The quick and reliable quantification of the relationship between ecosystem and economic system is important in policymaking for sustainable urban agglomerations facing enormous pressure from high population density and development intensity. This is especially true in China, where urban agglomeration has been part of the country’s strategy for reform, modernization, and urbanization. This study applied the coupling coordination degree (CCD) model to assess the coupling coordination relationships between the ecosystem and economic system at the county level for the Chengdu–Chongqing agglomeration for the period of 2005–2019, and then, the local indicator of spatial association analysis (LISA) was used to illustrate the spatial distribution of CCDs further, hoping to capture the spatiotemporal dynamics of CCDs. The results found that (1) fringe counties and districts in the urban agglomeration were on the brink of ecological–economic disorder with low CCDs, (2) urbanized areas near Chongqing coordinated well with high CCDs, and (3) sound spatial governance and territorial planning may be better achieved by using the county-level unit than the city-level unit.

With respect to a traditional point-to-point (P-P) network, a hub-and-spoke (H-S) network not only uses a smaller number of links/paths but also utilizes the scale economy advantage on consolidated flows on hub–hub links and at hubs. However, the inevitable delays through hubs have always been a critical concern. Therefore, this paper develops an H-S model considering flow delay costs and applies the model to a logistics case in Eastern China. The integer quadratic term in the model’s objective function is linearized using the algebraic method. Our model is applied to develop an H-S network for its 13-node express package delivery operation, using the particle swarm optimization (PSO) algorithm. The results show using the H-S can save more than 14.1% of the total cost annually. The model also provides an applied case to the H-S configuration, especially for urban express delivery logistics in China.

Urban green-blue spaces (UGBS) are increasingly recognized for their benefits to physical and mental well-being. However, research on real-time gender-specific emotional responses to UGBS remains limited. To address this gap, a dual-method approach combining facial expression recognition (FER) and self-reported measures to investigate gender differences in real-time emotional evaluations of UGBS was developed. Using static images from Google Street View as stimuli, a self-reporting experiment involving 108 participants provided insights into subjective emotional experiences. Subsequently, a FER experiment, utilizing 360-degree video stimuli, captured over two million data points, validating the feasibility and advantages of real-time emotion monitoring. The findings revealed distinct gender-specific emotional patterns: women experienced stronger pleasant emotions and preferred scenes evoking higher arousal, while men demonstrated sharper responses and rated scenes with peak valence emotions more favorably. Grass elicited relaxation and delight in women and arousal in men, whereas blue spaces induced calmness across genders, with men reporting greater relaxation as water content increased. The study underscores the potential of FER technology in assessing real-time emotional responses, providing actionable insights for inclusive urban planning. By integrating advanced tools and participatory design approaches, urban planners can develop strategies that enhance emotional well-being and create livable cities that support diverse user needs.

Planned capitals across the Global South frequently experience unplanned land use transitions that contradict their founding visions. Despite six decades of planning and academic inquiry, Islamabad’s research remains fragmented. Environmental studies have documented land use and land cover changes through remote sensing, while governance-oriented analyses have highlighted institutional weaknesses and policy failures. However, these domains rarely intersect, and few studies systematically link spatial transformations with the underlying governance structures and political–economic processes that drive them. Consequently, the existing literature provides valuable but partial explanations for why Islamabad’s planned order unraveled. This study examines Islamabad, conceived in 1960 as a model of order and green balance, where the built-up area expanded by 377 km² (from 88 to 465 km²; +426%) and forest cover declined by 83 km² (−40%) between 1979 and 2019. Using a PRISMA-guided systematic review integrating spatial, governance, and policy data, we synthesized 39 peer-reviewed and gray literature sources to explain why Islamabad’s planned order unraveled. The findings reveal that governance fragmentation between the Capital Development Authority (CDA) and Metropolitan Corporation Islamabad (MCI), combined with elite capture and weak enforcement of the 2020–2040 Master Plan, has produced enduring contradictions between policy intent and urban reality. These conditions mirror those of other planned capitals, such as Brasília and Abuja. Grounded in Pakistan’s institutional context, the study proposes four actionable reforms: (1) regularization frameworks for informal settlements, (2) cross-agency spatial and fiscal coordination, (3) ecological thresholds within zoning by-laws, and (4) participatory master-plan reviews. Islamabad’s experience illustrates how planned capitals can evolve toward inclusive and ecologically resilient futures through governance reform and adaptive planning.

Mountainous urban areas like Guiyang face unique fire safety challenges due to rugged terrain and complex road networks, which hinder fire station accessibility. This study proposes a GIS-based framework that integrates nighttime light (NPP/VIIRS) and point of interest (POI) data to assess fire risk and accessibility. Kernel density estimation quantified POI distributions across four risk categories, and the Spatial Appraisal and Valuation of Environment and Ecosystems (SAVEE) model combined these with NPP/VIIRS data to generate a composite fire risk map. Accessibility was evaluated using the enhanced two-step floating catchment area (E2SFCA) method with road network travel times; 80.13% of demand units were covered within the five-minute threshold, while 53.25% of all units exhibited low accessibility. Spatial autocorrelation analysis (Moran’s I) revealed clustered high risk in central basins and service gaps on surrounding hills, reflecting the dominant influence of terrain alongside protected forests and farmlands. The results indicate that targeted road upgrades and station relocations can improve fire service coverage. The approach is scalable and supports more equitable emergency response in mountainous settings.