Objective Counties are key spatial units for implementing the new-type urbanization and the "dual carbon" strategies， and their spatiotemporal evolution and driving mechanisms of carbon emission efficiency are crucial for addressing the green transformation challenges in resource-based regions. Methods Based on multi-source heterogeneous data from 102 counties in Shanxi Province from 2012 to 2023， this study employed a Super-SBM model with undesirable outputs to measure carbon emission efficiency. It then integrated the Dagum Gini coefficient， exploratory spatiotemporal data analysis （ESTDA）， the XGBoost-SHAP interpretable machine learning model， and panel quantile regression to systematically analyze the spatiotemporal patterns， regional disparities， dynamic transitions， and nonlinear driving mechanisms of carbon emission efficiency. Results 1） Spatiotem-porally， carbon emission efficiency exhibited a stable distribution pattern of "high in the north and south， low in the central" with phased increases， with high-efficiency areas gradually agglomerating in the core zones of urban agglomerations and areas along the Yellow River. 2） Regional disparities primarily originated from inter-group differences and super-variable density， exhibiting a fluctuating process of "divergence-convergence-re-divergence"， with persistent spatial imbalance. 3） Global and local spatial analyses indicated that carbon emission efficiency had significant positive spatial autocorrelation and path dependence. LISA time paths and spatiotemporal transitions revealed that southern and southwestern Shanxi were active zones of local structural changes， showing evident signals of state transitions and restructuring. 4） The driving mechanisms exhibited significant nonlinearity and quantile heterogeneity. Economic density and land use efficiency had positive promoting effects on carbon emission efficiency with threshold effects， while population density and fiscal expenditure intensity generally acted as negative constraints. The positive effects of new-type urbanization and industrial structure upgrading were more pronounced in medium- and high-efficiency counties. Conclusion The study reveals the complex adaptive mechanisms of county-level low-carbon development in resource-based regions. The spatiotemporal heterogeneity and threshold effects of the driving factors indicate that future efforts require the formulation of differentiated and precise emission reduction policies to synergize high-quality county-level development with the "dual carbon" goals.

Objective This study aims to investigate the effects of water and nitrogen regulation on the emission patterns of NH₃ and N₂O and the nitrogen fertilizer utilization rate in saline-alkali farmland. Methods Sunflower was used as the research subject. Two irrigation gradients were set： T1， mild deficit irrigation （field capacity 65%-75%）， and T2， full irrigation （field capacity 75%-80%）. Four nitrogen application levels were established： N1 （0 kg/hm²）， N2 （150 kg/hm²）， N3 （225 kg/hm²）， and N4 （300 kg/hm²）. By analyzing the effects of different irrigation gradients and nitrogen application levels on NH₃ and N₂O emissions， the pattern characteristics of nitrogen fertilizer utilization， residue， and loss in saline-alkali land were clarified. Results Under the two irrigation conditions， with increasing nitrogen application rate， both the N₂O emission flux and NH₃ volatilization rate significantly increased. Peak emissions under the N4 treatment were 4.4%-21.4% higher than those under N3 and N2. Under the same nitrogen application level， the cumulative NH₃ volatilization and N₂O emissions under the T2 treatment were 10.10%-20.41% and 16.41%-39.77% higher than those under the T1 treatment， respectively. The gaseous nitrogen fertilizer loss rate under the T1 treatment was 7.05%-32.38% lower than that under the T2 treatment. The ¹⁵N isotope tracing results showed that the fate of fertilizer nitrogen followed the pattern： nitrogen fertilizer loss rate > nitrogen fertilizer residual rate > nitrogen fertilizer utilization rate. The residual fertilizer nitrogen was mainly concentrated in the 0-20 cm soil layer， accounting for 46.13%-63.14% of the total residual amount， and gradually decreased with increasing soil layer depth. Under the T1 treatment， the sunflower yield was higher than that under the T2 treatment by 4.12%-12.38%. The nitrogen fertilizer utilization rate under the T1 treatment was 8.46%-14.96% higher than that under the T2 treatment. Among them， the T1-N3 treatment achieved the highest yield and nitrogen fertilizer utilization rate， reaching 2 796.01 kg/hm² and 26.10%， respectively. Additionally， the effects of salinity on the nitrogen transformation process were mainly related to the composition of salt ions， and Na⁺+K⁺ and \mathrm{H}\mathrm{C}{\mathrm{O}}_{\mathrm{3}}^{-} were positively correlated with the gaseous nitrogen emission rate （r = 0.57-0.86）. Conclusion For severely saline-alkali farmland， under mild deficit irrigation conditions， a nitrogen application level of 225 kg/hm² （T1N3） can effectively reduce nitrogen fertilizer residue and gaseous losses while ensuring nitrogen fertilizer utilization efficiency， representing the optimal water and nitrogen management mode for emission reduction and efficiency improvement.

Objective This study aims to achieve efficient utilization and value transformation of the space under photovoltaic panels in rocky desertification areas， and to reveal the effects of different "medicinal herb-liana" utilization models on improving soil nutrients and enzyme activities， thereby selecting a model that can efficiently utilize the space under panels while having socioeconomic value. Methods Based on a near-natural indoor simulation test site， composite utilization models of Polygonum multiflorum with various lianas were established. Drawing from soil data in both dry and rainy seasons， one-way ANOVA， nonlinear regression， and redundancy analysis were employed to systematically analyze the temporal variation trends of total soil nutrients and available nutrients under different utilization models， thereby further clarifying the correlation characteristics between soil physicochemical properties and enzyme activity. Results 1） The model of P. multiflorum with Lonicera japonica exhibited a stable nutrient accumulation effect； the model of P. multiflorum with Puerarialobata showed strong seasonal fluctuations， with a net depletion of nutrient content throughout the year； the model of P. multi-florum with Wisteria showed an initial increase followed by a decrease in nutrients. In comparison， the control group exhibited continuous nutrient depletion. 2） The seasonal dynamics of available nutrients showed significant species specificity. The L. japonica model achieved accumulation of available phosphorus and potassium in both dry and rainy seasons. In addition， despite an explosive increase of 4.02 mg/kg in alkali-hydrolyzable nitrogen in the Wisteria model during the dry season， this increase was rapidly lost during the rainy season. 3） Organic carbon and available phosphorus served as key factors driving enzyme activity variation， with explanatory rates of 71.8% （dry season） and 83.5% （rainy season）， respectively. The L. japonica model alleviated carbon and phosphorus limitations through the accumulation of organic carbon， resulting in a steady increase in both soil nutrients and enzyme activity， whereas the Wisteria model presented a state of high enzyme activity but low nutrient content. Conclusion The "medicinal herb-liana" composite utilization model plays an important role in enhancing the stability of the soil biochemical system and nutrient cycling efficiency in the area under photovoltaic panels. The composite utilization model of P. multiflorum with L. japonica exhibits the best adaptation potential and ecological benefits. The research results can provide a theoretical basis and practical guidance for under-panel economic deve-lopment and sustainable soil ecological management of photovoltaic power stations in rocky desertification areas.

Objective This study aims to scientifically delineate land use functional zones in Anhui Province， identify the key driving factors of landscape ecological risk， and reveal the spatiotemporal evolution characteristics and spatial conflict patterns of ecological risk， providing scientific support for regional ecological security and territorial space planning. Methods Taking Anhui Province as the study area， a land use functional zoning scheme was developed by integrating the morphological spatial pattern analysis （MSPA）， Conefor， and minimum cumulative resistance （MCR） models and applying the resistance threshold method. On this basis， This study combines the XGBoost model with the SHAP interpretation method to quantitatively analyze the effects of topographic conditions， climatic factors， and socio-economic factors on landscape ecological risks. Results 1）According to land use functions， Anhui Province can be divided into three core areas， among which the ecological protection core area accounts for 30.96%， the agricultural production core area accounts for 35.52%， and the construction and development core area accounts for 33.52%. Spatially， the three areas are respectively concentrated in the southern Anhui region， the northern Anhui region， and the urban agglomeration in central Anhui. 2） From 2000 to 2020， the overall landscape ecological risk in the region has been continuously intensifying， with the proportion of areas with high and very high risks increasing from 16.95% to 32.81%. In terms of spatial distribution， low-risk zones largely coincided with ecological protection zones， while high-risk zones were concentrated in construction development zones. 3） The analysis of influencing factors showed that topographic factors exerted the greatest influence on the provincial risk pattern. Climatic factors were the dominant factors in ecological protection and construction development zones， whereas socio-economic factors had the most significant influence on risk in agricultural production zones. 4） Ecological risk conflicts within the province were significant， with relatively high- and high-conflict zones accounting for 30.78%， mainly distributed in cities such as Anqing and Hefei. Conclusion Landscape ecological risk in Anhui Province shows an intensifying trend. The province is influenced by topographic factors， while different zones are driven by climatic and socio-economic factors， and high-risk zones are concentrated in construction development core zones. It is recommended to implement differentiated management strategies based on functional zoning， focusing on mitigating spatial conflicts in areas such as Anqing and Hefei to improve regional ecological security. The findings provide a scientific basis for the synergistic governance of territorial space planning and ecological risk.

Objective This study aims to analyze hydrological cycle patterns under different climatic and vegetation conditions and evaluate vegetation water carrying capacity in arid and semi-arid regions using an ecohydrological model. Methods Eagleson′s ecohydrological model was applied to analyze vegetation water carrying capacity and growing-season water budget under different climatic zones and land use types in the study area. Results 1） Eagleson′s model showed good applicability in simulating vegetation water carrying capacity in the study area， with a coefficient of determination （R²） of 0.85. 2） Current vegetation coverage and vegetation water carrying capacity showed obvious regional differences. Fractional vegetation coverages （FVC） values in five core ecological functional zones-water conservation and management area in the Bashang Plateau and hilly mountainous area of northern North China， desertification control area in the Ordos Plateau， desertification control area in the Hunshandake-Horqin sandy land， degraded desert grassland control area in the Ulanqab Plateau， and degraded grassland control area in the Xilin Gol Plateau-Ujimqin Basin-were 0.54， 0.28， 0.36， 0.20， and 0.31， respectively. 3） In the water conservation and management area of the Bashang Plateau and hilly mountainous areas of northern North China， 49.83% of the land area still has potential for increased FVC， whereas the degraded grassland control area in the Xilin Gol Plateau-Ujimqin Basin， vegetation in 82.04% of the land area has reached the upper limit of water carrying capacity. Across the entire region， vegetation interception and transpiration during the growing season accounted for nearly 90% of precipitation， whereas bare-soil evaporation accounted for less than 10%. Vegetation transpiration increased with increasing FVC. Water budget patterns differed markedly among different vegetation types. Conclusion Eagleson′s model shows good performance and can be used to predict vegetation water carrying capacity in arid and semi-arid regions. Vegetation water carrying capacity shows obvious spatial differentiation， and the potential for increasing FVC varies significantly among regions. Shrublands and grasslands show better climatic adaptability in vegetation-hydrology coupling. Climatic differentiation is the fundamental factor driving spatial differentiation of vegetation-hydrology coupling effects. The results provide theoretical reference and data support for determining FVC in ecological restoration.

Objective This study aims to investigate the spatiotemporal evolution characteristics and coupling relationship between landscape patterns and ecological surplus in the Kuye River basin， thereby providing guidance for ecological restoration and sustainable development in this region. Methods Taking the Kuye River basin as the study area， land use data were obtained based on six periods of Landsat remote sensing images from 2000 to 2024 using support vector machine classification and visual interpretation， and multiscale landscape pattern indices were extracted. Using statistical yearbook data from 2000， 2010， and 2024， ecological surplus was calculated based on ecological footprint and ecological carrying capacity models， and the relationship between landscape patterns and ecological surplus was quantified using Spearman rank correlation analysis. Results 1） From 2000 to 2010， under the influence of high-intensity industrial and mining development and land use expansion， landscape fragmentation in the river basin significantly intensified， with patch density （PD） and division index （DIVISION） increasing markedly. After 2010， driven by policies such as the Grain for Green Program and mine ecological restoration， contagion （CONTAG） and cohesion （COHESION） exhibited increasing trends， indicating a transition of landscape patterns from fragmentation towards overall structural optimization. 2） Changes in ecological surplus were mainly driven by the rapid growth of the energy footprint. Specifically， the ecological deficit of fossil energy land expanded from 1.376 1×10⁶ hm² in 2000 to 9.28×0⁷ hm² in 2024， accounting for an overwhelming proportion of the total ecological deficit and becoming the key factor constraining the decline of ecological carrying capacity in the river basin. 3） Significant correlations were observed between landscape pattern indices and ecological surplus. CONTAG and COHESION showed extremely significant positive correlations with ecological surplus （p<0.01）， while patch density （PD）， diversity index （DIVISION）， and Shannon's diversity index （SHDI） exhibited significant negative correlations. The "fragmentation and disturbance-driven diversity" under industrial and mining disturbance exerted a significant inhibitory effect on ecological surplus. Conclusion Landscape fragmentation is the core factor regulating ecological surplus in the Kuye River Basin， and the turning point in 2010 is directly associated with regional industrial and mining regulation policies. Restoration measures that reduce landscape fragmentation and enhance the connectivity of ecological land can effectively promote the steady recovery of ecological surplus in the river basin. The findings can provide a quantitative reference for ecological restoration planning in industrial and mining-affected river basins of the Pisha sandstone region.

Objective This study aims to construct a gully erosion susceptibility assessment model in order to provide an important scientific basis for early warning of gully erosion occurrence and its precise management in the black soil region of Northeast China. Methods Kedong County in Heilongjiang Province， a typical thick-layer black soil region， was selected as the study area. Erosion gullies were extracted based on 0.4 m high-resolution remote sensing images. Redundant variables were eliminated using the variance inflation factor （VIF）. Four machine learning models covering linear， nonlinear， and ensemble learning architectures-logistic regression （LR）， support vector machine （SVM）， random forest （RF）， and extreme gradient boosting （XGBoost）-were compared. The SHAP （Shapley Additive exPlanations） interpretability model was introduced to quantify the contribution of each influencing factor to gully erosion susceptibility. Results 1） XGBoost demonstrated the best predictive capability for gully erosion susceptibility （training set AUC=0.81， test set AUC=0.75）， followed by the SVM and RF （test set AUC=0.74）， while LR showed the weakest predictive capability （AUC=0.67）. 2） The gully erosion susceptibility predicted by the four models all exhibited a spatial distribution pattern of "low in the periphery and high in the center" at the county scale， but there were significant differences in the susceptibility levels. The RF model predicted the largest proportion of high gully erosion susceptibility， accounting for 45.0%. The gully erosion susceptibility predicted by the SVM and XGBoost models in the study area was predominantly moderate and very low levels， accounting for 56.2% and 59.2%， respectively. In contrast， the LR prediction results indicated that low gully erosion susceptibility was dominant， covering 39.4% of the study area. 3） SHAP analysis revealed that the digital elevation model （DEM） was the dominant factor influencing gully erosion susceptibility （SHAP value=0.55）， exhibiting a significant positive effect. The stream power index （SPI） had the second strongest positive driving effect on gully erosion susceptibility （SHAP value=0.30）， while the normalized difference vegetation index （NDVI） showed a clear negative effect on gully erosion susceptibility （SHAP value=0.37）. Conclusion In the thick-layer black soil region of Kedong County， significant differences are observed in the gully erosion susceptibility levels evaluated by the four machine learning models. The integration of the XGBoost model with the SHAP method enhances the interpretability of machine learning models in predicting gully erosion susceptibility. Moreover， gully erosion susceptibility is jointly driven by the coupled effects of "terrain-dominated control， vegetation regulation， and confluence hydrodynamics triggering". These findings deepen the understanding of gully erosion occurrence and provide theoretical guidance for optimizing gully erosion control measures in black soil regions.

Objective This study aims to systematically characterize the overall spatial patterns， regional differentiation， and dynamic evolution of territorial space utilization efficiency within China′s urban agglomerations， and to reveal the role of enhanced efficiency in optimizing resource allocation and spatial structure within these agglomerations. Methods 14 urban agglomerations in China were selected as the research objects. The super-efficiency SBM model and Gini coefficient decomposition were employed to reveal the regional differences and sources of territorial space utilization efficiency. Additionally， kernel density estimation and Markov transition probability matrix were applied to analyze the evolution trends from 2010 to 2022. Results 1） The region as a whole showed a temporal pattern of urban space utilization efficiency > ecological space utilization efficiency > agricultural space utilization efficiency. During the study period， the average efficiency values were 0.645， 0.326， and 0.163， respectively. 2） The overall disparity in the territorial space utilization efficiency among urban agglomerations showed a trend of first decreasing and then increasing， with the Gini coefficient rising from 0.131 in 2010 to 0.145. Moreover， the differences between urban agglomerations were the primary source of the overall disparity. 3） The level of comprehensive territorial space utilization efficiency demonstrated strong spatial spillover effects. Furthermore， the state transition of efficiency levels within each urban agglomeration was more likely driven by the positive effect of high-level neighboring areas. Conclusion Adopting a development model of coordinated and mutual assistance within and outside the region can break the constraints on internal resource allocation and factor circulation. Meanwhile， actively leveraging the spillover effects of high-level urban agglomerations can promote the optimization and enhancement of the territorial space layout of urban agglomerations.

Objective This study investigates the effects and mechanisms of biochar application on the accumulation of total soil organic carbon （SOC） and key organic carbon fractions such as microbial necromass carbon （MNC） in paddy soils. Methods A field experiment was conducted to analyze the changes in SOC fractions， such as MNC and enzyme activities， five years after a one-time application of maize straw biochar at rates of 20 t/hm² （M20）， 30 t/hm² （M30）， and 40 t/hm² （M40）. Results Compared with the control （no biochar application）， biochar treatments increased the contents of SOC， particulate organic carbon （POC）， and mineral-associated organic carbon （MAOC） by 19.4%-45.0%， 36.3%-65.3%， and 13.4%-47.2%， respectively， whereas dissolved organic carbon （DOC） and microbial biomass carbon （MBC） decreased by 9.8%v29.2%， and 19.5%-29.1%， respectively. Biochar treatments had no significant effects on the contents of biomarkers such as mannosamine， glucosamine， muramic acid， and galactosamine， nor on fungal necromass carbon （FNC） and bacterial necromass carbon （BNC）. However， biochar treatments significantly reduced the contribution rates of FNC， BNC， and MNC to SOC by 16.6%-32.4%， 16.7%-27.2%， and 17.0%-30.8%， respectively. The M30 treatment significantly decreased the activities of polyphenol oxidase （PHOX）， peroxidase （PERO）， α-glucosidase （AG）， β-D-cellobiohydrolase （CB）， and β-xylosidase （XYL） by 19.8%， 24.7%， 32.7%， 34.2%， and 33.7%， respectively. Correlation analysis indicated that the contribution rate of MNC to SOC was significantly negatively correlated with pH， POC， and MAOC， and positively correlated with MBC and some hydrolytic enzyme activities. Random forest model prediction showed that MAOC and pH were the main driving factors determining the contribution of MNC to SOC. Conclusion Biochar application enhances the accumulation and stability of SOC mainly by promoting the accumulation of POC and MAOC rather than MNC. The results provide a theoretical reference for improving soil carbon sequestration in paddy soils through biochar application.

Objective This study aims to investigate the water transport mechanisms in the root-soil composites of typical landslide-prone areas on the Loess Plateau and its influence on slope stability. Methods A systematic analysis was performed on the variation process of the soil water breakthrough curve within the root zone of the region. By measuring soil physical properties， macropore structural parameters， and root parameters indicators， the key influencing factors of saturated hydraulic conductivity （K_sat） were identified in combination with linear correlation analysis. Furthermore， redundancy analysis （RDA） and variance partitioning analysis （VPA） were applied to quantify the contribution of each influencing factor to the change ratio of outflow velocity （CR_i）， which is a key indicator of the dynamic process. Results 1） Non-capillary porosity （NCP）， macropore area ratio （PAR）， and root surface area density （RSAD） were the main influencing factors of K_sat， with the coefficient of determination （R²） between PAR and K_sat reaching up to 0.99. 2） Soil gradation and macropore parameters overall had a significant effect on the change ratio of outflow velocity （CR _i ） （F=2.67）， explaining 78.05% of the constrained variation based on redundancy analysis. 3） Gradation influenced the variation mechanisms of water flow paths by regulating macropore structure， explaining 34.8% of the variation based on variance partitioning analysis. Conclusion The saturated hydraulic conductivity （K_sat） of root-soil composites on the Loess Plateau is jointly influenced by basic soil physical properties， macropore parameters， and root parameters. It is primarily dominated by non-capillary porosity and macropores ranging from 0.8 mm to 4.0 mm， and is negatively correlated with soil bulk density. Roots improve permeability by promoting pore development. Furthermore， the influence of particle size distribution on saturated hydraulic conductivity （K_sat） exhibits significant variations among soils under different vegetation types. Regarding the change ratio of outflow velocity， the proportions of sand， silt， and clay are the most dominant influencing factors. This study provides a theoretical basis for further understanding the water transport mechanisms in root-soil composites of loess regions and for evaluating slope stability.