Search Results (2,018)

In highly urbanised and industrialised settings, managing environmental pressures and enhancing urban resilience demand integrated, spatially explicit approaches. This study presents a methodological framework that integrates topographic data, land cover information, and open geodata to produce a high-resolution vulnerability map. A multi-criteria analysis was performed using indicators such as land use, population density, proximity to emission sources, vegetation cover, and sensitive services (e.g., schools and hospitals). The result is a high-resolution vulnerability map that classifies the urban, peri-urban, and coastal zones into five levels of environmental risk. These evaluation levels are derived from geospatial analyses combining pollutant dispersion modelling with land-use classification, enabling the identification of the most vulnerable urban zones. These findings support evidence-based planning and can guide local governments and environmental agencies in prioritising Nature-based Solutions (NBSs), enhancing ecological connectivity, and reducing exposure for vulnerable populations. Full article

Barn districts—ensembles of agricultural buildings situated at the edges of rural settlements—once played a key role in the spatial and economic organization of agrarian communities in Central Europe. Today, many of these structures remain marginalized and underexplored in contemporary landscape and heritage planning. This paper presents a comparative study of six barn districts in Poland’s Kraków-Częstochowa Upland, where vernacular construction, ecological adaptation, and local tradition shaped distinctive rural–urban interfaces. We applied a mixed-methods approach combining cartographic and archival analysis, field surveys, and interviews with residents and experts. The research reveals consistent patterns of landscape transformation, functional decline, and latent adaptive potential across varied morphological and material typologies. Despite differing levels of preservation, barn districts retain symbolic, spatial, and socio-cultural value for communities and local landscapes. The study emphasizes the importance of reintegrating these marginal heritage structures through adaptive reuse strategies rooted in the values of the New European Bauhaus—sustainability, aesthetics, and inclusion. The findings contribute to broader discussions on rural socio-ecological resilience and landscape-based development, highlighting how place-based strategies can bridge past identities with future-oriented spatial planning. Full article

The ongoing global climate change has led to an increase in the frequency and complexity of drought events. Pinus yunnanensis, a native tree species in southwest China that possesses significant ecological and economic value, exhibits a high sensitivity to drought stress, particularly in its seedlings. This study investigates the response mechanisms of non-structural carbohydrates (NSCs, defined as the sum of soluble sugars and starch) and the stoichiometric characteristics of carbon (C), nitrogen (N), and phosphorus (P) to repeated drought conditions in Pinus yunnanensis seedlings. We established three treatment groups in a potting water control experiment involving 2-year-old Pinus yunnanensis seedlings: normal water supply (CK), a single drought (D1), and three drought–rewatering cycles (D3). The findings indicated that the frequency of drought occurrences, organ responses, and their interactions significantly influenced the non-structural carbohydrate (NSC) content and its fractions, as well as the C/N/P content and its stoichiometric ratios. Under D3 treatment, stem NSC content increased by 24.97% and 29.08% compared to CK and D1 groups (p < 0.05), respectively, while root NSC content increased by 41.35% and 49.46% versus CK and D1 (p < 0.05). The pronounced accumulation of soluble sugars and starch in stems and roots under D3 suggests a potential stress memory effect. Additionally, NSC content in the stems increased significantly by 77.88%, while the roots enhanced their resource acquisition by dynamically regulating the C/P ratio, which increased by 23.26% (p < 0.05). Needle leaf C content decreased (18.77%) but P uptake increased (8%) to maintain basal metabolism (p < 0.05). Seedling growth was N-limited (needle N/P < 14) and the degree of N limitation was exacerbated by repeated droughts. Phenotypic plasticity indices and principal component analysis revealed that needle nitrogen and phosphorus, soluble sugars in needles, stem C/N ratio (0.61), root C/N ratio (0.53), and stem C/P ratio were crucial for drought adaptation. This study elucidates the physiological mechanisms underlying the resilience of Pinus yunnanensis seedlings to recurrent droughts, as evidenced by their organ-specific strategies for allocating carbon, nitrogen, and phosphorus, alongside the dynamic regulation of nitrogen storage compounds (NSCs). These findings provide a robust theoretical foundation for implementing drought-resistant afforestation and ecological restoration initiatives targeting Pinus yunnanensis in southwestern China. Full article

Species and phylogenetic diversity play vital roles in sustaining the structure, function, and resilience of plant communities, particularly in tropical rainforests. However, the mechanisms according to which environmental filtering and competitive exclusion influence diversity across forest layers remain insufficiently understood. In this study, we investigated the species and phylogenetic diversity patterns in two representative tropical rainforest sites—Bawangling and Jianfengling—within Hainan Tropical Rainforest National Park, China, focusing on communities associated with the endangered species Hopea hainanensis. We employed a one-way ANOVA and Pearson’s correlation analyses to examine the distribution characteristics and interrelationships among diversity indices and used Mantel tests to assess the correlations with environmental variables. Our results revealed that the plant community in Jianfengling exhibited a significantly higher species richness at the family, genus, and species levels (a total of 288 plant species have been recorded, belonging to 82 families and 183 genera) compared to that in Bawangling (a total of 212 plant species, belonging to 75 families and 162 genera). H. hainanensis held the highest importance value in the middle tree layer across both sites (IV(BWL) = 12.44; IV(JFL) = 5.73), while dominant species varied notably among other forest layers, indicating strong habitat specificity. Diversity indices, including the Simpson index, the Shannon–Wiener index, and Pielou’s evenness, were significantly higher in the large shrub layer of Jianfengling, whereas Bawangling showed a relatively higher Shannon–Wiener index in the middle shrub layer. Phylogenetic diversity (PD) and the phylogenetic structure indices (NRI and NTI) displayed distinct vertical stratification patterns between sites. Furthermore, the PD in Bawangling’s large shrub layer was positively correlated with total phosphorus in the soil, while community evenness was influenced by soil organic carbon and total nitrogen. In Jianfengling, species richness was significantly associated with soil bulk density, altitude, and pH. These findings enhance our understanding of the ecological and evolutionary processes shaping biodiversity in tropical rainforests and highlight the importance of incorporating both species and phylogenetic metrics into the conservation strategies for endangered species such as Hopea hainanensis. Full article

This paper proposes the Open Competency Optimization (OCO) approach, based on adaptive combinatorial operators, to solve the Dynamic Green Traveling Salesman Problem (DG-TSP), which extends the classical TSP by incorporating dynamic travel conditions, realistic road gradients, and energy consumption considerations. The objective is to minimize fuel consumption and emissions by reducing the total tour length under varying conditions. Unlike conventional metaheuristics based on real-coded representations, our method directly operates on combinatorial structures, ensuring efficient adaptation without costly transformations. Embedded within a dynamic metaheuristic framework, our operators continuously refine the routing decisions in response to environmental and demand changes. Experimental assessments conducted in practical contexts reveal that our algorithm attains a tour length of 21,059, which is indicative of a 36.16% reduction in fuel consumption relative to Ant Colony Optimization (ACO) (32,994), a 4.06% decrease when compared to Grey Wolf Optimizer (GWO) (21,949), a 2.95% reduction in relation to Particle Swarm Optimization (PSO) (21,701), and a 0.90% decline when juxtaposed with Genetic Algorithm (GA) (21,251). In terms of overall offline performance, our approach achieves the best score (21,290.9), significantly outperforming ACO (36,957.6), GWO (122,881.04), GA (59,296.5), and PSO (36,744.29), confirming both solution quality and stability over time. These findings underscore the resilience and scalability of the proposed approach for sustainable logistics, presenting a pragmatic resolution to enhance transportation operations within dynamic and ecologically sensitive environments. Full article

The genus Trichoderma plays a pivotal role in sustainable agriculture through its multifaceted contributions to plant health and productivity. This review explores Trichoderma’s biological functions, including its roles as a biocontrol agent, plant growth promoter, and stress resilience enhancer. By producing various enzymes, secondary metabolites, and volatile organic compounds, Trichoderma effectively suppresses plant pathogens, promotes root development, and primes plant immune responses. This review details the evolutionary adaptations of Trichoderma, which has transitioned from saprotrophism to mycoparasitism and established beneficial symbiotic relationships with plants. It also highlights the ecological versatility of Trichoderma in colonizing plant roots and improving soil health, while emphasizing its role in mitigating both biotic and abiotic stressors. With increasing recognition as a biostimulant and biocontrol agent, Trichoderma has become a key player in reducing chemical inputs and advancing eco-friendly farming practices. This review addresses challenges such as strain selection, formulation stability, and regulatory hurdles and concludes by advocating for continued research to optimize Trichoderma’s applications in addressing climate change, enhancing food security, and promoting a sustainable agricultural future. Full article

Accurate estimation of actual evapotranspiration (ET) is critical for understanding hydrothermal cycles and ecosystem functioning in arid regions, where water scarcity governs ecological resilience. To address persistent gaps in ET quantification, this study integrates multi-source remote sensing data, energy balance modeling, and ground-based validation that significantly enhances spatiotemporal ET accuracy in the vulnerable desert steppe ecosystems. The study utilized meteorological data from several national stations and Landsat-8 imagery to process monthly remote sensing images in 2019. The Surface Energy Balance System (SEBS) model, chosen for its ability to estimate ET over large areas, was applied to derive modeled daily ET values, which were validated by a large-weighted lysimeter. It was shown that ET varied seasonally, peaking in July at 6.40 mm/day, and reaching a minimum value in winter with 1.83 mm/day in December. ET was significantly higher in southern regions compared to central and northern areas. SEBS-derived ET showed strong agreement with lysimeter measurements, with a mean relative error of 4.30%, which also consistently outperformed MOD16A2 ET products in accuracy. This spatial heterogeneity was driven by greater vegetation coverage and enhanced precipitation in the southeast. The steppe ET showed a strong positive correlation with surface temperatures and vegetation density. Moreover, the precipitation gradients and land use were primary controllers of spatial ET patterns. The process-based SEBS frameworks demonstrate dual functionality as resource-optimized computational platforms while enabling multi-scale quantification of ET spatiotemporal heterogeneity; it was therefore a reliable tool for ecohydrological assessments in an arid steppe, providing critical insights for water resource management and drought monitoring. Full article

The intensification of anthropogenic pressures, particularly those related to agriculture driven by increasing demands for food and cash crops, generates negative environmental externalities. Assessing these externalities is essential to better identify and implement measures that promote the environmental sustainability of rural landscapes. This study aims to develop a multi-criteria assessment method of the negative environmental externalities of rural landscapes in the northern Benin agricultural basin, based on satellite-derived data. Starting from a 12-class land cover map produced through satellite image classification, the evaluation was conducted in three steps. First, the 12 land cover classes were reclassified into Human Disturbance Coefficients (HDCs) via a weighted sum model multi-criteria analysis based on nine criteria related to the negative environmental externalities of anthropogenic activities. Second, the HDC classes were spatially aggregated using a regular grid of 1 km² landscape cells to produce the Landscape Environmental Sustainability Index (LESI). Finally, various discretization methods were applied to the LESI for cartographic representation, enhancing spatial interpretation. Results indicate that most areas exhibit moderate environmental externalities (HDC and LESI values between 2.5 and 3.5), covering 63–75% (HDC) and 83–94% (LESI) of the respective sites. Areas of low environmental externalities (values between 1.5 and 2.5) account for 20–24% (HDC) and 5–13% (LESI). The LESI, derived from accessible and cost-effective satellite data, offers a scalable, reproducible, and spatially explicit tool for monitoring landscape sustainability. It holds potential for guiding territorial governance and supporting transitions towards more sustainable land management practices. Future improvements may include, among others, refining the evaluation criteria and introducing variable criteria weighting schemes depending on land cover or region. Full article

Common prosperity serves as a pivotal condition for achieving sustainable development by fostering social equity, bolstering economic resilience, and promoting environmental stewardship. Differential land revenue, as a crucial form of property based on spatial resource occupation, significantly contributes to the achievement of common prosperity, though empirical evidence of its impact is limited. This study explores the potential influence of land utilization revenue disparity on common prosperity from the perspective of urban macro differential rent (UMDR). Utilizing panel data from 280 Chinese cities spanning 2007 to 2020, we discover that UMDR and common prosperity levels exhibit strikingly similar spatiotemporal evolution. Further empirical analysis shows that UMDR significantly raises urban common prosperity levels, with a 0.217 standard unit increase in common prosperity for every 1 standard unit rise in UMDR. This boost stems from enhanced urban prosperity and the sharing of development achievements, encompassing economic growth, improved public services, enhanced ecological civilization, and more equitable distribution of development gains between urban and rural areas and among individuals. Additionally, we observe that UMDR has a more pronounced effect on common prosperity in eastern cities and those with a predominant service industry. This study enhances the comprehension of the relationship between urban land revenue disparities, prosperity, and equitable sharing, presenting a new perspective for the administration to contemplate the utilization of land-based policy tools in pursuit of the common prosperity goal and ultimately achieve sustainable development. Full article

In river-connected lake regions, both land use and hydrological regime changes may affect the ecosystem services; however, few studies have attempted to elucidate their complex influences. In this study, the spatiotemporal dynamics of eight ecosystem services (crop production, aquatic production, water yield, soil retention, flood regulation, water purification, net primary productivity, and habitat quality) were investigated through remote-sensing images and the InVEST model in the Dongting Lake Region during 2000–2020. Results revealed that crop and aquatic production increased significantly from 2000 to 2020, particularly in the northwestern and central regions, while soil retention and net primary productivity also improved. However, flood regulation, water purification, and habitat quality decreased, with the fastest decline in habitat quality occurring at the periphery of the Dongting Lake. Land-use types accounted for 63.3%, 53.8%, and 40.3% of spatial heterogeneity in habitat quality, flood regulation, and water purification, respectively. Land-use changes, particularly the expansion of construction land and the conversion of water bodies to cropland, led to a sharp decline in soil retention, flood regulation, water purification, net primary productivity, and habitat quality. In addition, crop production and aquatic production were higher in cultivated land and residential land, while the accompanying degradation of flood regulation, water purification, and habitat quality formed a “production-pollution-degradation” spatial coupling pattern. Furthermore, hydrological fluctuations further complicated these dynamics; wet years amplified agricultural outputs but intensified ecological degradation through spatial spillover effects. These findings underscore the need for integrated land-use and hydrological management strategies that balance human livelihoods with ecosystem resilience. Full article

Improving the ecological resilience in the Yellow River Basin is a crucial way to achieve ecological conservation and high-quality development in the region. Based on the panel data from 2011 to 2023 of 57 cities in the Yellow River Basin, the ecological resilience of each city was measured by using the Catastrophe Progression Model, and its spatial differences and dynamic evolution characteristics were analyzed by the Dagum Gini coefficient and kernel density estimation. At the same time, the STIRPAT model was integrated with the random forest model to identify the key factors influencing urban ecological resilience. The results demonstrated the following: (1) The urban ecological resilience in the Yellow River Basin exhibited a slight upward trend during 2011–2020 and presented a gradient spatial pattern with “high in the east and low in the west”. (2) Hypervariation density is the main source of spatial difference in urban ecological resilience, with trailing and polarization phenomena across the entire basin and its three major subregions. (3) There was significant regional heterogeneity of influences in the urban ecological resilience, with upstream, midstream, and downstream regions characterized by low interference intensity, high sensitivity, and strong adaptability, respectively. Full article

Due to climate effects and human influences, wildfire regimes in boreal forests are changing, leading to profound ecological consequences, including shortened fire return intervals and elevated tree mortality. However, a critical knowledge gap exists concerning the spatiotemporal dynamics of fire-induced tree mortality specifically within the vast North American boreal forest, as previous studies have predominantly focused on Mediterranean and tropical forests. Therefore, in this study, we used satellite observation data obtained by the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua and Terra MCD64A1 and related database data to study the spatial and temporal variability in burned area and forest mortality due to wildfires in North America (Alaska and Canada) over an 18-year period (2003 to 2020). By calculating the satellite reflectance data before and after the fire, fire-driven forest mortality is defined as the ratio of the area of forest loss in a given period relative to the total forest area in that period, i.e., the area of forest loss divided by the total forest area. Our findings have shown average values of burned area and forest mortality close to 8000 km²/yr and 40%, respectively. Burning and tree loss are mainly concentrated between May and September, with a corresponding temporal trend in the occurrence of forest fires and high mortality. In addition, large-scale forest fires were primarily concentrated in Central Canada, which, however, did not show the highest forest mortality (in contrast to the results recorded in Northern Canada). Critically, based on generalized linear models (GLMs), the results showed that fire size and duration, but not the burned area, had significant effects on post-fire forest mortality. Overall, this study shed light on the most sensitive forest areas and time periods to the detrimental effects of forest wildfire in boreal forests of North America, highlighting distinct spatial and temporal vulnerabilities within the boreal forest and demonstrating that fire regimes (size and duration) are primary drivers of ecological impact. These insights are crucial for refining models of boreal forest carbon dynamics, assessing ecosystem resilience under changing fire regimes, and informing targeted forest management and conservation strategies to mitigate wildfire impacts in this globally significant biome. Full article

Achieving a win-win situation for both economy and ecology is crucial for promoting sustainable social development and shaping new advantages in high-quality developments. This article constructs an ecological economic resilience (EER) analysis framework by integrating both ecological and economic dimensions from a resilience perspective. Based on panel data from 290 cities in China, it explores the dynamic evolution characteristics, regional differences, and convergence trends of EER. The findings indicate that the EER has weakened nationwide and in the four major economic regions, overall tending towards stability. Significant disparities exist in EER, particularly pronounced in the northeast. There is $\sigma$ convergence in the nation as well as in the northeast and east regions. Additionally, both absolute and conditional $\beta$ convergence is evident nationwide and in all regions, with conditional convergence occurring at a faster pace. The research findings in this paper provide solid theoretical support for promoting regional coordinated development and constructing a new development paradigm. Full article

The Tibetan-Himalayan region, recognized as a global biodiversity hotspot, is increasingly threatened by the dual pressures of climate change and human activities. Understanding the vulnerability of plant species to these forces is crucial for effective ecological conservation in this region. This study employed an improved Climate Niche Factor Analysis (CNFA) framework to assess the vulnerability of six representative alpine endemic herbaceous plants in this ecologically sensitive region under future climate changes. Our results show distinct spatial vulnerability patterns for the six species, with higher vulnerability in the western regions of the Tibetan-Himalayan region and lower vulnerability in the eastern areas. Particularly under high-emission scenarios (SSP5-8.5), climate change is projected to substantially intensify threats to these plant species, reinforcing the imperative for targeted conservation strategies. Additionally, we found that the current coverage of protected areas (PAs) within the species’ habitats was severely insufficient, with less than 25% coverage overall, and it was even lower (<7%) in highly vulnerable regions. Human activity hotspots, such as the regions around Lhasa and Chengdu, further exacerbate species vulnerability. Notably, some species currently classified as least concern (e.g., Stipa purpurea (S. purpurea)) according to the IUCN Red List exhibit higher vulnerability than species listed as near threatened (e.g., Cyananthus microphyllus (C. microphylla)) under future climate change. These findings suggest that existing biodiversity assessments, such as the IUCN Red List, may not adequately account for future climate risks, highlighting the importance of incorporating climate change projections into conservation planning. Our study calls for expanding and optimizing PAs, improving management, and enhancing climate resilience to mitigate biodiversity loss in the face of climate change and human pressures. Full article

Effective management of cascade reservoir systems is essential for balancing hydropower generation, flood control, and ecological sustainability, especially under increasingly uncertain runoff conditions driven by climate change. Traditional optimization methods, while widely used, often struggle with high dimensionality and fail to adequately address inflow variability. This study introduces a novel deep reinforcement learning (DRL) framework that tightly couples probabilistic runoff forecasting with adaptive reservoir scheduling. We integrate a Long Short-Term Memory (LSTM) neural network to model runoff uncertainty and generate probabilistic inflow forecasts, which are then embedded into a Proximal Policy Optimization (PPO) algorithm via Monte Carlo sampling. This unified forecast–optimize architecture allows for dynamic policy adjustment in response to stochastic hydrological conditions. A case study on China’s Xiluodu–Xiangjiaba cascade system demonstrates that the proposed LSTM-PPO framework achieves superior performance compared to traditional baselines, notably improving power output, storage utilization, and spillage reduction. The results highlight the method’s robustness and scalability, suggesting strong potential for supporting resilient water–energy nexus management under complex environmental uncertainty. Full article