In the context of unbalanced regional development posing severe challenges to the coordinated advancement of the "dual carbon" goals， it is of critical scientific significance to explore the spatio-temporal evolution characteristics and driving factors of carbon emission and economic growth decoupling at the provincial level in China. Based on panel data from 30 provincial regions from 2003 to 2022， this study systematically analyzes the spatio-temporal evolution characteristics and influencing factors by integrating the Tapio decoupling model， exploratory spatio-temporal data analysis （ESTDA）， and LMDI decomposition model. The results show： ① The carbon decoupling status exhibited a significant regional gradient pattern of "eastern leading， central and western regions catching up." ② The carbon decoupling effect in provinces showed significant spatial dependence， with 83.3% of provinces exhibiting path dependence characteristics， and a prominent spatial club convergence effect forming between neighboring provinces. ③ Economic growth was the most essential positive driving factor for the increase in carbon emissions， with a cumulative contribution of 531 843 tons from the east， central， and western regions. The reduction of energy intensity was the primary negative inhibiting factor， with a cumulative contribution of -320 516 tons from the east， central， and western regions. Carbon emission intensity had turned into a significant negative suppression in the eastern region， while showing fluctuating positive driving in the central and western regions. The suppressive effect of industrial structure optimization primarily appeared in the later stage in the eastern region， and its impact on carbon emissions， along with population size， showed significant regional heterogeneity. ④ The efforts towards decoupling showed phase evolution and regional gradient differentiation； improvements in energy intensity were the only continuous positive factor promoting decoupling. This research provides important scientific evidence for formulating precise and coordinated regional emission reduction strategies.
Excessive exploitation of land resources has driven global land-use changes that diminish ecological spaces， posing significant challenges to biodiversity conservation. To synergize ecological protection with high-quality development in the Yellow River Basin while balancing biodiversity conservation and socioeconomic needs in Shaanxi Province， this study designed three scenarios-Baseline （natural evolution）， Beautiful Shaanxi （ecological priority）， and Harmonious Shaanxi （coordinated development）-based on intrinsic linkages among land-use types， disturbance intensity， and biodiversity. Using the GeoSoS-FLUS and FLUS-Biodiversity models， we simulated land-use patterns and spatial heterogeneity of Mean Species Abundance （MSA） for 2035 and 2050. XGBoost-SHAP modeling further deciphered MSA drivers. Key findings reveal that： ① Structural shifts in most land-use types occurred， with moderately disturbed forests expanding significantly across all scenarios while dryland areas decreased most markedly. ② Under natural trends， Shaanxi's MSA rose from 0.533 （2020） to 0.539 （2050）， whereas the Beautiful and Harmonious scenarios elevated MSA to 0.546 and 0.544 by 2050， confirming their positive biodiversity effects. ③ MSA exhibited pronounced regional disparities-lower in densely populated economic zones and higher in natural resource-rich areas. ④ Driver contributions to MSA exhibited significant heterogeneity： Population density was the dominant negative factor， while annual potential evapotranspiration and temperature acted as primary positive drivers. This study demonstrates that scientifically coordinating land-use changes with biodiversity conservation， implementing targeted protection policies， and enhancing governance efficacy constitute effective pathways to achieve biodiversity targets.
Under the "dual carbon" strategy， this study systematically analyzes the spatiotemporal patterns， regional disparities， and dynamic evolution of the synergy between carbon reduction and pollution control in the Chengdu-Chongqing Economic Circle. Based on panel data of 144 districts and counties from 2000 to 2022， empirical research was conducted using methods such as the coupling coordination model， standard deviational ellipse （SDE）， spatial autocorrelation analysis， Dagum Gini coefficient decomposition， and both traditional and spatial Markov chain models. The results show that： ① The synergistic effect demonstrated a phased evolution of "rapid improvement-high-level fluctuation-differentiation adjustment." The coupling coordination degree rose from 0.256 to a peak of 0.323 before falling back to 0.262. The spatial autocorrelation was significant （Moran's I > 0.556）， with high-value areas concentrated in the Chengdu Plain and the main urban area of Chongqing. ② The average Dagum Gini coefficient was 0.214， and the contribution of intra-regional disparities exceeded 60%， with the imbalance being higher on the Chongqing side than on the Sichuan side. ③ Dynamic evolution analysis revealed that the distribution of synergistic effects had evolved from a single peak to a double peak. The synergy exhibited significant "state stability" （the minimum probability of maintaining the original state was 86.1%） and a "state locking" effect influenced by spatial neighborhoods. This resulted in limited upward transition possibilities and made "leapfrog" jumps difficult， indicating a gradual and stable evolutionary process. The synergy of carbon reduction and pollution control in the Chengdu-Chongqing Economic Circle exhibited significant spatiotemporal differentiation and polarization trends and demonstrated a stable， progressive evolutionary characteristic， providing a theoretical and practical basis for the region's green transition.
Nanhua County in central Yunnan is the largest and most important wild mushroom production area in Southwest China. To precisely quantify the origins of soil heavy metals and evaluate their associated health risks， thereby establishing a robust theoretical foundation for the prevention of soil heavy metal contamination and the safeguarding of human health， 571 surface soil samples were systematically collected from Nanhua County. To elucidate the origins of soil heavy metals， we employed a combination of multivariate statistical analysis and the UNMIX model， which facilitated the identification and quantification of distinct heavy metal sources within the soil matrix. The pollution levels of soil heavy metals in the study area and their potential health risks to humans were evaluated using the geoaccumulation index method and a health risk model based on Monte Carlo simulation. The findings reveal that： ① The levels of As and Cr in the surface soil of the investigated region markedly exceeded the regional background values for Yunnan Province. The spatial pattern of the eight heavy metals was largely influenced by the geological background， yet it exhibited considerable deviations attributable to external influences. ② The primary sources of soil heavy metals in the study area were high geological background sources closely associated with gold-arsenic deposits （17.08%）， atmospheric deposition sources related to coal mining and combustion （17.87%）， mixed sources of transportation and agriculture （12.78%）， industrial and mining activity sources closely linked to lead-zinc deposits （12.66%）， and natural parent material sources （39.61%）. ③ The geoaccumulation index （Igeo） evaluation revealed that the pollution levels of Cd， Cr， Cu， Ni， Pb， and Zn were relatively low， while As and Hg exhibited varying degrees of contamination. ④ The non-carcinogenic health risks of soil heavy metals to the local population were negligible. However， there were notable carcinogenic health risks， particularly for children， with As being the dominant carcinogenic factor. The heavy metal element As and ingestion frequency （IngR） were identified as the main factors influencing human health risks in the study area. High geological background sources closely related to gold and arsenic deposits and natural parent material sources were significant contributors to human health risks and should be prioritized for control， with enhanced monitoring and regulation.
To reveal the evolutionary patterns of regional ecological quality under human-nature interactions， we analyzed Henan Province （from 2001 to 2023） using MODIS data， Google Earth Engine （GEE）， and a vegetation ecological quality index （EQI）. Integrated with Theil-Sen trends， Mann-Kendall tests， spatial autocorrelation， and optimal parameter Geodetector （OPGD）， the results showed that： ① EQI increased significantly （0.21%·a-1， P < 0.05； mean： 36.67 to 41.34）， with spatial divergence （30.99% western improvement vs. 13.51% eastern degradation） reducing global autocorrelation （Moran's I：0.84 to 0.74）. ② Drivers transitioned from natural dominance （temperature∩elevation， q=0.30， 2001） to human-nature coupling （nighttime light ∩ elevation， q=0.409， 2012）， and finally land use-human synergy （land type ∩ nighttime light， q=0.5045， 2023）. ③ Policy effectiveness was evident in stable periods （CV < 33%， 2013 to 2023）， while eastern high-variability areas （CV > 35%） indicated compounding anthropogenic and natural pressures. This study validates a "natural→human→integrated" driver progression and proposes actionable zoning strategies， with OPGD-based methods offering broader applicability.
