Search Results (4,848)

Balcony gardening has become increasingly relevant in dense urban environments, providing residents with a way to address limited access to green spaces and reduced everyday contact with nature. Despite the growing importance of urban green infrastructure (GI), there is a significant lack of understanding of balcony gardening as a decentralized, distributed, and resident-driven component of urban greening, as it is often viewed as an isolated hobby. This study conceptualises balcony gardening as a micro-scale, resident-driven component of urban GI. Data were collected in Sweden via a semi-structured online survey. The results show that among the surveyed group, balcony gardening is mainly motivated by food self-provision (77%) and aesthetic enjoyment (67%), while also providing intangible benefits such as improved mood (65%) and reduced stress (36%). Knowledge is primarily acquired through self-learning (90%) and digital communities, compensating for the lack of formal support. Respondents perceived balcony gardening as falling outside formal governance structures, with limited interaction with local authorities. Respondents face structural barriers, including limited space (51%) and insufficient lighting (41%). The findings highlight the governance gaps of a practice that respondents associate with multiple urban sustainability functions, pointing to the need for more inclusive planning approaches. Full article

Historic urban landscapes are at continuous risk of loss due to urban modernization and the demolition of built heritage. Scan-based documentation methods such as laser scanning and photogrammetry become inapplicable, leaving demolished sites undocumented and unrecoverable through existing digital heritage workflows. This study develops a documentary-based Urban Digital Twin (UDT) framework for reconstructing demolished historic urban landscapes through the integration of historical documentation, geospatial analysis, and parametric modeling within the Historic Urban Landscape (HUL) approach. As a feasibility study, the framework reconstructs Chahar-Bagh Bala Street in Isfahan, Iran, a four-century-old promenade among the oldest urban streets in the Middle East, whose royal garden entrances, towers, water features, and promenade have been almost entirely replaced by industrial and modern structures. The methodology applies a nine-step workflow, integrating georeferencing, viewpoint reconstruction, cross-source validation, and HBIM parametric modeling, using historical maps, travel account engravings, archival photographs, and measured plans. The reconstruction confirms the historical existence and spatial locations, established through convergent visual evidence, of two lost royal garden entrances. The study further quantifies long-term historic green infrastructure loss, finding that approximately 70% of the original garden cover has been replaced. This replicable framework supports evidence-based heritage governance and sustainable urban regeneration, including the reintegration of historic green infrastructure into contemporary urban planning, particularly for rapidly transforming cities of the Global South. Full article

This study analyzes land use dynamics in Fălticeni Municipality over four decades (1985–2025), examining the influence of major political, socio-economic, and demographic changes on urban development. The transition from a centrally planned economy to a market economy, Romania’s democratic transformation and European integration, together with migration and population dynamics, have driven largely unregulated urban expansion at the expense of agricultural land and natural landscapes. Recent urban growth has also extended into areas with varying geomorphological vulnerability, increasing exposure to landslide hazards, while the city continues to face challenges related to abandoned industrial areas and insufficient forested land and green spaces. By integrating land use change analysis with physical vulnerability indicators, this study highlights the need for risk-informed and sustainable urban planning in medium-sized cities. It proposes a planning framework based on ecological zoning, controlled urban expansion on suitable terrain, brownfield redevelopment, the establishment of peri-urban forests, and the implementation of essential infrastructure supported by comprehensive geomorphological susceptibility assessments. The proposed approach provides practical guidance for enhancing urban resilience and can be replicated in other cities facing similar environmental and development challenges. Full article

The popularity of electric vehicles (EVs) is growing at a fast pace, creating a need for the strategic deployment of charging stations (CSs) to provide enough coverage, cost effectiveness, and compliance with grid and urban planning regulations. The deployment of large-scale infrastructure under multiple, often conflicting constraints remains a challenging engineering decision-making problem. In this paper, we propose a hybrid optimization framework that combines greedy initialization with reinforcement learning to efficiently explore the charging station deployment problem. The proposed approach employs Q-learning and Deep Q-Network (DQN) agents to iteratively refine the initial deployment while simultaneously optimizing deployment cost, charging demand coverage, and operational utility under practical planning constraints. The constraints include grid capacity limitations, renewable energy utilization, and fairness considerations. The proposed framework is evaluated in realistic urban scenarios. The experimental results demonstrate that the reinforcement learning (RL) approach achieves superior trade-offs among competing objectives compared to baseline heuristic strategies, while maintaining computational scalability for large candidate location sets. The proposed framework demonstrates stable performance across three evaluated deployment scenarios, indicating its potential applicability to increasingly complex charging infrastructure planning problems. The proposed methodology is scalable to other complex engineering planning and resource allocation problems characterized by multi-objective trade-offs and dynamic constraints. Beyond improving optimization performance, the proposed framework contributes to sustainable transportation planning by supporting the efficient deployment of electric vehicle charging infrastructure. Optimized charging station placement promotes greater accessibility to charging services, encourages electric vehicle adoption, reduces unnecessary travel associated with charging activities, and contributes to lower greenhouse gas emissions. Consequently, the proposed methodology provides decision-makers with a scalable and intelligent planning tool that supports the transition toward more sustainable and energy-efficient urban mobility systems. Full article

Urban and peri-urban regions increasingly face climate-related pressures, competing land-use demands, and the need to expand renewable-energy infrastructure while maintaining agricultural land and landscape functions. Agrivoltaics, which combines photovoltaic energy generation with agricultural production, represents a potentially multifunctional approach to land use; however, regional GIS assessments often stop at environmental suitability surfaces without translating those results into planning-relevant cadastral inventories. This study develops and applies a planning-oriented Geographic Information System (GIS) framework for preliminary statewide agrivoltaic screening in Connecticut. Annual global solar radiation and terrain slope were integrated through a weighted suitability model, while incompatible land-cover classes were treated as hard exclusions through a binary land-cover mask. The workflow subsequently excluded protected and open-space lands, associated suitable areas with cadastral parcels, normalized and dissolved parcel identifiers using ParcelKey, and a recalculated suitable area from the resulting unique parcel geometries and then applied a minimum requirement of 1 ha of cumulative suitable area per retained parcel. The final baseline inventory contained 3497 normalized unique cadastral parcels encompassing 16,366.49 ha of GIS-identified suitable area, with suitable land representing an average of 42.46% of total parcel area. Peri-urban contexts accounted for the largest share of the final suitable area, containing 2497 parcels and 73.16% of the total, compared with 476 urban and 524 rural parcels. Sensitivity analysis indicated strong stability under alternative weighting schemes, with spatial overlap exceeding 99% relative to the baseline. Reducing the suitability-score threshold from 3.0 to 2.5 produced only minor changes, whereas increasing it to 3.5 reduced the inventory to 3095 parcels and 13,712.89 ha. From a sustainability perspective, the framework provides a spatial decision-support approach for coordinating renewable-energy planning with agricultural land stewardship, conservation constraints, and more efficient use of already fragmented land resources. By making the effects of exclusions, parcel thresholds, and analytical assumptions explicit, the approach supports more transparent and reproducible evaluation of land-use trade-offs relevant to sustainable development. The resulting inventory is intended as a first-stage planning resource rather than a determination of project feasibility or site-level sustainability performance. Full article

Rapid population growth, urbanization, and industrialization are continuously increasing global energy demand while intensifying climate change associated with fossil fuel consumption. In this context, renewable energy production from organic waste has gained increasing attention as a sustainable and environmentally friendly strategy. Anaerobic digestion (AD) offers significant potential for simultaneous waste stabilization and biomethane generation. However, many previous studies investigating lignocellulosic or nutrient-rich substrates have relied on physical, chemical, or thermal pretreatment methods to enhance biodegradability, despite their additional operational costs, energy consumption, and environmental impacts. Therefore, developing low-cost and pretreatment-free co-digestion strategies remains an important research need. This study investigated the biomethane production potentials of untreated chicken manure (CM) and duckweed (Lemna minor-LM) collected from the final sedimentation tanks of wastewater treatment plants under mono-digestion and co-digestion conditions. The study hypothesized that rapidly growing and widely available LM biomass could enhance methane production without requiring pretreatment. Among all reactors, CM0.75 (75% of the total TS derived from CM and 25% from LM and inoculum) achieved the highest performance with a cumulative biogas production of 5350 mL (74.2% of CH4) and a methane yield of 327 mL CH4/g VS, while mono-digestion of CM resulted in the lowest methane yield of 104 mL CH4/g VS. The results demonstrated that LM biomass naturally proliferating in wastewater treatment plants can be directly utilized as an effective co-substrate to improve biomethane production from poultry wastes. The proposed approach provides a cost-efficient, eco-friendly, and circular-economy-oriented alternative by eliminating the need for pretreatment while simultaneously valorizing problematic biomass generated in wastewater treatment facilities. Full article

Highly automated vehicles often rely on takeover requests (TORs) that lack contextual transparency, treat drivers as passive fallbacks, and lead to poor situational awareness. To address this challenge, this study presents the Empowering Language Interaction (ELI) framework, a conversational interface powered by a large language model that supports bidirectional negotiation and collaborative human–AI teamwork. Using the CARLA driving simulator, 28 participants compared ELI with a conventional TOR baseline in both urban and peri-urban driving scenarios. The study employed a multidimensional evaluation approach, integrating telemetry data on driving performance with continuous monitoring of physiological indicators (electrocardiogram and electrodermal activity) and subjective questionnaires to assess driver trust and engagement. Results indicated that ELI sustained continuous driver engagement and improved the subjective comprehension of the vehicle’s state. Physiologically, the conversational interface maintained active cognitive load, preventing the abrupt autonomic spikes characteristic of traditional takeover requests. Furthermore, ELI outperformed the TOR baseline in safety metrics by reducing out-of-lane events and maintaining greater safety margins. Conversational interaction has shown potential to transform drivers from passive supervisors into active teammates, improving joint decision-making without inducing over-reliance on the automated system. Full article

Urban transmission of dengue and other Aedes aegypti-borne diseases is shaped by the interaction of vector ecology, human mobility, climate, and spatial heterogeneity. Capturing these interactions in city-specific settings requires models that are detailed enough to represent local transmission processes, while remaining computationally feasible for calibration, validation, and sensitivity analysis. This study presents a hybrid agent-based modeling and simulation (HABMS) approach, supported by high-performance computing (HPC), for simulating urban vector-borne disease transmission. Human residents are represented as mobile agents with stochastic infection dynamics, while mosquito populations are represented at the patch level through discrete-time equations. The model incorporates geospatial structure, temperature, land-use-based human movement, and local contextual information to represent transmission within urban environments. The framework was applied to Santa Marta, Colombia, as a city-specific case study. Transmission parameters were calibrated using surrogate-based Bayesian optimization, and their influence was assessed through sensitivity analysis. High-performance computing made the large number of stochastic simulations required for calibration and sensitivity analysis feasible. The calibrated model reproduced the magnitude and main seasonal shape of the observed dengue epidemic, including the peak and early decline. However, the model did not fully reproduce the late low-incidence tail of the season, indicating the need to incorporate external introductions of infection and rainfall-driven seasonal forcing of vector recruitment in future versions. A control scenario run on the calibrated baseline, a 30% reduction in larval carrying capacity, lowered the simulated seasonal attack rate by about three quarters and moved the system below the threshold at which local transmission is self-sustaining, illustrating the relative comparisons the calibrated model supports. This study contributes an adaptable hybrid model architecture and a high-performance implementation that make city-specific calibration and sensitivity analysis computationally feasible, demonstrated through a case study in Santa Marta. Full article

The impacts of international tourism on economic performance, sustainable development, natural resources and environmental outcomes have been extensively researched during the last couple of decades in the available body of knowledge. However, the specific role of international tourism on women’s empowerment is rarely researched despite the fact that it creates numerous employment and entrepreneurial opportunities in host economies for women. Accordingly, this research study attempts to examine the impacts of international tourism on women’s empowerment using the framework of Vision 2030 introduced by Saudi Arabia in 2016. Using the ARDL cointegration approach and utilizing annual time series data from 1995 to 2024, this study demonstrates that international tourism has improved women’s empowerment both in the long run as well as in the short run in Saudi Arabia. Besides tourism, this study found that urbanization and female’s labor force participation have accelerated the pace of women’s empowerment both in the long run and short run. In addition, the results underscored that trade openness only matters for women’s empowerment in the long run. Finally, this study demonstrates that internet use is helpful in promoting women’s empowerment in the short run only. The study’s research findings highlight the significance of international tourism as a determinant of women’s empowerment and advocates that the policymakers of Saudi Arabia should introduce gender-inclusive tourism policies. The findings of this research study contribute to the existing body of literature on tourism-led development and gender equality, which is consistent with SDG-5 and Vision 2030 of Saudi Arabia. Full article

The increasing development pressure observed in rural areas leads to progressive land-use transformation and a reduction of biologically active areas (BAA). Maintaining the required share of biologically active areas is an important element of sustainable spatial planning, climate-change adaptation, and ecosystem-based urban and rural land management. However, conventional field-based assessments are time-consuming and may not accurately reflect current land-use conditions. This study evaluated the potential of high-resolution UAV-derived multispectral imagery for estimating biologically active areas on residential parcels located in rural areas under increasing development pressure. Multispectral imagery was used to calculate selected vegetation indices, while Receiver Operating Characteristic (ROC) analysis and the Youden index were applied to determine optimal classification thresholds. The obtained classifications were compared with manually delineated reference data prepared for individual cadastral parcels. Among the analysed vegetation indices, NDVI and RGBVI achieved the highest agreement with the reference data and provided the most reliable estimation of biologically active areas. NDVI achieved the highest pixel-level classification accuracy, whereas RGBVI produced the lowest parcel-level area estimation errors. The results also demonstrated that ROC-based threshold selection reduced subjectivity and improved the repeatability of land-cover classification. Although classification accuracy was influenced by shadows, heterogeneous land cover, and tree canopies, the proposed workflow proved effective for parcel-scale monitoring. The developed approach can support spatial planning by enabling rapid identification of parcels where the actual share of biologically active areas may differ from planning requirements, thereby facilitating environmental monitoring, supporting sustainable spatial planning, and contributing to climate-resilient land management. Full article

Climate change increasingly impacts urban areas, particularly in developing countries. Although some Brazilian cities have advanced climate adaptation policies, these initiatives remain concentrated in a limited number of municipalities. In this context, the objective of this study is to analyze climate adaptation strategies implemented in Spanish cities and evaluate their potential applicability to the Brazilian urban context. The study adopts a qualitative comparative approach combining bibliographic and documentary research, analysis of climate policies, and technical field visits to fourteen Spanish cities. The comparative analysis focused on identifying adaptation measures with the greatest potential for transfer to Brazil while considering differences in governance capacity, financial resources, urban infrastructure, and climatic conditions. The results indicate that climate shelters, sustainable urban drainage systems (SUDS), infiltration gardens, public drinking-water fountains, urban shading structures, and green infrastructure constitute feasible strategies for many Brazilian municipalities. The study concludes that international cooperation and the exchange of successful urban experiences can support the development of more resilient Brazilian cities, provided that adaptation policies are tailored to regional climatic diversity, institutional capacities, and local socioeconomic conditions. Full article

Rapid and unplanned urbanization in Dhaka is reshaping land use, intensifying peripheral expansion, and increasing pressure on urban and ecological resources. Understanding these growth dynamics is essential for effective urban growth boundary delineation and sustainable planning, yet integrated assessments of historical and future urban growth remain limited. The current study evaluates spatiotemporal urban expansion from 2010 to 2025, delineates the urban growth boundary using a morphological framework, and simulates future growth for 2030 through a coupled cellular automaton-neural network model. Multi-temporal Landsat imagery (2010, 2015, 2020, and 2025) was classified in Google Earth Engine using supervised Maximum Likelihood Classification. Urban growth patterns were quantified using the urban expansion intensity index (UEII), annual urban expansion rate (AUER), and landscape expansion index (LEI). The urban largest continuous patch index (ULCPI) approach was applied to extract functional urban boundaries. Model performance was validated using the Chi-square (χ²) goodness-of-fit test. Results show a substantial increase in built-up land from 115.85 km² to 171.42 km² between 2010 and 2025, accompanied by a decline of approximately 60 km² in urban green spaces. LEI results demonstrate a transition from compact infilling growth (2010–2015) to dominant edge and outlying expansion (2015–2020), indicating progressive peri-urbanization. The urban largest continuous patch (ULCP) nearly doubled from 78.58 km² to 152.13 km² over the same period, accentuating rapid spatial consolidation. The 2030 projection anticipates continued corridor-oriented expansion, particularly toward the northern and eastern peripheries, with predictive agreement from the CA–ANN model (χ² = 0.03 < 7.8). The study identifies a clear transition from monocentric compactness to polycentric expansion, emphasizing the necessity for enforceable growth containment, transit-oriented development, and ecologically responsive planning strategies to ensure long-term urban sustainability. Full article

The assessment of sustainable ecological carrying capacity (SECC) serves as a crucial scientific foundation for supporting high-quality urbanization, advancing ecological civilization, and achieving the strategic goals of the “Dual Carbon” initiative. However, existing assessment methods largely rely on subjective expert scoring, making them difficult to apply at the large-scale urban level; simultaneously, traditional fuzzy assessment frameworks lack effective mechanisms for representing uncertainty when dealing with objective panel data. This paper proposes a temporal-uncertainty-driven interval-valued Fermatean fuzzy set (TU-IVFFS) theoretical framework and integrates it with an improved decision-making trial and evaluation laboratory (DEMATEL), the method based on the removal effects of criteria (MEREC), and the measurement of alternatives and ranking according to compromise solution (MARCOS) approach to construct an integrated urban ecological carrying capacity assessment framework: TU-IVFF-DEMATEL-MEREC-MARCOS. Using panel data from 2021 to 2024 for 690 major Chinese cities (at the county-level-city level and above) as the sample, the analysis found that Beijing, Guangzhou, Shenzhen, Nanjing, and Chongqing ranked in the top five for SECC, while some small cities in the northeast and northwest ranked lower. Sensitivity analysis showed that the city rankings remained stable across the entire range of weight combination coefficients λ ∈ [0, 1], verifying the robustness of the proposed framework. This study provides a methodological breakthrough for the reproducible and generalizable assessment of urban ecological carrying capacity in large-scale samples. Full article

Environmental noise is a major environmental and public health concern in rapidly urbanizing cities, yet integrated studies combining field measurements, GIS-based spatial analysis, and predictive modelling remain limited in Central Asia. This study assessed the spatial distribution of urban noise pollution in Samarkand (Uzbekistan) and explored its statistical association with selected public health indicators, forecasting future trends. Measurements were conducted at 50 georeferenced sites covering more than 300 streets. The measured data were processed and mapped using ArcGIS 10.5 (Esri, Redlands, CA, USA) to produce the spatial distribution of environmental noise across the study area. Official data on registered vehicles, industrial enterprises, and disease incidence (2014–2024) were analysed using Pearson correlation, Autoregressive Integrated Moving Average (ARIMA), and its extension incorporating exogenous variables (ARIMAX) models. Results revealed pronounced spatial heterogeneity in noise levels, highest along transport corridors and industrial zones. Industrial enterprises showed the strongest correlations with disease incidence; vehicle registrations were excluded from final models owing to collinearity with industrial activity. ARIMA projected continued industrial growth through 2030, while ARIMAX models identified significant associations between industrial activity and diseases of the ear and mastoid process and of the nervous system (MAPE 28.55% and 19.78%). As an ecological, exploratory study using infrastructural proxies rather than measured noise exposure, findings should be interpreted as associations rather than causation. The framework offers a transferable approach for environmental risk assessment and sustainable urban planning. Full article

Rapid urbanization in China has intensified ecological degradation, loss of architectural and cultural heritage, and declining well-being in traditional villages. Existing conservation approaches often address ecological, economic, or physical heritage issues in isolation, while insufficiently incorporating residents’ quality of life (QoL) as a central criterion of sustainability. This study develops a human-centred traditional village preservation framework grounded in the ecovillage concept and a QoL-oriented sustainability paradigm, using Langde Miao Village in Guizhou Province as a representative ethnic heritage settlement. A mixed-methods approach combining systematic literature review, Delphi expert consultation, household surveys, and partial least squares structural equation modelling (PLS-SEM) was employed to examine the interrelationships among ecological, cultural, social, and economic dimensions of sustainable built heritage. Based on valid responses, the empirical results indicate that the ecological dimension exerts the strongest direct effect on residents’ quality of life, followed by the cultural and economic dimensions, while the social dimension shows a comparatively weaker effect. The proposed framework provides a community-centred model for sustainable architectural heritage conservation and rural revitalization in traditional villages. Full article