Search Results (4,890)

Globally, intensive groundwater extraction has led to widespread land subsidence, posing severe threats to urban infrastructure, structural safety, and flood control capacity, and resulting in substantial economic losses and ecological degradation. Based on dynamic monitoring data and a poroelastic fluid–solid coupling model developed using COMSOL Multiphysics 6.2, this study systematically investigates the characteristics and evolution of land subsidence in Decheng District before and after the implementation of a groundwater extraction ban. Furthermore, recommendations and strategies for the sustainable management of regional groundwater resources are proposed. The results indicate that after the ban was enforced in 2020, the extraction volumes of deep and shallow groundwater in Decheng District decreased from 830,000 m³/a and 33,070,000 m³/a to 178,000 m³/a and 20,775,000 m³/a, respectively. The ban significantly influenced groundwater levels, with the recovery rate of deep groundwater increasing markedly from approximately 0.5 m/a before the ban to about 5 m/a afterward. Groundwater levels directly govern the rate of land subsidence; their decline increases the effective stress within the strata, leading to aquifer compaction and subsequent subsidence. Following the ban, the subsidence rate in Decheng District decreased significantly, with the annual subsidence volume reduced by more than 80% compared to the pre-ban period. Predictive analysis using the fluid–solid coupling model reveals that extraction from deep confined aquifers is the main driver of regional subsidence, with a time lag of approximately five years between groundwater level changes and subsidence response. After the implementation of the extraction ban, the subsidence rate slowed considerably. Over the long term, the subsiding strata tend to stabilize, although most of the subsidence that has already occurred is irreversible, making it difficult for the strata to return to their original state. In summary, the groundwater extraction ban has effectively facilitated groundwater recovery and mitigated land subsidence in Decheng District, though the response exhibits both temporal lag and spatial variability. Future work should focus on establishing an integrated monitoring and regulation system for land subsidence and groundwater dynamics to ensure the coordinated security of both water resources and the geological environment. These findings provide a scientific basis for informing land subsidence prevention and guiding the rational exploitation of groundwater resources in Decheng District. Full article

Salinity is a long-standing global environmental stressor of terrestrial agroecosystems, with important implications for viticulture sustainability, especially in arid and semi-arid environments. Salt-induced physiological and biochemical disruptions to grapevines undermine yield and long-term vineyard sustainability. This review aims to integrate physiological, molecular, and omics-based insights to elucidate how grapevine rootstocks confer salinity tolerance and to identify future breeding directions for sustainable viticulture. This review critically assesses the ecological and molecular processes underlying salt stress adaptation in grapevine (Vitis spp.) rootstocks, with an emphasis on their contribution to modulating scion performance under saline conditions. Core adaptive mechanisms include morphological plasticity, ion compartmentalization, hormonal regulation, antioxidant defense, and activation of responsive genes to stress. Particular emphasis is given to recent integrative biotechnological developments—including transcriptomics, proteomics, metabolomics, and genomics—that reveal the intricate signaling and regulatory networks enabling rootstock-mediated tolerance. By integrating advances across eco-physiological, agronomic, and molecular realms, this review identifies rootstock selection as a promising strategy for bolstering resilience in grapevine production systems confronted by salinization, a phenomenon increasingly exacerbated by anthropogenic land use and climate change. The research highlights the value of stress ecology and adaptive root system strategies for alleviating the environmental consequences of soil salinity for perennial crop systems. Full article

This paper focuses on the aircraft landing problem with time windows (ALP-TW), which consists of determining a landing schedule for each aircraft within a specified time window and determining the minimum required separation interval between successive operations. This NP-hard state-dependent scheduling problem plays a key role in the operational efficiency of busy airports. We propose a fast and efficient heuristic, called the CAS-TW (Closest Aircraft Sequence with Time Windows), to generate landing sequences that minimize total delay while respecting operational constraints. The method combines a greedy algorithm with a discretization strategy to explore feasible landing intervals. We validate the approach using real data from São Paulo/Guarulhos International Airport (GRU), comparing the CAS-TW to traditional scheduling strategies and optimal solutions obtained via a commercial solver. Computational experiments show reductions in makespan up to 21% in theoretical datasets and 5% in real-world datasets. The CAS-TW solved instances with 50 aircraft in less than 1 s of computation time. The results showed that our algorithm was quickly implemented, equitable, easy to use, and obtained good solutions. These results translated into an increase in airport capacity. Full article

Green belts in metropolises face a significant contradiction between ecological protection constraints and urban sprawl, necessitating effective planning and management. Existing studies have primarily focused on a single dimension, while the factors influencing the spatial evolution of green belts are complex and diverse. This study establishes a multi-objective quantitative analysis framework, utilizing quantitative analysis methods such as average nearest neighbor analysis, landscape ecological index analysis, land–use transition matrix, kernel density estimation, and spatial autocorrelation models. Taking the green belt area of Shijiazhuang as a case study, this research systematically analyzes the spatial evolution characteristics of the region from 2015 to 2024. The findings reveal spatial patterns such as the small-scale and dispersed expansion of industrial land, increasing fragmentation of ecological spaces, ongoing encroachment on agricultural land, differentiated growth of service industry spaces, and the uncontrolled sprawl of residential areas in villages and towns during rapid urbanization. These patterns lead to increased ecological risks, imbalanced urban–rural development, and lagging infrastructure. To address these challenges, this study proposes a planning strategy of “adjusting the primary industry, restricting the secondary industry, and promoting the tertiary industry,” aiming to resolve the conflict between ecological protection and urban expansion in metropolitan green belts, ensuring their orderly development. This research provides insights for the sustainable development of green belts in Metropolises of developing countries during the rapid urbanization process. Full article

Urban CO₂ mitigation strategies typically aim at particular zones or sectors but do not account for spatial interdependencies among different components within the city. Understanding how land uses emit within and across districts can reveal systemic leverage points for climate-resilient urban planning. This study applies a bipartite spatial network approach using high-resolution Urban Atlas land-use data and a hierarchical spatial framework for emissions and sequestration estimation. The approach links urban land uses to their emissions profiles, offering a structural view of how different areas interconnect within urban carbon dynamics, moving beyond fragmented emission accounting. Using the Reggio Calabria Functional Urban Area in Italy as a case study, the analysis identifies influential areas and emission-intensive land uses. Subsequently, using centrality metrics highlights the spatial units with strong connections to emission-dense land uses, marking them as points of intervention. Results show that although 53% of districts act as net carbon sinks, their sequestration capacity is outweighed by the intensity of a smaller group of emitter districts. Among these, five central districts (IDs 94, 82, 107, 108, and 72) emit over 500 million kg CO₂ per year, making them leverage points for systemic mitigation. The integration of bipartite spatial network and multiscale territorial analysis provides a replicable, data-driven framework for urban CO₂ mitigation. Ultimately, the study demonstrates that mapping emissions through spatial interdependencies enables planners to target interventions where localized action yields the greatest network-wide climate impact. Full article

Over the past few decades, the concept of sustainable agriculture has gained popularity. However, the notion of sustainable agriculture is highly imprecise and unclear, making its application and execution exceedingly challenging. Moreover, disagreements about what sustainability means can lead to a deeper understanding of the intricate empirical procedures and possibly debatable principles involved in any effort to achieve sustainability in agriculture. Practices to increase crop resilience, lower chemical inputs, and boost efficiency are examples of future developments. This review identifies how agricultural life cycle assessment (LCA) studies engage with climate-related metrics such as GHG emissions and land use changes, offering insights for adaptation and mitigation strategies. This review also addresses the need to synthesize existing research on how agriculture and food systems can become more environmentally friendly through LCA. LCA enables the identification of environmental hotspots within agricultural systems, therefore, guiding efforts to limit resource consumption and emissions. For this purpose, a search of a bibliographic database was carried out and the results obtained were analyzed with the open-source tool bibliometrix. There were 2328 results in total with publication years from 1993 to 2025, the latter of which refers to a pre-publication. Then, a post-processing analysis of 1411 articles was conducted and a narrative review of around 100 publications was carried out, where agricultural practices with LCA, current trends, and research gaps were explored. Finally, this paper contributes by identifying three major research gaps derived from the literature synthesis: firstly, the underrepresentation of dynamic LCA models in agriculture; secondly, the lack of geographical balance in case studies; and thirdly, the insufficient integration of socio-economic dimensions in environmental assessments. Full article

Autonomous vehicles (AVs), including privately owned self-driving cars and shared autonomous vehicles (SAVs), hold great potential to transform urban mobility by enhancing safety, accessibility, efficiency, and sustainability. However, their widespread deployment also carries the risk of significantly increasing vehicle miles traveled (VMT), a phenomenon known as the rebound effect. This paper examines the VMT rebound effects resulting from AV and SAV deployment, drawing on recent studies and global case insights. We conducted a systematic narrative review of 48 studies published between 2019 and 2025, drawing on academic sources and credible agency reports. We do not conduct a meta analysis. We quantify how different automation levels (SAE Levels 3, 4, 5) impact VMT and identify the primary factors driving VMT growth, namely: reduced perceived travel time cost, induced demand from new user groups, modal shifts away from transit, and empty VMT. Global case studies from North America, Europe, Asia, and the Middle East are reviewed alongside regional policy responses. Quantitative analyses indicate moderate to significant VMT increases under most scenarios—for example, approximately 10 to 20% increases with conditional automation and potentially over 50% with high/full automation, under the circumstances of no effective policy interventions. Meanwhile, aggressive ride-sharing and policy interventions, including road pricing and transit integration, can mitigate or even reverse these increases. The discussion provides a critical assessment of policy strategies such as mileage pricing, SAV incentives, and integrated land-use/transport planning to manage VMT growth. We conclude that without proactive policies, widespread AV adoption is likely to induce a rise in VMT, but that a suite of well-designed measures can steer automated mobility towards sustainable outcomes. These findings help policymakers and planners balance AV benefits with congestion, energy use, and climate goals. Full article

Rapid urbanization and limited land resources have intensified parking shortages in China’s core and old urban districts, highlighting the tension between parking supply and public space. This study investigates the staged impacts of shared autonomous vehicles (SAVs) on private car ownership and parking demand within the context of urban renewal. Using a case study of Qintai Community in Wuhan, we combined resident surveys (135 valid samples), on-site parking facility assessments, and demand forecasting models to evaluate changes in parking requirements across different timeframes. Results indicate that SAVs can substantially reduce private car ownership and reshape parking demand structures, with short-term transitional pressures followed by long-term demand contractions. Furthermore, SAV adoption offers opportunities to reallocate parking land for multifunctional urban uses, alleviating land-use conflicts in high-density neighborhoods. The findings contribute to a dynamic framework for staged parking optimization, integrating technological innovation with community-level urban renewal strategies. This study underscores the importance of linking residents’ behavioral shifts with infrastructure adaptation, providing evidence-based guidance for sustainable urban transport and space management. Full article

Heatwaves are intensifying across Mediterranean cities, where the Urban Heat Island (UHI) effect amplifies thermal stress. This study updates the spatial characterization of the Surface Urban Heat Island (SUHI) in Tarragona using multi-sensor remote sensing data within a Local Climate Zone (LCZ) framework. Land surface temperature, albedo, and the Normalized Difference Vegetation Index (NDVI) were analyzed during heatwaves from 2015–2025 to assess spatial patterns and drivers of urban heating. Results reveal a daytime urban cool island associated with low albedo and scarce vegetation, and a nocturnal SUHI caused by heat retention in dense built-up areas. High-resolution mapping identifies industrial and commercial zones as hotspots, while vegetated and water-covered areas act as cooling sites. These findings clarify the spatial dynamics and key biophysical controls of SUHI and provide an actionable basis for prioritizing locally tailored adaptation strategies in Mediterranean coastal cities. Full article

Urban heat island (UHI), a significant environmental issue caused by urbanization, is a pressing challenge in modern society. To mitigate it, urban thermal policies have been implemented globally. However, despite differences in topographical and environmental characteristics between cities and within the same city, these policies are largely uniform and fail to reflect contexts, creating notable drawbacks. This study analyzed three cities in Korea with high land surface temperatures (LSTs) to identify factors influencing LST by applying Extreme Gradient Boosting (XGBoost) with Shapley Additive explanations (SHAP) and Geographically Weighted Regression (GWR). Each variable was derived by calculating the average values from May to September 2020. LST was the dependent variable, and the independent variables were chosen based on previous studies: Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), ALBEDO, Population Density (POP_D), Digital Elevation Model (DEM), and SLOPE. XGBoost-SHAP was used to derive the relative importance of the variables, followed by GWR to assess spatial variation in effects. The results indicate that NDBI, reflecting building density, is the primary factor influencing the thermal environment in all three cities. However, the second most influential factor differed by city: SLOPE had a strong effect in Daegu, characterized by surrounding mountains; POP_D had greater influence in Incheon, where population distribution varies due to clustered islands; and DEM was more influential in Seoul, which contains a mix of plains, mountains, and river landscapes. Furthermore, while NDBI and ALBEDO consistently contributed to LST increases across all regions, the effects of the remaining variables were spatially heterogeneous. These findings highlight that urban areas are not homogeneous and that variations in land use, development patterns, and morphology significantly shape heat environments. Therefore, UHI mitigation strategies should prioritize improving urban form while incorporating localized planning tailored to each region’s physical and socio-environmental characteristics. The results can serve as a foundation for developing strategies and policy decisions to mitigate UHI effects. Full article

Global coastal reclamation areas face significant land subsidence, threatening infrastructure and sustainable development. China’s large-scale projects show particularly severe subsidence. For example, Tianjin’s Binhai New Area contains 413.6 km² of reclaimed land, and subsidence is driven by soft soil consolidation, industrial loads, and dynamic land use changes. This study addresses the unique geology of coastal reclamation zones: thick, soft clay layers; high porosity; and low soil strength. We employed optimized Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technology using 48 Sentinel-1A radar images (2019–2022), which generated high-resolution annual deformation rate maps revealing a north-high, south-low subsidence gradient. Crucially, validation against leveling data confirmed reliability. The systematically quantified results demonstrate built areas and the bare ground intensifies subsidence through structural loads and soil compression. Land use transitions also exacerbate differential settlement. For coastal cities and reclamation zones, key strategies emerge, including regulating structural loads in high-subsidence areas, managing soft soil consolidation, and implementing dynamic monitoring. Aligning development intensity with geological capacity is essential, and adopting adaptive spatial planning can mitigate subsidence hazards. This approach offers a scientific framework for enhancing global coastal resilience. Full article

China’s rapid urbanization, characterized by extensive land allocations, operates within a framework of binding quotas imposed by upper-level governments, while local governments exercise broad discretion over the zoning of newly transacted land parcels. In this context, investigating the evolving patterns of land supply structure during this period is therefore of critical importance. The central government’s 2018 articulation of the “Houses are for living in, not for speculation” (fangzhubuchao) sought to mitigate housing market speculation and curb potential asset bubbles, including through changes to residential land supply. Using a panel of 266 prefecture-level cities across China, this study employs a generalized difference-in-difference model to examine how housing market regulations affect the industrial sector through adjustments in land supply. To capture cross-city variations in local policy interventions, we construct a measure based on the land price wedge between residential (and commercial) and industrial land derived from a hedonic pricing model, which reflects underlying housing market conditions. The results indicate that a reduction in residential land supply caused by these policies results in a corresponding increase in industrial land supply, while the total land supply remains unchanged. These effects are more pronounced in cities with stringent policy regulations and relaxed urban land quotas. The short-term economic outcomes are inadequate. As of 2023, our analysis reveals no substantial increase in either the number of industrial enterprises or the industrial value added, notwithstanding the augmented industrial land supply. Consequently, these findings identify a secondary determinant of industrial location patterns and provide a scientific basis for designing efficient land-use regulations and sustainable urban development strategies. Full article

Pastoralist sedentarization has accelerated globally since the late 20th century, driven by climate change, government policies, and economic transitions. In Inner Mongolia, China, this process advanced under 1950s socialist initiatives and the 1980s Grassland Household Contract Policy (GHCP), which allocated land use rights to individual households. This study examines the 1960–2020 transition from seasonal nomadism to settled pastoralism in a Gacha, emphasizing changes in grazing strategies and water access. Migration distances declined from about 55 km in the 1960s to 4 km in the 1980s, with sedentarization becoming permanent after the GHCP. Grazing practices shifted toward fixed facilities and supplementary feed, while water use moved to deep wells and storage tanks, increasing both costs and groundwater risks. These transformations modestly improved productivity but heightened social vulnerability. Full article

Extreme precipitation events, rapid urbanization, and irregular land use have significantly increased flood risk in recent years. In order to mitigate risks and enhance urban resilience, there is a need for the integration of innovative approaches with classical disaster management methods. This study uses geospatial artificial intelligence (GeoAI) methods to develop a flood risk analysis model. The proposed methodology is applied in the Marmara Region of Türkiye as a case study to highlight flood risk by evaluating factors such as precipitation, drainage density, and distance to waterways, population density, topography, water flow direction, and accumulation. Areas with high flood risk in the region are identified through the integration of hazard and vulnerability assessments, and explainable artificial intelligence (XAI) techniques are employed to identify the most significant factors contributing to flood susceptibility. Thus, a flood risk map of the Marmara Region is produced for eleven cities, utilizing open-source and government data to serve as an accessible guide for decision makers. This study aims to develop a flood risk analysis model through the integration of AHP-based hazard analysis and machine learning-based vulnerability assessment. This comprehensive hybrid approach facilitates the development of strategies for practical disaster risk reduction studies in a data-driven manner. Full article

The three northeastern provinces of China are the country’s most important grain-producing region, particularly for maize, soybean, and rice, and form its largest commercial grain base. Over the past two decades, cropping structures in this region have undergone notable shifts driven by both climate change and human activities. Generating long-term, high-resolution maps of multi-crop distribution and evaluating their suitability is essential for understanding cropping dynamics, optimizing land use, and promoting sustainable agriculture. In this study, we integrated multi-source satellite imagery from Landsat and Sentinel-2 to map the distribution of rice, maize, and soybean from 2000 to 2023 using a Random Forest classifier. A crop suitability assessment framework was developed by combining a multi-criteria evaluation model with the MaxEnt model. Reliable training samples were derived by overlaying suitability evaluation results with stable crop growth areas, and environmental variables—including climate, topography, soil, hydrology, and anthropogenic factors—were incorporated into MaxEnt to assess suitability. Furthermore, the spatial consistency between actual cultivation and suitability was evaluated to identify areas of misallocated land use. The results show that: (1) the six classification maps achieved an average overall accuracy of 91.05% and a Kappa coefficient of 0.857; (2) the cultivation area of all three crops expanded, with maize showing the largest increase, followed by soybean and rice, and the dominant conversion being from soybean to maize; (3) suitability areas ranked as soybean (376,692 km²) > maize (329,056 km²) > rice (311,869 km²), with substantial spatial overlap, particularly between maize and soybean, suggesting strong competition; and (4) in 2023, highly suitable zones accounted for 57.39% of rice, 39.69% of maize, and 28.89% of soybean cultivation, indicating a closer alignment between actual distribution and suitability for rice, weaker for maize, and weakest for soybean, whose suitable zones were often displaced by rice and maize. These findings provide insights to guide farmers in optimizing crop allocation and offer a scientific basis for policymakers in designing cultivated land protection strategies in Northeast China. Full article