Ambivalent governance and urban resilience in Kabukicho's sex industry: Local actors, built environment, and the survival of a contested urban space

Planning urban heat resilience projects inherently involves the distribution of benefits and burdens, which raises concerns about justice. The just outcome of these projects requires clarity on how justice is conceptualized, defined, measured, and translated into practice. Here, we demonstrate how alternative conceptualizations of justice result in significant variations in the distribution of urban heat resilience resources. We analyze these alternatives through the choices of interpretations of justice theories, indicators for resource distribution, the scale of jurisdictional autonomy in urban governance, modeling constraints for urban morphologies, and the granularity of data. We use the case of Greater Sydney to examine the implications of operationalizing justice, including instances where the increased provision of tree canopies as a resource in the face of urban heat has unintentionally increased injustices. Our findings highlight the significance of contextualized strategies and urban governance structures to organize the just outcomes of resilience projects. • Operationalization of justice theories for heat resilience resource distribution • Quantified spatial injustices using statistical measures across granular levels • Uncovered divergent outcomes between centralized and local decision-making • Demonstrated human-centered metrics reveal higher deviations from justice • Emphasized scale and built environment constraints in just resource distribution

Dynamic simulation of urban agglomeration network resilience under disturbances: An integrated deep learning and agent-based approach – Case study of the Beijing-Tianjin-Hebei region

A systematic review of climate change impacts on sewer overflow.

Uncovering the foundation-capacity-performance logic of urban resilience: Identifying dynamic weights and nonlinear relationships through a spatial machine learning framework

Natural hazards such as earthquakes, floods, and tropical cyclones pose significant threats to the operation of critical infrastructure systems (CISs) in urban environments. Rapid recovery of post-disaster CISs is essential not only for mitigating immediate socio-economic impacts but also for strengthening urban resilience against future shocks. A key challenge in this recovery process is the efficient scheduling of resources to repair damaged infrastructure, a task complicated by the dynamic and uncertain post-disaster environment, the interdependencies within infrastructure networks, and the diverse priorities and demands of various stakeholders. Given the multifaceted nature of these challenges, numerous repair resource scheduling models have been developed, each incorporating distinct algorithmic strategies tailored to different disaster types and infrastructure systems. Despite a growing body of literature on optimization problems in disaster recovery, a comprehensive understanding of the variations in these models and methods remains lacking. This review aims to systematically explore and synthesize the landscape of repair resource scheduling models, highlighting model variants and their solution algorithms. In particular, it addresses the emerging challenges in post-disaster recovery, exacerbated by the coupled effects of climate change and rapid urbanization. By categorizing the variants and extensions of existing models, this study seeks to refine current frameworks and inspire the development of more comprehensive models, ultimately contributing to more informed restoration decisions and enhanced resilience of urban infrastructure systems.

Abstract The current U.S. federal administration has sought to intervene into every aspect of academic life, university functioning, and the research enterprise including by attacking academic freedom and integrity and canceling and retreating from publicly funded research. Such actions have profound adverse effects on the U.S. public, especially its most marginalized communities, and on science, itself. This perspective provides a telling example of such impacts through our own experience of funding cancellation, the disruptions it causes and the effects it has on urban systems and the communities they support. By focusing on our project that sought to center environmental justice communities in urban transportation and climate planning we offer insights into the wide-ranging effects of such disinvestment, including on sustainability and air quality efforts, with recommendations for moving forward to advance sustainable, equitable, and resilient cities.

This study examines near-surface air-temperature variability during extreme heatwaves in Coimbra (Portugal), focusing on Urban Heat Island (UHI) dynamics through a hotspot-based assessment of intra-urban thermal hotspots (IUTHs), defined as localized zones of recurrent elevated near-surface temperatures. Using an extensive multi-site dataset collected at multiple times of the day across heterogeneous urban environments, the analysis evaluates how urbanization intensity, surface cover, green infrastructure, and site-specific context influence diurnal temperature contrasts and patterns of heat exposure. Statistical results reveal clear spatial thermal disparities, with densely built-up and highly impervious areas such as Santana and the Seminary surroundings consistently emerging as intra-urban hotspots, particularly during afternoon peak temperatures. In contrast, green spaces (Botanical Garden and Mermaid Garden) act as cooling refugia, exhibiting lower near-surface air temperatures and reduced thermal amplitude compared with surrounding urban areas. Proximity to water bodies further moderates ambient conditions, highlighting the buffering role of blue infrastructure during extreme heat periods. These findings demonstrate that analysing UHI intensity through fine-scale intra-urban hotspot patterns provides valuable insights for urban climate adaptation. The results support the strategic integration of green spaces and nature-based solutions in urban planning to mitigate heat risk, strengthen climate resilience, safeguard public well-being, and promote more adaptive and liveable cities.

Urban food insecurity in African cities reflects deep structural gaps in planning, governance, and service delivery. This review examines how guerrilla agro-urbanism, the (in)formal community-led socio-economic use of vacant urban land, can be strategically integrated into resilient urban planning in Harare, Zimbabwe. Its low-cost, adaptive practices outside formal regulation, guerrilla agrourbanism, is critical in enhancing food security, generating income, and delivering environmental benefits. However, it remains largely invisible in policy as it is seen as a threat to what is viewed as “modern cities” landscapes. The paper identifies both opportunities and constraints based on historical analysis, global and African case studies, and Zimbabwe’s legal and planning frameworks. Key barriers include insecure land tenure, inadequate legal recognition, and environmental risks. The review proposes reforms centred on formal policy recognition, secure and equitable land access, safe agricultural practices, and community participation, aligned with the African Union’s Agenda 2063, the SDGs, and devolved governance systems. By reframing informality as an asset rather than a threat, this study offers a pathway for transforming guerrilla agro-urbanism from a survival strategy into a pillar of inclusive, climate-resilient urban food systems. The findings intrigue further debates on sustainable urban planning, putting into perspective the realities of urban residents. It also provides actionable insights for policymakers, planners, and stakeholders committed to sustainable urban development in the Global South

Amid intensifying climate change, climate physical risks (CPR)—including extreme heat, heavy rainfall, and drought—have become critical constraints on urban development. This study constructs an Urban Sustainability and Resilience (USR) index using data from 177 Chinese cities from 2000 to 2023, and applies a spatial Durbin model to examine the spatial impacts of CPR. Technological Innovation (TI) development and government support (GS) are incorporated to identify the underlying transmission mechanisms. The results show the following: (1) CPR exerts a significant negative influence on USR, displaying a spatial pattern of “mild local enhancement but strong spillover attenuation,” consistently observed for both sustainability and resilience. (2) Notable regional heterogeneity exists: CPR effects are strongest in western cities, while eastern and central cities experience weaker impacts due to higher adaptive capacity. (3) Mechanism analysis indicates that TI and GS serve as partial mediators, forming a “risk–technology–governance” transmission pathway. These findings provide empirical evidence for strengthening urban climate adaptation, promoting regional collaborative governance, and enhancing digital risk-response capabilities.

Climate driven drought risk and machine learning approaches for urban resilience and sustainable water governance.

Driven by the development of engineering technology, the “dual carbon” goal, and the construction of urban resilience, modern structural engineering is undergoing comprehensive innovation [...]

The growing importance of urban seismic resilience highlights the need for effective strategies to minimize earthquake-induced losses of building groups. Urban seismic capacity assessment requires rapid and accurate prediction of buildings’ seismic responses. However, in large urban building groups, the design information of many buildings is often difficult to obtain completely. In addition, post-earthquake interruption of economic activities will cause significant indirect losses to the city. Based on this background, this paper developed a convolutional neural network (CNN) model using structural characteristic parameters and ground motion parameters, by which the seismic response of structures in a building group can be rapidly predicted. On this basis, the repair priority of buildings was determined according to the repair efficiency value, and an optimal repair strategy considering indirect losses was proposed to minimize the post-earthquake indirect losses of building groups. Taking 908 buildings in Shanghai as an example, the effectiveness of the model is verified, and a comparison is made with the existing conventional repair strategies. Results showed that the proposed neural network accurately predicted the damage states of buildings. Compared with other existing strategies, the proposed optimal repair strategy effectively reduced the indirect economic loss. Further, the increased repair resources can reduce the indirect economic loss and repair time, but the reduction ratio decreases with the further increase of resource allocation.

ABSTRACT In April 2024, the United Arab Emirates (UAE) experienced its heaviest rainfall in 75 years, which triggered widespread urban flooding and prolonged disruptions. This study assesses the spatial impact and post-rainfall recovery of the event using the U-Net deep learning architecture and daily PlanetScope satellite imagery captured on a pre-rainfall date and on a series of post-rainfall dates between 14 April 2024 and 27 April 2024. Multi-temporal land use and land cover (LULC) classifications were generated using a U-Net model trained via transfer learning, and the LULC categories were water, vegetation, built area, and bare ground. The multi-temporal LULC classifications were further used for change detection analyses to map flooded areas and track temporal recovery across LULC categories. The U-Net model for LULC classification achieved over 95% overall accuracy and a Kappa statistic of 0.927. The change detection and inundation recovery showed that approximately 23.8 km2 – nearly ten times the area of Downtown Dubai – was flooded. The flood affected the built area and bare ground most, while vegetation exhibited higher flood resilience. Although rainfall ended by 17 April, 95% of flooded areas remained submerged three days later, and 37% were still underwater by day 10. These findings reveal the limitations of urban drainage systems in Dubai and the value of high-temporal-resolution remote sensing and deep learning for flood monitoring. This study offers a practical and replicable framework to support urban flood risk assessment, resilience planning, and climate adaptation in the Persian Gulf region, characterized by rapid urbanization and fragile arid environments similar to Dubai.

ABSTRACT As nations pursue ambitious decarbonization targets, questions arise regarding whether technological innovation can amplify the effectiveness of environmental institutions. This study examines how artificial intelligence (AI) integration transforms environmental judiciary capacity to reduce carbon emissions, leveraging China’s establishment of specialized environmental courts beginning in 2007 as a quasi-natural experiment. Employing a triple-differences identification strategy that combines the staggered rollout of environmental courts with spatial and temporal variation in AI patent intensity, we find that AI adoption in cities with environmental courts reduces carbon emissions by 2.65% relative to control groups, with this effect statistically significant at the one percent level. Heterogeneity analysis reveals that this decarbonization impact is substantially stronger in cities with higher AI application maturity, greater urban ecological resilience, and higher industrial carbon dependency, while regional disparities do not significantly moderate the effect. Mechanism analysis, drawing on semi-structured interviews with government officials and quantitative validation, demonstrates that AI enhances judicial performance through improved evidence processing capabilities, increased legal consistency, reduced litigation barriers, and strengthened enforcement monitoring. These findings suggest that digital transformation of environmental adjudication represents a viable pathway for climate governance.

Virginia has exceptional landscape diversity, both in ecoregions present within its borders and types and location of cities and other forms of human settlement; this presents both opportunities and challenges for researching environmental issues such as climate change. A group of 13 primarily undergraduate–serving institutions in Virginia collaborated to measure and evaluate the extent of the urban heat island in 10 diverse localities across Virginia. This case study article describes how faculty and students analyzed and applied the data collected through a citizen science initiative in six of the urban areas, and how they used the data to work with community members on projects such as urban tree planting. The article also shares the benefits and challenges of this multi-institutional project, providing examples of how participating institutions were able to achieve greater outcomes through their collaboration than would have been possible individually. Through engaging with this case, readers will understand how a multi-institutional environmental research collaboration substantially focused around undergraduate participation and research formed and how such a collaboration has contributed to climate change/urban heat research, urban heat mitigation action, community outreach, and education, including undergraduate research mentorship.

Abstract. Urban soil plays a fundamental role in supporting a range of ecosystem services important for both human health and urban resilience. While many ecosystem services rely on soil microbial communities, studies on urban soil microbes have remained scarce. A major complexity in understanding urban soil bacteria is the heterogeneity of soil throughout the city. In this study, we investigated the urban soil bacterial communities of street tree areas in relation to soil characteristics. We sampled soils under London plane trees at 20 street sites throughout the city of Antwerp (Belgium) and looked into the horizontal and vertical spatial variation in soil bacterial communities. Using 16S rRNA gene amplicon sequencing, we found great bacterial diversity (over 1800 bacterial genera) and heterogeneity in the urban soil of a single city. Our findings suggest that, first and foremost, urban soil community variation is determined by the sampling site, indicating that samples grouped together in space are more similar. Additionally, but to a smaller extent, the bacterial communities are affected by soil characteristics as is the case for non-urban soils. Significant relationships were observed between composition and soil pH, moisture, density, depth, air pollution, and land use class (all p values < 0.01). We found that soils, especially those with relatively low moisture content in the city under study, showed a decline in soil bacterial biodiversity with decreasing moisture content (p value = 0.047), indicating that soil moisture content may be an important aspect of sustainable urban soil management. Furthermore, soils under trees with a smaller circumference varied greatly in bacterial community composition relative to soils under trees with a bigger circumference, which were significantly more alike in bacterial composition. This indicates that, to better predict urban resilience, factors such as soil and vegetation development should be taken into account. In conclusion, we recommend including microbial soil diversity in urban sustainable soil management and focusing future urban soil research on understanding what the desired microbial functions and compositions for urban soils are.

Purpose This study introduces a systematic and reproducible Neighbourhood Quality Index (NQI) based on quantifiable environmental attributes to provide a consistent system for evaluating neighbourhoods undergoing urban transformations and tests its feasibility across diverse contexts. Design/methodology/approach Four environmental attributes and 19 theoretically grounded and quantifiable measures are identified for pilot neighbourhoods in Newcastle upon Tyne, UK, and Sydney, Australia – cities implementing new planning policies in response to recent urban changes. These measures, calculated using published statistics, quantity calculations, and GIS-based syntactic and attraction analyses, are synthesised using a hybrid TOPSIS-EM multi-criteria method combined with structured expert weighing to derive the NQI. Findings By integrating systematic data across multiple spatial scales, the multi-domain NQI captured subtle neighbourhood characteristics, including disparities in service distribution, and enabled population-specific insights. The hybrid TOPSIS-EM approach demonstrated improved transparency and robustness compared to earlier methods, yielding consistent neighbourhood rankings. Practical implications The data-led NQI could inform policy decisions on allocating resources towards developing and retrofitting healthy, resilient, and sustainable neighbourhoods. Originality/value This study is a response to recent urban changes driven by climate change, hybrid working, and the COVID-19 pandemic, which highlight the need to enhance neighbourhood quality (NQ) for health and well-being. It addresses gaps in existing NQ indices by developing an integrated, data-driven assessment to support urban resilience and liveability in practice and policy.

Artificial Intelligence is increasingly embedded in urban mobility and emergency response systems, where real-time decision-making, infrastructure coordination, and public safety converge. This article examines Urban Artificial Intelligence (Urban AI) through the domain of traffic management and emergency mobility, using these systems as a strategic entry point for analyzing broader questions of governance, equity, and resilience in AI-enabled cities. The paper develops a theoretical framework that distinguishes among cognitive, data-driven, and hybrid Urban AI models, highlighting how each approach shapes urban knowledge production, operational performance, and accountability. This framework is grounded through three U.S.-based case studies: AI-enabled emergency vehicle preemption in Fremont, California; AI-assisted subway infrastructure monitoring in New York City; and AI-driven signal coordination for emergency routing in Seattle. Together, these cases illustrate how Urban AI systems are deployed in real-world contexts to enhance efficiency, safety, and resilience. The analysis demonstrates that while Urban AI can significantly improve urban operations, its long-term legitimacy depends on integrating principles of equity, transparency, environmental responsibility, and participatory governance. The article concludes by arguing for integrated Urban AI models that balance technical effectiveness with democratic oversight, positioning Urban AI as a critical component of just, resilient, and inclusive urban futures.

Urbanization in India has intensified environmental health challenges, creating a critical need to integrate health considerations into city planning. We conducted a comprehensive literature mapping on the intersection of urban planning and environmental health in India. By employing bibliometric analysis on 511 articles from both global and Indian sources, we identified key thematic areas, gaps, and opportunities. We highlighted the importance of land-use distribution, air quality, and green spaces as significant factors influencing public health in Indian cities. Additionally, the study reveals a lack of comprehensive research addressing the multifaceted health risks posed by rapid urbanization and environmental degradation in India. Through thematic mapping, we identified underexplored areas in the literature, particularly the need for more integrative frameworks to address environmental health risks within urban planning in India. The findings are to provide insights for policymakers and urban planners to design and maintain resilient and health-promoting cities.