Search Results (2,520)

The global shift towards renewable energy sources necessitates efficient methods for assessing solar potential in urban areas. Rooftop photovoltaic (PV) systems present a sustainable solution for decentralized energy production; however, their effectiveness is influenced by structural and environmental factors, including roof slope, azimuth, and shading. This study aims to develop and validate a UAV-based methodology for assessing rooftop solar potential in urban areas. The authors propose a low-cost, innovative tool that utilizes a commercial unmanned aerial vehicle (UAV), specifically the DJI Air 3, combined with advanced photogrammetry and 3D modeling techniques to analyze rooftop characteristics relevant to PV installations. The methodology includes UAV-based data collection, image processing to generate high-resolution 3D models, calibration and validation against reference objects, and the estimation of solar potential based on rooftop characteristics and solar irradiance data using the proposed Model Analysis Tool (MAT). MAT is a novel solution introduced and described for the first time in this study, representing an original computational framework for the geometric and energetic analysis of rooftops. The innovative aspect of this study lies in combining consumer-grade UAVs with automated photogrammetry and the MAT, creating a low-cost yet accurate framework for rooftop solar assessment that reduces reliance on high-end surveying methods. By being presented in this study for the first time, MAT expands the methodological toolkit for solar potential evaluation, offering new opportunities for urban energy research and practice. The comparison of PVGIS and MAT shows that MAT consistently predicts higher daily energy yields, ranging from 9 to 12.5% across three datasets. The outcomes of this study contribute to facilitating the broader adoption of solar energy, thereby supporting sustainable energy transitions and climate neutrality goals in the face of increasing urban energy demands. Full article

The conversion of ecological product value is vital for reconciling economic growth with environmental sustainability. As a financial innovation that combines digital technology with inclusive finance, digital finance has emerged as a key driver of this process. Drawing on Chinese provincial panel data from 2011 to 2020, this study shows that digital finance significantly enhances the conversion efficiency of ecological product value (CEEPV), and the results remain robust after addressing endogeneity and sensitivity concerns. The analysis reveals that the depth of use and the level of digitalization of digital finance strongly promote CEEPV, while coverage breadth has no significant effect. Mechanism tests indicate that digital finance improves CEEPV mainly through alleviating rural financing constraints, fostering entrepreneurship, encouraging green innovation, enhancing agricultural social services, and supporting rural e-commerce. In addition, traditional finance and financial regulation complement digital finance in strengthening CEEPV. Heterogeneity analysis further shows that the positive effect of digital finance is concentrated in provinces with higher levels of marketization and urbanization. Overall, the findings underscore the importance of accelerating digital finance development and implementing region-specific policies to maximize its potential in advancing ecological product value realization. Full article

The paper contributes to the debate on the holistic sustainability assessment of real estate projects, integrating economic, financial, environmental, and social aspects. A methodological study is presented to support decision-making processes involving the preferability ranking of alternative investment scenarios: new building production vs. retrofitting the existing stock, in the context of urban transformation interventions. The study integrates life cycle approaches by introducing the social components besides the economic and environmental ones. Firstly, a composite unidimensional (monetary) indicator calculation is illustrated. The sustainability components are internalized in the NPV calculation through a Discounted Cash-Flow Analysis (DCFA). Life Cycle Costing (LCC) and Life Cycle Assessment (LCA) are suggested to assess the economic and environmental impacts, and the Social Return on Investment (SROI) to assess the intervention’s extra-financial value. Secondly, a methodology based on multicriteria techniques is proposed. The Hierarchical Analytical Process (AHP) model is suggested to harmonize various performance indicators. Focus is placed on the criticalities emerging in both the methodological approaches, while highlighting the relevance of multidimensional approaches in decision-making processes and for supporting urban policies and urban resilience. Full article

Driven by environmental concerns and aligned with the principles of the circular economy, urban plastic waste—including packaging materials, disposable items, non-functional objects, and industrial scrap—is increasingly being collected, recycled, and marketed as a potential substitute for virgin polymers. However, the use of recycled polymers introduces uncertainties that can significantly affect both the durability and the further recyclability of the resulting products. This paper demonstrates how spectroscopic analysis in the mid-infrared (MIR) and near-infrared (NIR) regions can be applied well beyond the basic identification of the main polymeric component, typically performed during the sorting stage of recycling processes. A detailed interpretation of spectral data, based on well-established correlations between spectroscopic response and material structure, enables the classification of recycled polymers according to specific physicochemical properties, such as chemical composition, molecular architecture, and morphology. In this context, infrared spectroscopy not only provides a reliable comparison with the corresponding virgin polymer references but also proves particularly effective in assessing the homogeneity of recycled materials and the reproducibility of their properties—factors not inherently guaranteed due to the variability of input sources. As a case study, we present a robust protocol for determining the polypropylene content in recycled polyethylene samples. Full article

Urban traffic noise has become an increasingly significant environmental and public health issue, with many cities—particularly those experiencing rapid urban growth, such as Kuwait—recording levels that often exceed recommended limits. In this study, we present a detailed, data-driven approach for assessing and predicting equivalent continuous noise levels (LAeq) in residential neighborhoods. The analysis draws on measurements taken at 12 carefully chosen sites covering different road types and urban settings, resulting in 21,720 matched observations. A range of predictors was considered, including road classification, traffic composition, meteorological variables, spatial context, and time of day. Four predictive models—Linear Regression, Support Vector Machine (SVM), Gaussian Process Regression, and Bagged Trees—were evaluated through 5-fold cross-validation. Among these, the Bagged Trees model achieved the strongest performance (R² = 0.91, RMSE = 2.13 dB(A)). To better understand how the model made its predictions, we used SHAP (SHapley Additive Explanations) analysis, which showed that road classification, location, heavy vehicle volume, and time of day had the greatest influence on noise levels. The results identify the main determinants of traffic noise in Kuwait’s urban areas and emphasize the role of targeted design and planning in its mitigation. Full article

High-precision semantic segmentation of remote sensing imagery is crucial in geospatial analysis. It plays an immeasurable role in fields such as urban governance, environmental monitoring, and natural resource management. However, when confronted with complex objects (such as winding roads and dispersed buildings), existing semantic segmentation methods still suffer from inadequate target recognition capabilities and multi-scale representation issues. This paper proposes a neural network model, LMVMamba (LoRA Multi-scale Vision Mamba), for semantic segmentation of remote sensing images. This model integrates the advantages of convolutional neural networks (CNNs), Transformers, and state-space models (Mamba) with a multi-scale feature fusion strategy. It simultaneously captures global contextual information and fine-grained local features. Specifically, in the encoder stage, the ResT Transformer serves as the backbone network, employing a LoRA fine-tuning strategy to effectively enhance model accuracy by training only the introduced low-rank matrix pairs. The extracted features are then passed to the decoder, where a U-shaped Mamba decoder is designed. In this stage, a Multi-Scale Post-processing Block (MPB) is introduced, consisting of depthwise separable convolutions and residual concatenation. This block effectively extracts multi-scale features and enhances local detail extraction after the VSS block. Additionally, a Local Enhancement and Fusion Attention Module (LAS) is added at the end of each decoder block. LAS integrates the SimAM attention mechanism, further enhancing the model’s multi-scale feature fusion capability and local detail segmentation capability. Through extensive comparative experiments, it was found that LMVMamba achieves superior performance on the OpenEarthMap dataset (mIoU 52.3%, OA 69.8%, mF1: 68.0%) and LoveDA (mIoU 67.9%, OA 80.3%, mF1: 80.5%) datasets. Ablation experiments validated the effectiveness of each module. The final results indicate that this model is highly suitable for high-precision land-cover classification tasks in remote sensing imagery. LMVMamba provides an effective solution for precise semantic segmentation of high-resolution remote sensing imagery. Full article

High-rise building facades offer an alternative site for installing photovoltaic panels, which are traditionally placed on rooftops. The unique spatial configuration of high-rise buildings, characterized by a small footprint area relative to their height, supports the application of vertical facades for this purpose. Photovoltaic panels installed in these areas not only generate electricity but also enhance the aesthetic dimension of the urban landscape. The proposed methodology uses the EnergyPlus software to simulate the electricity generation of photovoltaic panels mounted on the walls of high-rise buildings in the city of Kragujevac, Serbia. A technical, economic, and environmental analysis was conducted for two scenarios: (1) photovoltaic panels installed on two facade areas with the highest solar potential, and (2) photovoltaic panels installed on all four available facade areas. In Scenario 1, the annual reduction in electricity consumption, annual cost savings in electricity consumption, and investment payback period range from 13 to 38%, 11 to 31%, and 8.4 to 10.6 years, respectively. In Scenario 2, these values range from 23 to 58%, 18 to 47%, and 10.9 to 12.9 years, respectively. The results indicate that southeast and southwest facades consistently achieve higher levels of electricity generation, underscoring the importance of prioritizing high-performing orientations rather than maximizing overall surface coverage. The methodology is particularly efficient for analyzing the solar potential of numerous buildings with comparable shapes, which is a characteristic commonly found in Eastern European architecture from the late 20th century. The study demonstrates the applicability of the proposed methodology as a practical and adaptable tool for assessing early-stage solar potential and providing decision support in urban energy planning. The approach addresses the identified methodological gap by offering a low-cost, flexible framework for assessing solar potential across diverse urban contexts and building typologies, while significantly simplifying the modeling process. Full article

Food waste (FW) presents a critical issue, representing an environmental liability and a largely untapped resource. Its heterogeneity and low valorization rate among main-stream alternative treatments challenge its integration into economically and environmentally sustainable bioprocesses. We explore biorefineries as a solution that can address the complexity of urban food waste through biological strategies capable of converting food waste into valuable products. Exploring the current landscape of FW biorefineries, this study focused on the interplay between feedstock heterogeneity, pretreatment strategies, microbial dynamics, and integration potential. We propose a framework distinguishing between robust fermentations that can use minimally treated FW and tailored fermentations, which require refined media pretreatment and/or supplementation to yield higher-value compounds. Drawing on recent techno-economic and life cycle assessments, this article evaluates process viability and environmental impacts across multiple scales, reinforcing the need for robust analysis to support decision-making. Real-world initiatives and policy frameworks are analyzed to contextualize technological advances within regulatory and infrastructural realities. By linking practical constraints to biochemical and operational strategies, this work outlines how food waste biorefineries can contribute meaningfully to circular economy goals. Instead of treating FW as an intractable problem, it is seen as a versatile feedstock that demands integration, investment, and adaptive process design. Full article

The integration of artificial intelligence (AI) into bioclimatic building design is reshaping the architecture, engineering, and construction (AEC) industry by addressing critical challenges in sustainability and efficiency. By aligning structures with local climates, bioclimatic design addresses global challenges such as energy consumption, urbanization, and climate change. Complementing these principles, AI technologies—including machine learning, digital twins, and generative algorithms—are revolutionizing the sector by optimizing processes across the entire building lifecycle, from design and construction to operation and maintenance. Amid the diverse array of AI-driven innovations, this research highlights digital twin (DT) technologies as a key to AI-driven transformation, enabling real-time monitoring, simulation, and optimization for sustainable design. Applications like façade optimization, energy flow analysis, and predictive maintenance showcase their role in adaptive architecture, while frameworks like Construction 4.0 and 5.0 promote human-centric, data-driven sustainability. By bridging AI with bioclimatic design, the findings contribute to a vision of a built environment that seamlessly aligns environmental sustainability with technological advancement and societal well-being, setting new standards for adaptive and resilient architecture. Despite the immense potential, AI and DTs face challenges like high computational demands, regulatory barriers, interoperability and skill gaps. Overcoming these challenges will be crucial for maximizing the impact on sustainable building, requiring ongoing research to ensure scalability, ethics, and accessibility. 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

In contemporary spatial planning, territorial resilience is rapidly gaining relevance, referring to a territory’s capacity to withstand, adapt to, recover from, and transform in response to environmental, social, and economic pressures. However, several constraints limit its operationalisation in planning. A key element to addressing this gap is to investigate where and which interventions are most urgently needed to tackle the impact of hazards on territories. This can be achieved by understanding and localising the vulnerabilities of territorial systems, thereby enabling the definition of appropriate mitigation and adaptation measures. This paper presents the application of R3C-GeoResilience, an open-source GIS tool and its methodological framework, which allows mapping territorial vulnerabilities across different geographical contexts and spatial scales. The methodology is applied to the Italian case of the Union of Bassa Romagna (UBR), aiming to build capacity for local practitioners to implement resilience thinking in decision-making processes. Findings underscore the potential of R3C-GeoResilience to enhance evidence-based planning and policymaking, supporting adaptive and transformative strategies to address territorial vulnerabilities. The application of the research demonstrates the replicability and adaptability of the methodological framework for integrating participatory vulnerability mapping into local governance and urban planning strategies, thereby enhancing the resilience of territories. Full article

Effective waste management is important for reducing environmental harm, improving recycling operations, and building urban sustainability. However, accurate waste classification remains a critical challenge, as many deep learning models struggle with diverse waste types. In this study, classification accuracy is enhanced using transfer learning, ensemble techniques, and custom architectures. Eleven pre-trained convolutional neural networks, including ResNet-50, EfficientNet variants, and DenseNet-201, were fine-tuned to extract meaningful patterns from waste images. To further improve model performance, ensemble strategies such as weighted averaging, soft voting, and stacking were implemented, resulting in a hybrid model combining ResNet-50, EfficientNetV2-M, and DenseNet-201, which outperformed individual models. In the proposed system, two specialized architectures were developed: EcoMobileNet, an optimized MobileNetV3 Large-based model incorporating Squeeze-and-Excitation blocks for efficient mobile deployment, and EcoDenseNet, a DenseNet-201 variant enhanced with Mish activation for improved feature extraction. The evaluation was conducted on a dataset comprising 4691 images across 10 waste categories, sourced from publicly available repositories. The implementation of EcoMobileNet achieved a test accuracy of 98.08%, while EcoDenseNet reached an accuracy of 97.86%. The hybrid model also attained 98.08% accuracy. Furthermore, the ensemble stacking approach yielded the highest test accuracy of 98.29%, demonstrating its effectiveness in classifying heterogeneous waste types. By leveraging deep learning, the proposed system contributes to the development of scalable, sustainable, and automated waste-sorting solutions, thereby optimizing recycling processes and minimizing environmental impact. Full article

Nature-Based Solutions (NBS) represent an alternative for achieving environmental and resilience goals in diverse global contexts with varying needs. As such, NBS can be understood as processes involving actions that promote circular economy (CE) strategies within their function. Therefore, this research aims to conduct a systematic literature review to identify and analyze the main NBS applied and explore how they are associated with CE strategies. This study performs a systematic literature review of NBS and their relationship with the CE using the PRISMA methodology, analyzing a total of 32 articles retrieved from the SCOPUS database. The main NBS include constructed wetlands, green infrastructure, and soil restoration and enrichment solutions. Constructed wetlands are linked to strategies such as recycling and reuse due to their role in treating urban and domestic wastewater for reuse, thereby increasing water availability. Green infrastructure is associated with strategies like redesign and reduction, as it involves the use of lower-impact materials and designs for rainwater harvesting and thermal comfort improvement. Soil enrichment and remediation solutions are connected to reuse and recycling strategies, as most derive from organic waste composting or microorganisms. NBS and CE strategies highlight how these solutions not only provide direct environmental benefits but also, when analyzed from a sustainability perspective, can offer social and economic benefits. Furthermore, understanding their relationship will facilitate their integration into regulations for transitioning toward circularity in industries and cities. The contribution of this article lies in synthesizing and systematizing the evidence on how NBS operationalizes CE strategies, identifying the main mechanisms and gaps, and proposing a conceptual model that can guide future research and policy design. Full article

Rapid urbanization across Southern Asia’s coastal regions has significantly increased electricity demand, driving India’s solar sector expansion under the National Solar Mission and positioning the country as the world’s fourth-largest solar market. Nonetheless, methodological limitations remain in applying GIS-based multi-criteria decision analysis (MCDA) frameworks to coastal urban microclimates, which involve intricate land-use dynamics and resilience constraints. To address this gap, this study proposes a multi-criteria GIS- based Analytical Hierarchy Process (AHP) framework, incorporating remote sensing and geospatial data, to assess Solar Farm Sites (SFSs) suitability, supplemented by sensitivity analysis in Thoothukudi coastal city, India. Ten parameters—covering photovoltaic, climatic, topographic, environmental, and accessibility factors—were used, with Global Horizontal Irradiance (18%), temperature (11%), and slope (11%) identified as key drivers. Results show that 9.99% (13.61 km²) of the area has excellent suitability, mainly in the southwest, while 28.15% (38.33 km²) exhibits very high potential along the southeast coast. Additional classifications include good (22.29%), moderate (32.41%), and low (7.16%) suitability zones. Sensitivity analysis confirmed photovoltaic variables as dominant, with GHI (0.25) and diffuse radiation (0.23) showing the highest impact. The largest excellent zone could support approximately 390 MW, with excellent and very high zones combined offering up to 2080 MW capacity. The findings also underscore opportunities for dual-use solar deployment, particularly on salt pans (17.1%), as well as elevated solar installations in flood-prone areas. Overall, the proposed framework provides robust, spatially explicit insights to support sustainable energy planning and climate-resilient infrastructure development in coastal urban settings. Full article

Extreme climate events such as heatwaves are becoming more frequent and pose serious challenges in cities. Urban areas are particularly vulnerable because built surfaces absorb and release heat, while human activities generate additional greenhouse gases. This increases health risks, making it crucial to study population exposure to heat stress. This research focuses on Naples, Italy’s most densely populated city, where intense human activity and unique geomorphological conditions influence local temperatures. The presence of a Surface Urban Heat Island (SUHI) is assessed by deriving high-resolution Land Surface Temperature (LST) in a time series ranging from 2013 to 2023, processed with the Statistical Mono Window (SMW) algorithm in the Google Earth Engine (GEE) environment. SMW needs brightness temperature (Tb) extracted from a Landsat 8 (L8) Thermal InfraRed Sensor (TIRS), emissivity from Advanced Spaceborne and Thermal Emission Radiometer Global Emissivity Database (ASTERGED), and atmospheric correction coefficients from the National Center for Environmental Prediction and Atmospheric Research (NCEP/NCAR). A total of 64 nighttime images were processed and analyzed to assess long-term trends and identify the main heat islands in Naples. The hottest image was compared with population data, including demographic categories such as children, elderly people, and pregnant women. A risk index was calculated by combining temperature values, exposure levels, and the vulnerability of each group. Results identified three major heat islands, showing that risk is strongly linked to both population density and heat island distribution. Incorporating Local Climate Zone (LCZ) classification further highlighted the urban areas most prone to extreme heat based on morphology. Full article