Sustainable disaster waste management (DWM) plays a critical role in improving resource efficiency and reducing environmental and socio-economic impacts during post-disaster recovery. Unlike conventional solid waste systems, post-earthquake debris arrives at temporary sites as mixed, unsorted material, requiring on-site classification before transport to appropriate treatment or disposal facilities. To address this challenge, this study proposes a multi-objective mixed-integer linear programming (MILP) model that captures three integrated stages: mixed waste collection from disaster zones, waste sorting and separation at temporary waste management sites (TWSs), and classified transportation of waste streams to terminal facilities such as cement plants, iron-steel factories, and landfills. In contrast to conventional transfer site models that only perform transshipment, the proposed TWS structure integrates waste classification, facility location, and transportation decisions within an integrated optimization framework. The model simultaneously optimizes four sustainability objectives: total economic cost, carbon emissions, psychological impact on nearby populations, and TWS suitability. Five multi-objective decision-making (MODM) methods are applied, and the most appropriate approach is selected using the displaced ideal solution (DIS) method. The proposed framework is validated through a case study of three provinces heavily affected by the February 6, 2023 earthquake in Türkiye, accompanied by sensitivity analyses. The findings provide a comprehensive and sustainability-oriented decision-support framework for post-earthquake DWM, improving economic, environmental, and social performance during recovery operations.
Urban mobility directed to medical institutions represents an important yet underexplored component of urban public service systems, as these trips involve medical institutions that are both spatially distributed and hierarchically differentiated. Focusing on ride-hailing orders directed to medical institutions, this study investigates the spatiotemporal heterogeneity and hierarchical orientation embedded in the selective ride-hailing segment of medical-institution-oriented mobility. A three-dimensional tensor is constructed to represent arrival time, origin subdistricts or towns, and medical institution levels, and Canonical Polyadic (CP) decomposition is employed to extract latent structural components. A component-based structural diagnosis framework is further applied to quantify structural contribution, temporal regularity, origin-side spatial concentration, and institution-level orientation. The results identify six latent structural regimes characterized by differentiated combinations of temporal synchronization, spatial concentration, and institution-level orientation. Dominant regimes exhibit clearly structured arrival-time profiles, core-oriented origin-side patterns, and strong Tertiary Class A orientation, whereas lower-contribution regimes show weaker temporal structuring, localized origin-side concentration, and substantially weaker Tertiary Class A orientation. These findings indicate that ride-hailing trips directed to medical institutions are organized into multiple coexisting structural regimes rather than a homogeneous demand pattern. By identifying structurally concentrated arrival patterns, the framework supports more targeted allocation of monitoring and management resources and provides a diagnostic basis for sustainability-oriented coordination among medical-institution access planning, ride-hailing operations, and urban traffic management.
Understanding the three-dimensional structure of the urban heat island (UHI) is essential for climate-adaptive planning. Beyond surface and canopy-layer warming, UHIs involve vertically integrated heat accumulation; however, most studies assess temperature differences at a single height. This study infers vertical UHI coupling from the joint yet differentiated responses of surface (SUHII), canopy-layer (CUHII), and vertically integrated boundary-layer (VUHII) indicators, without directly quantifying coupling or simulating energy exchange. It further examines morphology-dependent relationships using morphology-zone classification and double machine learning (DML)-based statistical attribution. The results show that SUHII and CUHII exhibit continuous urban-rural gradients, whereas VUHII is more localized and fragmented. At night, mean VUHII reaches 963.5 K·m in dense central districts, compared with 206.6 K·m in peripheral plains, indicating that conventional surface- or canopy-layer metrics alone may not fully represent vertically integrated heat accumulation within the lower atmosphere. Thermal intensity generally follows high-rise compact (HC) > low-rise compact (LC) > low-rise sparse (LS) > low-rise open (LO). Nighttime VUHII trends are 59.1, 18.8, and 13.1 K·m·a⁻¹ in HC, LC, and LS zones, respectively, and winter nighttime VUHII exceeds 2500 K·m in HC zones. Transition-zone analysis indicates that VUHII changes lag behind those of SUHII and CUHII during urban-form evolution: during LS to HC transitions, VUHII remains 200–400 K·m, far below the 1500–2000 K·m observed in stable HC zones. After adjustment for observed covariates, DML-based statistical attribution indicates that land cover and surface properties have the strongest estimated conditional associations with SUHII and CUHII. In contrast, VUHII is more strongly associated with three-dimensional urban structure, atmospheric conditions, and human activity. At night, building-height heterogeneity, impervious-surface connectivity, and population density show positive conditional effects on VUHII, whereas vegetation shows a negative conditional effect. These findings provide evidence-informed planning priorities for morphology-aware mitigation of heat accumulation.
AI is increasingly embedded in local government systems, shaping how cities organise, automate, and govern public services. However, public support for AI-enabled services remains uneven across governance contexts, raising critical questions for responsible and sustainable urban innovation. This study investigates how citizens in Australia, Hong Kong, and Saudi Arabia evaluate different AI modalities in municipal service delivery, and how these evaluations vary depending on the populations targeted by algorithmic systems. Drawing on a cross-country scenario-based survey (n = 1,183) incorporating six AI service scenarios, the findings suggested three key patterns. First, public support varies significantly across governance contexts, with consistently higher acceptance in Saudi Arabia, greater variability in Australia, and more cautious attitudes in Hong Kong. Second, general, lower-sensitivity scenarios were evaluated more favourably than individually targeted scenarios, although targeting co-varied with service purpose and sensitivity. Third, perceptions differed across modality-based scenario types: scenarios involving image recognition were rated more useful, chatbots as more human-like, and scenarios using predictive analytics in local services received lower support. These findings demonstrate that public acceptance of AI in local government is shaped critically by who the technology is applied to and what the personal consequences are. They contribute to responsible and sustainable urban AI governance by showing that public support depends not only on technical capability, but also on perceived fairness, accountability, and contextual fit.
