Search Results (276)

By addressing the challenges of management difficulties, insufficient integration of driver analysis, and single-dimensional analysis in the governance of illegal land use and illegal construction (collectively referred to as the “Two Illegalities”) under rapid urbanization, this study designs and implements a GIS-based governance system using Xiamen City as the study area. First, we propose a standardized data-processing workflow and construct a comprehensive management platform integrating multi-source data fusion, spatiotemporal visualization, intelligent analysis, and customized report generation, effectively lowering the barrier for non-professional users. Second, utilizing methods integrated into the platform, such as Moran’s I and centroid trajectory analysis, we deeply analyze the spatiotemporal evolution and driving mechanisms of “Two Illegalities” activities in Xiamen from 2018 to 2023. The results indicate that the distribution of “Two Illegalities” exhibits significant spatial clustering, with hotspots concentrated in urban–rural transition zones. The spatial morphology evolved from multi-core diffusion to the contraction of agglomeration belts. This evolution is essentially the result of the dynamic adaptation between regional economic development gradients, urbanization processes, and policy-enforcement synergy mechanisms. Through a modular, open technical architecture and a “Data-Technology-Enforcement” collaborative mechanism, the system significantly improves information management efficiency and the scientific basis of decision-making. It provides a replicable and scalable technical framework and practical paradigm for similar cities to transform “Two Illegalities” governance from passive disposal to active prevention and control. Full article

Amidst the increasing aerial traffic and road traffic congestion, Urban Air Mobility (UAM) has emerged as a new mode of aerial transport offering less travel time and ease of portability. A critical factor in reducing travel time is the emerging electric Vertical Take-Off and Landing (eVTOL) vehicles, which require infrastructure such as vertiports to operate smoothly. However, the dynamics of vertiport operations, particularly the integration of battery charging facilities, remain relatively unexplored. This work aims to bridge this gap by delving into vertiport management by utilizing separate taxing and parking levels. The study also focuses on the time eVTOLs spend at the vertiport to anticipate potential delays. This factor helps optimise arrival and departure times via a scheduling strategy that accounts for hourly demand fluctuations. The simulation results, conducted with hourly demand, underscore the significant impact of battery charging on operational time while also highlighting the role of parking spots in augmenting capacity and facilitating more efficient scheduling. Full article

This study investigates the effects of saline reclaimed water recharge on soil salt accumulation and water migration in Xinjiang, China, aiming to provide scientific guidance for the sustainable utilization of reclaimed water in arid regions. Indoor vertical infiltration simulation experiments were conducted using reclaimed water with varying salinity levels (0, 1, 2, 3, and 4 g L⁻¹) to evaluate their impacts on soil water–salt distribution and infiltration dynamics. Results showed that irrigation with saline reclaimed water increased soil pH and significantly enhanced both the infiltration rate and wetting front migration velocity, while causing only minor changes in the moisture content of the wetted zone. When the salinity was 2 g L⁻¹, the observed improvement effect was the most significant. Specifically, the cumulative infiltration increased by 22.73% after 180 min, and the time required for the wetting peak to reach the specified depth was shortened by 21.74%. At this salinity level, the soil’s effective water storage capacity reached 168.19 mm, with an average moisture content increase of just 6.20%. Soil salinity increased with the salinity of the irrigation water, and salts accumulated at the wetting front as water moved downward, resulting in a characteristic distribution pattern of desalination in the upper layer and salt accumulation in the lower layer. Notably, reclaimed water recharge reduced soil salinity in the 0–30 cm layer, with salinity in the 0–25 cm layer decreasing below the crop salt tolerance threshold. When the salinity of the reclaimed water was ≤2 g L⁻¹, the salt storage in the 0–30 cm layer was less than 7 kg ha⁻¹, achieving a desalination rate exceeding 60%. Reclaimed water with a salinity of 2 g L⁻¹ enhanced infiltration (wetting front depth increased by 27.78%) and desalination efficiency (>60%). These findings suggest it is well suited for urban greening and represents an optimal choice for the moderate reclamation of saline-alkali soils in arid environments. Overall, this study provide a reference for the water quality threshold and parameters of reclaimed water for urban greening, farmland irrigation, and saline land improvement. Full article

Population expansion, urban development, climate change, and precipitation patterns are complicating sustainable natural resource management. Subbasin prioritization enhances the efficiency and cost-effectiveness of resource management. Artificial intelligence and data analytics eradicate the constraints of traditional methodologies, facilitating more precise evaluations of soil erosion, water management, and environmental risks. This research has created a comprehensive decision support system for the multidimensional assessment of sub-basins. The Erosion and Flood Risk-Based Soil Protection (EFR), Socio-Economic Integrated Basin Management (SEW), and Prioritization Based on Basin Water Yield (PBW) functions were utilized to prioritize sustainability objectives. EFR addresses erosion and flood risks, PBW evaluates water yield potential, and SEW integrates socio-economic drivers that directly influence water use and management feasibility. Our approach integrates principal component analysis–technique for order preference by similarity to ideal solution (PCA–TOPSIS) with machine learning (ML) and provides a scalable, data-driven alternative to conventional methods. The combination of machine learning algorithms with PCA and TOPSIS not only improves analytical capabilities but also offers a scalable alternative for prioritization under changing data scenarios. Among the models, support vector machine (SVM) achieved the highest performance for PBW (R² = 0.87) and artificial neural networks (ANNs) performed best for EFR (R² = 0.71), while random forest (RF) and gradient boosting machine (GBM) models exhibited stable accuracy for SEW (R² ~ 0.65–0.69). These quantitative results confirm the robustness and consistency of the proposed hybrid framework. The findings show that some sub-basins are prioritized for sustainable land and water resources management; these areas are generally of high priority according to different risk and management criteria. For these basins, it is suggested that comprehensive local-scale studies be carried out, making sure that preventive and remedial measures are given top priority for execution. The SVM model worked best for the PBW function, the ANN model worked best for the EFR function, and the RF and GBM models worked best for the SEW function. This framework not only finds sub-basins that are most important, but it also gives useful information for managing watersheds in a way that is sustainable even when the climate and economy change. Full article

Employment promotion and employment realization are the core and fundamental problems in the resettlement of land-expropriated farmers transferred to citizens. To solve this problem, it is necessary to clarify the key factors and mechanisms that affect the employment behavior of “farmers to citizens” workers. Taking the labor force from land-expropriated “farmers to citizens” in the construction of Beijing city sub-center as the research object, this paper utilizes Logistic ISM to determine the key factors affecting the employment behavior of the labor force when changing from rural to urban, as well as the internal logical relationship and hierarchical structure among the influencing factors. The results show that only 40% of the migrant workers in the sample have achieved employment, while 69% of the unemployed population have a willingness to work but are limited by age, skills, and family factors. The logistic regression model identifies that the employment behavior of land-expropriated farmers is significantly affected by 10 factors, including gender, age, work experience, hobbies, employment demand, expenditure change, employment difficulty cognition, government training, policy satisfaction and social security. Among them, ISM further reveals that these factors form a three-level hierarchical mechanism of “structure–cognition–behavior”; gender, social security and policy satisfaction are the deep-root factors, and the intermediate factors, such as hobbies and government training, affect employment demand, employment difficulty cognition and other surface factors, and ultimately affect the employment behavior of land-expropriated “farmers to citizens”. Based on this, it is proposed to start from four aspects: differentiated employment guidance, policy transmission optimization, service efficiency improvement, and industrial driving, to systematically promote the realization of more comprehensive and stable employment for the rural-to-residential population, and provide institutional guarantees and practical paths for their sustainable livelihoods. Full article

Characterized by zero-carbon, congestion-free, and high-capacity features, the utilization of metro systems for collaborative passenger-and-freight transport (the metro-based underground logistics system, M-ULS) has been recognized as a favorable alternative to facilitate automated freight transport in future megacities. This article constructs a three-echelon M-ULS network and establishes a multi-objective bilevel programming model, considering the interests of both government investment departments and transport enterprises. The overall goal of the study is to establish a transportation network with the lowest construction cost, lowest operating cost, and highest facility utilization rate, taking into account factors such as population density, transportation conditions, land resources, logistics demand, and metro station location, under given cost parameters and demand conditions. The upper-level model takes government investment as the main body and aims to minimize the total cost, establishing an optimization model for location selection allocation paths with capacity constraints; the lower-level model aims to minimize the generalized cost for freight enterprises by simulating the competition between traditional transportation and the M-ULS mode. In addition, a bi-level programming model solving framework was established, and a multi-stage precise heuristic hybrid algorithm based on adaptive immune clone selection algorithm (AICSA) and improved plant growth simulation algorithm (IPGSA) is designed for the upper-level model. Finally, taking the central urban area of Beijing as an example, four network scales are set up for numerical simulation research to verify the reliability and superiority of the model and algorithm. By analyzing and setting key indicators, an optimal network configuration scheme is proposed, providing a feasible path for cities to improve logistics efficiency and reduce the impact of logistics externalities under limited land resources, further strengthening the strategic role of subway logistics systems in urban sustainable development. Full article

Soil health is vital for the stability of agricultural production and ecosystem functions. However, the rapid urbanization process and environmental pollution have led to a sharp reduction in available arable land and accelerated soil degradation. Meanwhile, human activities generate a large amount of waste, which needs to be treated for resource recovery to reduce its potential pollution risks to the environment. By upcycling waste to mimic pedogenesis, Technosols offer a sustainable platform for land rehabilitation, environmental remediation, carbon sequestration and greenhouse gases emission reduction. However, the wide range of waste sources and complex compositions pose challenges to the standardized construction of Technosols suitable for agricultural production. This review systematically examines the sources and characteristics of waste, current utilization status and challenges in Technosol construction, and puts forward suggestions for developing agriculture-oriented Technosols through waste-novel nanomaterial composites. Finally, critical research directions are proposed regarding the relationship between Technosol fabrication and farmland environmental effects, including the targeted design, nanomaterial-enhanced construction, ecological impact assessment, and economic efficiency of agricultural Technosols. Full article

Smart cities rely on digital infrastructures and utilize data-driven frameworks to enhance quality of life, optimizing public services by promoting transparency in urban and heritage management. Based on the ArchTerr project for archeological heritage protection, this study introduces an integrated framework uniting two components: GIS-based land mapping and blockchain-enabled document management. The system supports urban planning, land administration, and governance by combining spatial intelligence with secure data handling. The GIS module enables precise land mapping using geographic coordinates, facilitating spatial analysis, land use monitoring, and infrastructure planning. The document management system employs blockchain storage functionalities to ensure the immutability, transparency, and traceability of records such as land ownership documents, permits, and regulatory filings. Developed using the Design Science Research methodology, the framework translates abstract principles of data immutability and interoperability into a functional architecture that addresses persistent issues of fragmented datasets, insecure records, and limited institutional accountability and improves scalability, efficiency, and transparency in a variety of urban situations. We explored its implications for policy and governance, illustrating how interdisciplinary technology serves as a basis for transparent, accountable, and resilient urban management. This study advances theoretical understanding of how the convergence of spatial and trust-based technologies can foster geo-trusted governance and contribute to more transparent and resilient heritage management. Full article

China, the world’s predominant carbon emitter, is instrumental in advancing green and low-carbon urban land development globally. Urban land green use efficiency (ULGUE) in China is shaped by a multifaceted array of economic and social factors. Given the incongruous results observed in prior research, a comprehensive evaluation of these factors is paramount. This study consolidates data from previous research that explored the determinants of ULGUE in China. Utilizing the IPAT model as a foundational framework, the influencing factors were classified, and meta-analysis was employed to quantify their overall impact. The results show the following: (1) Population agglomeration exhibits a nonlinear effect on ULGUE, with moderate density enhancing efficiency but excessive concentration yielding diminishing returns; (2) Economic development efficiency positively affects ULGUE, with both gross domestic product (GDP) per capita and industrial structure advancement showing significant positive associations; (3) Social development equity demonstrates a threshold effect, where excessive governmental intervention or disproportionate investment in science and education may constrain ULGUE; (4) Resource endowment sustainability, including per capita green space and road infrastructure, consistently enhances ULGUE; (5) The impacts of these factors vary across regions, highlighting the importance of context-specific strategies. These findings provide robust evidence for policymakers to design targeted interventions that account for nonlinearities, threshold effects, and regional heterogeneity, thereby supporting sustainable, green, and low-carbon urban land use in China. Full article

This study takes Sichuan’s major economic zones as a case to explore the evolution of ecological environment development. By improving the traditional ecological footprint model based on the concept of “provincial hectare,” we analyzed the characteristics of ecological utilization and ecological security status in Sichuan’s economic zones from 2013 to 2022. A comprehensive evaluation was conducted through factor analysis driven by geographical detectors. The results indicate that (1) from 2013 to 2022, Sichuan Province generally exhibited a “supply falling short of demand” situation, with a slight decrease in per capita ecological carrying capacity and a significant decreasing trend in GDP ecological footprint. The industrial structure of the Panxi and Chengdu Plain economic zones was relatively reasonable, with a notable improvement in resource utilization efficiency, while the Northeastern Sichuan Economic Zone lagged behind. (2) The ecological security focus was distributed in the southeastern region, showing an overall “northeast–southwest” distribution pattern. The ecological restoration in the Southern and Northwestern Sichuan Economic Zones needs to be strengthened. (3) The output value of the secondary industry, R&D expenditure, and urban construction land were the main factors influencing ecological footprint changes. The average urban temperature interacted with other factors, showing a significant effect. Full article

As urban boundaries continue to expand and core city areas undergo optimization, megacities such as New York, London, Beijing, Shanghai, and Guangzhou exert a siphon effect on surrounding regions, intensifying population concentration and land demand. However, the imperative for coordinated production-living-ecological space development has placed limits on uncontrolled urban sprawl, highlighting the need for connotative, high-quality urban growth. Recent initiatives in urban village renewal and regeneration aim to enhance land-use efficiency but face persistent challenges—including preserving indigenous settlements and cultural heritage, while creating livable and friendly communities within high-density contexts. Utilizing a mixed-methods approach—combining bibliometrics analysis, questionnaire surveys, and enterprise interviews—this research investigates core challenges to urban renewal. Results indicate that multi-party collaborative governance integrating policy innovation, cultural preservation, human-centered planning, smart technologies, and sustainable development is essential for advancing “people-industry-city integration” in renewal models. Full article

To address the utilization challenges of rural settlements in metropolitan suburbs, this study takes the suburban areas of Wuhan as its research object. Based on mobile signaling data and multi-source geographic data, it evaluates their utilization efficiency and construction suitability from both dynamic and static dimensions, and proposes zoning optimization strategies. The study constructed a population mobility network, revealing that rural population flow in Wuhan radiates primarily from the main urban core, with net inflow or balance prevalent in near-suburban areas and net outflow dominant in distant suburbs. The results indicate that only 11.45% of villages achieve medium-to-high utilization efficiency, while 94.50% of the area is classified as highly suitable for development. Based on the “Efficiency-Potential” matrix, villages are categorized into five types, including key development type, gradual optimization type, potential activation type, steady-state improvement type and priority exit type. This study contributes to our understanding of the relationship between rural population and land, and provides support for the optimization of suburban settlements and rural revitalization. Full article

Urban green spaces (UGS) are crucial for mitigating rising urban land surface temperatures (LST). Rapid urbanization presents unresolved questions regarding (a) seasonal variations in the spatial co-distribution of UGS and LST, (b) the temporal and spatial changes in UGS cooling, and (c) the dominant factors driving cooling effects during different periods. This study focuses on Beijing’s Fifth Ring Road area, utilizing nearly 40 years of Landsat remote sensing imagery and land cover data. We propose a novel nine-square grid spatial analysis approach that integrates LST retrieval, profile line analysis, and the XGBoost algorithm to investigate the long-term spatiotemporal evolution of UGS cooling capacity and its driving mechanisms. The results demonstrate three key findings: (1) Strong seasonal divergence in UGS-LST correlation: A significant negative correlation dominates during summer months (June–August), whereas winter (December–February) exhibits marked weakening of this relationship, with localized positive correlations indicating thermal inversion effects. (2) Dynamic evolution of cooling capacity under urbanization: Urban expansion has reconfigured UGS spatial patterns, with a cooling capacity of UGS showing an “enhancement–decline–enhancement” trend over time. Analysis through machine learning on the significance of landscape metrics revealed that scale-related metrics play a dominant role in the early stage of urbanization, while the focus shifts to quality-related metrics in the later phase. (3) Optimal cooling efficiency threshold: Maximum per-unit-area cooling intensity occurs at 10–20% UGS coverage, yielding an average LST reduction of approximately 1 °C relative to non-vegetated surfaces. This study elucidates the spatiotemporal evolution of UGS cooling effects during urbanization, establishing a robust scientific foundation for optimizing green space configuration and enhancing urban climate resilience. Full article

Urban rail transit, as a green, environmentally friendly, safe, and efficient mode of transportation, plays a crucial role in urban sustainable development. However, the influencing mechanism of build environment factors on rail transit ridership still needs to be further investigated. Also, the interaction effects between these factors have not been considered. This study aims to explore the relationship and impact of built environmental factors on metro ridership. The research employs the Multiscale Geographically Weighted Regression (MGWR) model to analyze the temporal and spatial effects of built environmental factors on the rail transit ridership. The GeoDetector model is utilized to investigate the interactive effects of these factors on rail transit ridership. The Shanghai Metro ridership data and built environment data are applied to validate the model. Based on data analysis results, we found that Food & Beverages and Accommodation services, respectively, have the greatest impact on metro ridership on weekdays and weekends. Furthermore, the interaction effects between other variable and Land use diversity significantly enhance rail transit ridership, validating the promoting effect of land use diversity on metro ridership. By proposing recommendations for relevant urban planning and policy formulation, we can foster the sustainable development of urban rail transit. Full article

Compact urban land use planning and smart growth are essential strategies for tackling the issues of sustainable urban transportation development. In the context of swift global urbanization, examining the intrinsic relationship between urban spatial structure and transport systems might furnish a measurable foundation for urban planning decisions. This study utilizes various data sources, including Chinese city compactness and the Didi traffic index, to integrate exploratory spatial analysis and regression analysis methods. It examines the influence of city compactness on urban transportation by comparing average commuting time and speed relative to city compactness. The following findings are derived: The compactness of Chinese cities demonstrates notable regional differentiation, with western cities expanding uniformly and efficiently, whereas eastern cities display multi-centered, differentiated development in their spatial structures. Furthermore, Chinese cities exhibit a pronounced high-value agglomeration in commuting patterns, where major cities are characterized by high speeds and extended durations. The study reveals that city compactness creates a “concentration paradox” in commuting efficiency, which may reduce commuting distances but significantly decreases speed and extends travel time. The solution to this conflict is to prioritize the enhancement of public transport systems, as the increase in passenger volume is strongly positively connected with improved commuting speed and reduced commuting time. These findings offer a crucial scientific foundation for developing diverse regional spatial plans and transport development strategies. Full article