Search Results (6,383)

Rural settlements are the fundamental socio-economic units of China’s countryside. In line with national strategies that emphasize place-based and category-specific pathways for rural revitalization, accurate classification of rural settlements is essential for differentiated planning and policy delivery. However, given the sheer number of settlements, manual classification is time-consuming and resource-intensive, limiting scalability. This study proposes an AI-driven, multi-model framework to automate rural settlement classification with high stability and accuracy. First, informed by a rigorous literature review, we construct a multidimensional indicator system that integrates natural conditions, socio-economic attributes, and land-use factors to capture spatial and functional characteristics at the settlement scale. Using Gaoqing County (Shandong Province) as the study area, we collect and curate survey data and apply outlier detection for preprocessing. We then benchmark multiple machine learning models and find that algorithms with native handling of missing values perform markedly better—a critical advantage given the prevalence of missingness in survey-based datasets. Finally, we assemble the three best-performing models—LightGBM, CatBoost, and XGBoost—into a weighted-voting ensemble, achieving an overall classification accuracy of approximately 88%. The results demonstrate that the refined indicator system, coupled with a multi-model ensemble, substantially improves both accuracy and robustness. This work provides a methodological foundation and empirical evidence to support differentiated planning and targeted rural revitalization at the settlement level, offering a scalable blueprint for broader regional and national implementation. Full article

The rapid growth of the aviation industry and increasing air traffic demand more careful attention to environmental concerns. Among these, aircraft noise is considered one of the main sources of environmental noise, especially after land-based transportation. The World Health Organization highlights noise pollution as the second-most important environmental factor after air pollution, with serious consequences for public health. Long-term exposure to high noise levels has been linked to problems such as cardiovascular disease and sleep disruption. In response, ICAO has introduced stricter standards especially in Annex 16, Volume I requiring aircraft to meet tighter noise limits. This study focuses on estimating the noise levels of Airbus and Boeing aircraft during approach, lateral, and flyover phases. The models use parameters such as maximum take-off and landing weights, engine thrust, and bypass ratio. Three approaches are compared: Support vector regression (SVR), a classical machine learning method, multi-layer perceptron(MLP), and a CNN–Transformer hybrid model, which combines deep learning and attention-based techniques. Their predictive performances were evaluated using MSE, RMSE, MAE, MAPE, and R². The CNN–Transformer showed better results in all metrics. At the flyover point, it reached an R² of 0.981, compared to 0.898 for SVR and 0.919 for MLP. At the lateral point, its MAE dropped to 0.58, while SVR had 1.64 and MLP 1.17. The attention-based model found patterns that the traditional one missed. It gave better results in several cases. Apart from this, some technologies used to reduce noise may also help save fuel and increase energy efficiency. For example, engines with a high bypass ratio can lower both noise and emissions. These kinds of solutions connect performance with environmental benefits. These insights could be useful for those involved in airport planning, aircraft engine design, or regulatory planning. Full article

Global climate warming and the evolution of land-use patterns are jointly reshaping the spatial configuration of fruit tree cultivation. Focusing on apricot (Prunus armeniaca L.) in China, this study constructs a comprehensive suitability assessment framework driven by the dual forces of climate change and farmland transformation. By integrating multi-source climate datasets, projected land-use data, and geostatistical analysis, the study evaluates the impacts of climate and farmland changes on the potential cultivation suitability of apricot under four SSP scenarios (SSP126, SSP245, SSP370, and SSP585) during 2021–2100. The results indicate that: (1) climate warming generally expands potential suitable areas, showing a latitudinal shift from low to high regions; (2) under moderate- to high-emission scenarios, moderately suitable areas increase significantly, whereas highly suitable areas degrade in the long term due to excess heat and water stress; (3) farmland transformation exerts a crucial constraint between climatic potential and actual plantability, as resource reduction and spatial mismatch limit development potential; and (4) climate factors contribute approximately 72% to suitability variation, while farmland factors contribute about 28%, with a significant spatial interaction between the two. This study reveals the dynamic evolution of apricot suitability patterns under the dual drivers of climate and land changes, providing a scientific basis for fruit industry optimization and spatial land-use planning. Full article

Urban green spaces, as vital land use components, play a crucial role in promoting public mental health and well-being. This study investigates the differential restorative benefits and stress relief pathways in urban green spaces for populations with varying stress thresholds. This study employed a controlled experiment (pre-test–free activity–post-test) with 120 park users, integrating subjective scales (DASS-21, SRRS, etc.). We innovatively stratified participants by stress threshold to analyze recovery mechanisms. Key findings reveal: (1) Park visits were associated with significant restorative benefits across all stress groups (p < 0.05), yet the recovery patterns and potential pathways appear to be stress-threshold-dependent. (2) Our findings suggest distinct patterns: low-stress individuals benefit via cognitive-behavioral routes (environmental awareness, dynamic activities), while medium-high stress groups rely more on physiological regulation (environmental enclosure, static relaxation). (3) Crucially, these mechanisms suggest stratified landscape design strategies: multi-sensory interactive spaces for low-stress, static rest areas for medium-stress, and low-interference, high-enclosure meditative environments for high-stress individuals. However, given the single-group pre-post design, observed benefits should be interpreted as associations and plausible pathways rather than definitive causal effects. By introducing stress threshold stratification into restorative landscape research, this study provides actionable, evidence-based guidelines for optimizing urban green space planning and design. It offers a crucial scientific foundation for creating healthier, more inclusive, and sustainable urban environments that effectively address diverse mental health needs and contribute to public health promotion through sustainable land use practices. Full article

Research on culture-led urban change in China has shifted from inner-city clusters to peripheral zones where formal planning meets managed informality, yet two gaps remain. First, artistic production continues to be interpreted through a leasing lens that positions artists as temporary occupiers. Second, land is analysed largely at the macro scale of municipal supply and branding, while internal property rules and meso-level governance are overlooked. This paper mobilises the concept of assetisation to show how precarious cultural spaces become rule-bound assets through property-rights design. Fieldwork in Chengdu’s Blue Roof Art District draws on qualitative methods, including semi-structured interviews, policy and registration documents, and on-site observation. We examine which resources are assetised, how this occurs, and with what effects on publicness and spatial form. Our findings show that planning endorsement, the transfer of collective construction land for cultural use, and title registration with mortgageability codify eligibility, use and transfer. Studios are converted into owner-occupied assets tied to land value. Governance shifts from direct administrative control to asset management by owners and the site operator. While production stabilises and overt conflict declines, public interfaces narrow and enclave risks intensify when city priorities change. Empirically, the paper demonstrates how property-rights design operates as a meso-level governance tool that sets explicit trade-offs between stability and openness. Theoretically, it links producers to land value, bridging macro land regimes with micro political practice in urban peripheries and informing urban policy-making that prioritises sustainability. Full article

Agriculture is one of the many sectors facing significant risks from climate change. To manage potential crop losses, whether climate-related or not, farmers widely rely on crop insurance to increase their resilience. However, farmers in Sri Lanka demonstrate a limited acceptance of crop insurance schemes. This study aimed to investigate the perceptions and Willingness-to-Pay (WTP) for crop insurance schemes among the paddy farmers in Kurunegala district. A total of 248 paddy farmers from the Kurunegala district were recruited as the study sample using the stratified random sampling approach. A pre-tested structured questionnaire and choice cards were used for primary data collection. The Conditional Logit Model (CLM) was used for data analysis. Around 77.8% of respondents were males engaged only in paddy farming, while the majority (62.5%) received an income of LKR 50,000 to 75,000. Complications experienced during the claim form-filling process (mean = 4.6), gaps in covering all crops on the crop land (mean = 4.6), and poor service quality (mean = 4.5) were perceived as the major limitations in existing crop insurance schemes. Outcomes of the CLM indicated that farmers with a positive attitude toward crop insurance significantly prefer plans with drought coverage (β = 0.823; p < 0.05), on-field assessments (β = 0.251; p < 0.05), and higher no-hazard returns (β = 0.318; p < 0.05) while showing a notable sensitivity to premium costs (β = −0.590; p < 0.05). The model also revealed an apparent willingness to switch from the status quo when presented with better-designed alternatives. The findings emphasized the need to implement responsive crop insurance schemes to enhance climate resilience and ensure the sustainability of paddy production in Sri Lanka. Full article

Floods increasingly threaten semiarid regions, yet their long-term soil ecological impacts remain underdocumented. This study quantifies the hydrologic change and flood-induced soil transformation on the Zhabay River floodplain (Akmola, Kazakhstan) using integrated field, laboratory, and remote sensing data. Gauge records (2012–2024) were analyzed; inundation was mapped from a 0.30 m DEM (Digital Elevation Model) merging SRTM (Shuttle Radar Topography Mission), Landsat 8/Sentinel 2, and UAV (Unmanned Aerial Vehicle) photogrammetry (NDWI (Normalized Difference Water Index) > 0.28) and validated with 54 in situ depths (MAE (Mean Absolute Error) 0.17 m). Soil samples collected before and after floods were analyzed for texture, bulk density, pH, Eh, macronutrients, and heavy metals. Annual maxima increased by 0.08 m yr⁻¹, while extreme floods became more frequent. Thresholds of ≥0.5 m depth and >7 days duration marked compaction onset, whereas >1 m and ≥12 days produced maximum organic carbon loss and Zn/Ni enrichment. The combination of high-resolution DEMs, ROC (Receiver Operating Characteristic) analysis, and soil microbial monitoring provides new operational indicators of soil degradation for Central Asian steppe floodplains. Findings contribute to SDG 13 (Climate Action) and SDG 15 (Life on Land) by linking flood resilience assessment with sustainable land-use planning. Full article

The Dhaka District is experiencing rapid industrial growth alongside uncontrolled urban expansion, leading to significant land-use conflicts and environmental pressures. This study investigates how to identify the optimal sites for industrial development that support sustainable urban growth by leveraging Geographic Information Systems (GISs), combined with a structured decision-making approach. The analysis incorporates key environmental and infrastructural factors to guide responsible planning aligned with global sustainability objectives. This study integrates spatial variables such as transport accessibility, land use, environmental sensitivity, and infrastructure presence. Up-to-date satellite imagery and land-use information from recent years ensure relevant and precise analysis. The findings indicate that roughly 10–15% of Dhaka District is suitable for industrial activities, predominantly the western and northwestern edges of the district. However, a considerable portion of existing industries are situated outside the officially designated zones, with nearly 9% infringing on protected environments, pointing to gaps in land management policies. Additionally, industrial expansion resulted in the conversion of over thousands of hectares of natural land, underscoring urgent ecological concerns. Scenario modeling further demonstrates how strategic land allocation can balance industrial growth with environmental conservation. This research highlights the value of integrating a GIS with multi-criteria evaluation using Analytical Hierarchy Process (AHP) to provide a flexible, data-driven framework for sustainable industrial land-use planning. Full article

Integrating ecosystem services into urban planning requires analytical tools that connect spatial land-use data with environmental performance. This paper applies a multi-scale, data-driven approach to assess urban carbon dynamics using spatial units that reflect both ecological functions and planning relevance. The study examines the Reggio Calabria Functional Urban Area (FUA) in Southern Italy, using Copernicus Urban Atlas land-use data to characterize spatial patterns and estimate CO₂ emissions and sequestration using parameters derived from established literature and institutional sources. A Spatial Durbin Model (SDM) identifies land uses with direct and spillover effects, revealing how spatial organization shapes urban carbon outcomes. Results reveal a net emission imbalance of approximately 1.85 billion kg CO₂ per year, confirming the region’s role as a net emitter. Transport corridors and discontinuous low-density urban areas show the strongest positive SDM coefficients (+3.48 and +0.78 kg CO₂ m⁻² yr⁻¹, respectively). Forests and agricultural lands show negative effects, indicating potential sequestration functions, though not statistically significant. This suggests that natural and semi-natural land uses contribute little to measurable CO₂ reduction within the FUA. Emissions and sinks display a polarized spatial pattern, with coastal urban zones acting as hotspots and inland areas serving as potential sinks. These findings underscore the need to strengthen ecological connectivity and integrate green infrastructure within dense urban areas to enhance mitigation capacity. The proposed framework shows how spatially explicit, hierarchical analysis can bridge ecosystem services and urban planning, offering a replicable basis for data-informed, climate-responsive strategies. Full article

With the intensification of climate change and the urban heat island effect, there is growing awareness of the role of urban greening in improving the urban climate. The aim of this study is to explore how various characteristics of green spaces—including type, configuration (size and shape), location, and distance from the urban centre—affect their cooling effect. Landsat remote sensing land surface temperature data were analysed through Geographic Information Systems, using Sheffield as a case study. The results show that the cooling effect of woodland was significantly stronger than that of grassland and urban parks, with a cooling intensity reaching up to 2.93 °C, and a cooling extent that can reach up to 500 m beyond its boundary. When closer to the city centre, both the shape and size of green spaces show a positive correlation with their cooling effect, but this relationship becomes less evident as the distance from the city centre increases. The size of a woodland had a greater effect in terms of a reduction in land surface temperature than the shape of the woodland. The findings of this study can provide a better framework for landscape architects and urban planners to plan for climate change and propose stronger green strategies to mitigate the urban heat island effect. Full article

The ongoing biodiversity crisis, driven by human activity, climate change, and disease spread, is reflected by the rapid decline of animal populations across all phylogenetic groups. Bats exemplify a group highly susceptible to these threats. While threats to bats are often studied locally, global interactions remain overlooked. Using a literature-based analysis and the metacoupling framework (including the telecoupling framework), which analyzes human–nature interactions across local to global scales, we take a holistic approach to understanding how conservation strategies can support both biodiversity and ecological and socioeconomic sustainability. Focusing on the Indiana bat (an endangered species with an accelerating population decline for which such a comprehensive analysis is urgently needed), we find how local, regional, and global factors contribute to the shrinking population. Results indicate that local factors include habitat disturbance, cave tourism, and public perceptions. Regional factors include inconsistent regulations and land-use change (e.g., suburban sprawl). Global factors include ecotourism, distant consumer demand (e.g., the timber market), and climate change. White-Nose Syndrome affects bats across scales. The results also suggest that conservation strategies limited to local interventions alone are insufficient. This paper advances sustainability research by applying the metacoupling framework to species conservation, demonstrating how local-to-global human–nature interactions can inform more effective and sustainable management strategies. Full article

Urban agriculture is increasingly recognized not only for its role in enhancing ecological resilience, food security, and social inclusion, but also for its potential to challenge dominant urban development paradigms. Agroparks, as a spatial typology, have traditionally been associated with multifunctionality, productivity, and land preservation. This Perspective argues that agroparks can also serve as instruments for degrowth-oriented urban transitions, particularly in the context of climate emergency and the need to reconfigure urban land use beyond growth imperatives. Through landscape architectural analysis, the Bernex Agropark (now Parc des Molliers) in Geneva is examined as a spatial prototype that transforms underutilized land into a coherent system of crop zones, civic amenities, and ecological infrastructure. The project demonstrates how landscape architecture can contribute to the regeneration of urban edges while promoting ecological productivity, cooperative stewardship, and spatial limits to urban expansion. We introduce the concept of “Agroparks and Degrowth Urbanism”, framing Bernex as both a post-growth design strategy and a governance experiment. The Perspective concludes with recommendations for integrating agroparks into urban planning: connecting them to green infrastructure networks, prioritizing ecological over economic outputs, enabling commons-based management, and supporting climate adaptation through spatial design and food system relocalization. Full article

The present research aims to analyze, assess, and interpret the territorial reality of the south of the Community of Madrid, with the case study of Aranjuez, revealing the interrelationship between urban growth, its link with the mechanisms that operate in the real estate sector, the processes of transformation of rural areas into spaces where rurbanization and marginal urbanization related to the reclassification of land (generally rural) prevail, based on the application of a novel interpretive methodology of the so-called “Third Landscape”, based on the “Key Performance Indicators (KPIs)”, applied to formal, socioeconomic, functional, environmental paradigms, among others. Thus, throughout the study, we address the Third Landscape and elements such as Lost Spaces, Garbage Spaces, Shadow Places, etc., in short, vacant lots or residual spaces (formless and unused anti-spaces), which are framed around the conceptualization of the Third Landscape, of that of no man’s land, which no one uses, whose maintenance is unknown, and which currently predominates in the rural and rururban world of Aranjuez. With all this, the motivation for the research and, therefore, its results, is based on the knowledge of the regeneration and adaptation of the nature of the study area in marginal and unplanned spaces—such as roadside ditches, vacant lots, and urban interstices—promoting the foundations for future territorial development models, as well as territorial planning processes that allow for urban regeneration, ecological design, and the socioeconomic enhancement of a city in expansion and transformation. Full article

In response to escalating urban waterlogging crises exacerbated by global warming and accelerated urbanization, an innovative waterlogging risk assessment framework was advanced in this study to bolster urban resilience and promote sustainable urban development. Current methodologies often suffer from subjective bias in weight assignments for evaluation indicators. To overcome this limitation, the projection pursuit (PP) technique was integrated with a real-coded accelerated genetic algorithm (RAGA) to derive objective indicator weights. Focusing on the built-up area of Xiushan County in Chongqing, the InfoWorks ICM was employed to develop a 1D-2D coupled hydrodynamic model for simulating the dynamic spatiotemporal evolution of waterlogging events. Based on three dimensions namely hazard, sensitivity, and vulnerability, an urban waterlogging risk assessment model was developed and ArcGIS was utilized to precisely generate risk distribution maps under rainfall scenarios with return periods of 20 years and 100 years. Additionally, to enhance flood mitigation capabilities in identified high-risk zones, this study proposed implementing stormwater storage tank systems. Simulation results demonstrated that these measures achieve a 50.88% reduction in overflow volumes in critical areas, effectively lowering peak waterlogging depth from 0.74 m to 0.53 m. Key findings revealed that high-risk areas exhibit significant spatial clustering in low-elevation districts characterized by high population density and economic development intensity, where extreme rainfall events amplify water accumulation vulnerabilities, highlighting the importance of sustainable land use planning and climate adaptation strategies. The proposed assessment methodology not only enables objective quantification of urban waterlogging risks but also facilitates evidence-based formulation of targeted mitigation strategies, facilitating the goals of urban sustainability and long-term environmental resilience. Full article

Vegetation in urban green spaces plays a critical role in mitigating surface heat, yet the magnitude of this effect remains uncertain across scales and measurement methods. This study assesses the cooling performance during the summer of 94 green spaces in three Chilean cities—classified in three types according to their size—combining satellite-derived land surface temperature (LST) data with high-resolution in situ thermal imaging. We performed comparisons of the cooling effects of green spaces and their components (vegetation, impermeable and semi-permeable surfaces). Spearman’s correlation analysis, the Mann-Whitney U test and Kruskal-Wallis and Dunn post hoc were used to evaluate associations and differences. Results demonstrate that vegetation quantity and composition—particularly tree and shrub cover—are key determinants of cooling performance. In situ measurements reveal that green spaces are on average 9.3 °C cooler than their urban surroundings, substantially exceeding differences captured by LST. Additionally, shaded surfaces within green spaces exhibit temperature reductions of 12 °C to 17 °C compared to sun-exposed areas, underscoring the role of vegetation in mitigating surface heat extremes. These findings challenge the sole reliance on remote sensing for urban heat assessments and highlight the value of integrating ground-based observations. This study advances understanding of vegetation’s localized cooling potential in Latin American cities and provides actionable insights for urban climate resilience planning. Full article