Search Results (715)

Urban heat islands pose increasing risks to human settlements, yet the differential mechanisms by which grey–green spaces regulate diurnal and nocturnal land surface temperature across local climate zones remain insufficiently understood. This study addresses this gap through a Hangzhou case study, integrating a ten-indicator grey–green space system with two local spatial identification approaches—K-means clustering and an LCZ-inspired simplified scheme—and a Random Forest-SHAP framework. The LCZ-inspired scheme outperformed K-means clustering, with a mean diurnal–nocturnal Test R² of 0.4344 across twelve models, compared to 0.2977 for K-means. Diurnal and nocturnal LST were driven by systematically different factors: building density dominated daytime LST in most LCZ types (22.0% to 27.8%), while canopy height dominated nighttime LST (22.7% to 30.2%), revealing a systematic shift from building-dominated daytime to vegetation-dominated nighttime. This shift did not occur in compact built-up areas, suggesting that built-up density may be a threshold condition for the shift. Key variables exhibited nonlinear threshold effects with saturation points varying by LCZ type: canopy height cooling saturated at approximately 4 m in LCZ2 but required 17–21 m in LCZ3 and LCZA. These SHAP-based patterns and turning points should be regarded as exploratory, sample-dependent associations evaluated within the training data; their spatial stability across held-out regions was not assessed. Factor interactions were interval-dependent rather than globally fixed. Spatial cross-validation confirmed that random splitting substantially overestimated model performance, highlighting the necessity of spatially explicit validation. The methodological framework provides a replicable approach for urban thermal environment research and offers LCZ-specific threshold hypotheses for thermal regulation planning in subtropical megacities, subject to further spatial and cross-city validation. Full article

Dog ownership is rising quickly across dense Asian cities, coinciding with increasing competition for scarce urban green space. However, the cognitive factors associated with residents’ acceptance of human–dog shared park use remain poorly understood. Most existing evidence comes from low-density cities that rely on dedicated off-leash dog parks and therefore provides limited insight into dense, multi-use park systems where spatial segregation is difficult to implement. This study examines and tests a model of public acceptance of human–dog shared use in Shanghai, integrating the Stimulus–Organism–Response (S-O-R) paradigm with the Theory of Planned Behaviour (TPB). PLS-SEM was applied to 1045 cross-sectional intercept-survey responses collected across 229 parks in all 16 districts of Shanghai. Perceived risk and incivility were negatively associated with attitude toward shared use, whereas perceived social friction was not significantly associated with attitude. The two conflict constructs explained 12.5% of the variance in attitude. Attitude, subjective norm, and perceived behavioural control made positive and statistically significant formative contributions to the higher-order pro-sharing cognition composite (PSC). PSC was strongly and positively associated with acceptance intention (β = 0.796) and explained 63.3% of its variance. The findings suggest that acceptance intention is associated with a multidimensional pro-sharing cognitive orientation encompassing favourable evaluation, perceived social approval, and perceived manageability. Supplementary variance decomposition indicated that ATT, SN, and PBC made broadly balanced contributions to the model’s explanatory power. The study provides a locally adapted analytical framework for examining human–dog shared park acceptance and offers a basis for comparative research in other high-density cities. Full article

Urban waterfront spaces that are ecologically sensitive often remain visually prominent yet inaccessible, posing a management challenge: how to generate public engagement and economic value without physical access. This paper proposes a four-stage socio-ecological framework integrating sensory restoration, behavioral transformation, economic generation, and ecological reinvestment. Using Xianyang Lake in China as a demonstrative case, the study applies six years of phenological records from 36 plant species. Analytic hierarchy process and k-means clustering derive four seasonal sensory palettes, ensuring year-round visual, olfactory, and auditory engagement. A proposed WeChat-based guidance system illustrates how passive sensory exposure could be converted into active behavioral engagement across walking, bridge viewing, and boating. The framework links enhanced experience to a dynamic revenue model and closes the loop by reinvesting proceeds into maintenance and sediment reuse for greenbelt expansion. The empirical component consists of phenological data analysis and seasonal palette derivation, while the socio-economic pathways are presented as testable hypotheses requiring future empirical validation. The findings shift the paradigm from physical to sensory accessibility, offering a conceptually transferable framework for managing inaccessible urban blue-green spaces. Full article

To address the limitation of traditional cellular automata models in effectively integrating temporal and spatial information, this study extends the previously developed Land use Simulation and Decision-Support system (LandSDS). By incorporating a graph attention network (GAT), a transformer, and an agent-based model (ABM) into a cellular automata framework informed by remote sensing time series, GT-LandSDS is constructed. Specifically, GAT dynamically captures higher-order spatial dependencies among land parcels; the self-attention mechanism of the transformer extracts land use change characteristics from multi-period observations; and ABM captures human behavioral decisions of three types, namely traffic, resident, and government. Based on this framework, GT-LandSDS derives CA transition rules from multiple dimensions and enhances the dynamic exploration of land use change across space and time. Using Guangxi Zhuang Autonomous Region as a case study, the model was validated with remote sensing land use data from six periods (2000, 2005, 2010, 2015, 2020, and 2023), and scenario-based future predictions were generated. The results show that: (1) The overall accuracy reaches 0.926, while the Kappa coefficient is 0.820, and the figure of merit (FoM) for change simulation is 0.034, indicating a relative advantage over ANN-CA, LSTM-CA, and UESP in overall pattern simulation, although fine-scale change reproduction remains limited; (2) Three development scenarios were then assessed: continuing historical trends, theoretical high-intensity urban expansion, and karst landform conservation under a green transformation development policy. The land use pattern of the area from 2023 to 2035 was predicted. The findings reveal that accelerating urbanization leads to rapid expansion of construction land, increasing by more than 88% compared with 2023, and causes substantial cropland loss. In contrast, intervention through the green transformation development policy limits construction land growth to 26.5%, effectively curbing urban sprawl while protecting forest, grassland, and cropland resources in the karst landscape. This study offers new insights into land use change simulation in ecologically fragile regions subject to strong policy interventions. It provides a scientific basis for coordinating ecological conservation and high-quality development in karst areas. Full article

Heavy metal contamination of agricultural soils and urban green spaces has become a major environmental concern, threatening ecosystem functioning, food safety, and sustainable land management. Silicon-based growing media have emerged as an environmentally friendly approach for reducing metal mobility while enhancing plant establishment in contaminated environments. Despite growing research interest, a comprehensive evaluation of the mechanisms, effectiveness, and practical applications of silicon-amended growing media across diverse plant systems remains lacking. This systematic review addresses this gap by synthesizing current evidence following the PRISMA 2020 framework. A systematic search of Web of Science, Scopus, PubMed, ResearchGate and Google Scholar identified 247 publications published between 2010 and 2025, of which 32 peer-reviewed studies met the predefined inclusion criteria for qualitative analysis. The reviewed literature demonstrates that silicon incorporation into growing media improves substrate functionality by modifying physicochemical properties, immobilizing heavy metals, regulating metal transport within plants, strengthening antioxidant and osmo-protective defense systems, preserving photosynthetic activity, and improving nutrient acquisition and water-use efficiency. Furthermore, silicon influences molecular signaling pathways and promotes beneficial rhizosphere interactions that collectively enhance plant resilience under metal stress. Among the evaluated materials, silicon nanoparticles consistently exhibited greater remediation efficiency than conventional silicon sources because of their higher surface reactivity and improved bioavailability. Overall, silicon-based substrate engineering represents a multifunctional and sustainable strategy for mitigating heavy metal contamination while improving the performance of agricultural crops and urban vegetation. Future research should focus on validating these findings under long-term field conditions, optimizing silicon formulations for different substrate types and contamination scenarios, evaluating environmental safety, and integrating silicon-based technologies into climate-resilient agricultural practices and urban green infrastructure. Full article

Urban expansion, peri-urban growth, and evolving mobility service systems are reshaping the functional links between urban and rural areas. As these relationships become increasingly complex, conventional urban–rural classifications may underestimate the spatial differences in everyday accessibility, environmental conditions, and demographic exposure. To capture these interrelated dimensions, this study develops a GeoAI-enabled Accessibility–Environment–Equity (AEE) framework to analyse the urban–rural continuum as a multidimensional functional space. Using Odense municipality as a case study, the framework incorporates network accessibility indicators, environmental benefit and pressure proxies, and demographic exposure indicators within a hexagonal tessellation. The indicators are combined into a standardised AEE feature matrix, interpreted through a principal component analysis, and classified using Gaussian mixture modelling with posterior-probability uncertainty mapping. The resulting typology shows a distinct but non-uniform core–periphery gradient: the central areas exhibit high accessibility and population density but lower green space availability and higher environmental pressure proxies, while the peripheral areas are greener but less accessible by non-car modes. The benchmarking against single-domain reference classifications reveals that the functional typology is primarily shaped by accessibility and demographic density structures rather than by greenness alone. This study advances urban–rural continuum (URC) research by demonstrating how GeoAI-enabled workflows can identify functional transition zones and accessibility–environment mismatches, supporting more evidence-based planning for medium-sized European cities and their surrounding urban–rural transition zones. Full article

Urban green space supply (UGSS) is a core component of territorial spatial planning, and the advancement of the park city initiative has been temporally associated with a systematic transformation. Differentiation in UGSS is a comprehensive issue concerning ecological environment, public welfare, and high-quality urban development, and it is closely related to the achievement of United Nations Sustainable Development Goal (SDG) 11.7. This study employs a comprehensive approach combining spatiotemporal dynamic analysis (Mann–Kendall trend test and Theil–Sen slope estimation), differentiation measures (Gini coefficient and Theil index), and the explainable machine learning SHAP model to conduct a large-sample empirical analysis of 1760 towns in China from 2015 to 2024. The findings show the following: First, park city construction corresponds to notable spatiotemporal evolution of UGSS in China’s towns, with approximately 85% of towns showing a significant rise. Second, park city construction coincides with a reduction in differentiation in both the outcomes and processes of UGSS in China’s towns, with both the Gini coefficient and Theil index declining to varying degrees. An analysis of the Theil index further confirms that the observed changes in the Theil index are more pronounced in disadvantaged towns, and the differentiation in UGSS originates more from intra-regional disparities than from inter-regional gaps. Third, the differentiation in UGSS shows deep structural correlates, with the associations of socio-economic and natural ecological factors exhibiting various complex nonlinear associations such as inverted U-shape, arc shape, U-shape, and wave shape. These associations are specifically manifested as mixed directionality of associations, hierarchical intensity of associations, threshold-based evolutionary pathways, geographic spatial heterogeneity, and interactive relationships among factors. This study recommends that the policy design of park city construction and green space system planning should promptly establish a new model combining situational response, threshold management, and collaborative governance. The nonlinear and interpretable analytical paradigm constructed in this study holds significant value for achieving precise supply and equitable sharing of green space resources. Full article

Urban green spaces play an important role in supporting ecosystem functions, landscape connectivity, and urban resilience; however, their potential contribution may be constrained by fragmentation, inadequate maintenance, and limited integration among spatial planning instruments. This study develops a preliminary green infrastructure (GI) planning framework for the municipality of Noicattaro (Bari, Apulia, Italy) by integrating planning provisions with the observed condition and spatial distribution of existing public green spaces. A GIS-based spatial analysis, complemented by field surveys, assessed 89 public green spaces for compliance with planning requirements and maintenance status. The analysis also considered the spatial structure of the surrounding peri-urban landscape, including the karstic dry valleys (Lame), agricultural land uses, and relevant landscape and hydrogeological planning constraints. The results show that only 20.2% of the surveyed green spaces met both planning and maintenance criteria, whereas 78.4% fell into categories characterised by planning non-compliance, inadequate maintenance, or both. These findings highlight potential gaps between formal planning provisions and the observed condition and management of the municipal green system. The analysis of the Lame further revealed a predominance of agricultural land over natural vegetation, emphasising the importance of considering the wider agricultural and peri-urban landscape when identifying potential ecological connections. Based on the combined spatial and field assessment, a preliminary GI network framework was developed around core ecological areas, buffer zones, and strategically located green spaces, with the aim of identifying potential spatial connections and areas for further investigation. Overall, the study demonstrates the potential of integrating planning analysis, GIS-based assessment, and field observations as a screening and decision-support approach for more context-sensitive GI planning in peri-urban Mediterranean landscapes. The proposed framework should be regarded as a preliminary planning proposal requiring further validation through biodiversity, ecosystem-service, accessibility, and implementation assessments. Full article

Urban public spaces in cold-climate cities commonly face the challenge of seasonal vitality attenuation. Taking the central urban area of Shenyang as a case study, this paper constructs a HEVA four-dimensional framework based on “spatial diagnosis, mechanism analysis, and design response.” By integrating Multiscale Geographically Weighted Regression (MGWR) with GIS spatial analysis, the study reveals the spatial heterogeneity and driving mechanisms of winter vitality attenuation in linear green spaces. The findings are threefold: (1) Winter wind fields induce significant local wind-chill effects that severely suppress winter stay vitality in green spaces; (2) MGWR results show that greenery coverage exhibits a predominantly positive effect (approximately 81% of sample points have positive coefficients), bus stop density exhibits a “bifurcated spatial pattern”, with positive coefficients at approximately 53% of sample points and negative at the remaining 47%, indicating a highly localized effect., commercial service density is positive across the entire study area (100% positive), building density is predominantly negative (approximately 87%), and road network density has a relatively weak effect; (3) Based on the above mechanisms, a three-pronged elastic design strategy system is proposed, encompassing “climate-adaptive regulation, functional composite implantation, and regional cultural response.” This study explores a data-driven, mechanism-guided geospatial design pathway for the renewal of underperforming spaces in cold-climate cities. The generalizability of the methodological framework warrants further validation through multi-city and multi-seasonal data. Full article

The 1960s to early 1990s marked a pivotal era in Kazakhstan’s mass housing history, particularly in Almaty, which was driven by a sector-wide policy shift toward apartment-based occupancy, regulated space standards, full utility provision, and organised residential courtyards. Large-panel buildings from this period still comprise over one-third of Almaty’s housing stock, yet their alignment with modern comfort standards remains understudied. This study establishes a historical periodisation of Almaty’s large-panel housing and assesses its comfort levels at apartment and built-environment scales. The two-part methodology combined a theoretical strand (comfort-level theory, literature review, historical retrospective, and periodisation) with an analytical strand (comparative graphical-analytical assessment of series 1Kz-464-AS, 1Kz-464-DS, 69, E-147 and 158), converging in a Basic–Supplementary–Advanced comfort classification. The results of the current study showed that three sub-periods emerged. Series 1Kz-464-AS and 1Kz-464-DS used a narrow-bay (2.6–3.2 m), economy-driven two-bay system with minimal zoning. Series 69 widened bays to 3.6–5.4 m, improving spatial comfort and utility provision. Series E-147 and 158 introduced a three-bay scheme with an added 2.1 m bay and raised ceilings (3.0 m), yielding the most flexible layouts. At the urban scale, microdistrict planning enlarged planning units by 7–10 times, preserved green connectivity through mid-rise buildings, and shielded courtyards using perimeter high-rises, supporting walkable, infrastructure-rich neighbourhoods. Apartment-scale comfort improved incrementally through bay widening rather than layout diversification, leaving persistent redevelopment constraints from panel structures. Built-environment comfort, however, reached a comparatively advanced, durable standard. Adaptive reuse should prioritise structural refurbishment, reduced occupancy density, courtyard–microdistrict continuity, and diversified public-space functions. This study’s results can inform renovation strategies for Soviet-era housing across post-Soviet cities. Full article

Community-scale sustainable built-environment practices are increasingly important for linking urban sustainability policy with everyday spatial transformation, yet the evidence base remains fragmented across nature-based solutions, housing retrofit, settlement upgrading, public-space transformation, digital mapping, circular infrastructure, and governance-oriented planning. This scoping review, reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR), maps and synthesizes peer-reviewed evidence on community-scale sustainable built-environment practices, with publication years from 2015 to 2026 and indexed by the final search date of 13 June 2026. Searches were conducted in Scopus and the Web of Science Core Collection, yielding 3506 records. After duplicate removal, title-and-abstract screening, and full-text eligibility assessment, 130 studies were included for Spatial–Social–Governance (SSG) coding and thematic synthesis. This review identified eight thematic clusters, led by nature-based and green–blue infrastructure practices, followed by housing, retrofit, and settlement upgrading; neighborhood sustainability assessment and planning; public-space transformation and placemaking; digital tools and mapping; circular neighborhood infrastructures; mobility-related practices; and heritage-led regeneration. The largest concentration of evidence was found in nature-based and green–blue infrastructure practices, followed by housing, retrofit, and settlement upgrading; smaller clusters extended the field towards neighborhood assessment, public-space transformation, digital evidence systems, circular infrastructures, mobility, and heritage-led regeneration. As a review-derived synthesis output, the Spatial–Social–Governance Mechanism Framework does not introduce spatial, social, and governance as new analytical dimensions. Instead, it operationalizes these established concerns as a common comparison structure for heterogeneous community-scale built-environment practices, linking spatial intervention, social embedding, and governance enablement. The framework is neither a general theory nor an effectiveness model; its contribution is an integrative and review-specific analytical instrument. Full article

Green-building research has examined energy efficiency and indoor environmental quality in depth, whereas site-related strategies are often represented through aggregate certification outcomes with limited visibility into criterion-level evidence and weighting assumptions. This study develops the Climate-Referenced SS–LT Site-Performance Assessment Framework (CR-SSAF) for an exploratory documentation-based comparison of a combined Sustainable Sites, Location, and Transportation (SS–LT) construct across five green-certified smart office buildings in three Köppen climate zones. Six SS–LT criteria were assessed using a four-level operational rubric, an exact-normalized weighted Site-Performance Index (SSPI), a descriptive Technology Enablement Factor (TEF), ordinal inter-rater agreement analysis, three weighting schemes, and a TOPSIS ranking-concordance check. SSPI values ranged from 2.00 to 2.65. The Af case recorded the highest SSPI in this sample, while the lowest scores occurred where no qualifying project-specific heat-mitigation or green-/open-space evidence could be verified. The first, second, and last ranks remained unchanged across the three weighting schemes, while The Edge and Shanghai Tower were tied under the equal and ecology-sensitive schemes. Because the sample is small and heterogeneous, and does not control for urban form, building scale, infrastructure, certification system, or documentation availability, differences cannot be attributed independently to climate. CR-SSAF is therefore presented as a transparent exploratory workflow, not as a validated climate effects model or as evidence of transferability. Full article

Fine-scale site selection of urban parks serves as a core measure to guarantee spatial equity of public service spaces for residents and advance the sustainable development of urban ecological spaces. Among relevant tasks, balancing the supply and demand of green spaces stands out as an essential foundation for maintaining long-term stability of urban human well-being and ecosystems. Green-space supply is defined as the stock of various existing urban parks within the city, while green-space demand is quantified via grids generated based on residential communities in Changsha. Existing research on urban park site selection lacks a full-process coupled framework, fails to accommodate differentiated layout demands for multi-level parks, and struggles to reconcile the sustainable operation and long-term ecological empowerment of urban green-space systems. Taking the main urban area of Changsha as the research scope, this study divides the study area into grid units to analyze the spatial differentiation of green-space accessibility and identify service blind zones. The XGBoost model is adopted to predict areas suitable for green-space construction, and the NSGA-III algorithm is applied to realize collaborative multi-objective optimization covering service efficiency, ecological benefits, and land development costs. The results reveal that the 15-min walking coverage of community parks in central Changsha only reaches 57.29%. Respectively, 34.52% and 41.04% of residential communities record accessibility levels below the municipal average of urban parks and forest parks, with prominent shortages of green-space supply in peripheral urban areas. This study optimizes and screens twenty-eight candidate sites for community parks, twelve candidate sites for urban parks, and eight candidate sites for forest parks. The proposed scheme effectively narrows the gap in green-space accessibility across the whole city and coordinates ecological conservation with land development costs. Compared with research relying on a single model or two-stage coupling frameworks, this paper constructs a systematic workflow spanning supply–demand status assessment to multi-objective layout decision-making, enabling differentiated optimized layout of multi-tiered parks. The integrated framework effectively enhances the spatial resilience and resource utilization efficiency of urban green-space systems, facilitates high-quality and sustainable upgrading of urban living environments, and provides a referable innovative approach for multi-level urban park arrangement and refined multi-objective planning. Full article

In response to the growing demand for sustainable regeneration and efficient operation of urban spaces, the adoption of digitalised facility management (FM) has emerged as an effective strategy. FM facilitates data-driven planning and optimised resource use, contributing to more sustainable and resilient urban environments. This research combines Building Information Modelling (BIM) with Visual Programming Languages (VPLs) to support the regeneration and optimisation of an urban park. Autodesk Revit is employed to develop an LOD 300 semantic and geometric model, while Dynamo enables the implementation of two parametric scripts: Script A, governing on/off control of monitored systems based on environmental, temporal and contextual conditions, and Script B, providing continuous operational monitoring, fault diagnosis and color-coded visual feedback directly within the BIM environment. The framework is tested at Parco Gioeni, an 8.6-hectare historically and geologically urban park in Italy, across three management systems: irrigation, nebulization and green area monitoring. A total of 28 what-if scenarios are conducted across systems, indicating the framework’s functional consistency and contextual adaptability under the tested conditions. The proposed approach is built on accessible open software tools, making it transferable to public administration contexts, and is designed to be scalable and replicable across different urban green space typologies. Full article

Faced with both global climate change and rapid urbanization, understanding how the built environment affects surface runoff is essential for strengthening urban hydrological resilience. However, elucidating the nonlinear and interactive effects of three-dimensional buildings and two-dimensional green spaces on surface runoff potential in urban blocks remains a scientific challenge for precise flood-mitigation spatial planning. Using six typical waterlogging-prone blocks in Shenyang as case studies, this study constructs a morphological index system for urban gray–green spaces and reveals the nonlinear effects of each index on surface runoff potential using an interpretable Random Forest (RF)–SHAP model. The results indicate that the RF model reliably captures the complex spatial patterns of simulated local surface runoff potential (R² = 0.723–0.825). At the block scale, the surface runoff response exhibits a dual character: it is predictively dominated by three-dimensional morphological dominance and two-dimensional base regulation. Three-dimensional building morphology generally demonstrates pronounced unidirectional thresholds and high-value saturation in model prediction. In particular, when the core indicator, building spatial congestion degree (B_SCD), crosses a critical threshold, surface runoff potential rises sharply. The coefficient of variation in building height (B_HVC) shows a “V-shaped” reversal in areas of extreme surface runoff potential, whereas two-dimensional green space indicators display clear asymmetric critical points. Significant reductions in surface runoff potential appear only when green space scale (G_LPI), boundary complexity (G_LSI), or fragmentation (G_PD) exceed specific model-identified thresholds. Furthermore, the study demonstrates marked interactive effects between the morphologies of gray–green spaces. High surface runoff risk from dense, large buildings can be substantially offset by large green space patches (G_LPI) with highly complex boundaries (G_LSI). The advantage of vertically staggered buildings (B_HVC) requires a green space base with low fragmentation (G_PD) to realize a gray–green synergistic mitigating effect. These findings provide theoretical and methodological support for enhancing the hydrological resilience of urban blocks. Full article