Accelerating the transition to low-carbon urbanisation was critical for climate change mitigation. The delineation of local carbon emission zones (LCEZs) offers a promising approach for integrating urban morphology with CO2 emissions patterns for targeted planning. However, existing LCEZs frameworks are affected by scale dependence and often provided limited, context-insensitive explanations of emission heterogeneity. This study developed a cross-scale LCEZs framework and applied it to London, New York, Paris, and Sydney. First, the collapse method, grounded in finite-size scaling, was used to identify morphology factors exhibiting cross-scale statistical regularity, thereby mitigating the scale effect of the modifiable areal unit problem. Second, an optimal parameter-based geographical detector (OPGD) model was used to identify city-specific combinations of morphology factors associated with heterogeneity in CO2 emissions to construct LCEZs. Eleven factors passed the collapse screening, including transport, building, and landscape factors. In the constructed LCEZs results of the four cities, at least one transportation-related variable was retained in every combination, while mean building volume (MBV) emerged as a core factor in three of the four metropolises, demonstrating high universality and influence. The resulting LCEZs were statistically validated by Kruskal-Wallis tests, with effect sizes ranging from 0.143 to 0.233, and intra-zone coefficients of variation were reduced by over 80% relative to the global baseline in each metropolis, confirming strong internal homogeneity. This framework provided an basis for comparing morphology–emission associations and informing low-carbon interventions. It offered a repeatable diagnostic procedure to quantify trade-offs and tailor measures to local contexts, bridging computational urban science with planning practice.

Understanding the scale-dependent mechanisms linking landscape patterns to ecosystem services is crucial for sustainable land management, especially in fragmented hilly regions. This study, conducted in the hilly areas of southern China, aimed to quantitatively unravel these mechanisms at an optimal spatial scale. We first identified 14,400 km2 as the scale where landscape metrics stabilized. Using Spatial Error Models (SEM) to control for spatial autocorrelation, we analysed the distinct effects of landscape configuration on key ecosystem services. At the class level, forest aggregation was a consistent positive driver for multiple services; for example, it maintained a stable, significant positive relationship with carbon sequestration across all study years (P < 0.01). Conversely, farmland edge (total edge) significantly promoted nutrient export (P < 0.001), highlighting a functional contrast with natural landscapes. At the landscape level, total edge exhibited a consistent positive effect on several ecosystem services (P < 0.001), whereas increased landscape evenness was a primary inhibitory factor, showing a significant negative correlation with habitat quality (P < 0.001) and a strengthening negative effect on nutrient retention over time (P < 0.01). These findings provide a scale-specific, quantitative foundation for spatial planning, underscoring the necessity of maintaining forest connectivity and strategically managing agricultural-natural land interfaces to enhance ecosystem services bundles in heterogeneous landscapes.

Urbanization introduces heavy-metal contamination while reshaping soil habitats through greenspace management, but their relative roles in structuring soil microbial communities remain unclear. We analyzed soils from industrial areas, urban parks and natural mountainous sites in Xiamen, China, using 16S rRNA gene sequencing, PICRUSt2 functional prediction, generalized linear models, distance-based redundancy analysis and piecewise structural equation modelling. Microbial α-diversity was significantly lower in mountainous soils than in industrial and park soils, and reached its maximum when Cd < 0.25 mg.kg-1 and TP > 6 mg.kg-1. Land use was the main driver of richness, while soil fertility (mainly TP and pH) was the main driver of community composition; heavy metals played only a secondary role because their concentrations were below toxicity thresholds. Mountainous, park and industrial soils were enriched in biosynthesis, energy metabolism and stress-response pathways, respectively. Our research indicates that systematic greenspace management can maintain microbial diversity in moderately contaminated cities without large-scale remediation.

Accurate aboveground biomass (AGB) estimation in subtropical forests is critical for regional carbon accounting and sustainable forest management. However, standardized multi-source feature screening and integrated machine learning–geostatistical analysis of AGB remain limited. This study integrated six heterogeneous datasets: Landsat-8 optical imagery, Sentinel-1 SAR, topographic, meteorological, soil data and the 2014 National Forest Inventory (NFI), and established 48 predictors in subtropical forests of Anhui Province, China. A two-stage variable selection framework was applied, with Pearson correlation screening reducing the initial 48 predictors to 36 less-correlated variables, followed by the recursive feature elimination (RFE) with 5-fold spatial block cross-validation for further predictor selection. Random Forest (RF), eXtreme Gradient Boosting (XGB), Empirical Bayesian Kriging Regression Prediction (EBKRP), hybrid RF_EBKRP and XGB_EBKRP models were evaluated. Stand age and stand density were dominant predictors in both RF and XGB, contributing 33.6% and 24.0% in RF and 36.5% and 17.3% in XGB, respectively. Elevation, precipitation, and canopy cover showed secondary importance, whereas vegetation indices contributed relatively little. RF_EBKRP achieved the highest prediction accuracy (R2 = 0.77), reducing RMSE by 17.20% and 43.75% compared with RF and EBKRP, respectively. This study provides a reproducible RF–EBKRP workflow integrating nonlinear machine-learning prediction with geostatistical residual correction, supporting improved subtropical forest AGB mapping and management.

Regression-based aboveground biomass (AGB) prediction from Earth-observation data often compresses the upper tail of the biomass distribution, yet the relative effectiveness of geospatial residual correction and ensemble learning in fragmented mountain landscapes remains unclear. Thus, we compared the two paths for reducing high-value underestimation: geospatial residual reconstruction using empirical Bayesian kriging regression prediction, and feature-space optimization using Stacking ensemble learning. SHapley Additive exPlanations (SHAP) interpreted feature contributions, and quantile regression forests (QRF) converted high-AGB point estimates into prediction intervals. Results show that geospatial optimization brought limited gain because residual spatial autocorrelation was weak (Moran’s I = 0.10), whereas Stacking improved overall R2 from 0.75 to 0.79 and reduced high-AGB bias (AGB > 80 t/ha) from −13.56 to −5.49 t/ha. This improvement was mainly attributed to complementary heterogeneous learners, with XGBoost capturing the primary non-linear trends, SVR extrapolating to correct high-AGB errors, and RF providing minor marginal calibration. SHAP analysis suggests that LiDAR-derived cubic mean height (Elev_curt_mean_cube) was the dominant feature explaining high-AGB variability, with a threshold response consistent with biomass-height allometry. QRF achieved coverage of 92.8% with a mean interval width of 74.51 t/ha, while coverage in the high-AGB subset was 84.2% with a mean width of 90.50 t/ha. The proposed comparison-and-diagnosis framework provides an interpretable approach for selecting an appropriate correction pathway and supports forest carbon monitoring, carbon accounting, and management decisions in complex mountain ecosystems.

With the rapid development of ecological agriculture, microbial inoculants and organic fertilizers have been widely applied in various agroecological systems. Despite the great prospect of microbial inoculants for improving soil fertility and plant growth, it remains poorly understood whether and how microbial inoculants can amplify the effects of organic fertilizers on soil microbial community structures and tea yields. Here, we conducted a field experiment in tea plantations of Rougui and Shuixian cultivars, where the control group only applied organic fertilizer, and the treatment group used a mixed fertilizer (microbial inoculant and organic fertilizer). After four months of fertilization, soil organic matter, total nitrogen, total phosphorus, and alkali-hydrolyzable nitrogen contents were higher in the Rougui tea plantation applied with mixed fertilizer than in the control, whereas these soil nutrients were lower in the Shuixian tea plantation with mixed fertilizer than in the control. PCoA revealed significant separations of soil bacterial and fungal communities between the Rougui and Shuixian tea plantations, and different fertilization regimes in the Rougui tea plantations (p = 0.001). The addition of microbial inoculants enhanced the stochastic assembly processes of both soil bacterial and fungal communities, with the normalized stochasticity ratio of bacterial communities increased by 42.86% one month after the fertilization (p ≤ 0.0001), and that of fungal communities increased by 79.40% after six months (p ≤ 0.0001). Meanwhile, the co-occurrence networks of soil microbial communities treated with microbial inoculants exhibited higher complexity and stronger negative cohesion and robustness. Importantly, the mixed fertilizer increased the yields of fresh teas of Rougui and Shuixian by 83.61% and 11.81%, respectively, compared with the application of organic fertilizer alone, a pattern consistent with elevated stochastic processes and network complexity in soil microbial communities. Overall, this study provides crucial insights into the soil microbial communities' assemblies and network dynamics under different fertilization strategies, which is essential for improving microbial inoculants to further increase the yields of organic tea plantations.

As a key driver of urban net-zero transitions, climate action (SDG13) can generate synergies with other Sustainable Development Goals (SDGs), but may also create trade-offs and spatially uneven burdens. Evidence remains scarce on how low-carbon policies affect production-side eco-efficiency and its distribution at sub-city scales. Using panel data from 2042 Chinese counties (2000-2023), this study evaluates China's Low-Carbon City Pilot (LCCP) through a staggered difference-in-differences design with spatial econometrics. We construct an industrial water-environment load intensity (IWELI) indicator, defined as the ratio of endpoint water-quality indicators to industrial output, to measure production-side water-environment eco-efficiency. Results show that LCCP raises IWELI by 12.9-13.5%, revealing a distinct short-run trade-off between climate action (SDG13) and sustainable production (SDG12). Mechanism analysis suggests that this trade-off stems mainly from industrial contraction and firm dynamics, with industrial output contracting more sharply in the short run than endpoint water-environment pressure adjusts. Notably, endpoint water-quality regressions show small, mixed coefficients, indicating limited clean-water co-benefits (SDG6) in this phase. Moreover, the policy's effect follows an inverted-U pattern moderated by environmental regulation and innovation capacity, implying that LCCP only enhances eco-efficiency beyond certain thresholds. Spatial analyses further uncover a distance-dependent "halo-haven" pattern: IWELI decreases in counties adjacent to pilot areas but increases within about 100 km among non-adjacent counties, exacerbating spatial inequalities in transition burdens (SDG10). Overall, the findings provide empirical evidence on SDG13-SDG12-SDG6 linkages and underscore the need for integrated policy packages combining stronger enforcement, innovation support, and cross-jurisdictional coordination to foster a just urban transition.

This study develops an equitable carbon intensity allocation scheme to support China’s “14th Five-Year Plan” target of an 18

Land degradation is a significant global environmental challenge that undermines ecosystem services and jeopardizes sustainable development. Robust assessment of degradation status is critical for guiding effective policy interventions. This study assesses the status of Land Degradation Neutrality (LDN) in Asia, utilizing the UN Sustainable Development Goal (SDG) indicator 15.3.1, which encompasses changes in land cover, land productivity, and soil organic carbon. The analysis encompasses 48 Asian countries, covering an area of approximately 31.22 million square kilometers. The results indicate that 25.21% of the land is degraded, with significant regional and country differences. Central Asia has the highest degradation rate at 40.61%, followed by Southern Asia at 25.19% and Eastern Asia at 25.13%. On a national scale, countries such as Armenia, Azerbaijan, Lebanon, and Palestine are experiencing severe land degradation, with rates exceeding 60%. This poses considerable challenges to achieving LDN by 2030. In contrast, Yemen, Saudi Arabia, Oman, and the United Arab Emirates are making notable progress, as their degradation rates are below 9%. Large countries, including China (25.45%), India (25.05%), and Kazakhstan (44.34%), are facing substantial pressure from land degradation. These findings offer an essential foundation for policymakers, development agencies, researchers, and conservation practitioners to create targeted restoration strategies and monitor progress toward achieving LDN goals by 2030 across Asia.

China's pursuit of high-quality development highlights carbon productivity as a vital metric for balancing growth and decarbonization. However, current research on digital inclusive finance (DIF) focuses primarily on emissions, neglecting its impact on carbon productivity and the heterogeneous local-spatial consequences across diverse urban types. This study employed the Spatial Durbin Model and index decomposition analysis to investigate how DIF shaped carbon productivity through the lens of urban unevenness, uncovering a complex landscape of positive local impacts and inhibitory spatial spillovers across 284 prefecture-level cities. The results revealed a dual spatial character where DIF significantly enhanced local carbon productivity while generating negative spatial externalities on neighboring regions. DIF acted as a driver in central and eastern China but presented a suppressive effect in the west. Notably, the effects of DIF were strongly conditioned by city type. Gains were concentrated in industry-based and service-based cities, though spillover directions differed. Only high-end service-based cities exhibited positive spatial externalities, whereas DIF development in light industry-based and general service-based cities created negative cross-city effects. This contrasted with the positive spillovers observed for heavy industry-based cities. Mechanism analysis further showed that DIF enhanced carbon productivity primarily through economic structure optimization and industrial energy efficiency, although the dominant pathway varied across city types. This necessitated region-specific and city-type differentiated DIF deployment strategies to optimize carbon productivity gains and support national climate goals.

The Urban Heat Island (UHI) effect has garnered significant attention due to its detrimental effects, such as increased near-surface temperatures, reduced resident comfort, heat-related illnesses, and damage to urban ecosystems. While strategies including expanding green spaces, optimizing building layouts, adjusting vegetation, and using high-albedo materials are known to mitigate urban thermal conditions, a targeted, comprehensive approach to urban thermal management remains elusive. Our study addresses this gap by introducing a socio-economically driven method to segment the urban landscape into Urban Functional Zones, identifying and prioritizing zones with the most substantial thermal impact for enhancement. We stratify target zones into those requiring no adjustment, temporary non-adjustment, and those needing adjustment, based on the statistical distribution of land surface temperatures. We then employ landscape indices that encapsulate the spatial arrangement of green spaces and built environments, pinpointing specific structural elements within these zones for targeted thermal improvement. Adjustments are made to the building-green space landscape, focusing on high-temperature areas with the aim of aligning temperatures with low-temperature regions, guided by the identified structural elements indicated by landscape indices. Our research presents a clear, actionable framework for urban managers to improve thermal conditions, applicable to various cities requiring such interventions.

The significant impact of urban morphology on CO2 emissions is widely recognised. However, existing studies exhibit considerable variations in findings and conclusions. These discrepancies stem from differences in research scales, selected urban morphology factors, CO2 inventory data, and analysis methods, hindering the development of robust and generalizable knowledge. These discrepancies underscore the need for a comprehensive synthesis to identify the root causes of inconsistency and comparable results. This review synthesises 408 articles to address these discrepancies. Key findings include the identification and categorisation of 147 urban morphology factors into six groups (e.g., building embodied, transport operational, tangible production), and the development of a multi-scale factor selection framework. The identification of 'tele-connection' factors as a primary source of cross-scale inconsistency. Moreover, a comparative assessment shows that uncertainties across nine CO2 inventory datasets remain below 20 Developed a multi-scale framework with 147 factors across seven categories. Identified ‘tele-connection’ factors as a key mechanism for resolving cross-scale inconsistencies. Explored evolving analysis methods through evolutionary trees. Evaluated nine emission inventories' uncertainty to guide robust data selection. Integrated framework linking factors, methods, and data across multi-scale research.

With the acceleration of urbanization, the urban heat island effect has garnered increasing attention. However, few studies have explored the differential impacts of urban green spaces on the UHI across various urban functional zones (UFZs). This study takes Xiamen Island as the research object and selects nine representative landscape pattern indices to characterize the spatial patterns of UGS in each urban functional zone. Through Pearson correlation analysis, four landscape indices—largest patch index (LPI), mean patch area (AREA_MN), area-weighted average shape index (SHAPE_AM), and aggregation index (AI)—were chosen to reveal the varying influences of UGS spatial patterns on the UHI in different urban functional zones. These four landscape indices reflect aspects such as area, shape complexity, density size, and variation, as well as the aggregation of UGS. To address the spatial autocorrelation of variables, a spatial regression model was established. Given that the parameters of the spatial lag model outperformed those of the spatial error model, the spatial lag model was selected. Key findings reveal that the cooling efficiency of UGS varies across UFZs. In urban residential zones (URZs), UGS with complex shapes significantly enhances cooling, as indicated by a negative correlation between SHAPE_AM and LST (β = −0.446, p < 0.05). In urban village zones (UVZs), larger green patches have a stronger cooling effect, with AREA_MN showing a significant negative correlation with LST (β = −1.772, p < 0.05). The results indicate that UGS in different urban functional zones plays distinct roles in mitigating the UHI, with its cooling effects being associated with the spatial patterns of UGS. Therefore, it is recommended to adopt differentiated planning strategies for UGS in various urban functional zones to contribute to a more sustainable and thermally comfortable urban environment.

Urbanization transforms landscapes from natural ecosystems to configurations of impervious surfaces and green spaces, leading to urban heat island effects that impact health and ecosystem sustainability. This study in Xiamen City, China, categorizes urban areas into functional zones, employs Random Forest and Stepwise Regression models to assess thermal differences, and proposes optimization measures for the building–green space landscape. The optimization involves altering the characterization of the building–green space landscape pattern. Results indicate: (1) due to the spatial heterogeneity of the building–green space landscape pattern in different functional zones, the surface temperature also shows strong spatial heterogeneity in different functional zones; (2) different optimization measures for the building–green space pattern are needed for different functional zones; taking the urban residential zone as an example, the Normalized Difference Vegetation Index (NDVI) in the hot spot area can be adjusted according to the value range of the cold spot area; (3) considering the solar radiation process, Sun View Factor (SunVF) plays an important role in indicating the change in surface temperature in the commercial service area, and as SunVF increases, the surface temperature of the functional zone tends to rise. This research offers insights into urban thermal environment improvement and landscape pattern optimization.

Urban heat islands significantly exacerbate thermal discomfort, energy consumption, and public health risks in dense urban cores with limited green space. While landscape optimization is a recognized mitigation strategy, practical and quantifiable approaches for highly urbanized areas remain scarce. This study reconceptualizes the city as a continuous mosaic of intertwined grey (built) and green (vegetated) spaces. We apply a "downscale-classify-attribute" framework to analyze Urban Functional Zones along a grey-to-green continuum. Focusing on Beijing's Fifth Ring Road area, we analyzed 11 landscape metrics across socioeconomic functional zones with Pervious Surface Fraction (PSF) segments (0-1 at 0.05 intervals). Results identified PSF = 0.5 as a critical threshold distinguishing two thermal regulation regimes. Below this value, building patterns (e.g., coverage ratio, height, sky view factor) dominate temperature regulation. In these low-PSF zones (<0.5), a quantile-based optimization framework showed that stringent adjustments (90th/10th percentiles) yielded optimal cooling (up to 2.3 degrees C reduction) with broader spatial coverage, outperforming moderate and neutral approaches. Above PSF = 0.5, vegetation health (NDVI) becomes the primary regulator. For these areas, maintaining healthy vegetation is the priority. This study provides scientifically-grounded, fine-grained solutions tailored to mixed urban landscapes. Our dual-focused framework-architectural optimization for dense zones and vegetation standards for greener areas-offers a transferable strategy to resolve the urban density-thermal comfort paradox.

The synergistic effects of large-scale surface compound ozone and heat (SCOH) present a more extensive and persistent risk to population exposure and environmental safety compared to isolated extreme heat or ozone events. Quantifying the spatiotemporal mechanisms and diffusivity of SCOH in urban areas is therefore critical for risk mitigation. This study integrates the air pollutants spatiotemporal dataset named Multiple Air Pollutants dataset (MuAP) and surface heat datasets to map the 1 km-scale time delay correlation between surface ozone and heat. Combining BayesConvLightGBM and SHapley Additive exPlanations (SHAP), the quantitative influence of urban factors such as building/canopy height and road length on SCOH in predominant urban is examined through scene analysis and diffusion potential analysis. The results show that SCOH has significant temporal and spatial distribution characteristics. Based on more effective spatiotemporal response BayesConvLightGBM modeling of SCOH (The BayesConvLightGBM's R2 is 0.03-0.07 higher than LightGBM), we found that buildings, roads, and trees have the ability to significantly affect SCOH in urban, locally or globally. Meanwhile, more compact planning of urban areas will help reduce the complex risk of SCOH. Even so, it is still important to be aware of the risk of exposure of SCOH to the population at a range of 4 km or more during a 30-day time delay period. This study deepens the quantification of nonlinear interactions between urban infrastructure and SCOH propagation, the understanding of surface compound ozone and heat, and strengthens key elements and quantification approaches using optimized machine learning. This is of great significance to explain the spatiotemporal response of SCOH, and provides an important reference for the study of compound exposure.

Monitoring and evaluating surface water dynamics is crucial for addressing climate change and fostering growth in various sectors. The spectral water index method is a predominant approach for mapping and monitoring surface water. The study was conducted in the southern low mountain and hilly areas of China. This study presents a combined approach to enhancing extraction accuracy in surface water mapping. Five distinct water and vegetation indices were employed alongside various bands. The modified normalized difference water index (mNDWI), combined with the near-infrared (NIR) band, has demonstrated superior extraction accuracy across different types of water compared to the other combinations. The combination of (mNDWI_NIR) was validated with the Joint Research Center (JRC) product Global Surface Water (GSW) dataset and the available surface runoff data. The accuracy of the combined method was assessed using a confusion matrix, which yielded an overall accuracy of 96.90 % and a kappa value of 0.868. It also shows a strong linear correlation with areal surface runoff distributions, with an R2 value of 0.946, compared to GSW and land use and land cover (LULC) values of 0.933 and 0.926, respectively. The method demonstrated a comprehensive approach, stability, and versatility across various environmental conditions over the years in efficiently extracting slender waters. Its usefulness is shown by analyzing spatiotemporal dynamics in the Southern low mountain and hilly areas of China, highlighting its capacity to expand to larger regions, which supports the efforts of the government and water management authorities to recover and restore water resources.

Land degradation (LD) is a critical environmental challenge caused by human activities and climate change. Reversing degraded land requires effective LD monitoring. The UN Sustainable Development Goal (SDG) indicator 15.3.1, "Proportion of land that is degraded over total land area," was established to assess and report LD status at regional and global levels. However, SDG indicator 15.3.1 requires comprehensive, consistent, easily accessible data and would induce large uncertainty, especially in mountainous regions. This study assesses LD in Southern China's mountainous regions by integrating national and global land cover (LC) datasets with a customized LC transition matrix to improve the effectiveness of UN SDG indicator 15.3.1 for LD assessment. The national LC transition matrices were tailored to align with the specific context of the study region and the country's ecological restoration policies and ecosystem services. The results of LD by national LC datasets were compared with the global (default) datasets provided by the Trend.Earth plugin in QGIS. Both sets of results were then compared with the validated LD findings. Using default LC datasets, 20.58% of land areas were classified as degraded, compared to 12.74% with national LC datasets. Land improvement assessed by national data was 7.58% higher than the default datasets. The LD results by national datasets and customized LC transition were closest to the validated LD data, with 94% overall accuracy. Therefore, incorporating national LC datasets and a customized LC transition matrix into UN SDG indicator 15.3.1 could enhance the effectiveness of assessing LD in mountainous regions.