Search Results (80)

Many cities worldwide face decline due to mineral-resource exhaustion, with mining-induced subsidence and land degradation posing urgent land use challenges. At the same time, carbon neutrality has become a global agenda, promoting ecological restoration, emissions reduction, and green transformation in resource-exhausted cities. However, empirical evidence on how carbon neutrality strategies drive land use transition remains scarce. Taking Xuzhou, China, as a case study, we integrate the GeoSOS–FLUS land use simulation model with a Markov chain model to project land use patterns in 2030 under three scenarios: natural development (ND), land recovery (LR), and carbon neutrality (CN). Using emission factors and a land use carbon inventory, we quantify spatial distributions and temporal shifts in carbon emission and sequestration. Results show that LR’s rigid recovery policies restrict broader transitions, while the CN scenario effectively reshapes land use by enhancing the competitiveness of low-carbon types such as forests and new-energy land. Under CN, built-up land expansion is curbed, forests and new-energy land are maximized, and emissions fall by 4.95% from 2020. Carbon neutrality offers opportunities for industrial renewal and ecological restoration in resource-exhausted cities, steering transformations toward approaches that balance ecological function and carbon benefits. Long-term monitoring is required to evaluate policy sustainability and effectiveness. Full article

Soil heavy metal contamination in agricultural land has emerged as a critical environmental issue, threatening both food security and ecological sustainability. However, the contamination characteristics and associated potential ecological risks under different land use types remain poorly understood. This study presents a systematic comparison of heavy-metal pollution between three distinct agricultural land use systems (suburban vegetable fields, paddy fields, and maize fields) using an integrated approach that combines spatial analysis, pollution indices, and receptor modeling. Dehui City, a major grain-producing region in Northeast China, was selected as the study region, in which 73 topsoil samples were systematically collected. The concentrations and spatial distributions of heavy metals (Cd, Cr, Cu, Hg, Ni, Pb, Zn, and As) were analyzed. Source apportionment of soil heavy metals was performed using principal component analysis (PCA) and positive matrix factorization (PMF), while pollution assessment employed the geo-accumulation index (I_geo), Nemerow integrated pollution index (NIPI), and potential ecological risk index (PERI). The results showed that the mean concentrations of all heavy metals exceeded the soil background values for Jilin Province. The enrichment factors for Hg, Pb, and Cu were 3.51, 1.32, and 1.31, respectively, while all metals remained below the risk screening values (GB 15618-2018, China) for agricultural soils. Land use-specific patterns in heavy-metal accumulation were evident. Suburban vegetable fields showed elevated levels of Ni, As, and Cr, paddy fields showed elevated levels of Cd, Hg, and As, and maize fields showed elevated levels of Hg and Pb. Source apportionment revealed that agricultural fertilization, traffic emissions, industrial and coal-combustion activities, and natural sources were the main contributors. Notably, industrial and coal-combustion sources accounted for 77.7% of Hg in maize fields, while agricultural fertilization contributed 67.7% of Cd in suburban vegetable fields. The I_geo results indicated that 65.75% of the sampling sites exhibited slight or higher pollution levels for Hg. However, the NIPI results showed that 97.26% of the sampling sites remained at a safe level (NIPI < 0.7). The PERI results revealed a moderate ecological risk across the study area, with the risk levels following the order: maize fields > paddy fields > vegetable fields. Although agricultural soils generally met the safety standards, Hg-dominated ecological risks warrant priority attention and mitigation measures. Full article

Historical irrigation districts (HIDs) are integrated systems of natural and cultural assets, with cultivated land providing critical functions such as food security, environmental conservation, and cultural inheritance. This study presents a research framework for evaluating multifunctional potential, performance, and geographical matching along the “potential-performance” dimensions using analytical tools such as SPSS26.0, ArcGIS pro3.5.2, GeoDa1.22, InVEST3.13, and bivariate spatial autocorrelation. We use Mulanbei HID in China as a case study because of its thousand-year irrigation history and unique location at the intersection of coastal urban and rural communities. The results show the following: (1) In the Mulanbei HID, multifunctional cultivated land exhibits functions in the following order: producing functions, ecological functions, landscape–cultural functions, and social functions. The production function has a homogenous distribution characterized by high values. The ecological function, on the other hand, is distinguished by high-value clusters that decrease significantly as building land approaches its periphery. Social and landscape–cultural roles continue to be undervalued, with high-value places isolated on metropolitan margins. (2) In terms of matching multifunctional potential and performance, in the High-Potential–High-Performance cluster, production and ecological functions account for 19% and 20%, respectively, while in the High-Potential–Low-Performance cluster, social and landscape–cultural functions account for 33% and 27%. The Low-Potential–Low-Performance cluster has 4% production, 4% ecological, 10% social, and 13% landscape–cultural functions, but all four functions are less than 4% in the Low-Potential–High-Performance cluster. These findings provide a scientific foundation for improving cultivated land zoning and governance with a focus on heritage protection. Full article

Amid climate change and land-use transformation, the scientific identification of high-quality arable land reserves is critical for safeguarding both cropland quantity and quality. Conventional approaches, largely based on spatial autocorrelation and heterogeneity theories, inadequately capture the multi-scale integration of ecological functions and carbon cycling, particularly in ecologically high-risk areas where systematic identification and mechanism analysis are lacking. To address these challenges, this study introduces a geographically similar “grain-carbon” synergistic framework, paired with a “bidirectional optimization” strategy (negative elimination + positive selection), to overcome the shortcomings of traditional methods and mitigate grain–carbon trade-offs in high-risk areas. Using land-use data from Yunnan’s mountainous areas (2000–2020), integrated with InVEST-PLUS model outputs, multi-source remote sensing, and carbon pool datasets, we developed a dynamic land-use–carbon storage simulation framework under four policy scenarios: natural development, urban expansion, arable land protection, and ecological conservation. High-quality arable lands were identified through a geographic similarity analysis with the Geo detector, incorporating ecological vulnerability and landscape risk indices to delineate priority high-risk zones. Carbon storage degradation trends and land-use pressures were further considered to identify optimal areas for cropland-to-forest conversion, facilitating the implementation of the bidirectional optimization strategy. Multi-scenario simulations revealed an increase of 454.33 km² in high-quality arable land, with the optimized scenario achieving a maximum carbon storage gain of 23.54 × 10⁶ t, reversing carbon loss trends and enhancing both farmland protection and carbon sequestration. These findings validate the framework’s effectiveness, overcoming limitations of traditional methods and providing a robust strategy for coordinated optimization of carbon storage and arable land conservation in ecologically high-risk regions, with implications for regional carbon neutrality and food security. Full article

The contemporary landscape is characterised by overlapping values and pressures, where ecosystem services and cultural spaces are used by diverse categories of users. In fragile contexts such as the Phlegraean Fields in Italy, the exponential growth of mass tourism has intensified the anthropogenic impacts, exacerbated by limited landscape awareness among local communities. Thus, walkability fosters direct exploration, while experiential transects provide a lens to read ecological, cultural, and perceptual layers of places. Together with digital storytelling, these approaches converge in a phygital approach that enriches physical experience without supplanting it. The study covered approximately 115 km of routes across five municipalities, combining road audits, an 11-item survey, participatory mapping, and ArcGIS StoryMaps. Results showed a structurally complex and functionally fragile mobility system: sidewalks are discontinuous, lighting insufficient, less than one quarter of the network is fully pedestrian, and cycling facilities are almost absent. At the same time, digital layers diversified routes and supported situated learning. By integrating geo-spatial analysis and phygital tools, the research demonstrates a replicable strategy to enhance the awareness and sustainable enjoyment of complex landscapes. The present research is part of the PNRR project Changes ‘PE5Changes_Spoke1-WP4-Historical Landscapes Traditions and Cultural Identities’. Full article

Tailings dam failures are often caused by seepage, posing severe threats to mine safety and downstream ecological environments. Conventional tailings stacking methods are prone to drainage blockage and slope instability under long-term seepage conditions. To address this issue, this study proposes a novel structural form that combines waste rock pillars with tailings stacking to construct a drainage system characterized by high permeability, anti-clogging capability, and load-bearing performance. A prototype-similar physical model test was conducted to systematically analyze the seepage characteristics and stability variations in the tailings dam under different dry beach lengths. In addition, numerical simulations using Geo-Studio 2022.1 (SEEP/W and SLOPE/W) were performed to verify and extend the experimental results. The findings show that the introduction of waste rock pillars forms effective preferential drainage channels, significantly reduces pore water pressure, and lowers the phreatic line within the dam body, thereby enhancing its overall stability. Compared with the conventional stacking method without waste rock pillars, the safety factor of the dam increased by 8.6–20.0% as the dry beach length extended from 70 m to 150 m, confirming the remarkable reinforcement and drainage performance of the composite structure. The study demonstrates that the proposed “high-permeability, anti-clogging, and load-bearing” waste rock pillar design not only achieves efficient reuse of waste rock resources but also provides a novel and sustainable technical approach for improving tailings dam safety through coupled physical and numerical verification. Full article

Intensive human interference has severely disrupted the natural and ecological environments of coastal areas, threatening ecosystem services (ESs). Meanwhile, the relationships between ESs exhibit certain variations across different spatial scales. Therefore, identifying the scale effects of interrelationships among ESs and their underlying driving mechanisms will better support scientific decision-making for the hierarchical and sustainable management of coastal ecosystems. Therefore, employing the Integrated Valuation of ESs and Tradeoffs (InVEST) model combined with GIS spatial visualization techniques, this investigation systematically examined the spatiotemporal distribution of four ESs across three scales (grid, county, and city) during 2000–2020. Complementary statistical approaches (Spearman’s correlation analysis and bivariate Moran’s I) were integrated to systematically quantify evolving ES trade-off/synergy patterns and reveal their spatial self-correlation characteristics. The geographical detector model (GeoDetector) was used to identify the main driving factors affecting ESs at different scales, and combined with bivariate Moran’s I to further visualize the spatial differentiation patterns of these key drivers. The results indicated that: (1) ESs (except for Water yield) generally increased from coastal regions to inland areas, and their spatial distribution tended to become more clustered as the scale increased. (2) Relationships between ESs became stronger at larger scales across all three study levels. These ESs connections showed stronger links at the middle scale (county). (3) Natural factors had the greatest impact on ESs than anthropogenic factors, with both demonstrating increased explanatory power as the scale enlarges. The interactions between factors of the same type generally yield stronger explanatory power than any single factor alone. (4) The spatial aggregation patterns of ESs with different driving factors varied significantly, while the spatial aggregation patterns of ESs with the same driving factor were highly similar across different spatial scales. These findings confirm that natural and social factors exhibit scale dependency and spatial heterogeneity, emphasizing the need for policies to be tailored to specific scales and adapted to local conditions. It provides a basis for future research on multi-scale and region-specific precision regulation of ecosystems. Full article

Retrieving and predicting summer average near-surface air temperature (SANSAT) across China remain challenging due to the country’s complex topography and heterogeneous vegetation cover. This study proposes an innovative deep learning framework that incorporates vegetation regionalization to achieve high-precision spatiotemporal temperature retrieval and prediction. Using MODIS land surface temperature, vegetation indices, weather station data (2000–2019) and other relevant datasets, we first apply GeoDetector to identify key influencing factors (e.g., nighttime surface temperature, elevation, vegetation index, and population density) within each vegetation region. Based on these findings, we develop a deep neural network (DNN) model, which achieves high accuracy in SANSAT retrieval (with validation R² ranging from 0.90 to 0.97 and RMSE from 0.46 to 0.64 °C). Results indicate that temperature variations in the eastern monsoon region are primarily influenced by human activity and topography, whereas natural factors dominate in the western regions. Subsequently, using a Long Short-Term Memory (LSTM) network with an optimal seven-year time step, we predict SANSAT for 2020–2023, achieving R² values of 0.71 in training and 0.69 in testing, which confirms the model’s high reliability in SANSAT prediction. The core innovation of this work lies in its vegetation-regionalized deep learning approach, which explicitly addresses landscape heterogeneity by customizing models to specific eco-climatic zones, thereby quantifying human-nature interactions more effectively than traditional, spatially uniform methods. This framework enhances the understanding of summer temperature dynamics and provides valuable spatial data to support applications in agricultural disaster prevention, ecological conservation, and carbon neutrality. Future research will incorporate multi-seasonal data and enhance the spatiotemporal resolution to further improve NSAT modeling. Full article

This study employs a systems theory approach to investigate the coupling coordination and driving mechanisms within the economic–social–environmental (ESE) system in China’s ethnic regions. It analyzes 67 ethnic counties in Sichuan Province, using an integrated framework that combines dynamic Shannon entropy, coupling coordination modeling, and GeoDetector. Based on data from 2005 to 2024, the study reveals the spatiotemporal patterns of ESE coupling coordination. The key findings are as follows: (1) The coupling coordination degree has gone through four stages: moderate imbalance → mild imbalance → primary coordination → moderate coordination. By 2024, 81.8% of counties had achieved coordinated development, and “highly coordinated” counties emerged for the first time. (2) The Western Sichuan Plateau has formed a high–high agglomeration zone by monetizing ecological assets and utilizing ethnic cultural resources. In contrast, the hilly and parallel ridge–valley regions in central and eastern Sichuan remain in low–low agglomerations due to their dependency on traditional industrialization paths. The decrease in high–low and low–high outliers indicates the recent policy polarization effects. (3) The interaction between habitat quality and per capita GDP has the strongest explanatory power. The rising marginal contributions of energy and carbon emission intensity suggest that green industrialization is crucial to breaking the “poverty–pollution” trap. Full article

Municipal sewage sludge is a potential soil amendment rich in organic matter and nutrients, yet its reuse is often constrained by heavy metal contamination. This study evaluated six heavy metals (Cd, Cr, Cu, Ni, Pb, and Zn) in sludge collected from seven centralized wastewater treatment plants in Bangkok, Thailand, by analyzing physicochemical properties, total metal concentrations, and chemical speciation. Three ecological risk indices, the geo-accumulation index (I_geo), risk assessment code (RAC), and potential ecological risk index (PERI), were applied to assess contamination status, mobility, and ecological threat. The sludge exhibited high levels of organic matter and essential nutrients, indicating potential for agricultural reuse; however, elevated electrical conductivity at some sites may pose salinity risks if unmanaged. Speciation analysis revealed that Cd and Zn were largely present in mobile and redox-sensitive fractions, Cr and Pb were primarily in stable residual forms, and Cu and Ni occurred in moderately mobile forms influenced by environmental conditions. Across all indices, Cd consistently posed the highest ecological risk, followed by Zn, in a site-dependent manner, while Cr and Pb represented low risk. These findings provide a clearer understanding of metal behavior in sewage sludge and underscore the importance of integrating chemical speciation with multi-index risk assessment in sludge management. Incorporating such approaches into national guidelines, particularly in countries lacking established heavy metal limits, can strengthen monitoring frameworks, guide safe and sustainable reuse, and support regulatory development in contexts with limited monitoring data. Full article

Coastal zones face mounting pressures from rapid urban expansion and ecological degradation, posing significant challenges to achieving synergistic carbon storage and emissions reduction under China’s “dual carbon” goals. Yet, the identification of spatially explicit zones of carbon synergy (high storage–low emissions) and conflict (high emissions–low storage) in these regions remains limited. This study integrates the PLUS (Patch-generating Land Use Simulation), InVEST (Integrated Valuation of Ecosystem Services and Trade-offs), and OPGD (optimal parameter-based GeoDetector) models to evaluate the impacts of land-use/cover change (LUCC) on coastal carbon dynamics in China from 2000 to 2030. Four contrasting land-use scenarios (natural development, economic development, ecological protection, and farmland protection) were simulated to project carbon trajectories by 2030. From 2000 to 2020, rapid urbanization resulted in a 29,929 km² loss of farmland and a 43,711 km² increase in construction land, leading to a net carbon storage loss of 278.39 Tg. Scenario analysis showed that by 2030, ecological and farmland protection strategies could increase carbon storage by 110.77 Tg and 110.02 Tg, respectively, while economic development may further exacerbate carbon loss. Spatial analysis reveals that carbon conflict zones were concentrated in major urban agglomerations, whereas spatial synergy zones were primarily located in forest-rich regions such as the Zhejiang–Fujian and Guangdong–Guangxi corridors. The OPGD results demonstrate that carbon synergy was driven largely by interactions between socioeconomic factors (e.g., population density and nighttime light index) and natural variables (e.g., mean annual temperature, precipitation, and elevation). These findings emphasize the need to harmonize urban development with ecological conservation through farmland protection, reforestation, and low-emission planning. This study, for the first time, based on the PLUS-Invest-OPGD framework, proposes the concepts of “carbon synergy” and “carbon conflict” regions and their operational procedures. Compared with the single analysis of the spatial distribution and driving mechanisms of carbon stocks or carbon emissions, this method integrates both aspects, providing a transferable approach for assessing the carbon dynamic processes in coastal areas and guiding global sustainable planning. Full article

The Santurbán paramo is a sensitive high-mountain ecosystem exposed to pressures from extractive and agricultural activities, as well as increasing tourism. In response, this study presents a context-aware recommendation system designed to support sustainable tourism through the integration of deep neural networks and ontology-based semantic modeling. The proposed system delivers personalized recommendations—such as activities, accommodations, and ecological routes—by processing user preferences, geolocation data, and contextual features, including cost and popularity. The architecture combines a trained TensorFlow Lite model with a domain ontology enriched with GeoSPARQL for geospatial reasoning. All inference operations are conducted locally on Android devices, supported by SQLite for offline data storage, which ensures functionality in connectivity-restricted environments and preserves user privacy. Additionally, the system employs geofencing to trigger real-time environmental notifications when users approach ecologically sensitive zones, promoting responsible behavior and biodiversity awareness. By incorporating structured semantic knowledge with adaptive machine learning, the system enables low-latency, personalized, and conservation-oriented recommendations. This approach contributes to the sustainable management of natural reserves by aligning individual tourism experiences with ecological protection objectives, particularly in remote areas like the Santurbán paramo. Full article

This paper outlines a methodology to compile geo-referenced observational data of Australian birds acting as pollinators of Strelitzia sp. (Bird of Paradise) flowers and dispersers of their seeds. Given the absence of systematic published records, a crowdsourcing approach was employed, combining data from natural history platforms (e.g., iNaturalist, eBird), image hosting websites (e.g., Flickr) and, in particular, social media. Facebook emerged as the most productive channel, with 61.4% of the 301 usable observations sourced from 43 ornithology-related groups. The strategy included direct solicitation of images and metadata via group posts and follow-up communication. The holistic, snowballing search strategy yielded a unique, behavior-focused dataset suitable for analysis. While the process exposed limitations due to user self-censorship on image quality and completeness, the approach demonstrates the viability of crowdsourced behavioral ecology data and contributes a replicable methodology for similar studies in under-documented ecological contexts. Full article

A system of field protective forest belts (FPFBs) was created in the middle of the 20th century in Southern Dobrudzha (Northern Bulgaria) to reduce wind erosion, improve soil moisture storage, and increase agricultural crop yields. Since 2020, prolonged climatic drought during growing seasons and the advanced age of trees have adversely impacted the health status of planted species and resulted in the decline and dieback of the FPFBs. Physiologically stressed trees have become less able to resist pests, such as insects and diseases. In this work, an original new methodology for the integrated assessment of the condition of FPFBs and their protective capacity is presented. The presented methods include the assessment of structural and functional characteristics, as well as the health status of the dominant tree species. Five indicators were identified that, to the greatest extent, present the ability of forest belts to perform their protective functions. Each indicator was evaluated separately, and then an overlay analysis was applied to generate an integrated assessment of the condition of individual forest belts. Three groups of FPFBs were differentiated according to their condition: in good condition, in moderate condition, and in bad condition. The methodology was successfully tested in Southern Dobrudzha, but it could be applied to other regions in Bulgaria where FPFBs were planted, regardless of their location, composition, origin, and age. This methodological approach could be transferred to other countries after adapting to their geo-ecological and agroforest specifics. The methodological approach is an informative and useful tool to support decision-making about FPFB management, as well as the proactive planning of necessary forestry activities for the reconstruction of degraded belts. Full article

This study investigates rural settlement patterns and land suitability in Rwanda’s Western Highlands, a mountainous region highly vulnerable to geohazards like landslides and flooding. Its primary aim is to inform sustainable, climate-resilient development planning in this fragile landscape. We employed high-resolution satellite imagery, a Digital Elevation Model (DEM), and comprehensive geospatial datasets to analyze settlement distribution, using Thiessen polygons for influence zones and Kernel Density Estimation (KDE) for spatial clustering. The Analytic Hierarchy Process (AHP) was integrated with the GeoDetector model to objectively weight criteria and analyze settlement pattern drivers, using population density as a proxy for human pressure. The analysis revealed significant spatial heterogeneity in settlement distribution, with both clustered and dispersed forms exhibiting distinct exposure levels to environmental hazards. Natural factors, particularly slope gradient and proximity to rivers, emerged as dominant determinants. Furthermore, significant synergistic interactions were observed between environmental attributes and infrastructure accessibility (roads and urban centers), collectively shaping settlement resilience. This integrative geospatial approach enhances understanding of complex rural settlement dynamics in ecologically sensitive mountainous regions. The empirically grounded insights offer a robust decision-support framework for climate adaptation and disaster risk reduction, contributing to more resilient rural planning strategies in Rwanda and similar Central African highland regions. Full article