Search Results (471)

Quercus variabilis is one of the primary species for plantation regeneration across China’s warm-temperate and subtropical zones. However, its long-term monoculture leads to ecosystem instability. Soil fungi are essential for nutrient cycling and ecosystem functioning, yet their responses to oak-dominated mixed plantations remain insufficiently understood. This study investigated the soil fungal communities among Q. variabilis monoculture (QV), mixed plantations of Q. variabilis and Platycladus orientalis (PO), Q. variabilis and Pinus tabuliformis (PT), and Q. variabilis, P. orientalis and P. tabuliformis (PPQ). The results showed that PO and PPQ plantations contained significantly higher concentrations of SOC, TN, and TP compared to QV monoculture. Ascomycota and Basidiomycota were identified as the dominant fungal phyla across four plantation types, with PO exhibiting the highest relative abundance of Ascomycota (60.85%) and fungal alpha diversity. The soil fungal communities across all plantations were predominantly saprotrophic, followed by mixotrophic modes. The relative abundance of saprotrophic fungi was significantly greater in the mixed plantations, peaking in PO at 44.69%. The soil fungal communities in both PO and PPQ plantations exhibited higher network interaction density. The SOC, TN, TP, water content, zinc, and β-glucosidase activity served as key environmental drivers of fungal community composition. Overall, the mixed plantation of Q. variabilis and P. orientalis most effectively improved soil fertility, enhanced fungal diversity, and increased network complexity, suggesting its potential as a sustainable afforestation strategy for oak-dominated ecosystems in the low hilly regions of western Henan. However, these findings are based on a single sampling period, and long-term monitoring is required to confirm its sustained ecological benefits. Full article

Freshwater ecosystems are increasingly threatened by eutrophication and other anthropogenic and climate-driven pressures that undermine ecological functioning and biodiversity. This study evaluates the transferability of a GIS-based multi-criteria decision analysis (GIS–MCDA) framework with Fuzzy Analytic Hierarchy Process (F-AHP), originally developed for a shallow coastal lake, to a morphologically distinct deep upland lake (Lough Tay, Ireland). Monthly in situ measurements at a single monitoring point in 2024 were analysed together with meteorological variables using Spearman rank correlations. Because spatial interpolation of in-lake water quality parameters was not feasible, eutrophication susceptibility was mapped using four external spatial drivers: distance from water resources (River Cloghoge inflows), land-based nitrogen export potential, distance from environmental pollutants represented by the transportation network, and a wind exposure index derived from a DEM and wind-rose analysis. Criteria were standardized with fuzzy membership functions, weighted using F-AHP (consistency index 0.056), and aggregated using weighted linear combination at 25 m resolution. The resulting Eutrophication Susceptibility Index (ESI) ranged from 0.18 to 0.81, indicating generally moderate to good conditions, with higher ESI values concentrated in the northern lake sector near inflow zones. The results demonstrate that GIS–MCDA can be adapted to lakes with limited monitoring by relying on external drivers, providing a spatial proxy for susceptibility rather than measured trophic status. Full article

This study was conducted in Northwest Ethiopia in 2025 to estimate genetic parameters for dairy cattle hybrids under a group-based mass selection scheme. The objective was to investigate lactation milk yield (MY), lactation length (LL), and key fitness traits across varying breed compositions, aligned with suitable agro-ecological zones and milkshed systems. The findings may then serve as a framework to develop economically efficient and sustainable dairy genotypes tailored to the region. Data were collected from 355 dairy households using semi-structured questionnaires and monthly monitoring of MY. A mass selection scheme was applied to evaluate the productive and reproductive performance of Holstein-Friesian (HF) and Jersey hybrids across varying levels of exotic breed compositions. To identify superior genotypes, a total merit index (TMI) was developed, utilizing economic weights of +0.20 for production traits and −0.12 for reproductive traits. General liner model (GLM) analyses were performed to evaluate the performance of different breeds and exotic breed composition. Realized genetic parameters including genetic correlations (rg) as an indicator of pleiotropy, genetic gain (GG) per trait, and aggregate genetic response (AGG) were estimated for each group using specialized procedures in R software. Breed type (stratified by exotic breed composition), agro-ecology zone, and milkshed system were defined as the main and sub-fixed effects. The genetic contribution to the performance of hybrids indicated that the Holstein-Friesian (HF) hybrid baseline scheme achieved significantly higher efficiency, with an aggregate genetic gain) (AGG) of 155.50, compared with 136.03 for the Jersey hybrid schemes. Specifically, the >75% HF hybrid group exhibited the highest predicted AGG (183.00), a result primarily underpinned by significant gains in MY (182.53 L) and extended LL (0.28 months). This indicated that higher exotic breed composition in HF crosses maximizes the genetic gain when selection is weighted toward productivity. Conversely, the 62.5% Jersey hybrid exhibited the lowest AGG (110.38) and GG for MY (109.86 L), indicating that intermediate Jersey breed compositions may be suboptimal under the studied conditions. Analysis of interaction effects revealed environment-specific superiorities: in the Bahir Dar midland milkshed, the >75% HF hybrids achieved the highest genetic gains in MY (182.53 L) and a superior AGG (181.34). In contrast, within the Gondar midland milkshed, >75% Jersey hybrids reached the highest overall AGG (177.11), with a corresponding GG for MY of 178.75 L per lactation. The observed variance in MY (δ² = 362.44) indicated significant potential for genetic improvement through group-based selection. Pleiotropy was identified between MY and LL (r_g = 0.14), whereas an antagonistic trade-off was observed between maturity and conception efficiency (r_g = −0.34). The consistent upward trend in the performance of hybrids as breed composition increased from 50% to >75% across both main and sub-effects suggests that these genotypes are suited to the environment. In conclusion, single- and multiple-trait predictions based solely on breed and breed comparisons were suboptimal; instead, selection strategies incorporating genotype-by-environment (G × E) interactions offered the most effective alternative for regional dairy selection alternatives. Full article

The forest within the Ben En National Park has a diverse flora, which, although protected, remains subject to degradation. The analysis and management strategies for forest degradation within this park were conducted using a phytogeographical approach supplemented by satellite imagery and a SWOT analysis. As a result, the area is characterized by nine distinct vegetation types comprising 1417 vascular plant species (from 902 genera and 196 families). These species belong to endemics from Northern, Central, and all of Vietnam, as well as 16 other phytogeographical elements. Tropical Asian and South China elements dominate the community structure in evergreen broad-leaved closed forests on both limestone and non-limestone mountains. Forest degradation is evident in changes to both floristic composition and vegetation structure. Floristic composition shows a trend of decreasing native elements while simultaneously increasing non-native or introduced elements. This “anthropogenic tropicalization” leads to a declining chain of ecological function from palaeotropical to introduced elements, resulting in biological invasion. For instance, the invasive species, Mimosa pigra, currently occupies about 442 ha in the semi-submerged zone of the Song Muc reservoir, indicating a loss of ecological function and a likely hydrological pathway for further spread. As a consequence of “anthropogenic tropicalization”, the vegetation is fragmented and gradually altered from a natural system to an anthropogenic one through a regressive succession from primary forest to bare land/invaded area. Based on the SWOT analysis, four management actions were proposed: 1—Establish a “sustainable native forest” program and “invasive species control” in the Song Muc reservoir; 2—Launch a “green livelihoods for the buffer zone” initiative; 3—Implement a “Smart forest monitoring” system; and 4—Forge an “ecotourism-conservation-community” alliance. Full article

Moroccan installations of solar photovoltaic panels experience operational difficulties due to shading and vegetation-related soiling, which reduce energy output by 15–30%. Most monitoring systems depend upon a single vegetation index, which can reduce the accuracy of detecting even moderate anomalies. This paper presents a novel integration of multi-criteria hierarchical labeling with dual-branch deep learning for enhanced vegetation anomaly detection. We combined MODIS (2000–2015) and Sentinel-2 (2015–2025) images and NASA POWER weather records to study a 25-year vegetation record using multi-source satellite data in 5 of Morocco’s ecologically diverse zones. We introduced a three-class hierarchical labeling scheme (normal, moderate, severe) for dynamic vegetation models based on combined vegetation indices (NDVI, EVI, NDWI) and meteorological thresholds. The proposed dual-branch architecture uses independent data streams for unfused data, which include temporal multi-scale CNNs (TMSCNN) for spatiotemporal modeling and bidirectional LSTMs for weather-integrated vegetation data. Systematic ablation studies show improvements from using NDVI (68.98%) to multispectral indices (77.74%), meteorological integration (81.02%), and a final accuracy of 82.34% ± 0.88%. The moderate anomaly class exhibits lower precision (65%), demonstrating the challenge of operationalizing severity-based anomaly classification. This work integrates hierarchical, multi-criteria labeling and hybrid deep learning for solar photovoltaic vegetation monitoring. Full article

Accurately monitoring the surface stabilization of waste dumps in open-pit coal mines is critical for hazard prevention and ecological reclamation. In arid and semi-arid regions, traditional optical remote sensing vegetation indices suffer from a systematic “response lag” in assessing physical stability due to the slow establishment of pioneer vegetation. To overcome this biological limitation, this study proposes a quantitative spatiotemporal monitoring framework based on time-series Interferometric Synthetic Aperture Radar (InSAR) coherence to detect early-stage geotechnical stabilization. Using Sentinel-1 imagery of the Balongtu coal mine, a sliding-window detection algorithm was developed to capture the physical transition of surface electromagnetic scattering mechanisms from active disturbance to stable consolidation. The main findings are as follows: (1) Statistical analysis identified a critical geophysical coherence threshold of 0.15, which effectively and objectively distinguishes active dumping disturbance zones from structurally stable areas. (2) The spatiotemporal evolution dynamics of the completed dump areas from 2017 to 2023 were successfully characterized, revealing that 87.6% of the open-pit areas achieved physical stabilization within three years post-mining, with a spatial distribution highly consistent with the objective operational rule of “mining first, dumping later”. (3) Accuracy assessment using 700 spatiotemporally balanced validation points—derived through strict visual interpretation of high-resolution optical imagery—demonstrated high algorithm reliability, achieving overall accuracies (OA) of 87.57% and 90.43% at half-yearly and annual monitoring intervals, respectively. By decoupling physical surface stabilization from optical greenness, this study provides a timely abiotic precursor indicator, offering scientific, quantitative decision support for precision ecological zoning and accelerated land turnover approval in mining areas. Full article

Cross-border regions often exhibit complex vegetation-related land-cover patterns due to contrasting natural conditions and divergent development trajectories, causing multi-source land-cover products to suffer from disagreements in class assignment and boundary delineation, especially for cropland, forestland, and grassland. Because border zones are rarely mapping priorities, classification instability near national boundaries undermines transboundary comparisons. To address this, we propose a Conflict-aware Adaptive Distillation Fusion Network (CADF-Net) that fuses multi-source land-cover products to improve the discrimination and spatial consistency of key vegetation classes in cross-border regions. Taking the transnational China–Russia border (Sanjiang Plain and Primorskiy Kray) as a representative case, we integrate geo-environmental factors and introduce a pixel-level Conflict Index (CI) to explicitly steer the model toward discrepancy-prone areas. Building on this, we develop an Adaptive Distillation U-Net (AD-UNet) with uncertainty-adaptive distillation and employ a confidence-guided, dynamically weighted ensemble to generate the final fused land-cover product (CADF-LC). Quantitative assessments demonstrate that CADF-LC achieved an OA of 0.8600, a Kappa of 0.8133, and an mIoU of 0.7589, outperforming all input land-cover products. Compared with the strongest input product, Esri Land Cover, CADF-LC improved OA by 0.0150 and mIoU by 0.0222. Furthermore, it effectively mitigates the trade-off between detail loss and morphological fragmentation. Ultimately, CADF-Net enhances classification stability for key vegetation classes, offering a reliable foundation for transboundary ecological monitoring and land management. Full article

The unchecked expansion of tea plantations onto steep, forest-adjacent slopes in subtropical mountains engenders a conflict between agricultural productivity and ecosystem integrity, particularly by exacerbating habitat fragmentation and soil erosion. While precise monitoring is essential to navigate this trade-off for sustainable management, accurate inventorying remains a challenge due to the plantations’ strong phenological variability, heterogeneous canopy structures, and high spectral confusion with surrounding vegetation. This study proposes a feature-optimized deep learning framework for mapping and characterizing tea plantations in complex landscapes, using Xinyang City, China, as a study area. The framework integrates multi-temporal Sentinel-1/2 observations with a sequential Jeffries-Matusita (JM)-Pearson feature filtering strategy. This approach effectively condenses a 132-variable high-dimensional pool (including optical spectra, vegetation indices, textures, and SAR polarimetry) into a compact 28-feature subset (a 78.8% reduction), preserving critical phenological and structural cues while minimizing redundancy. These optimized predictors drive a hybrid VGG16–UNet++ segmentation network, which couples transfer-learning-based semantic encoding with detail-preserving dense skip fusion. Extensive experiments across 18 model–feature configurations demonstrate that the optimal setting achieves an Overall Accuracy of 97.82%, an F1-score of 0.9093, and a mean IoU of 0.7968. Notably, the method significantly reduces misclassification in rugged, cloud-prone terrain, yielding a User’s Accuracy of 91.14% for tea. Based on the generated wall-to-wall map, we derived two decision-support indicators: multi-threshold steep-slope exposure and a normalized tea–forest interface density. This framework provides actionable, high-precision spatial products to support slope-based zoning, ecological restoration, and sustainable management in fragile mountain agroforestry systems. Full article

The rapid expansion of seaweed aquaculture has profound impacts on coastal ecosystems, yet the lack of long-term, high-precision spatiotemporal monitoring methods has constrained systematic understanding of aquaculture dynamics and their environmental effects. This study integrated Landsat (1984–2025) and Sentinel-2 (2015–2025) imagery with an attention-enhanced U-Net deep learning model to achieve 41 years of continuous monitoring of seaweed aquaculture in the Dongtou Archipelago, Zhejiang Province, China. The model achieved high extraction accuracy for both Landsat and Sentinel-2 aquaculture areas (F1 scores of 0.972 and 0.979, respectively). On this basis, the cultivation zones were further classified into Porphyra sp. and Sargassum fusiforme cultivation areas by incorporating local aquaculture planning and field survey data. Results showed that the aquaculture area underwent three developmental stages: slow initiation (1984–2000, <3 km²), rapid expansion (2001–2015, 3–8 km²), and high-level fluctuation (post-2015, typically 8–20 km²), reaching a peak of ~30 km² during 2018–2019. Long-term retrieval of water quality parameters revealed that the decline in total suspended matter (from ~80 to 60 mg/L) and chlorophyll (from ~3 to 2 μg/L) within aquaculture zones was significantly greater than that in non-aquaculture areas, providing direct observational evidence for local water quality improvement by appropriately scaled aquaculture. Meanwhile, sea surface temperature showed a sustained increasing trend, with extremely high-temperature days (≥25 °C) exhibiting strong interannual variability, posing potential thermal stress risks to cold-preferring seaweed species. The NDVI (Normalized Difference Vegetation Index) and FAI (Floating Algae Index) indices effectively captured aquaculture phenology (seeding, growth, maturation, harvest), with their interannual peaks exhibiting an inverted U-shaped correlation with corresponding yields (R = 0.82 and 0.79, respectively, based on quadratic regression fitting), preliminarily demonstrating the potential of remote sensing in indicating density-dependent effects. This study systematically demonstrates the comprehensive capability of multi-source satellite remote sensing in long-term dynamic monitoring, environmental effect assessment, and yield relationship analysis of seaweed aquaculture, providing key technical support and scientific basis for aquaculture carrying capacity management and ecological risk prevention in island waters. Full article

The effects of environmental factors on zooplankton within the marginal ice zone (MIZ) of the Barents Sea remain poorly understood. To address this knowledge gap, we investigated mesozooplankton communities across the central, northern, and northeastern regions in April 2016. Abundance and biomass ranged from 90 to 997 individuals m⁻³ and from 1.1 to 48.6 mg dry mass m⁻³ (0.3 to 13.6 g dry mass m⁻²), respectively. Oithona similis was the most abundant taxon, while calanoid copepods, including Calanus spp., Metridia longa, and Pseudocalanus spp., dominated total biomass. The spatial distribution of mesozooplankton communities was closely linked to the physical properties of water masses. Cluster analysis identified two distinct assemblages associated with Polar Front Water and Arctic Water. Redundancy analysis and generalized linear models identified temperature, mean salinity, mean chlorophyll a concentration, and sea ice concentration as significant predictors of abundance and biomass. The dominance of older life stages within major copepod taxa indicated a winter status for the mesozooplankton community. Regional and temporal comparisons of mesozooplankton biomass with prior May–June data from central and northwestern areas highlighted higher productivity in the northern and northeastern MIZ. This increase is potentially related to the warming trends observed in the Arctic since the 2000s. Our research provides novel insights into Arctic marine zooplankton assemblages and serves as a valuable baseline for future ecological monitoring and modeling of the Barents Sea ecosystem in the context of global climate change. Full article

Urban micro-tidal canals frequently suffer from severe hypoxia due to restricted hydrodynamic exchange and untreated discharges. Field monitoring during a 2022 mass fish mortality event at the Dongsam tidal canal revealed that during the ‘tidal window gap’—a hydraulic stagnation period required for passive tidal flushing—bottom-layer dissolved oxygen (DO) plummeted to a lethal 0.44 mg/L. To address the limitations of passive tidal exchange, this study proposes a conceptual hybrid water purification framework integrating active ecological interventions: wall-mounted spiral flow aeration for continuous oxygenation and vertical bio-curtains for pollutant interception. By synergizing fluid mechanics with ecological engineering, core design parameters were systematically derived: an effective mixing width ($W_{eff}=2.2 h$), longitudinal spacing ($L_s$ = 13.6$\times W_{eff}$), an optimal root immersion ratio ($D_r/h$ = 0.6), and climate-adaptive planting densities (${\rho }_p\approx$12–32 plants/m²). Additionally, a corrosion-resistant FRP guide rail system was incorporated to facilitate autonomous adaptation to tidal fluctuations. The framework was conceptualized through a prototype design for the Dongsam canal and subsequently scaled to 15 international micro-tidal canals across diverse climatic zones. The optimized bilateral staggered configuration established a continuous 528 m² ecological refuge, ensuring DO levels recover above the critical 3 mg/L threshold. Ultimately, this research presents a comprehensive methodological framework and a flexible engineering toolkit to guide water quality and ecological resilience enhancements in shallow urban waterways worldwide. Full article

Accurate identification of fly species composition and their responses to attractants is critical for risk assessment and targeted vector control. To evaluate the efficacy of different attractants in surveillance and their species-specific trapping biases, a standardized field study was conducted from June to September 2021 across seven representative cities in China’s major zoogeographical regions: Xining, Ürümqi, Yanji, Beijing, Chongqing, Kunming, and Sanya. Cage traps baited with either fish offal or sugar–vinegar solution were deployed, supplemented by hand-net collection. A total of 134 traps were set, yielding 2132 flies belonging to 21 species. Fish offal captured 1961 flies (91.9%), significantly more than the 101 flies (4.7%) caught with sugar–vinegar solution (χ² = 1582.3, p < 0.001). Lucilia sericata was the dominant species (885 individuals, 41.51%), followed by L. cuprina (178, 8.35%), Sarcophaga portschinskyi (127, 5.96%), and Sarcophaga africa (100, 4.70%). High-risk taxa (Calliphoridae and Sarcophagidae) were almost exclusively attracted to fish offal. Our findings demonstrate that protein-based baits, such as fish offal, are substantially more effective than traditional sugar–vinegar solutions for capturing epidemiologically relevant fly species across diverse ecological zones in China. We recommend prioritizing proteinaceous attractants in national fly surveillance programs and advocate for routine species-level identification to enable risk-informed vector monitoring. Full article

This study proposes a heterogeneous fusion path planning framework for unmanned aerial vehicles (UAVs) operating in complex emergency rescue and civil environments. Existing single-mechanism metaheuristics—including Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Genetic Algorithms (GAs)—suffer from fundamental limitations in three-dimensional kinematic path planning: PSO converges rapidly but stagnates at local optima due to population variance collapse; ACO offers robust local exploitation but incurs prohibitive cold-start overhead; GAs maintain diversity at the cost of expensive crossover operations. To address these complementary deficiencies simultaneously, the proposed framework introduces a spherical coordinate representation that reduces computational complexity and naturally enforces UAV kinematic constraints, combined with adaptive weight factors and a serial PSO-ACO fusion strategy, and subsequently incorporates adaptive weight factors. A serial fusion strategy is then introduced, wherein the sub-optimal trajectory generated by the Spherical PSO phase is mapped into the ACO pheromone field via a Gaussian Kernel Density Mapping (GKDM) mechanism, enabling the ACO phase to perform fine-grained local exploitation within a kinematically feasible corridor. Various constraints along the flight path are formulated into distinct cost functions, which cover aircraft track length, pitch angle variation, altitude difference variation, obstacle avoidance, and smoothness; the core task of the algorithm is to find the flight path with the minimum total cost. The proposed algorithm is dedicated to UAV path planning in complex emergency rescue environments (disaster-stricken areas, hazardous zones) and is further applicable to civil low-altitude logistics delivery, industrial facility inspection, ecological environment monitoring and urban air mobility (UAM) scenarios with complex obstacle constraints. It can effectively improve the safety and efficiency of UAVs in reaching rescue points, delivering emergency supplies, conducting disaster surveys, and completing various civil low-altitude operation tasks. Full article

This article presents a comprehensive assessment of sediment contamination in the Irtysh River within the industrial zone of the city of Ust-Kamenogorsk, using the Specific Combinatorial Sediment Pollution Index (SCSPI). This study includes a set of priority chemical elements characteristic of the region’s technogenic load (Be, Cu, Zn, As, Se, Cd, Te, Hg, Pb), taking into account their hazard class, persistence in bottom sediments, and ability to accumulate in fine-grained (pelitic) fractions. The assessment was carried out based on the calculation of the frequency index of background exceedance (S_α) and the exceedance multiplicity index (S_β), relative to the effective local background value, followed by the determination of the partial pollution indices (Ki) and the integral SCSPI indicator. It was established that, for most elements, the frequency of exceedance ranges from 75% to 100%, indicating widespread surpassing of the effective local background. The partial indices vary within 4–7 points, with cadmium and zinc making the greatest contribution to the formation of integrated pollution due to the presence of local accumulation zones. Correlation analysis showed that the proportion of the pelitic fraction (<0.01 mm) is most strongly associated with the accumulation of Cd (r = 0.67) and Se (r = 0.66), indicating the preferential accumulation of these elements in fine-grained sediments. Principal component analysis revealed stable geochemical associations among the elements. For the <2.0 mm fraction, the first three principal components explain 73.57% of the total variance, with PC1 mainly associated with Pb, Se, and Cd. For the <0.2 mm fraction, the first three components explain 72.44% of the total variance, and PC1 is characterized by high loadings of Zn, Cd, As, and Se, reflecting the strengthening of the technogenic association in fine-grained material. The SCSPI values across the studied cross-sections range from 5.0 to 5.6, corresponding to a moderately polluted state of bottom sediments (Classes 3a–3b). The spatial distribution of the index reflects the combined influence of technogenic sources and hydrodynamic processes responsible for the redistribution of fine-grained material. The obtained results confirm the applicability of the Specific Combinatorial Sediment Pollution Index (SCSPI) for an integrated assessment of the ecological condition of bottom sediments and for identifying zones of increased technogenic load. A comprehensive approach to the analysis of bottom sediment pollution is proposed, enabling a more accurate identification of spatial distribution patterns of contaminants and their accumulation zones. This provides a scientific basis for the development of adaptive strategies for monitoring and management of aquatic ecosystems. This study is of significant practical importance for advancing sustainable environmental management and the rational use of natural resources under increasing anthropogenic impact. Full article

Fine-scale accounting of land use carbon budgets and identification of their driving factors provides an essential scientific basis for constructing green and low-carbon territorial spatial systems. This is of great significance for optimizing territorial spatial structure and promoting low-carbon development in urban agglomerations. Taking the Central Guizhou Urban Agglomeration as the study area, this study employed a composite carbon coefficient method to construct a 30 m × 30 m grid-based carbon budget index and quantitatively assessed carbon budget changes induced by land use transitions from 2000 to 2024. POI data and a quantile regression model were further integrated to analyze the dominant spatial characteristics associated with carbon budgets, and a carbon budget monitoring and early-warning index was developed to delineate risk zones. The results show that: (1) From 2000 to 2024, the total area of land use change reached 0.95 × 10⁴ km² in the Central Guizhou Urban Agglomeration, accounting for 17.68% of the total land area, and leading to a net increase of 2.3821 million tons of carbon emissions. This increase was primarily associated with the conversion of cultivated land to construction land, with an accelerated growth rate observed in the later period. (2) The spatial patterns of carbon budgets and carbon emission risk levels exhibit a distinct “core–periphery” structure, with high carbon emission levels concentrated in built-up urban areas and lower levels observed in peripheral ecological land. (3) The expansion of construction land is the dominant contributor to the increase in net carbon emissions; industrial, transportation, and residential spaces exert significant positive driving effects, whereas commercial and service spaces show a negative association. (4) Carbon budget risk zoning based on dominant spatial characteristics identifies Guiyang and Anshun as extremely high-risk areas. The results further suggest that reducing carbon-increment spaces and increasing carbon-reduction spaces may play an important role in territorial carbon budget optimization. The integrated “accounting–driving–monitoring” analytical framework established in this study provides a scientific basis for territorial spatial optimization and carbon emission reduction in mountainous urban agglomerations. Full article