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

Urban inequality and the financialization of housing call for a reconsideration of centralized municipal finance. This study introduces Decentralized Behavioral Finance (DBF), a framework integrating behavioral economics, blockchain infrastructures, and participatory governance to realign individual incentives with collective urban outcomes. Grounded in Sen's capability approach, Nash equilibrium theory, and libertarian paternalism, DBF links tokenization and behavioral design to accessibility, capital efficiency, and cooperative stability. Using longitudinal data for Spain (2000–2024) and evidence from tokenized housing initiatives, the analysis shows that citizen participation and technological adoption are positively associated with governance stability and social housing outcomes, while capital concentration exhibits a negative relationship with stability. The paper advances a formal Cooperative Stability Condition, expressed as a structural inequality, under which decentralized governance remains stable when participation amplified by technological enforcement outweighs concentration pressures. By introducing a testable equilibrium condition rather than a descriptive governance model, the study offers an internationally transferable framework for participatory urban finance focused on transparency, inclusion, and institutional resilience. • Introduces a formal Cooperative Stability Condition for urban governance • Integrates behavioral economics and blockchain in municipal finance • Shows participation × technology offsets capital concentration • Provides longitudinal evidence (Spain, 2000–2024) • Proposes a transferable equilibrium framework for cities

Accurate prediction of urban land use changes at fine spatial scales is essential for developing healthy and sustainable cities, yet traditional simulation models struggle to capture local dynamics due to limited availability of fine-grained data and insufficient complexity in modeling urban systems. To address these limitations, we propose a novel approach that leverages advances in pre-trained vision-language foundation models combined with spatial dynamic modeling to forecast detailed urban land use patterns. Specifically, we collected a spatially dense collection of street view images (SVIs) throughout Shenzhen, China, and applied UrbanCLIP, a specialized vision-language prompting framework, to perform zero-shot inference of urban land use directly from images without labeled datasets and model retraining. The resulting fine-grained classifications delineate eight distinct urban land use types, producing a detailed urban functional map. These high-resolution patterns were then integrated into a spatial dynamic model enhanced by polynomial regression to simulate urban evolution toward 2035. This approach effectively captures neighborhood influences, socioeconomic drivers, and urban planning policies. Our simulation provides actionable insights for sustainable development in Shenzhen by identifying areas for balanced growth, targeted infrastructure investments, and ecological preservation. Compared to conventional methods, our methodology significantly improves predictive accuracy and spatial granularity. By incorporating foundation models, our approach addresses traditional data constraints, offering scalable and robust tools for informed urban governance and decision-making. • Proposed a VLM-enhanced framework to predict fine-grained urban land use changes. • Achieved zero-shot land use inference based on street view images. • Produced high-resolution simulations of Shenzhen's urban dynamics toward 2035.

Neighborhoods are critical arenas where urban form, accessibility, and daily life intersect, yet most megacities rely on coarse administrative boundaries that obscure local spatial and social dynamics. There is a persistent gap in objective, multicriteria, and data-driven methods for defining neighborhoods and supporting local planning. This paper proposes a transferable, data-light framework that clusters urban blocks into contiguous neighborhood units, contributing to a consistent socioterritorial definition. The framework incorporates barrier-aware adjacency constraints, PCA and multi-index/ARI-stability evidence based protocol to enable urban delineation. The method integrates three key planning dimensions: (A) built environment, (B) accessibility, and (C) sociodemographic context. After standardization and dimensionality reduction via Principal PCA, clustering is performed using three different algorithms. The algorithm dynamically adjusts the k-optimal value for each case. Applied to São Paulo, a 11.4-million–inhabitant city in Brazil, results indicate distance to high-capacity public transit as the most influential factor, correlating strongly with land value and commercial-service concentration. Population density remains relevant but not deterministic, underscoring the importance of a multi-criteria approach to neighborhood analysis. The clustering reveals socio-spatially cohesive areas that cut across formal administrative boundaries, exposing neighborhood-scale structures often obscured in conventional planning units. Core transit-rich clusters concentrate up to 75% of built area in commercial or service use, while peripheral zones remain underserved. The framework identifies neighborhood areas of influence that can support the delineation of reference perimeters for guiding the public policies, and offers planners a flexible tool for neighborhood-scale policy design, inclusive urban governance, and equitable spatial interventions. • Delimits objectively neighborhoods through unsupervised learning method • Replicable clustering approach strengthens neighborhood delineation in cities • Multi-metric clustering and validation method to analyse urban dynamics • São Paulo application evidences clustering’s relevance to neighborhood planning • Results showed strong alignment between cluster patterns and transit accessibility

Leveraging networks to access basic services: Understanding preference for patronage and collective action networks based on housing precarity

Despite the potential of Green-Adaptive Green Infrastructure (GAGI) to increase the provision of ecosystem services, and to mitigate urban climate risks while maintaining biodiversity, there is a critical research gap in the empirical identification of the context specific factors in developing nations. Therefore, this study examine the critical barriers, drivers and potential strategies to optimize GAGI by employing satellite imagery and survey data of 1232 respondents from Pakistan. The research rationale stems from the need to align urban expansion with ecosystem resilience. By employing remote sensing techniques on satellite imagery data, land use land cover analysis highlights the significant urban expansion in the study area. For the empirical analysis, a deep learning-based hybrid structural equation modeling-artificial neural network analysis was used to capture linear and complex non-linear relationships. Findings reveal that integrated urban transformation, socioeconomic equity, governance, and infrastructural barriers were critical barriers to implementing GAGI. Among the drivers, economic development and innovation, environmental sustainability, and infrastructure integration and efficiency are the main drivers of GAGI implementation. Moreover, education and R&D support, GAGI incentives, government standards and regulations, publicity programs, and awareness are the potential strategies for implementing GAGI in Pakistan. Specifically, sensitivity analysis of artificial neural network identifies governance and infrastructural barriers, economic development and innovation, and incentives towards GAGI as the most influencing factors. The study highlights the need for GAGI and provides a reference for decision-making on GAGI implementation and landscape transformation in response to extreme climate changes.

Mapping per- and polyfluoroalkyl substances contamination in England's surface waterbodies: Urban water cycle pathways and governance challenges.

ABSTRACT Focusing on recent developments regarding the area adjacent to the recently privatized port of Thessaloniki, Greece, the article sheds light on the relationship between processes aiming at the modernization and internationalization of the port itself and the spatial development plans promoted for the future of its urban hinterland. The study foregrounds processes of spatial transformation triggered after the port’s concession to a multinational consortium in 2018 and within the context of the long-lasting financial crisis that began in 2008. In doing so, recent development plans about the surrounding area of the port are presented and discussed on the basis of the spatial development orientations they promote, the actors involved, the governance processes they perform and the discourse they adopt. The study of spatial development plans enriches the research on current transformations of the port-city interface, unveiling discursive links between privatization processes, and dominant planning paradigms. Contributing to the discussion of the multiple institutional, administrative, spatial and geographically variegated dimensions of neoliberalization, the article argues that, in Greece, the privatization of major urban infrastructure catalyzed processes of neoliberalization of urban governance and the city itself.

ABSTRACT This paper examines illicit domestic cannabis cultivation as a form of urban infrastructural adaptation and informal atmospheric regulation. It introduces the concept of domesticating agritech to describe how cultivation technologies are repurposed within ordinary dwellings to produce controlled microclimates for plant growth. Clandestine home cultivation involves covertly transforming residential space into climate-controlled agronomic infrastructure, integrating LED lighting, hydroponic systems, and automated environmental controls. Operating in legal gray zones, these practices bypass formal agricultural and planning regulations while reconfiguring housing as decentralized sites of production. The result is a distributed form of urban agronomy in which households actively modulate indoor climates to sustain illicit crops while minimizing detection. Engaging debates on urban informality and infrastructure, the paper shows how these practices reshape domestic space, intensify energy use, and complicate regimes of urban governance and regulatory visibility. It argues that illicit cultivation reveals a wider process through which atmospheric control is being internalized within the home, extending the infrastructuralization of climate into everyday urban life.

ABSTRACT The widespread application of digital technologies in urban green landscape design has brought new opportunities while introducing cybersecurity challenges. Frequent occurrences of cyber attacks, data breaches, and privacy infringements severely threaten the stability and data security of urban green landscape systems. This study analyzes current applications in spatial thermal energy regulation and urban landscape governance, proposing strategies including thermal energy scheduling coordination, energy storage, optimized scheduling models, fault-tolerant scheduling strategies, and thermal energy regulation simulations. An AI-based urban landscape governance system is constructed, along with an ecological safety assessment methodology for landscapes, along with evaluations of its current status and system architecture. The research particularly focuses on cybersecurity issues, ensuring data security and operational stability through encrypted communication protocols, firewall configurations, vulnerability scanning, and real-time monitoring and early warning of cyber attacks using AI technology. The findings reveal that the AI-powered spatial thermal energy regulation system significantly enhances the stability and ecological security of urban green landscapes. Optimized scheduling effectively reduces urban heat island effects, strengthens internal ecological connectivity, and substantially improves the effectiveness of urban green landscape governance. In terms of cybersecurity, multi-layered protective measures successfully resist various cyberattacks, safeguarding data integrity and operational stability. Practical evidence demonstrates that AI systems integrated with cybersecurity measures exhibit significant advantages in urban green landscape governance.

This study investigates the political dimension of architecture and urban design, focusing on their potential to act as instruments of critical resistance within contemporary governance structures. The research problem centers on the tendency of architects and planners to operate within neoliberal frameworks, which reduces design to a technical and managerial function, limiting its capacity to foster solidarity, social justice, and environmental responsibility. In response, this study examines how design agencies -emerging institutional actors in urban governance- can reposition architecture as a form of political action rather than merely delivering projects. To address this problem, the study hypothesizes that architectural and urban design practices must be reframed through principles of ethics, solidarity, and social repair. It argues that design agencies can develop transformative DEPARCH 53 models that prioritize public benefit and spatial quality, thereby challenging the homogenizing logic of neoliberal urbanization. Methodologically, the research adopts a qualitative and comparative approach, analyzing global examples of design agencies to examine the relationship between spatial form production and modes of political representation. The theoretical framework draws on Ungers’s “City within the City” and Pier Vittorio Aureli’s notion of “absolute architecture.” These theories provide a lens for understanding how formal autonomy and antagonistic resistance can be institutionalized through design agencies. Ultimately, the study interprets these agencies not as administrative instruments but as urban apparatuses capable of generating political consciousness through spatial form.

ABSTRACT Gen AI is transforming city and urban governance in a way that enhances decision-making, management of infrastructure, and delivery of services to the population. This article considers the role of Gen AI in Singapore and Barcelona and determines that AI has a positive effect in Singapore in terms of traffic movement and healthcare and improving waste management and energy consumption in Barcelona. Governance models have a significant impact on the efficiency of AI: a centralized governmental body in Singapore would allow achieving the desired effect more quickly, and the participatory benefit of Barcelona can decelerate its implementation. Using the Diffusion of Innovations (DOI) Theory, the adoption behaviors and ethical consequences of AI were determined. Although the efficiency gains, which are made by AI, are impressive, there are instances where its impact is excessive to the extent of putting issues of trust in AI and fairness. Some of the recommendations are to enhance AI risk assessment, empower the people and hold the algorithms accountable in order to bring future smart city projects to democratic values and social justice.

ABSTRACT This article examines the implementation of the Social Rehabilitation of Uninhabitable Houses (RS-RTLH) program in Surabaya as an integrated model of social protection that bridges poverty reduction, local economic empowerment, and inclusive urban governance. Employing a qualitative approach through interviews, observations, and document analysis, the study reveals that RS-RTLH not only improves the physical quality of housing but also strengthens social capital, revitalises community solidarity, and generates micro-economic benefits such as home-based enterprises and increased household productivity. The program’s success is underpinned by adaptive institutional coordination, digital transparency, and co-production practices between government and communities. Surabaya’s experience demonstrates how local innovation and collaborative governance can transform housing interventions into instruments of socio-economic empowerment, advancing the achievement of SDG 1 (No Poverty) and SDG 11 (Sustainable Cities and Communities).

The proposed study puts forward an artificial intelligence-based framework to predict the needs of the urban public services and aid resource allocation based on data in the current social governance systems. A hybrid deep learning model is designed by combining a Graph Neural Network (GNN) based on spatial-relational reasoning with a Transformer network to model time-dependent connections, textual complaint semantics and structural relations between service requests, agencies, and locations, and via the joint learning of time-dependent patterns, textual complaint semantics, and structural relationships among service requests, agencies, and locations. In order to deal with the high-dimensional and non-convex problem of hyperparameter tuning on hybrid architectures, an Improved Heap-Based Optimizer (IHBO) is used, using opposition-based learning and chaotic search strategies to improve convergence and global search. The suggested model is tested using the Official Website of the City of New York (NYC 311) Service Requests large-scale data of nearly 12 million records that have mixed temporal, geographic, and categorical variables. It is experimentally proven that the IHBO-optimized Transformer-GNN has an overwhelming performance in comparison to the state-of-the-art baselines with a classification accuracy of 0.938 and lower prediction error by resolution time with Root Mean Square Error equal to 2.18days, and it is also robust to novel temporal variations and noisy labels. In addition to predictive performance, the suggested model can deliver policy implications to urban governance by making allocation of public service resources more adaptive, equitable, and efficient, which do attest to the utility of hybrid artificial intelligent models in citizen-focused government of any kind.

ABSTRACT The article investigates urban-environmental injustices through the empirical lens of the Harmandalı case in İzmir, Türkiye. It concentrates on the failures in waste governance, demonstrating how infrastructural and governance failures converge to produce compounding inequalities. Drawing on in-depth interviews with non-randomly selected key municipal officials, city planners, environmental engineers, local residents, academics, and civil society actors, alongside documentary and GIS-based spatial analyses, the study examines how the failures in waste governance crystallizes tripartite framework of urban-environmental injustices: distributive, procedural and recognition justice. The findings reveal that peripheral neighbourhoods bear disproportionate environmental burdens, highlighting stark distributional injustices. Governance failures – including fragmented institutional coordination, political conflicts, and symbolic participatory mechanisms – generate procedural injustices, locking the city into dependence on the Harmandalı facility. Furthermore, the marginalised socio-political visibility and weak organizational capacities of the affected communities exemplify recognition injustice, limiting their capacity to assert rights and shape urban-environmental decision-making. The article contributes to critical urban studies and urban-environmental justice scholarship by emphasizing that waste governance is not merely a technical or ecological challenge but a central arena of urban politics. By advancing a tripartite analytical framework and grounding it in the case of İzmir, the article foregrounds how urban-environmental injustices are both produced and reproduced through intersecting social, spatial, and political processes. The research provides an empirically grounded lens to explore the deep causes of urban-environmental injustices, offering insights for policy, activism, and comparative urban governance studies.

Air pollution associated with public transport systems constitutes a critical yet highly heterogeneous component of urban exposure and represents an important challenge for sustainable urban mobility and environmental health governance. Commuters and transport workers are frequently subjected to pollutant concentrations that exceed those reported by ambient background monitoring networks. This review provides a comprehensive synthesis of the global scientific literature on air quality in public transport microenvironments—including buses, bus stops, terminals, and underground stations—through a multidimensional analytical framework that considers climatic classification, socio-economic context, meteorological drivers, transport microenvironment typology, sampling strategies, analytical techniques, and exposure metrics. A large body of peer-reviewed studies published worldwide was examined to identify dominant patterns, methodological trends, and persistent knowledge gaps. Across regions, the evidence consistently reports elevated concentrations of particulate matter (PM2.5, PM10, and ultrafine particles) and traffic-related gaseous pollutants, particularly within confined or poorly ventilated environments and during peak traffic periods. Marked geographical, climatic, and socio-economic imbalances are evident, with most studies conducted in temperate and tropical climates and in countries with very high or high Human Development Index, whereas arid, continental, and low-HDI regions remain substantially underrepresented. From a methodological perspective, the literature is dominated by short- to intermediate-term monitoring campaigns relying on active sampling, mobile measurements, and increasingly calibrated low-cost sensors, while long-term stationary observations and standardized integrative monitoring frameworks remain scarce. Although advanced analytical approaches—such as chemical characterization, environmental magnetism, receptor modeling, computational fluid dynamics, and inhaled dose assessment—are increasingly applied, their systematic integration remains limited. Overall, this review reveals persistent methodological, geographical, and conceptual gaps and highlights the urgent need for standardized, interdisciplinary, and long-term monitoring strategies to improve exposure assessment and support evidence-based mitigation policies and sustainable urban transport planning aimed at reducing health risks associated with public transport-related air pollution.

Rapid urbanisation across Africa is placing increasing strain on urban governance systems, intensifying long-standing challenges such as infrastructure deficits, inefficient service delivery, and limited institutional capacity. Within this context, Artificial Intelligence (AI) is emerging as a potentially transformative tool for advancing smart city development. By enabling data-driven decision-making, predictive analytics, and real-time monitoring, AI offers opportunities to enhance operational efficiency and improve the delivery of essential urban services. This paper provides a perspective on the role of AI in shaping urban governance across African cities, highlighting both its transformative potential and the structural constraints that influence its adoption and effectiveness. While AI applications across sectors such as transport, energy, healthcare, and public administration can improve urban outcomes, their success is not guaranteed. The effectiveness of AI depends fundamentally on governance readiness, including the availability of quality data, institutional capacity, regulatory frameworks, and human capital. In many African contexts, these foundational elements remain underdeveloped, limiting the scalability and inclusivity of AI-driven solutions. Furthermore, dominant smart city models imported from developed economies often overlook the socio-economic complexities and informal dynamics that characterise African cities. Accordingly, this paper argues for a shift towards context-specific, citizen-centred approaches to smart city development. Such approaches prioritise inclusivity, local innovation, and participatory governance. Ultimately, sustainable and equitable urban transformation in Africa will depend not merely on technological advancement but on the strategic integration of AI within adaptive, accountable, and development-oriented governance systems.

ABSTRACT Research on the geography of religion has extensively examined the spatial politics of places of worship, yet it has largely treated them as fixed sites, paying limited attention to movement, impermanence, and intra-urban mobility. Despite early calls to study religion through both place and movement, the trajectories through which religious groups move within cities remain under-theorized. This paper addresses this gap by introducing the concept of places-of-worship pathways, adapted from Clapham's (2002) housing pathways framework, as a tool for analyzing religious mobility within secular urban contexts. The concept is developed through a qualitative case study of African Pentecostal churches in Turin, Italy – a city often described as relatively accommodating toward religious diversity, yet shaped by strong legal, planning, and market constraints. The analysis is based on nine in-depth interviews with Pentecostal pastors conducted between 2022 and 2024, reconstructing churches’ spatial trajectories and the meanings attributed to relocation. The findings identify three pathways highlighting how socio-economic vulnerability, regulatory frameworks, charismatic leadership, and theological orientations shape patterns of movement. Beyond contributing to scholarship on global Pentecostalism, the paper reveals insights into the relationship between the religious and the secular, showing how urban governance and market mechanisms reproduce differentiated forms of secularity.

This research analysed how sustainability features impacted the time-on-market of commercial rental properties in Lagos, Nigeria. A quantitative research design was utilised in the study, where 250 registered estate surveying and valuation firms were sampled to administer the structured questionnaires, of which 150 valid responses were collected and analysed. The research examined how the sustainability qualities, such as energy efficiency, water conservation, indoor environmental quality, and accessibility, impacted the leasing performance. Factor analysis was used to derive the latent dimensions of sustainability, and independent samples t-tests were used to establish significant variations in the TOM between properties that had and those that did not have sustainable features. The findings show that commercial properties with sustainability elements are rented much quicker than non-sustainable ones, which proves the existence of a tangible market benefit of a green attribute. These results support the argument that sustainability is neither an environmental nor an ethical concern but rather a performance determinant of market liquidity and an investment return. The study argued that developers, investors and policymakers should mainstream sustainability ideals in property development and urban governance to improve commercial efficiencies of the market, shorten vacancy cycles, and promote environmentally friendly growth in the emerging real estate markets.

Power, consensus, and conflict in urban heritage governance: A MACTOR analysis of stakeholder networks in Tabriz, Iran