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Biological invasions have become an important threat to global ecological security and forest health, and exploring the environmental driving mechanisms of invasive species is important for prevention and control. Bursaphelenchus xylophilus (Steiner and Buhrer, 1934), as a highly destructive invasive species, has its distribution and spread driven by a combination of various environmental factors. The study systematically evaluated the habitat suitability and key driving factors of B. xylophilus in the current period by applying an ensemble model and an optimized parameter‐based geographical detector. The results indicate that bioclimatic, vegetation indices, topographical features, and human activities are key environmental factors influencing the distribution of B. xylophilus, with highly suitable areas primarily located in southern, northern, and northeastern China. Meanwhile, the synergistic interaction between slope and population density (PD) significantly enhanced the suitability of B. xylophilus distribution, while the interaction between normalized difference vegetation index (NDVI) and global human influence index (GHII) exhibited a nonlinear weakening effect. Additionally, the habitat suitability of B. xylophilus increased with the expansion of isothermality, mean temperature of the wettest quarter, precipitation of the driest month, global human footprint, GHII, and PD, while it gradually decreased with the increase of UV‐B seasonality and NDVI. This study thoroughly explored the mechanisms by which various environmental factors influence the habitat suitability of B. xylophilus, revealing the complexity of regional driving factors. The findings not only provide theoretical support for predicting the ecological suitability of B. xylophilus but also offer scientific evidence for comprehensively analyzing the key factors affecting its distribution. While most studies focus on a single species distribution model, this study further analyzed the drivers of environmental factors by incorporating a geographic detector.

Revealing the spatiotemporal driving mechanisms of landslide activity is fundamental to improving long-term landslide hazard management and risk mitigation in mountainous cities. Focusing on landslide events in Hong Kong from 1990 to 2024, this study develops an integrated framework at the slope-unit scale that combines rainfall index optimization with multi-factor spatiotemporal driving analysis. First, Grey Relational Analysis (GRA) is employed to systematically evaluate the spatiotemporal associations between landslide occurrences and six commonly used rainfall indices, aiming to obtain a consistent and robust representation of rainfall triggering conditions. Subsequently, the Optimal-Parameter Geographical Detector (OPGD) model is introduced to quantitatively assess the explanatory power of individual factors—covering geological, topographic, hydro-meteorological, and human-related variables—as well as their pairwise interactions, thereby revealing the spatiotemporal evolution of landslide driving factors and their multi-factor coupling mechanisms over a 35-year period. The results indicate that the maximum 3-day cumulative rainfall index (RX3day) consistently exhibits the strongest association across different resolution parameter settings and is identified as the dominant rainfall indicator representing dynamic landslide triggering. Geological conditions and topographic factors constitute a stable background controlling the spatial heterogeneity of landslides throughout the entire study period, whereas the explanatory power of RX3day increases markedly after around 2000, gradually emerging as a primary dynamic driving factor of landslide activity. Interaction detection further demonstrates that landslide occurrence is mainly governed by nonlinear enhancement effects among multiple factors, with “geology–topography” and “rainfall–topography/geology” interactions showing the highest explanatory power, and rainfall-related interactions exhibiting continuous strengthening over time. Overall, the spatiotemporal distribution of landslides in Hong Kong is jointly controlled by long-term stable geological–topographic conditions and increasingly intensified extreme rainfall forcing.

Understanding the spatiotemporal distribution of land surface temperature (LST) is crucial for managing urban thermal environments and mitigating urban heat island (UHI) effects. This study addresses the challenge of quantifying the complex interactions among natural and anthropogenic factors driving LST variations, while leveraging advanced modeling techniques to predict future thermal risks in rapidly urbanizing regions. By analyzing the evolution of LST in Jinan city, China, from 2002 to 2022, and forecasts future trends using advanced spatial analysis and predictive modeling techniques. Directional shifts in LST were quantified using the quadrant azimuth method and the standard deviation ellipse method, both of which analyze spatial distribution and dispersion. To identify the key drivers of LST variations, 14 socioeconomic and environmental factors were assessed using the optimal parameter-based geographical detector (OPGD) model, which effectively handles spatial heterogeneity. Key findings include: (1) a significant northward shift in the LST centroid and a 26.64% expansion in high-temperature areas, with noticeable cooling effects in the city center. (2) A nonlinear relationship between LST and socioeconomic factors, particularly GDP, where cooling effects were observed when GDP exceeded 10,000 yuan/km 2 . (3) Synergistic interactions, especially between topographic factors (such as the Digital Elevation Model, DEM) and land-use indices (e.g., normalized difference built-up index, NDBI; normalized difference vegetation index, NDVI), were found to significantly influence LST variations. (4) The Oscillating Sequence Grey Model (OSGM), optimized for handling oscillating data sequences, demonstrated superior predictive accuracy, projecting a 20.72% increase in extreme high-temperature zones and a 40.61% reduction in moderate-high-temperature zones by 2047. These findings offer actionable strategies for urban planning and climate adaptation, aiming to mitigate thermal risks and inform future policies for urban sustainability and resilience. This research underscores the importance of integrating spatial and predictive analyses to inform urban planning and climate adaptation strategies, contributing to the mitigation of thermal risks and the development of sustainable urban policies.

In recent decades, accelerated economic growth has positioned non-point source pollution as a critical threat to China’s ecological security. Jiangxi Province, a pivotal ecological barrier in the middle-lower Yangtze River Basin, confronts escalating challenges in mitigating nitrogen pollution. This study systematically examined spatiotemporal land use dynamics in Jiangxi from 2000 to 2020 through statistical analysis and transition matrices, followed by multi-scenario projections (natural development, ecological conservation, and urban expansion) for 2030 using the Patch-generating Land Use Simulation (PLUS) model. The Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model was subsequently employed to simulate historical and scenario-based nitrogen emissions, while an optimal parameter geographical detector quantified driving mechanisms. Results revealed that 2,588 km 2 of cultivated and forest land was converted to construction land during 2000–2020. By 2030, the natural development scenario maintained historical trends, while the ecological protection scenario slowed construction land growth and increased ecological land area. Conversely, urban expansion intensified built-up land sprawl. Total nitrogen emissions initially rose then declined during 2000–2020, spatially characterized by localized growth in northern urban clusters and large-scale reductions in central-northern cropland emissions. Projected emissions for 2030 under natural development, ecological protection, and urban sprawl scenarios reached 5.05 × 10 4 tons, 4.59 × 10 4 tons, and 5.02 × 10 4 tons, respectively. Cultivated land use intensity (X6) emerged as the dominant driver of spatial heterogeneity (highest q-value), while its interaction with the human footprint index (X10) exhibited the strongest explanatory power. This study provides actionable insights for optimizing land use patterns and mitigating nitrogen pollution in Jiangxi Province.

What are the main findings? * QRNN outperformed RF and XGBoost and was selected as the optimal model for SOCD prediction. * SOCD showed a non-monotonic pattern with alternating decline and recovery phases, with consistently low values in the southwest. QRNN outperformed RF and XGBoost and was selected as the optimal model for SOCD prediction. SOCD showed a non-monotonic pattern with alternating decline and recovery phases, with consistently low values in the southwest. What are the implications of the main findings? * Latitude, elevation, and mean annual temperature were stable dominant drivers of SOCD in Changchun croplands. * Multi-source data and stage-based analysis improve SOC monitoring and support sustainable soil management. Latitude, elevation, and mean annual temperature were stable dominant drivers of SOCD in Changchun croplands. Multi-source data and stage-based analysis improve SOC monitoring and support sustainable soil management. Soil organic carbon (SOC) of cropland is a key indicator of soil fertility and contributes to climate regulation and carbon storage. The understanding of SOCchanges in cropland in Northeast China still lacks high-precision long-term empirical evidence. This study is of great significance for ensuring national food security and regional sustainable development. Taking Changchun, a representative black soil region, as the study area, this study integrated 953 field samples with 19 predictors to estimate cropland soil organic carbon density (SOCD) from 2000 to 2022. The performance of quantile regression neural network (QRNN), random forest (RF), and extreme gradient boosting (XGBoost) models was compared. QRNN showed the best overall performance (R[sup.2] = 0.74, RMSE = 0.57 kg/m[sup.2], MAE = 0.40 kg/m[sup.2], and RPIQ = 2.46) and also exhibited greater stability in temporal-stage validation. Results indicated that SOCD exhibited an overall declining trend with intermittent recoveries, decreasing from 3.72 kg/m[sup.2] in 2000 to 3.36 kg/m[sup.2] in 2005, then increasing to 3.55 kg/m[sup.2] in 2010, slightly declining to 3.46 kg/m[sup.2] in 2015, and recovering to 3.63 kg/m[sup.2] in 2022. Spatially, SOCD remained low in the southwest, fluctuated markedly in the north, and was relatively stable in the central region. The analysis of the optimal parameter geographic detector (OPGD) showed that Y-latitude, elevation, and mean annual temperature (MAT) were stable dominant factors, while precipitation (PRE) and remote sensing variables showed stage-dependent effects. Interactions among multiple factors further enhanced the explanation of SOCD variations. These findings provide theoretical support for enhancing soil carbon retention and promoting long-term cropland sustainability in black soil areas.

The new energy vehicle (NEV) industry is a key sector for achieving dual carbon goals and advancing regional green transformation. Its sustainable development depends on the deep coupling of the industrial chain and the innovation chain. Drawing on data from Shaanxi’s NEV industry covering the period 2014–2023, this study employed kernel density estimation (KDE), the entropy weight method, the coupling coordination degree model, and the optimal parameter geographical detector. Specifically, we examine Shaanxi’s national positioning and spatial pattern within the NEV industry, the spatiotemporal evolution of the coupling coordination degree between its industrial and innovation chains, and the key driving factors along with their interaction mechanisms. The results indicate that Shaanxi is situated within the secondary core growth zone of central and western China. Within the province, the industry exhibits a pronounced spatial pattern characterized by single core concentration in Xi’an, contiguous support across the Guanzhong region, and point-like distribution in northern and southern Shaanxi. The dual-chain coupling coordination degree in Shaanxi’s NEV industry has improved steadily, resulting in a four-tier structure comprising core breakthrough, secondary catch-up, weak foundation, and lagging predicament categories. The dominant driving factors are Industrial Agglomeration Degree, Research and Development (R&D) Funding Input, and Resource Utilization Rate. The interaction between Resource Utilization Rate and Integration Degree exerts the strongest effect.

New-type urbanization (NTU) is a key driver of high-quality development and progress toward the Sustainable Development Goals (SDGs) in China. While existing studies acknowledge the multidimensional nature of this process, they often measure it as a single composite aggregate. This approach masks the system’s local sensitivity to internal structural changes and obscures the spatially stratified heterogeneity of dominant drivers. To address this gap, this study constructs construct a comprehensive evaluation index system using panel data for 280 prefecture-level and above cities in China from 2001 to 2023. This study integrates the entropy-weighted TOPSIS method, a modified coupling coordination degree model (MCCD), geographically and temporally weighted regression (GTWR), and the optimal parameters geographical detector (OPGD). Using this framework, this study investigates the spatio-temporal characteristics of the coordinated high-quality development (CHQD) in NTU, systematically dissecting the spatial heterogeneity of local sensitivities and dominant drivers. The results indicate that the following: (1) CHQD exhibits a continuous upward trajectory characterized by significant regional convergence, with the center of gravity gradually shifting southwest. Structurally, green and social dimensions demonstrate the most rapid growth, progressively superseding spatial expansion as primary growth poles. (2) The structural decomposition reveals clear spatially stratified heterogeneity in local sensitivity. The coastal East faces “diminishing marginal utility” of traditional factor inputs, whereas the Central and Western regions continue to reap “structural dividends” from factor accumulation. (3) The dominant drivers shaping spatial heterogeneity have undergone a sequential evolution from an early “resource-space orientation” to a later “innovation-service orientation.” For instance, in the eastern region, the proportion of construction land (L2) had a single-factor explanatory power (q-statistic) of 0.791. However, its interactions with science and technology expenditure (E3) and other factors yielded q-statistics exceeding 0.820, indicating a marked synergistic effect. These findings support region-specific policy recommendations to promote CHQD and inform sustainable urbanization pathways in China.

Exploring the spatial patterns and associated mechanisms of Rural Tourism Demonstration Villages (RTDVs) and Potential Rural Tourism Villages (PRTVs) is crucial for rural tourism planning and regional coordination. This study focuses on the arid region of Northwest China. Based on national and provincial official directories, it selects villages listed under tourism-oriented categories as RTDVs, while designating other villages categorized for their ecological, cultural, and agricultural characteristics as PRTVs. Multiple geospatial analyses were conducted to identify spatial distribution characteristics and differences between RTDVs and PRTVs, while the optimal-parameter geographical detector model quantified the influences and interactions of natural, socioeconomic, locational, and cultural–tourism factors. Results show that rural tourism is concentrated in the Ili River Valley, the mid-Hexi Corridor, and the Urumqi–Turpan area. RTDVs follow this pattern but display stronger hierarchical differentiation. Cultural Potential Rural Tourism Villages (C-PRTVs) cluster in multi-ethnic areas. Ecological Potential Rural Tourism Villages (E-PRTVs) occur mainly in mountain oases, and agricultural Potential Rural Tourism Villages (A-PRTVs) agglomerate near provincial capitals and major transport corridors. Overall, influencing factors exhibit interactive enhancement, suggesting that spatial patterns are associated with multidimensional synergy. The findings provide empirical support for differentiated planning and sustainable development in arid regions.

Ecosystem services significantly impact human well-being and regional sustainable development, and clarifying the spatially heterogeneous characteristics of their supply and demand and the factors influencing them is the key to implementing sustainable development strategies. This study employs the InVEST model to quantitatively assess the supply and demand status of four primary ecosystem services in Sichuan Province: carbon sequestration(CS), grain production(GP), water production(WP), and soil conservation(SC), and analyze the spatial correlation of ecosystem services to identify different bundles of ecosystem services, thus presenting the regional heterogeneity characteristics, and finally explores the dominant factors in combination with the optimal parameterized geographical detector, taking into account the unique geographic location of Sichuan Province. The results showed that: (1) From 2010 to 2022, CS supply in Sichuan Province increased by 0.07 × 10¹⁰ t, GP supply increased by 3.28 × 10¹² t, WP supply decreased by 3.38 × 10¹² t, and SC supply decreased by 1.26 × 10¹² t. CS demand increased by 7.32 × 10¹¹ t, GP demand decreased by 3.47 × 10¹³ t, WP increased by 1.47 × 10¹³ t, and SC demand decreased by 2.25 × 10⁹ t; (2) The supply-demand dynamics of ecosystem services reveal a pattern of “high supply and high demand” in the eastern regions and “low supply and low demand” in the western regions. On the supply side, trade-offs predominate across the four categories of ecosystem services in eastern and western Sichuan. On the demand side, strong synergistic effects are evident across all categories; (3) During the study period, the distribution and percentage of four ecosystem service bundles exhibited significant changes. On the supply side, the area covered by the GP-WP bundle(S2) in eastern Sichuan decreased by 4%, while the CS-SC bundle(S3) increased by 3%. In western Sichuan, the CS-SC bundle(S3) area rose by 13%, while the WP bundle(S4) decreased by 14%. On the demand side, the area covered by the SC demand bundle(C1) in eastern Sichuan decreased by 4%, and the low-demand bundle(C3) area in western Sichuan decreased by 1%; (4) The supply of ecosystem services in eastern and western regions is generally more influenced by natural landscape factors, while demand is closely linked to socioeconomic conditions. Furthermore, the combined effects of interacting factors exert a far greater influence on ecosystem services than individual factors alone. The study can provide a scientific basis for the ecological function of zoning and the development of differentiated control strategies in Sichuan Province.

The ecological environment is important for the survival and development of human beings, and objective and accurate monitoring of changes in the ecological environment has received extensive attention. Based on the normalized difference vegetation index (NDVI), wetness (WET), normalized differential build-up and bare soil index (NDBSI), and land surface temperature (LST), the principal component analysis method is used to construct a comprehensive index to evaluate the ecological environment’s quality. The R package “Relainpo” is used to estimate the relative importance and contribution rate of NDVI, WET, NDBSI, and LST to the remote sensing ecological index (RSEI). The optimal parameter geographic detector (OPGD) model is used to quantitatively analyze the influencing factors, degree of influence, and interaction of the RSEI. The results show that from 2001 to 2020, the area with a poor grade quality of the RSEI in Guangzhou decreased from 719.2413 km2 to 660.4146 km2, while the area with an excellent quality grade of the RSEI increased from 1778.8311 km2 to 1978.9390 km2. The NDVI (40%) and WET (35%) contributed significantly to the RSEI, while LST and NDBSI contributed less to the RSEI. The results of single factor analysis revealed that soil type have the greatest impact on the RSEI with a coefficient (Q) of 0.1360, followed by a temperature with a coefficient (Q) of 0.1341. The interaction effect of two factors is greater than that of a single factor on the RSEI, and the interaction effect of different factors on the RSEI is significant, but the degree of influence is not consistent. This research may provide new clues for the stabilization and improvement of ecological environmental quality.