Search Results (87)

Rapid urbanization in South Asian coastal cities is systematically dismantling natural cooling infrastructure, driving unprecedented urban heat island (UHI) intensification with severe consequences for human health, energy systems, and urban livability. Despite growing research attention, comprehensive frameworks that simultaneously capture temporal UHI dynamics, machine learning-based thermal projections, and community-grounded validation remain scarce, particularly for secondary coastal cities in tropical developing regions. This study addresses these gaps by investigating UHI dynamics in Chattogram City Corporation (CCC), Bangladesh, through three integrated methodological pillars: (1) multi-temporal remote sensing analysis using Landsat 5 and 8 imagery spanning 2005–2025; (2) comparative evaluation of five machine learning algorithms (LightGBM, Random Forest, XGBoost, SVM, and MLP) for land use/land cover (LULC) classification and land surface temperature (LST) regression, with iterative scenario projections for 2029, 2033, and 2037; and (3) a structured public perception survey of 384 residents validated through participatory mapping and focus group discussions. Landsat analysis revealed dramatic LULC transformations: built-up areas expanded 88% (12,649 to 23,719 acres), while waterbodies declined 53.1% and vegetation decreased 21.9%. Mean LST increased by 9.09 °C (from 30.94 °C to 40.03 °C), with mean UHI intensity rising from 19.59 to 33.88 standardized units over two decades. LightGBM achieved optimal LULC classification (F1-weighted: 0.765) while Random Forest best predicted LST (RMSE: 1.51, R²: 0.809). Projections indicate continued thermal escalation, with mean LST reaching 43.64 °C and UHI intensity exceeding 37.41 standardized units by 2037. Persistent thermal hotspots were identified in the southwestern coastal corridor, western industrial belt, and central business district. Community survey data corroborated satellite-derived patterns, with 73.44% of respondents observing environmental degradation, yet only 22% aware of formal heat mitigation policies, and 87% supporting vegetation-based cooling interventions. This integrated framework advances urban thermal monitoring in tropical coastal cities and provides spatially targeted, community-endorsed evidence for climate-responsive urban planning. Full article

Anticipating the socio-environmental impacts of spatial planning strategies is a prerequisite for sustainable development pathways. Land change models are increasingly employed to evaluate the impacts of spatial planning on land use and land cover, and their subsequent effects on ecosystem services and environmental resources. Nevertheless, modelling land use and land cover changes, and their interactions, at a fine scale to preserve future landscape patterns has been identified as a key challenge in the land change science community. This paper presents an innovative process-based model—the FORecasting landscapE SCEnarios Model (FORESCEM)—designed to spatially simulate fine-scale future land use and land cover changes (LUCC) based on narratives developed through participatory or expert-driven approaches. By clearly distinguishing land covers and land uses as two different but related inputs, its conception and architecture enable the assessment of interactions among LUCC within human-managed landscapes. It relies on conventional functions and properties of LUCC models, and aims at completing the existing land change models. Applied on a French case study, the validation results demonstrate the model’s capability to replicate LUCC dynamics, effectively simulating trend-based and trend-breaking LUCC trajectories under contrasting scenarios. More broadly, this paper questions and discusses the validation of land change models used for simulating future LUCC. Full article

Public administrations increasingly seek to adopt digital tools for evidence-based policymaking, yet systematic frameworks guiding this adoption remain scarce. This paper aims to develop and apply a co-creation framework for technology adoption roadmaps in public sector policymaking. The objectives are threefold: (1) to systematically identify impacts, facilitators, and barriers through structured stakeholder engagement; (2) to structure these elements into Impact Pathways and Transition Scenarios; and (3) to derive actionable policy recommendations. Using a participatory action research design, a seven-step co-creation methodology was applied across all four cases addressing crisis management challenges: forest fires in Finland, floods and refugee reception in Italy, power outages in Greece, and wildfires in Spain. Through structured stakeholder engagement combining surveys, workshops, and online consultations, the study identified seven categories of policy support results; twelve impacts spanning technology adoption, policy process enhancement, public administration capacity, and citizen empowerment; nine facilitators across financial, organisational, legal, and technical dimensions; and eight frustrators assessed through a risk matrix. These elements were structured into Impact Pathways, visualising causal relationships among policy support tools, enabling factors, and transformation outcomes. Four Transition Scenarios were derived, aligned with the policy lifecycle stages of inception, negotiation, set-up, and operation, accompanied by fifteen actionable policy recommendations classified by thematic area, timeframe, and stakeholder responsibility. The findings reveal that evidence-based policies represent a central transformation target across all result categories, while effective stakeholder engagement and leadership commitment emerge as cross-cutting enablers. The integrated framework contributes to digital governance research by operationalising co-creation for strategic roadmap development and offers practitioners a decision-support tool for planning digital technology-assisted policymaking transformations. Full article

Rapid urbanization and climate change are intensifying heat exposure in cities, making effective adaptation strategies essential. This study presents a streamlined digital twin modeling framework for simulating the impact of nature-based solutions (NBSs) on outdoor thermal comfort, developed within the Intelligent Communities Lifecycle (ICL) software suite. The approach automates the import of urban geometry from OpenStreetMap and integrates geolocated weather data, enabling users to efficiently test scenarios involving NBSs and surface material modifications. Outdoor thermal comfort is quantified using the Universal Thermal Climate Index (UTCI), with results visualized through an interactive cloud-based 3D platform to support participatory urban planning. The methodology is demonstrated in Meunierstraat, Leuven (Belgium), where three planning alternatives are compared across seasonal extremes. Simulations show that targeted NBS interventions, particularly temporary participatory measures, can improve thermal comfort under extreme heat. However, the benefits are seasonally dependent and spatially heterogeneous, emphasizing the value of high-resolution, scenario-based analysis. This integrated workflow enhances both technical evidence and stakeholder engagement. While the tool is capable of linking outdoor comfort improvements with building energy performance and carbon emissions, the present paper focuses solely on the outdoor thermal comfort results, leaving indoor–outdoor coupling analysis as a direction for future work. Full article

Scenario planning supports strategic decision-making under uncertainty by comparing multiple plausible futures. Impact indicators help to prioritize scenarios, while rank-based evaluations clearly communicate indicator relevance for participatory planning, policymaking, and resource allocation. Ensuring that rankings are both sensitive and robust is therefore essential. However, conventional statistical measures fail to fully capture ranking dynamics. They describe overall dispersion but cannot jointly assess the magnitude of rank shifts and the frequency with which items occupy specific ranks across scenarios. This study explores the novel application of Rao’s Quadratic Entropy (Rao’s Q) in scenario analysis to quantify ranking variability. A theoretical test demonstrates that Rao’s Q captures full variability in rankings and continuous values, suggesting it as a promising alternative to existing approaches. Rao’s Q is then applied to a climate change hotspot in Central Italy to evaluate changes in bio-energy landscape connectivity across forty-eight scenarios. Results reveal how land-use and climate changes affect landscape unit connectivity over time, identifying which are highly stable across scenarios or consistently critical, and thus highlighting planning priorities for mitigation, conservation, and sustainable urban development. Supported by openly available R code, this study demonstrates the relevance of Rao’s Q for participatory, scenario-based decision-making processes. Full article

Groundwater is increasingly relied upon in cities, particularly during drought, yet its management often lacks coordination and systems-based decision-making. Effective governance requires inclusive participation across sectors and scales, engaging actors with diverse knowledge, experiences, and priorities. In cities, this is challenging due to the wide range of roles and responsibilities tied to groundwater. This study examines the value of urban water metabolism analysis (UWMA) for enhancing groundwater governance in Cape Town and Nelson Mandela Bay, South Africa—both recently affected by severe drought. Through a series of Learning Labs, we convened groundwater-related actors to co-develop a shared understanding of urban water systems. We brought together two methods of systems enquiry, UWMA and governance network analysis to explore physical stocks and flows of water across metropolitan boundaries with governance processes shaping groundwater management. The UWMA revealed that, prior to the 2015 drought, Nelson Mandela Bay’s water supplies were more diversified than those of Cape Town, despite Cape Town progressively pursuing managed aquifer recharge and wastewater reuse. The governance analysis surfaced the diversity of actors influencing groundwater flows across the public, private, and civil society sectors, yet highlighted the fragmented nature of the network, with geohydrology and engineering consultants often acting as intermediaries. This research found that UWMA was perceived to be most useful at larger scales (e.g., watershed/urban scales) and was considered a valuable tool for strategic discussion, though clearer language would increase accessibility. We conclude that UWMA helps identify knowledge gaps, integrate diverse perspectives, and foster stakeholder cooperation. Coupled with scenario planning, it can support participatory and inclusive decision-making. Full article

Marine Spatial Planning (MSP), the prevailing global governance paradigm for sustainable ocean development, confronts the critical challenge of integrating climatic uncertainty into its core processes. Reliance on the stationarity assumption compromises risk assessments for long-lifecycle assets within the Blue Economy, thereby impeding progress toward principal sustainability objectives. This article introduces and validates FARO (Framework for Adaptive Operational Risk Analysis), a methodological framework designed to operationalize the transition toward climate-smart MSP. The framework’s core innovation lies in furnishing a scalable quantitative structure that directly links high-resolution climatological projections with operational decision-making and capital planning, thereby converting climatic uncertainty into actionable operational risk indicators. Its applicability is demonstrated via a case study of Brazil’s emergent offshore wind industry (Southeastern Marine Region), analyzing impacts under the RCP 4.5 and RCP 8.5 scenarios (using INPE-Eta/CMIP5 regional projections). The findings quantify the critical role of technological resilience as a key adaptation variable, revealing a potential reduction in operational downtime from approximately 60% to 10% by enhancing operational capacity from Standard (SWH 2.0 m) to Flexible (SWH 2.5 m). In conclusion, the results indicate that FARO is a robust decision-support instrument, effectively bridging state-of-the-art regional climate science with participatory planning to foster genuinely sustainable and resilient maritime development. Full article

Urban modeling is being reshaped by advances in artificial intelligence (AI) and data-rich sensing. This review assembles an integrated evidence base connecting spatial dynamic modeling (SDM), planning support systems (PSSs), urban analytics, and governance concerns. We analyze 1290 publications (2000–2025) using a reproducible pipeline that combines structured literature retrieval with retrieval-augmented generation (RAG) for semantic screening and evidence extraction. Bibliometric mapping and a rigorous coding framework structure the synthesis. The results reveal three linked trajectories. First, SDM has progressed from rule-based simulation toward learned spatial representations using deep and multimodal learning. Second, PSS has evolved from static analytical tools to interactive and participatory environments that embed AI for scenario exploration and stakeholder engagement. Third, governance themes such as transparency, fairness, and accountability have gained importance but remain unevenly implemented in modeling workflows. Building on these findings, we advance AI-aligned SDM, which integrates explainability, uncertainty reporting, documentation, and participation into model design to strengthen institutional accountability and evidence-based planning. A forward research agenda emphasizes methodological fusion between simulation and learning, institutional design for continuous model stewardship, and epistemic pluralism connecting local knowledge with AI to advance equitable and transparent urban governance. Full article

Despite broad consensus on the importance of participatory processes in disaster risk management and urban resilience building, substantial gaps persist, including scarce research on monitoring and evaluating participation, lack of comparative studies, underexplored policy and institutional roles. The paper provides methodological and empirical insights by developing and validating two indicator-based tools: one for ex ante assessment of institutional capacity and the other for supporting monitoring and ex post evaluation of participatory processes. The paper also tests them through a comparative study employing a standardizable and reproducible methodology and synthesizes findings from a systematic review of case studies and a semi-systematic review of grey literature to compile a comprehensive pool of criteria and indicators. These are screened, assigned a weight (either by Equal Weight or Best Worst Method) and are aggregated in the two innovative tools mentioned above. These are tested on four case studies: recent local-scale participatory processes aimed at reducing disaster risk and promoting urban resilience addressing multi-hazard scenarios. The research quali-quantitatively demonstrates how, in the four case studies, greater institutional capacity turns into a higher-quality participatory process. Furthermore, the paper improves practical knowledge on participatory processes in disaster risk management and urban resilience building and lays the foundation for evidence-based innovative guidelines for their planning a priori. Full article

Crisis management in smart cities demands coherent, interoperable, and reusable semantic models to represent complex systems, their risks, crisis situations, interdependencies, and decision-making processes across all lifecycle phases, i.e., prevention, preparedness, response, and recovery. This paper presents the TERMINUS (TERritorial Management and INfrastructures ontology for institutional and industrial USage) ontology, a BFO (Basic Formal Ontology)-aligned conceptual model based on semantic patterns for the crisis management lifecycle operationalized as both ontology design patterns (ODPs) to structure the ontology and ontology query patterns (OQPs) to use it in specific contexts. ODPs capture reusable conceptual structures for modeling domains, while OQPs provide SPARQL (SPARQL Protocol and RDF Query Language)-based templates to retrieve and reason over knowledge graph instances derived from these model chunks. The approach ensures semantic continuity from conceptual modeling to operational applications, enabling automated scenario generation, cascading risk analysis, and participatory decision-making. We position the patterns within the crisis management lifecycle and demonstrate their use through real-world case studies, covering semantic spatio-temporal risk assessment, interdependent infrastructure risk cascades, creative emergency scenario design, and recovery planning. Evaluation results highlight the ontology’s ability to support domain experts in generating plausible context-specific models, fostering collaborative validation, and enhancing preparedness and resilience. TERMINUS thus provides a versatile and interoperable semantic infrastructure for integrating ontologies and knowledge graphs into urban crisis management workflows. Full article

The COVID-19 pandemic caused the world to pause temporarily on an almost planetary scale. The creation and diffusion of knowledge about environmental planning and public health are now almost taken for granted. However, such processes were rather different in pre-pandemic times. It took a substantial dose of labor and resources to generate the information needed to produce useful and usable knowledge, and especially to make it available to others in a timely and effective way. As automobility has come to occupy center stage in the lives of an increasing number of suburbanized dwellers, it has taken multiple energy and public health crises, bold leadership, and the real threat of climate change to create the conditions needed to bolster sustainable Non-Motorized Transportation (NMT) as a complement to cleaner and more convenient mass transit options in cities. How does knowledge about sustainable NMT get created? How are sustainable NMT innovations diffused? How can technological and societal transitions to more sustainable realities be nurtured and augmented? This article utilizes a longitudinal and integrated knowledge creation and diffusion model with a Participatory Planning Process to analyze the adoption of measures aimed at reducing the negative consequences of too much automobility and encouraging higher levels of walking, cycling, and mass transportation. The research methods comprised autoethnographic, qualitative, and policy evaluation techniques. The study makes use of the means and ends matrix to discuss cases from five distinct realms: personal, academic, institutional, volunteering NGO, and private sector. The key findings and lessons learned promote scenarios of managed degrowth and sustainable urban transitions. Full article

Landscape Character Assessments (LCAs) support planning decisions by offering structured descriptions of landscape character. However, producing these texts is often resource-intensive and shaped by subjective judgement. This study explores whether Generative Artificial Intelligence (GenAI), specifically ChatGPT, can support the drafting of LCA descriptions using a structured, prompt-based framework. Applied to Harku Municipality in Estonia, the method integrates spatial input, reference material, and standardised prompts to generate consistent descriptions of landscape character areas (LCAs) and facilitate scenario building. The results show that ChatGPT outputs align with core LCA components and maintain internal coherence, although variations in terminology and ecological specificity require expert review. A stakeholder role play using ChatGPT highlighted its potential for enhancing early-stage planning, education, and participatory dialogue. The limitations include the reliance on prompt quality, static inputs, and the absence of real-time community validation. Recommendations include piloting AI-assisted workflows in education and practice, adopting prompt protocols, and prioritising human oversight, both experts and stakeholders, to ensure contextual relevance and build trust. This research proposes a practical framework for embedding GenAI into planning processes while preserving the social and interpretive dimensions central to landscape governance. Full article

Effective forest planning in Mediterranean environments requires tools capable of managing ecological complexity, socio-economic pressures, and fragmented governance. This study develops and applies a GIS- and GNSS-based methodological framework for regional forest planning, tested in the “Bosco della Ficuzza, Rocca Busambra, Bosco del Cappelliere, Gorgo del Drago” Regional Nature Reserve (western Sicily, Italy). The main objective is to create a multi-layered Territorial Information System (TIS) that integrates high-resolution cartographic data, a Digital Terrain Model (DTM), and GNSS-based field surveys to support adaptive, participatory, and replicable forest management. The methodology combines the following: (i) DTM generation using Kriging interpolation to model slope and aspect with ±1.2 m accuracy; (ii) road infrastructure mapping and classification, adapted from national and regional forestry survey protocols; (iii) spatial analysis of fire-risk zones and accessibility, based on slope, exposure, and road pavement conditions; (iv) the integration of demographic and land use data to assess human–forest interactions. The resulting TIS enables complex spatial queries, infrastructure prioritization, and dynamic scenario modeling. Results demonstrate that the framework overcomes the limitations of many existing GIS-based systems—fragmentation, static orientation, and limited interoperability—by ensuring continuous data integration and adaptability to evolving ecological and governance conditions. Applied to an 8500 ha Mediterranean biodiversity hotspot, the model enhances road maintenance planning, fire-risk mitigation, and stakeholder engagement, offering a scalable methodology for other protected forest areas. This research contributes an innovative approach to Mediterranean forest governance, bridging ecological monitoring with socio-economic dynamics. The framework aligns with the EU INSPIRE Directive and highlights how low-cost, interoperable geospatial tools can support climate-resilient forest management strategies across fragmented Mediterranean landscapes. Full article

Nature-based solutions (NbS) are increasingly recognized as strategic alternatives and complements to grey infrastructure for addressing water-related challenges in the context of climate change, urbanization, and biodiversity decline. This article presents a critical, theory-informed review of the state of NbS implementation in European water management, drawing on a structured synthesis of empirical evidence from regional case studies and policy frameworks. The analysis found that while NbS are effective in reducing surface runoff, mitigating floods, and improving water quality under low- to moderate-intensity events, their performance remains uncertain under extreme climate scenarios. Key gaps identified include the lack of long-term monitoring data, limited assessment of NbS under future climate conditions, and weak integration into mainstream planning and financing systems. Existing evaluation frameworks are critiqued for treating NbS as static interventions, overlooking their ecological dynamics and temporal variability. In response, a dynamic, climate-resilient assessment model is proposed—grounded in systems thinking, backcasting, and participatory scenario planning—to evaluate NbS adaptively. Emerging innovations, such as hybrid green–grey infrastructure, adaptive governance models, and novel financing mechanisms, are highlighted as key enablers for scaling NbS. The article contributes to the scientific literature by bridging theoretical and empirical insights, offering region-specific findings and recommendations based on a comparative analysis across diverse European contexts. These findings provide conceptual and methodological tools to better design, evaluate, and scale NbS for transformative, equitable, and climate-resilient water governance. Full article

Addressing the economic–ecological imbalance within urban agglomeration integration and sustainable development is crucial, particularly in the context of achieving the Sustainable Development Goals of sustainable cities and communities. This study examines this imbalance using a unique ecosystem services (ESs) balance index that evaluates “supply” and “demand” tradeoffs. It emphasizes localization, mobility, and cooperation as regionalization strategies, utilizing multisource datasets. To address gaps from endogeneity and heterogeneity, the study regresses these strategies on ESs balance values, incorporating landscape patterns as endogenous variables across 214 YRDCA counties or districts in 2020, using a multilevel geographically weighted instrumental variable regression model. Employing the patch-generating land use simulation method, three scenarios were explored: non-intervened development (ND), mobility priority (MD), and localization priority (LP). These scenarios were assessed for their 2025 mitigation effects and health benefits to optimize balanced development strategies. Key findings include (1) a severe ecological–economic imbalance in supply and demand patterns; (2) localization boosts economic development, mobility enhances ecological development, and cooperation promotes both; and (3) LP and MP strategies, compared to ND, show promising potential to reduce the imbalance and generate health benefits, although the extent of the impact may depend on the implementation scale and regional context. By promoting inclusive urbanization and participatory and integrated planning, and enhancing urban resilience through targeted risk-reduction strategies, this study provides insights into fostering balanced development among cities. Full article