the high Cd concentration (1.0 mg/L) and PAMPs, the PAMPs blunted the toxicity of Cd by efficiently adsorbing it (antagonism, MDR < 0.7). Both individual and combined exposures decreased chlorophyll a and chlorophyll b synthesis and increased superoxide dismutase (SOD) and peroxidase (POD) activities as well as malondialdehyde (MDA) content in plant tissues. However, exposure to low and medium concentrations of MPs (0.1% and 0.3% w/w) alone had positive effects on plant growth and photosynthesis systems, while combined exposures exacerbated the damaging effects of PAMPs on the antioxidant defense system in V. natans. These results allow for a better understanding of the synergistic effect of co-contamination of microplastics and heavy metals in freshwater ecosystems, and highlight the necessity of ecological risk assessment during phytoremediation using submerged macrophytes.

Accurately estimating village-level winter wheat yield in coastal saline–alkali farmland is challenging because this region has strong spatial differences and multiple environmental stresses. In this study, Huanghua City, Hebei Province, was selected as a typical coastal saline–alkali area. Sentinel-2 images, climate factors, and topographic variables, including elevation, topographic wetness index, distance to the coastline, and distance to water systems, were combined to build a phenology-guided feature set for winter wheat yield prediction in coastal areas. The results showed that Phenology-Guided Feature Integration XGBoost achieved an R2 of 0.6382 and an RMSE of 450.15 kg/ha, which was slightly better than Gradient Boosting (R2 = 0.6256) and Random Forest (R2 = 0.6098), and clearly better than SVR (R2 = 0.4792), Ridge regression (R2 = 0.4582), and a single Decision Tree (R2 = 0.3088). Then, a three-stage branch was designed to identify the main drivers of SI, NDVI, and winter wheat yield at different stages, helping explain how environmental constraints and vegetation responses jointly affect final yield. The Three-Stage Fusion XGBoost Model achieved an R2 of 0.6439, an RMSE of 446.24 kg/ha, and an MAE of 363.38 kg/ha, showing a slight improvement in prediction accuracy. SHAP analysis showed that SI, distance-related factors, elevation, TWI, and NDVI were important drivers of winter wheat yield variation. Spatial prediction results showed higher winter wheat yield in inland areas (5145 kg/ha) and lower yield in coastal areas (4198 kg/ha). This framework supports village-scale winter wheat yield prediction in coastal saline–alkali farmland and improves model interpretability.

BACKGROUND: Following over two decades of eliminated endemic transmission, Mexico experienced a significant measles resurgence in 2025-26. Declining vaccine coverage and pandemic-related service disruptions created subnational immunity gaps. We aimed to characterize the spatiotemporal patterns of this outbreak to identify areas of sustained transmission. METHODS: We conducted a retrospective surveillance study of all confirmed measles cases reported nationally over a 50-week period (mid-February 2025 to February 10, 2026). Using municipal-level data and population projections, we applied Kulldorff's space-time scan statistic (discrete Poisson model) to identify statistically significant clusters where incidence exceeded population-based expectations. RESULTS: A total of 8,205 confirmed cases were reported across 337 municipalities during the study period. Overall, 23 space-time clusters were identified, of which 19 were statistically significant (p < 0.001). The primary cluster, located in northern Mexico between epidemiological weeks 6 and 30, represented the focus of transmission, encompassing 89 municipalities and 4,012 cases (relative risk [RR] = 103.7). Secondary clusters reflected additional transmission patterns, including a large late-phase cluster in western Mexico with 1,493 cases (RR = 31.5), as well as several smaller, short-duration micro-outbreaks with markedly elevated localized risk (RR > 200). CONCLUSIONS: The resurgence of measles was characterized by marked subnational heterogeneity, with both sustained regional transmission and localized surges. A late-stage, low-intensity cluster spanning 244 municipalities suggested a transition toward more diffuse national transmission. These spatiotemporal patterns suggest that geographically differentiated approaches, including subnational surveillance and targeted "mop-up" immunization campaigns, may be needed to address immunity gaps and support efforts toward restoring Mexico's elimination status.

Abstract. The article includes an overview of the current state of knowledge regarding climate in Poland (Central Europe) in the 16th century and its changes. For this purpose, we utilised all previously published reconstructions and five new quantitative reconstructions incorporating dendrochronological data and documentary evidence. New dendrochronological data were used to reconstruct the mean winter or late winter–early spring temperatures, while documentary evidence enabled the reconstruction of mean winter (DJF) and summer (JJA) temperatures. The climate of Poland in the 16th century, as reconstructed from documentary evidence, was colder than it is today (1991–2020), particularly in winter (by 3.6 °C). In summer, it was only 0.7 °C colder than today. Compared to the average for the entire 20th century, however, the summer average in the 16th century was 0.3 °C warmer, whereas the winter average was 2.5 °C colder. In both dendrochronological reconstructions of the temperature of south-eastern Poland, the temperatures in the 16th century were generally lower than those recorded today (1951–2000), particularly in the case of the reconstruction based on the fir chronology (December–March). Anomalies, however, both positive and negative, were usually of less than one standard deviation from the long-term mean. On the other hand, in northern Poland, the February–March temperatures in the 16th century were, on average, comparable to those of the present. Most available temperature reconstructions for Poland reveal cooling over the last few decades of the 16th century, particularly during the winter half-year. The climate in the 16th century was more continental than it is today.

The growing global population has increased energy and food demand, leading to a higher production of waste streams such as fly ash from the energy sector and wastewater from food and beverage industries. Without proper treatment, these wastes pose significant environmental concerns. One promising strategy is to repurpose industrial byproducts for wastewater treatment. Winery wastewater, for instance, contains acidic organic compounds and alcohol that are difficult to remove using conventional methods, while large amounts of fly ash remain underutilized. This study, therefore, examines a hybrid system that combines fly ash-assisted Fenton oxidation with membrane filtration for winery wastewater treatment. The process involved sequential Fenton pre-treatment followed by lab-scale nanofiltration using a 1 kg/mol ceramic membrane (13.1 cm2). A Design of Experiments approach was applied to evaluate system performance under varying H2O2 dosages (10–30 mL/L), fly ash loadings (1–3 g/L), and membrane fluxes (40–80 LMH). Filtration was performed through multiple constant-flux cycles, with energy requirements ranging from 400 to 800 kWh/m3 for the flux variations calculated from the lab-scale pump operating at a constant power supply. The hybrid method showed strong performance, achieving 70% TOC removal and 90% reduction of color and iron. However, considerable membrane fouling was observed, likely due to increased retention and deposition of organic matter, iron, and fly ash during filtration.

Currently, existing point cloud semantic segmentation methods do not fully exploit surface geometric features. In particular, the depiction of object boundaries and the transition areas of curved surfaces is rather rough. On the other hand, the neighbourhood aggregation mostly follows a single strategy, making it difficult to simultaneously take into account the context and fine-grained differences and ignoring local details. To address these issues, this paper proposes a geometry-enhanced adaptive local feature aggregation network (GALA-Net). First, a geometric information embedding (GIE) module is introduced, which extracts pseudo-normal vectors and pseudo-curvatures of local point cloud regions as geometric priors, and incorporates multi-frequency sine–cosine encoding to capture multi-scale spatial relationships, yielding enhanced local geometric representations. Then, an adaptive feature fusion (AFF) module dynamically allocates fusion weights between semantic and geometric features, thereby alleviating channel coupling and neighbourhood noise amplification caused by simple concatenation. Next, a dual-path adaptive attention aggregation (DAAA) module jointly models semantic and positional attention and adaptively fuses them with max-pooled features to improve the robustness of local aggregation. In addition, a self-enhanced attention encoding (SEAE) module is designed to expand the feature representation space by extracting features through independent mapping branches and fusing them in a residual manner. The proposed model is evaluated on the S3DIS and ScanNetV2 datasets, achieving mIoU scores of 78.0% and 71.6%, respectively, which demonstrates its strong segmentation performance on indoor scenes.

Estimating chlorophyll-a (Chl-a) concentrations from satellite ocean color data remains challenging in the Arctic, where freshwater inputs, colored dissolved organic matter (CDOM), suspended particles, and low sun elevation alter optical properties and influence blue–green reflectance. Here, we combine satellite and in situ observations to examine how freshwater-driven optical variability shapes satellite-derived Chl-a across the Canadian Arctic Archipelago (CAA). Continuous underway observations were collected by a FerryBox system aboard the MS Roald Amundsen during August–September 2022 and matched with MODIS-OC3M Level-3 Chl-a (4 km, ± 2 days, 0.1° bins; n = 758). Satellite-derived Chl-a showed large differences relative to in situ observations, with a mean positive bias of 0.69 log 10 units and a root-mean-square error of 0.73 log 10 units, corresponding to an approximate 4.9-fold difference. These differences were strongly structured by environmental gradients, with the largest discrepancies occurring in low-salinity, CDOM-rich waters influenced by the Mackenzie River and decreasing eastward toward clearer, marine-dominated regions of Lancaster Sound. Previously-developed Arctic-tuned algorithms were applied to examine how regional models represent these gradients with the CAA. These approaches reduced overall bias and also resulted in substantial spatial variability linked to freshwater and optical gradients. To further account for these nonlinear environmental effects, a generalized additive model (GAM) incorporating salinity, CDOM, and temperature was applied, resulting in closer agreement between satellite-derived and in situ Chl-a, particularly in the Kitikmeot Sea. These findings demonstrate that freshwater-driven optical variability is a primary control on the calculation of satellite-derived Chl-a in Arctic shelf systems and that integrating environmental predictors into observational frameworks improves the interpretation of ocean color data in optically complex regions.

Multimodal fusion unlocks the potential of point cloud semantic segmentation, thereby driving advancements in surface observation and visual perception tasks. Although light detection and ranging (LiDAR) systems capture precise 3D structural geometry and optical images provide rich semantic and textural information, existing fusion methods struggle with limited cross-modal perception and insufficient information complementarity. To address these limitations, we propose a multi-stage LiDAR-image collaborative perception fusion network (MCPFNet) for point cloud semantic segmentation of urban scenes. At the middle fusion stage, the network incorporates an elevation-guided geometric-aware fusion module and a semantic-aware cross-attention fusion module to enable bidirectional feature injection between LiDAR and image modalities. In the late fusion stage, a bidirectional adaptive fusion module further refines semantic representations through gated weighting and bidirectional cross-attention mechanisms. Extensive experiments on three multimodal datasets with different resolutions, i.e., ISPRS Vaihingen, N3C-California, and UAVScenes, demonstrate that MCPFNet outperforms existing fusion methods, achieving mIoUs of 74.51%, 95.15%, and 62.76%, respectively. Hence, our multi-stage fusion and bidirectional interaction strategy is more reliable and accurate than existing methods in performing segmentation across diverse and complex urban scenes.

Accurate soil salinity estimation under small-sample agricultural conditions continues to pose a formidable challenge, attributed to the scarcity of labeled data, inherent representational limitations of single-backbone neural networks, and the heightened complexity of subsurface salinity inversion. To mitigate these intertwined challenges, this study developed a UAV-enabled soil salinity estimation framework that integrated lightweight convolutional neural networks and staged feature optimization, leveraging both RGB and multispectral imagery. A feature selection framework integrating random forest recursive feature elimination (RF-RFE), the one-standard-error (One-SE) criterion, and variance inflation factor (VIF) analysis was employed to reduce 129 candidate variables to a unified 16-channel feature set, which served as the common input for estimating both surface and subsurface soil salinity. Three lightweight single-backbone (VGGNet, ResNet, and DenseNet) and dual-backbone feature-level fusion networks (DenseResNet, DenseVGGNet, and ResVGGNet) were constructed and systematically evaluated for their performance in estimating both surface and subsurface soil salinity. Among the single-backbone networks, ResNet yielded the highest overall statistical accuracy, while DenseNet exhibited superior performance in preserving estimation trends. For surface soil salinity estimation, ResVGGNet achieved the best performance among all evaluated models, with an R2 of 0.820, RMSE of 0.626 g/kg, MAE of 0.409 g/kg, and RPD of 2.31 on the test dataset. SHAP analysis further highlighted the dominant role of vegetation and salinity-sensitive indices, together with selected spectral mean features, and revealed spatially complementary response patterns among major input channels. Collectively, the integration of lightweight multi-backbone feature-level fusion with streamlined feature optimization strategies effectively enhances the accuracy, robustness, and interpretability of UAV-enabled soil salinity estimation, particularly under the constraint of small agricultural sample sizes.

Remanufacturing plays a vital role in sustainable manufacturing but is often hampered by significant capital requirements. Although traditional bank financing (BF) is commonly used, carbon quota pledge financing (CQPF) has emerged as a promising low-carbon alternative. However, whether and how CQPF outperforms BF in remanufacturing supply chain operations remains unclear. This study develops a Stackelberg game-theoretic model involving one capital-constrained manufacturer and one retailer. Four models are formulated to analyze equilibrium strategies under BF and CQPF, both with and without carbon emission reduction (CER). The effects of key parameters on pricing, environmental impact, and profitability are examined through analytical derivations and numerical simulations. Our analysis reveals three key findings. First, when the carbon quota pledge rate remains below a certain threshold, CQPF leads to lower wholesale and retail prices compared to BF, regardless of whether CER is implemented. Second, while implementing CER lowers product prices under both financing modes, it does not affect the supply chain’s decision to enter the remanufacturing market but raises the threshold for adopting complete remanufacturing. Third, CQPF consistently yields higher supply chain profits than BF; however, its effectiveness in enabling remanufacturing operations is subject to a specific threshold in the carbon trading price. These findings suggest that CQPF offers a viable and often superior financing alternative for capital-constrained remanufacturing firms, provided that the carbon quota pledge rate is appropriately set. For managers, prioritizing CQPF under low pledge rates enhances both market competitiveness and profitability. For policymakers, higher carbon trading prices can incentivize complete remanufacturing adoption. Banks should set pledge rates below the critical threshold to stimulate green manufacturing. The study also highlights that CER investment, while beneficial for demand, may increase overall environmental impact if it disproportionately expands new product production.

Subtropical mountain economic tree plantations are constrained by pronounced spatial heterogeneity in resource availability, yet the spatial scales at which soil properties, topography, and canopy structure regulate vegetation vigor remain poorly resolved. To address this gap, a spatially consistent multi-scale dataset combining 10 m high-density soil sampling, UAV-LiDAR, and multispectral remote sensing was used to quantify the scale-dependent drivers of the Leaf Chlorophyll Index (LCI) across 3–50 m within a Chinese hickory (Carya cathayensis Sarg.) plantation. The relative contributions of canopy, soil, and topography to LCI were decomposed across scales using an interpretable machine-learning framework (XGBoost–SHAP). At fine scales (3–10 m), vegetation vigor was primarily controlled by tree-level canopy structure, particularly tree height, reflecting localized resource acquisition. At intermediate scales (10–20 m), a distinct coupling window emerged, characterized by increased interaction complexity: LCI was predominantly driven by interactions between canopy structure and soil nutrient availability, whereas single-factor effects weakened. Notably, at 20 m this interaction pattern largely weakened and reverted to single-metric dominance. At broader scales (&gt;30 m), complex interactions re-emerged, and dominant SHAP contributions shifted from nutrients and canopy structure toward topography and soil texture. These findings reconcile strong fine-scale drivers with weaker predictability at intermediate extents and demonstrate that soil–canopy relationships reorganize across spatial scales rather than remaining static. On the basis of these findings, a scale-hierarchical framework for precision forestry is proposed that aligns management interventions with the ecological scales at which dominant correlates operate across spatial supports.