Search Results (511)

This study presents a data-driven framework for optimizing parking space allocation and pricing in large commercial complexes, addressing persistent spatial imbalances in occupancy between high- and low-demand zones. A mixed Logit (ML) model with interaction terms is estimated from stated preference survey data to capture heterogeneous user preferences across trip purposes. A dual clustering algorithm is then applied to generate spatially coherent pricing zones, integrating geometric, functional, and occupancy-based attributes. Two differential pricing strategies are formulated: an administered model with regulatory price bounds and a market-based model without such constraints. Both pricing models are solved using an improved multi-objective Particle Swarm Optimization–Grey Wolf Optimizer (PSO–GWO) algorithm that jointly optimizes spatial zoning and zone–time pricing schedules. Using data from the Kingmo Complex in Nanjing, China, the results show that both strategies significantly reduce spatio-temporal occupancy variance and improve utilization balance. The administered strategy reduces variance by up to 67% on weekdays, with only a 1% increase in revenue, making it suitable for contexts prioritizing regulatory compliance and price stability. In contrast, the market-based strategy reduces variance by over 40% while generating substantially higher revenue, particularly during periods of high and uneven demand. The proposed framework demonstrates the potential of integrating behavioral modeling, spatial clustering, and multi-objective optimization to improve parking efficiency. The findings provide practical guidance for operators and policymakers seeking to implement adaptive pricing strategies in large-scale parking facilities. Full article

Bangkok is the largest city in Thailand and the second largest city in Southeast Asia. Due to the rapid urbanization and upgrading of economic structures, the real estate market in Bangkok is not only constrained by domestic factors but also fluctuates with international economic cycles. Bangkok’s long history, diverse culture, developed economy, and incomplete land infrastructure make the formation of housing prices particularly complex. In this study, we collected 13,175 residence transaction data from 2076 different neighborhoods in Bangkok and explored multiscale effects of various land infrastructure factors on housing prices in Bangkok at the neighborhood level. Our analysis not only supports land planning departments of Bangkok to make more reasonable facility planning but also provides new insights into driving mechanisms of housing prices in other cities of Thailand and ASEAN countries. Full article

The construction of large-scale infrastructure, such as power facilities, requires extensive use of reinforced concrete. The durability degradation of reinforced concrete structures in chloride environments involves multi-physics coupling effects, chloride ion diffusion, rebar corrosion, and concrete damage. Existing models neglect the coupling mechanisms among these processes and the influence of mesoscale structural characteristics. Therefore, this study proposes a diffusive–mechanical coupled phase field by integrating the phase field, chloride ion diffusion, and mechanical equivalence for rebar corrosion, establishing a multi-physics coupling analysis framework at the mesoscale. The model incorporates heterogeneous meso-structure of concrete and constructs a dynamic coupling function between the phase field damage variable and chloride diffusion coefficient, enabling full-process simulation of corrosion-induced cracking under chloride erosion. Numerical results demonstrate that mesoscale heterogeneity significantly affects crack propagation paths, with increased aggregate content delaying the initiation of rebar corrosion. Moreover, the case with corner-positioned rebar exhibits earlier cracking compared to the case with centrally located rebar. Furthermore, larger clear spacing delays delamination failure. Comparisons with the damage mechanics model and experimental data confirm that the proposed model more accurately captures tortuous crack propagation behavior, especially suitable for evaluating the durability of reinforced concrete components in facilities such as transmission tower foundations, substation structures, and marine power facilities. This research provides a highly accurate numerical tool for predicting the service life of reinforced concrete power infrastructure in chloride environments. Full article

Indoor air quality (IAQ) is a critical determinant of health, comfort, and productivity, and is strongly connected to building energy demand due to the role of ventilation and air treatment in HVAC systems. This review examines recent applications of Artificial Intelligence (AI) and Machine Learning (ML) for IAQ prediction across residential, educational, commercial, and public environments. Approaches are categorized by predicted parameters, forecasting horizons, facility types, and model architectures. Particular focus is given to pollutants such as CO₂, PM2.5, PM10, VOCs, and formaldehyde. Deep learning methods, especially the LSTM and GRU networks, achieve superior accuracy in short-term forecasting, while hybrid models integrating physical simulations or optimization algorithms enhance robustness and generalizability. Importantly, predictive IAQ frameworks are increasingly applied to support demand-controlled ventilation, adaptive HVAC strategies, and retrofit planning, contributing directly to reduced energy consumption and carbon emissions without compromising indoor environmental quality. Remaining challenges include data heterogeneity, sensor reliability, and limited interpretability of deep models. This review highlights the need for scalable, explainable, and energy-aware IAQ prediction systems that align health-oriented indoor management with energy efficiency and sustainability goals. Such approaches directly contribute to policy priorities, including the EU Green Deal and Fit for 55 package, advancing both occupant well-being and low-carbon smart building operation. Full article

Under the dual backdrop of the Healthy China strategy and the concept of sustainable development, optimizing the spatial layout of primary healthcare facilities is important for fairly distributing healthcare resources and strengthening the resilience of the public health system in a sustainable way. This study introduces an innovative 3D spatial resilience evaluation framework, covering transmission (service accessibility), diversity (facility type matching), and stability (supply demand balance). Unlike traditional accessibility studies, the concept of “resilience” here highlights a system’s ability to adapt to sudden public health events through spatial reorganization, contrasting sharply with vulnerable systems that lack resilience. Method-wise, the study uses an improved Gaussian two-step floating catchment area method (Ga2SFCA) to measure spatial accessibility, applies a geographically weighted regression model (GWR) to analyze spatial heterogeneity factors, combines network analysis tools to assess service coverage efficiency, and uses spatial overlay analysis to identify areas with supply demand imbalances. Harbin is located in northeastern China and is the capital of Heilongjiang Province. Since Harbin is a typical central city in the northeast region, with a large population and clear regional differences, it was chosen as the case study. The case study in Harbin’s main urban area shows clear spatial differences in medical accessibility. Daoli, Nangang, and Xiangfang form a highly accessible cluster, while Songbei and Daowai show clear service gaps. The GWR model reveals that population density and facility density are key factors driving differences in service accessibility. LISA cluster analysis identifies two typical hot spots with supply demand imbalances: northern Xiangfang and southern Songbei. Finally, based on these findings, recommendations are made to increase appropriate-level medical facilities, offering useful insights for fine-tuning the spatial layout of basic healthcare facilities in similar large cities. Full article

Under the accelerating demographic aging trend, the rational allocation of elderly care facilities has emerged as a critical challenge. Although existing studies have investigated elderly care facilities planning using conventional methods, they frequently overlook the nonlinear interactions between built environment factors and heterogeneous demands across different elderly care facility types. This study addresses these gaps by proposing a data-driven framework that integrates machine learning with spatial analysis to optimize elderly care facility distribution in Xi’an City central area, Shaanxi Province, China. Leveraging multi-source datasets encompassing points of interest (POIs), road networks, and demographic statistics, we classify facilities into three categories (service-oriented, activity-oriented, and care-oriented) and employ an XGBoost model with SHAP interpretability to evaluate spatial distributions and influencing factors. The results demonstrate that the XGBoost model outperforms comparative algorithms (Random Forest, CatBoost, LightGBM) with superior performance metrics (accuracy rate of 97%, precision of 95%, and F1-score of 90%), effectively capturing nonlinear thresholds effects. Key findings reveal the following: (1) Accessibility and road density exert threshold effects on care-oriented facilities, with facility attractiveness saturating when these values exceed 6; (2) Land use intensity and medical resources positively correlate with activity-oriented facilities, while excessive retail density inhibits their distribution; (3) Service-oriented facilities thrive in areas with balanced accessibility and moderate commercial diversity. Spatial analysis identifies clustered distribution patterns in urban core areas contrasted with peripheral deficiencies, indicating need for targeted interventions. This research contributes a scalable methodology for equitable facility planning, emphasizing the integration of dynamic built environment variations with model interpretability. The framework provides significant implications for formulating age-friendly urban policies applicable to global cities undergoing rapid urbanization and population aging. Full article

Clarifying how the community-scale built environment shapes the spatial heterogeneity of cardiovascular disease (CVD) prevalence is essential for precision urban health interventions. We integrated CVD prevalence data from the Guangxi Zhuang Autonomous Region Hospital (2020–2022) with 14 built-environment indicators across 77 communities in Xixiangtang District, Nanning, and compared ordinary least squares (OLS), geographically weighted regression (GWR), and multiscale geographically weighted regression (MGWR). MGWR provided the best model fit (adjusted R² increased by 0.136 and 0.056, respectively; lowest AICc and residual sum of squares) and revealed significant scale-dependent effects. Distance to metro stations, road network density, and the number of transport facilities exhibited pronounced local-scale heterogeneity, while population density, building density, healthy/unhealthy food outlets, facility POI density, and public transport accessibility predominantly exerted global-scale effects. High-risk clusters of CVD were identified in mixed-use, high-density urban communities lacking rapid transit access. The findings highlight the need for place-specific, multi-scale planning measures, such as transit-oriented development and balanced food environments, to reduce the CVD burden and advance precision healthy-city development. Full article

With the acceleration of global population aging, rural areas face particularly severe challenges due to youth outmigration and uneven resource distribution. Taking Jiashan Village in Wuhan as a case study, this research combines the planning–activity model of new time–geography with Maslow’s hierarchy of needs to investigate the behavioral and emotional characteristics of the elderly and their spatial adaptation requirements. Using GPS tracking of 30 participants, questionnaires (152 valid responses; 73.4% response rate), facial expression recognition, and the stated preference (SP) method, the study classified elderly lifestyles into four types: leisure-oriented, agricultural-labor-oriented, caregiving-oriented, and self-employment-oriented. The results show significant heterogeneity in spatial needs, social intensity, and emotional responses. A quantitative analysis using the multinomial logit model indicates that farmland optimization had the greatest positive utility (+1.5873), followed by the addition of new plazas and leisure facilities, both significantly enhancing satisfaction. A correlation analysis further revealed that prolonged use of farmland, parks, and walking paths was negatively correlated with satisfaction, underscoring the urgency of targeted renovations. On this basis, the study proposes a three-tiered demand framework of “local service–social interaction–personal value”, offering both theoretical support and practical strategies for multi-level and collaborative retrofitting of suburban rural public spaces, aiming to mitigate “aging depression” and promote urban–rural integration. Full article

Background: Non-functional well-differentiated pancreatic neuroendocrine tumors (WD-PanNETs) are complex, heterogeneous malignancies with variable prognosis. Despite guideline recommendations, disparities in access to specialized care may impact survival. This study examines whether treatment facility type, geographic travel distance, and treatment modalities are associated with survival outcomes in patients diagnosed with WD-PanNETs. Results: Among 20,174 patients with WD-PanNETs, the median age was 62 years (IQR: 52–70), and 54% were men. The majority were treated at non-academic hospitals (76%), with 2.9% traveling >250 miles for care. Patients treated at non-academic hospitals (24%) had 50% lower 15-year survival rates compared to those treated at academic hospitals (58%) and integrated hospitals (56%) (p < 0.001). Patients traveling >250 miles had a 72% 15-year survival rate, compared to 43% for those traveling <12.5 miles (p < 0.001). In the context of facility-type and geographic distance, treatment at non-academic hospitals <250 miles was associated with a 21% higher mortality risk (HR 1.21, 95% CI 1.12–1.31, p < 0.001), and treatment at low-volume hospitals increased mortality risk by 25% (HR 1.25, 95% CI 1.14–1.37, p < 0.001). In contrast, primary tumor resection was associated with a 64% reduction in mortality risk (HR 0.36, 95% CI 0.33–0.38, p < 0.001), which remained significant at all disease stages. Conclusion: Treatment at academic or high-volume centers and longer travel distances were associated with improved OS in patients with WD-PanNETs. Primary tumor resection remains critical, while systemic therapies were primarily used in later-stage disease. These findings support policies that improve access to centralized, multidisciplinary care. Full article

The Qinghai–Tibet Plateau (QTP), a globally significant tourist destination and critical ecological barrier, faces an intrinsic conflict between development and conservation. The scientific identification of suitable tourism zones is therefore crucial for formulating sustainable development policies. Conventional suitability assessments, however, which typically rely on subjective, expert-based weighting and static, supply-side data, often fail to capture the complex, non-linear dynamics of actual tourist–environment interactions. To overcome these limitations, an innovative analytical framework is presented, integrating massive tourist trajectory big data (66.7 million GPS points) as an objective, demand-driven suitability proxy, a Geo-detector model to identify key drivers and their interactions, and a Random Forest algorithm for spatial prediction. The framework achieves high predictive accuracy (AUC = 0.827). The results reveal significant spatial heterogeneity: over 85% of the QTP is unsuitable for tourism, while suitable zones are intensely concentrated in southeastern river valleys, forming distinct agglomerations around core cities and along primary transport arteries. Analysis demonstrates that supporting conditions—particularly transport accessibility and service facility density—are the dominant drivers, their influence substantially surpassing that of natural resource endowment. Furthermore, the formation of high-suitability zones is not attributable to any single factor but rather to the synergistic coupling of multiple conditions. This research establishes a replicable, data-driven paradigm for tourism planning in environmentally sensitive regions, offering a robust scientific basis to guide the sustainable development of the QTP. Full article

Environmental control in closed environment agricultural systems (CEA) is challenging due to the high energy demand and the dynamic interactions between plants and their heterogeneous phylloclimate. Optimization of crop production in CEA systems therefore requires a thorough understanding of whole-plant functioning and the interconnected plant-climate interactions. Such optimization is limited by an incomplete knowledge of how leaf-level measurements of gas exchange relate to whole-plant processes and how to scale-up point measurements of the heterogeneous environment to inform plant-level decisions. To address both, a dynamic whole-plant gas exchange system was developed to quantify the effect of temperature, relative humidity and light intensity on whole-plant photosynthetic and transpiration rates in lettuce (Lactuca sativa L.). Results showed that light intensity was the primary driver for whole-plant photosynthesis, with temperature optima increasing from 5 °C at a photosynthetic photon flux density (PPFD) of 150 µmol·m⁻²·s⁻¹ to 13 °C at 400 µmolm⁻²·s⁻¹. These optima for lettuce plants were 10 to 20 °C lower than those observed at leaf level due to a shifted balance between respiration and photosynthesis within the complex habitus of lettuce. The results showed a decoupling of transpiration and photosynthesis under high relative humidity, with vapour pressure deficit (VPD) values of 0.5 kPa or lower, which physically limited transpiration. The newly developed dynamic gas exchange system has proven to be a helpful tool for examining the relative importance and combined effects of environmental factors on whole-plant photosynthesis and transpiration. Potential future applications of this system include research on phylloclimate, implementation in production facilities, and validation of crop models. Full article

As global climate change intensifies with unprecedented urgency, nations worldwide have increasingly adopted market-based environmental regulatory instruments to advance carbon reduction objectives. In 2017, China launched energy rights trading pilots, thereby providing a crucial policy instrument for controlling total energy consumption at its source. However, the specific impacts and transmission pathways through which this system influences corporate carbon reduction behavior remain insufficiently explored through rigorous empirical investigation. Drawing upon panel data from heavy-polluting companies listed on the Shanghai and Shenzhen A-share markets, this study employs a difference-in-differences methodology to identify the causal effects of energy rights trading systems on corporate carbon reduction. Our findings reveal that energy rights trading systems significantly reduce corporate carbon emission intensity, generating pronounced emission reduction effects. Further mechanism analysis demonstrates that this system operates through two principal pathways: first, by promoting increased green investment among enterprises, whereby short-term emission reductions are achieved through procurement of energy-saving equipment and environmental protection facilities, and second, by stimulating corporate green technological innovation, whereby long-term sustainable emission reductions are realized through the development of energy-saving technologies and clean processes. Additionally, the research reveals that enterprises with lower financing constraints and stronger supply chain bargaining power respond more actively to policy implementation, with policy effects exhibiting significant heterogeneity. This study not only enriches the theoretical understanding of market-based environmental regulatory policy effects but also provides crucial empirical evidence for improving the energy rights trading system design and enhancing policy implementation effectiveness, thereby offering important policy insights for promoting corporate green transformation and achieving “dual carbon” objectives. Full article

In the context of low-growth urban development, the interaction between the supply–demand structure of community public service facilities and the housing market has increasingly become a key research concern. Yet, systematic investigations into how supply–demand dynamics influence market value remain limited. To fill this gap, this study takes Wuchang District of Wuhan as the empirical case and establishes an integrated framework of “supply–demand evaluation—value effects” to assess both the equity of facility allocation and its capitalization effects. The results indicate that: (1) all categories of public service facilities in Wuchang District have Gini coefficients above 0.6, indicating substantial imbalance. Among them, elderly care, infant care, and child recreation facilities exceed 0.7, reflecting particularly severe inequality. (2) The “accessibility–housing price” quadrant model further reveals typical mismatch patterns, with “low accessibility–high price” and “high accessibility–low price” zones together accounting for 45.08%, suggesting that mismatches are widespread in the study area. (3) MGWR results show that different facility types exert differentiated effects across locations, with some even displaying opposite positive and negative effects, underscoring significant spatial heterogeneity. Overall, this study uncovers the intrinsic links between facility supply–demand structures and market value, clarifies the differentiated roles of facility types in shaping spatial value, and provides empirical evidence to support improvements in urban public service systems. Full article

Natural playgrounds have garnered growing attention as supportive environments for children’s mental health. This study develops an analytical framework grounded in affordance theory and incorporates the Pleasure–Arousal–Dominance (PAD) model to examine the relationships between physical environmental features—and their combinations—in natural playgrounds and children’s emotional perceptions. Using the Yunhu Natural Playground in Fuzhou, China, as a case study, we selected seven typical behavior setting units. Environmental features were assessed through UAV imagery and on-site observations, while PAD-based visual questionnaires were employed to collect emotional responses from 159 children. By applying correlation analysis, random forest, and regression tree models, this study identified key environmental predictors of children’s emotional responses and revealed heterogeneous mechanisms across the three emotional dimensions. The results indicated that seasonal flowering/fruiting plants, accessible lawns, and structured play facilities were critical in supporting children’s pleasure, arousal, and dominance. Specifically, pleasure was primarily associated with sensory enjoyment and contextual aesthetics, arousal favored open grassy areas, and dominance was linked to environments with clear structure and manipulability. Based on these findings, this study proposes a spatial configuration strategy characterized by “nature as foundation, play encouraged, and structure clarified” to promote the positive development of children’s multidimensional emotional experiences. This research contributes empirical evidence on the role of physical environmental features in supporting children’s play behaviors and expands the theoretical understanding of the “emotional effects” of green spaces. While the findings are exploratory and context-specific, they emphasize the critical role of the sensory–behavioral–emotional chain in shaping children’s well-being and provide theoretical and practical guidance for the design of emotionally supportive, child-friendly, natural play environments in schools, parks, and residential areas. Full article

Public healthcare infrastructure is among the most energy-intensive of public facilities; therefore, it needs to become more environmentally and economically sustainable by increasing energy efficiency and improving service reliability. Achieving these goals requires modernizing hospital energy systems with renewable energy sources (RESs). This process often involves Energy Service Companies (ESCOs), which propose integrated RES technologies with tailored contractual schemes. However, comparing ESCO offers is challenging due to their heterogeneous technologies, contractual structures, and long-term performance commitments, which make simple cost-based assessments inadequate. This study develops a structured Multi-Criteria Decision-Making (MCDM) methodology to evaluate energy projects in public healthcare facilities. The framework, based on the Analytic Hierarchy Process (AHP), combines both quantitative (net present value, stochastic simulations of energy cost savings, and CO₂ emission reductions) with qualitative assessments (redundancy, flexibility, elasticity, and stakeholder image). It addresses the lack of standardized tools for ranking real-world ESCO proposals in public procurement. The approach, applied to a case study, involves three ESCO proposals for a large hospital in Southern Italy. The results show that integrating photovoltaic generation with trigeneration achieves the highest overall score. The proposed framework provides a transparent, replicable tool to support evidence-based energy investment decisions, extendable to other public-sector infrastructures. Full article