Despite widely existing consensus that nature-based solutions (NbS) provide competitive and sustainable alternatives for grey infrastructure in urban water management, the implementation of NbS is lagging behind current needs. This study explores if and how regulatory measures can promote NbS adoption on privately owned land. Policy document analysis of Finnish stormwater programmes gathers how NbS are considered at municipal-level, key informant interviews aim to identify bottlenecks in stormwater regulations, and building permit analysis evaluates whether policy instruments have increased NbS implementation on private land. Results reveal fragmented municipal regulations due to a lack of national consensus, leading to bottlenecks for implementation such as limited knowledge, commitment, and institutional coherence. While current regulations have increased solution diversity, widespread NbS implementation requires mandating and consistent regulations aligned with sustainability goals, improved understanding of NbS functionality, examples and education, national cooperation, and recognition of NbS as viable stormwater management options.

Research on the assessment and forecasting of social resilience in urban flood risk management based on the BBN-HMM-ARIMA framework

Butterfly assemblages as indicators of wildfire disturbance in recently burnt urban ecosystems.

Assessing and improving the resilience of a stakeholder cooperation network for urban waterlogging disaster management from the perspective of social capital: A case study of China

Urban green spaces rely on bird–plant interactions that support biodiversity and ecosystem functioning, yet it remains unclear how urban landscape configuration shapes bird–plant interaction networks. We examined how landscape configuration influences the structure and beta diversity of bird–plant multilayer networks in 13 Tokyo urban parks, and which bird traits, landscape variables, and spatial scales best explain among‐park variation. We sampled bird-dispersed seeds and feces along transects and identified birds and plant genera using DNA and morphology. For each park, we constructed a multilayer network linking foraging and seed-dispersal interactions and related network metrics to bird traits and landscape variables within 500–1500 m buffers. Multilayer networks contained relatively few bird species but many plant genera. Longer bills, semi-open habitat use, and ground-dwelling or perching behaviors were associated with higher multilayer centrality, whereas body mass showed no clear effect. Within 1500 m buffers, higher green-space proportion increased network density and centrality and reduced average path length, whereas greater mean nearest-neighbor distance among patches was associated with higher network modularity. Park area, vegetation quality, and surrounding landscape attributes jointly explained among-park variation in interaction beta diversity, with most dissimilarity driven by interaction rewiring rather than species turnover. Overall, urban landscape configuration appears to shape bird–plant multilayer networks by filtering bird functional traits and reorganizing interactions. Maintaining well-connected green spaces within ~1500 m of parks, together with high within-park vegetation quality, may help sustain these networks and their associated ecological functions. • Bird–plant multilayer networks were constructed for 13 urban parks in Tokyo using DNA metabarcoding. • Birds with longer bills, a preference for semi-open habitats, and ground-dwelling or perching lifestyles were central species in the networks. • Green-space proportion within 500–1500 m increased network density and centrality and decreased average path length. • Patch isolation at 1500 m was positively associated with network modularity and overall network dissimilarity. • Urban green-space planning should prioritize landscape configuration within ~1500 m of parks, together with high within-park vegetation quality, to sustain interaction networks.

Depicting human mobility across functional zones to elevate urban heat exposure management—A case study of Shenzhen, China

In urban pluvial flood risk management, local authorities and local collectives (e.g. citizen groups) increasingly rely on each other's contributions to develop on-the-ground solutions, thereby creating an interdependent relationship. Although it is known that these interdependencies can be beneficial, most studies have focused on interaction processes between local authorities and local collectives, paying less attention to the underlying interdependencies. This study explores how interdependencies influence flood risk management by analysing two neighbourhood-level projects in the Netherlands: Oase Noord (Oasis North) in Amsterdam, which is characterised by a shared governance model, and Dakpark (Roof Park) in Rotterdam, which reflects a self-governance approach. Interviews were held with local authorities and local collectives from the two cases, and experts involved in similar projects. Our analysis demonstrates that interdependencies can benefit collaborative processes for developing integrated pluvial flood risk management strategies, and their forms are by and large similar in shared governance and self-governance. Our empirical findings also highlight institutional challenges in recognising and using interdependencies, particularly the absence of consistent government support across both approaches. This study underscores the interdependencies between local authorities and communities and the ability of citizens to take their part in the responsibility to manage flood risks and other climate-related transitions. • Interdependency enables mutual benefits for authorities and collectives in interconnected flood-societal challenges. • Government support is key to maintain the collaboration between authorities and collectives. • However, the continuity of government support is often lacking in practice. • Neutral neighbourhood managers and mediators can enhance authority–collective collaboration.

Integrating carbon-based nutrient fertilization for urban soil management: Impacts on plant growth and microbial communities

Adaptive infrastructure intelligence: integrated urban stormwater management framework for climate-resilient cities

Urban expansion in areas adjacent to industrial areas presents serious safety challenges, especially in fast developing areas. Nearly between residential and industrial activities increases the risk of accidents, environmental pollution and massive disasters. In response, industrial countries have adopted advanced management systems to reduce these risks. Of these, the Geographical Information System (GIS) provides a powerful tool for analyzing and mapping the areas coming in contact with industrial threats. The purpose of this study is to assess the role of GI in reducing industrial risks and focusing a specific focus on the Algerian context, reducing industrial risks and supporting permanent urban planning. Research adopts a spatial analysis approach using topological maps, geographical datasets and GIS software. Industrial areas and surrounding urban settlements were studied through layered spatial modeling. Supplementary area observation and case studies were included to validate the accuracy of GIS output. The analysis focused on identifying weak areas, assessing industrial threats and assessing the effectiveness of existing land-use and reaction strategies. Conclusions highlight the important weaknesses in current urban risk management systems, especially in terms of preparations and integration with spatial plan. The GIS proved to be effective in detecting high-risk areas, imagining dangerous areas and providing strategic support for the decisions of land-use. The device also featured landscape simulation, which improved the understanding of potential industrial accidents and their impact on nearby communities. The interpretation of these results suggests that GI increases urban flexibility, reduces exposure to industrial threa.

ABSTRACT Millions of trees are lost in urban areas globally, thereby increasing the vulnerability of urban dwellers to natural disasters. Jakarta is a city and province experiencing an increase in flooding and air pollution. This paper examines urban forest implementation in Jakarta, Indonesia, through a systematic literature review while examining its ecological benefits through i-Tree Canopy. This research finds that urban forests are less well managed in Indonesia, especially Jakarta, due to the government and community’s lack of attention and awareness of the importance of the ecological role of urban forests. However, in reality, the benefits of Jakarta’s urban forests from carbon absorption and pollution removal are estimated to reach at least 8 million USD/year. Community participation in urban forest management in Jakarta remains very limited and is often dominated by authority holders (tokenism). Thus, the importance of collaboration (process and effect) in effective urban forest management is an issue that needs to be studied in further research in the global and Jakarta context.

Freshwater lakes are critical sources of drinking water worldwide, yet contamination by trace elements (TEs) presents significant health risks. Phewa Lake, Nepal, a Ramsar-listed wetland supporting many people, irrigation, fisheries, and tourism, was selected as a case study due to its socio-economic and ecological value. This study investigates the spatiotemporal distribution and health risks of 10 TEs across pre-monsoon and monsoon seasons. 50 lake water samples were collected, split evenly between pre-monsoon and monsoon. Results showed elevated concentrations of arsenic (As), chromium (Cr), copper (Cu), manganese (Mn), nickel (Ni), lead (Pb), and zinc (Zn) during the pre-monsoon, with values declining by approximately half during the monsoon due to rainfall dilution. Spatially, higher concentrations were observed near urban settlements and drainage points. Subsequent statistical analyses identified geogenic sources as predominant, with minor anthropogenic influence mapped to urban shorelines. Water quality was assessed using the Water Quality Index (WQI) and Metal Index (MI): scores were 9.66 and 0.09 pre-monsoon, and 2.86 and 0.03 during monsoon, all well within the World Health Organization (WHO) guideline limits. Hazard Index (HI) values for all TEs were below unity, with As posing the highest non-carcinogenic risk (HIchildren = 0.115, HIadults = 0.076). Cancer risk was low to medium for Pb, Cr, and As. Although water quality was generally acceptable with low risks, proactive measures, such as routine monitoring, regulated runoff, and improved wastewater treatment in alignment with Sustainable Development Goals (SDGs) 6, 13, and 15, are recommended. These findings can inform sustainable urban lake management in the Himalayas and comparable regions globally.

Urban waste management is an environmental issue that directly impacts the comfort and quality of life of the community, particularly in market areas. The Ciputat Market area in South Tangerang City previously experienced significant waste accumulation, which disrupted daily activities and triggered public criticism of the local government. This study aims to analyze the level of community satisfaction regarding urban waste management in the Ciputat Market area. This research employs a quantitative descriptive approach using a survey method involving 100 respondents, comprising market vendors, visitors, local residents, and road users. Data were collected through a five-point Likert scale questionnaire and analyzed using descriptive statistics. The findings reveal that community satisfaction falls within the moderate to low category, with 62.5% of respondents expressing dissatisfaction or being less than satisfied with the waste management. The aspects receiving the lowest evaluations include the consistency of waste transportation and the sustainability of waste management policies. This condition reflects the suboptimal quality of public servis in waste management. Therefore, this study recommends that the state or local government must demonstrate a tangible presence through the reinforcement of strict policies and regulations. Furthermore, the government needs to act as a catalyst in fostering the active participation of citizens and the involvement of the private sector to develop a collaborative and sustainable waste management system.

Under the “dual-carbon” goal, hidden emissions in urban construction archives management demand urgent attention. Evidence from Nanjing's 2023 digitalization project reveals a 25% emission rise during transition due to procuring 200+ scanners and 5 servers, highlighting a carbon efficiency gap. Using measured data and scenario simulation, this study uncovers a two-stage “transition–stable” carbon mechanism, establishes a life-cycle accounting framework (formation, storage, inspection, destruction), and proposes a technology–management–policy reduction strategy. Emissions are projected to drop 76.4% by 2025, shifting from paper/transport to electronic storage. Management optimization drives over 50% of savings; a cross-department digital platform alone cuts 3.3 tons CO2e at ¥320/ton. Higher standardization enables the Planning Bureau to cut emissions by 65.4%, versus 46.4% for the Urban Management Bureau reliant on on-site access. The study offers practical tools to balance digital transition and carbon control, aiding governance of hidden urban emissions.

Dynamic routing and distribution network design represent critical challenges in modern logistics and transportation systems, where decisions must adapt to rapidly changing environmental conditions and operational constraints. Reinforcement learning (RL) has emerged as a powerful paradigm for addressing these challenges by enabling autonomous agents to learn optimal policies through interaction with complex, uncertain environments. This review examines recent advances in RL applications to dynamic routing problems and distribution network design, focusing on methodological innovations and practical implementations. The paper explores fundamental RL algorithms including deep Q-networks (DQN), policy gradient methods, and actor-critic architectures, analyzing their suitability for different routing scenarios. We investigate how RL approaches handle real-time traffic dynamics, demand uncertainty, and multi-objective optimization in distribution systems. The review synthesizes findings from recent literature on hybrid methods combining RL with traditional optimization techniques, multi-agent RL (MARL) for coordinated routing decisions, and transfer learning strategies for network adaptation. Key applications examined include vehicle routing problems (VRP), last-mile delivery optimization, urban traffic management, and supply chain network configuration. This comprehensive analysis reveals that RL methods demonstrate superior performance in handling dynamic uncertainties compared to conventional approaches, though challenges remain in scalability, sample efficiency, and real-world deployment. The paper concludes by identifying promising research directions including federated RL for privacy-preserving logistics optimization, graph neural network (GNN) integration for spatial reasoning, and explainable RL frameworks for decision transparency.

Perceiving multidimensional emotions from social media data and analyzing their spatiotemporal dynamics constitute a significant topic at the intersection of geographic information science and social management. However, existing methods often oversimplify emotions by neglecting mixed categories, providing representations of spatiotemporal emotion patterns with limited accuracy, and overlooking the influence of environmental factors on emotions. To address this issue, we proposed a framework that integrates the deep learning model with co-occurrence theory to classify individual emotions into single, dominant-subordinate, and compound classes. Emotional graphs and metrics were leveraged to characterize the spatiotemporal distribution of public emotions. The proposed method identified 34 types of complex emotions and revealed that urban environmental factors, such as building volume and night-time light intensity, were closely associated with variations in emotional distribution and dynamics. Compared with valence-based classification methods, the proposed method clearly illustrated the changes in public emotions following a respiratory infectious disease outbreak and subsequent health control measures. Uncertainty analysis conducted for grid- and street-based spatial units demonstrated the robust stability and cross-scale consistency of this method. This study provides deeper insights into collective emotion patterns and their geographic drivers, offering a valuable reference for urban governance and emergency management.

Rapid urbanization has led to significant accumulation of black carbon (BC) in urban soils, threatening ecological safety and human health. Existing methods for quantifying soil BC remain limited by complexity and poor applicability at large scale. To address these challenges, we developed two novel models to predict urban soil BC content using visible–near–infrared spectra and environmental covariates. A total of 110 soil samples from Wuhan were analyzed for BC content using chemical oxidation. We then developed the spectral inversion model by integrating spectral transformations with machine learning. In addition, a digital soil mapping (DSM) model based on environmental covariates was constructed by combining geographic aggregation cross-mapping with machine learning. Results showed that the spectral inversion model (R² = 0.91, RMSE = 0.95) outperformed the DSM model (R² = 0.32, RMSE = 2.62). Among spectral transformation methods, the first derivative (FD) method yielded the most significant accuracy improvement for BC inversion, and the combination of Extreme Gradient Boosting and FD achieved the highest accuracy for BC content inversion. The proposed spectral inversion method can achieve regional dynamic monitoring of BC in urban soils, providing critical evidence for urban soil ecosystem management, human health protection, and sustainable development.

Making waves: Enhancing stormwater quality models through memory bias dynamics.

Indicator-based assessment of social sustainability in urban water management across contrasting governance contexts.

Regional decision-making in urban planning, environmental management, and related fields requires the reliable and extensible integration of diverse geographic data and models. Traditional expert-driven approaches rely mainly on the manual configuration of data and models by human users, which is inefficient and suffers from knowledge gaps. Agents based on large language models (LLMs) offer the potential to integrate resources and automate computational processes, but face challenges in handling heterogeneous geospatial data and models and often produce unreliable outputs. We propose the regional decision-making agent (RDMA), which is a multiagent collaborative framework that enhances LLM-based agents with structured resource management and reliability mechanisms to support complex, multistep decision-making processes. The RDMA comprises three modules: data governance, model governance, and agent scheduling. To mitigate LLM hallucinations, three strategies are implemented: knowledge-enhanced retrieval-augmented generation (RAG), confidence self-assessment, and system transparency. Performance evaluations demonstrate high success rates and accuracy across diverse tasks at both the model configuration and workflow composition levels. Ablation studies confirm the essential contributions of quality control and knowledge-enhanced retrieval mechanisms. Case studies in urban planning and stormwater management validate RDMA’s ability to deliver actionable insights. This work advances AI-driven geographic analysis and provides a scalable solution for sustainable regional governance.