This study addresses the growing role of Renewable Energy Communities (RECs) in supporting decentralized renewable energy integration and improving local energy self-sufficiency within the European energy transition framework. The work aimed to evaluate the technical and economic feasibility of a municipal REC in Pattada, a small municipality located in Sardinia, Italy, through an energy balance analysis based on distributed photovoltaic generation and shared electricity consumption. A techno-economic assessment framework was developed by combining the estimated electricity production of municipally owned photovoltaic systems with the load profiles of municipal, commercial, and residential users participating in the REC. The photovoltaic energy production was estimated using the Photovoltaic Geographical Information System (PVGIS) simulation platform, while the shared energy within the REC was evaluated by considering the simultaneity between electricity generation and demand under different residential participation scenarios. The results showed that the municipal photovoltaic systems achieved an annual electricity production of approximately 506.41 MWh, while direct physical self-consumption remained limited to 3.10 MWh/year due to the mismatch between municipal demand and photovoltaic generation profiles. The analysis further showed that the REC reached an energy equilibrium condition with the participation of 285 residential users, corresponding to nearly 23% of the households within the municipality, allowing virtually shared energy to reach 425.92 MWh/year. The economic evaluation demonstrated that the municipal administration obtained the highest share of the overall economic return, mainly driven by electricity exported to the grid and incentive revenues associated with shared energy. The results indicate that the integration of municipally owned photovoltaic systems within REC configurations provides an effective approach for improving local energy sharing and enhancing the economic viability of distributed renewable energy systems in small municipalities. The proposed framework offers practical support for local administrations in planning renewable energy investments and optimizing REC configurations under real operating conditions.
Renewable Energy Communities (RECs) play an increasingly important role in decentralized energy systems by improving local renewable energy utilization, enhancing energy flexibility, and supporting low-carbon energy transitions. However, the integration of distributed energy resources (DERs), flexible electrical loads, and energy sharing mechanisms continues to create operational and management challenges for REC-based systems. This study investigates the energy management and optimization of a residential REC in Italy composed of photovoltaic (PV) generation, battery storage systems, and flexible air-conditioning loads. A detailed optimization framework was developed to coordinate DERs and flexible demand with the objective of maximizing shared energy utilization and related economic incentives while maintaining user comfort and avoiding additional electricity costs. The regulatory framework and operational structure of RECs in Europe and Italy were also examined to support the development of the proposed management strategy. The optimization process was conducted under different operating conditions to evaluate the influence of coordinated load management on REC performance. The results showed that the coordinated control of battery storage systems and air-conditioning units improved shared renewable energy utilization and increased the economic return associated with energy sharing. The optimized operation strategy also reduced electricity costs for users while improving the operational efficiency of the community energy system. The findings indicate that advanced energy management and load coordination strategies provide an effective approach for enhancing the performance of distributed renewable energy systems and supporting the practical implementation of REC-based energy infrastructures.
The poultry sector plays a critical role in food security, rural income generation, and economic development in South Africa. However, its rapid expansion has intensified environmental challenges such as waste accumulation, water contamination, greenhouse gas emissions, and pressure on natural resources. This study examines how poultry value chain financing influences environmental sustainability outcomes using a mixed methods approach. Primary data were collected from 45 respondents in Gauteng Province through structured questionnaires, complemented by 9 key informant interviews. Quantitative data were analysed using IBM SPSS version 26, while qualitative data were analysed using NVivo version 14. The results reveal a significant positive relationship between access to formal financing and the adoption of sustainable practices, including manure management, water efficiency, and energy-saving technologies. However, limited access to institutional credit constrains small-scale farmers, leading to continued reliance on environmentally harmful production methods. The study also highlights the role of governance frameworks and green financing mechanisms, including policy incentives, risk sharing instruments, and sustainability linked credit, in shaping environmental outcomes across the poultry value chain. The findings suggest that value chain financing plays an important role in promoting environmental sustainability and that targeted green financing instruments may facilitate the adoption of cleaner production systems. This study contributes empirical evidence to the growing discourse on sustainable agri-food systems and provides policy recommendations for strengthening environmentally responsible financing in the poultry sector.
Tourism provides considerable economic advantages; however, it also imposes environmental challenges, especially in coastal regions where unmanaged waste poses a threat to long-term sustainability. This research seeks to examine the behavioral and spatial elements that affect tourists’ willingness to pay (WTP) for circular waste management in eight coastal destinations in Southern Yogyakarta, Indonesia. Employing the Contingent Valuation Method (CVM), primary survey data were gathered from 984 visitors and analyzed using Ordinary Least Squares (OLS) regression, K-Means clustering, and spatial mapping techniques with geomap orange data mining. The analysis investigates how socio-economic factors such as age, income, gender, education level, and travel costs influence WTP, with behavioral theory serving as the interpretive framework. The findings indicate that younger and more educated tourists demonstrate a higher WTP, while age and travel costs negatively and significantly impact their WTP. The estimated average WTP of IDR 13,840 surpasses the official waste retribution fee, reflecting a considerable level of environmental concern among visitors. Additionally, spatial and cluster analyses uncover diversity in visitor segments across coastal areas, implying that standardized waste management policies may not be effective. In summary, the results underscore the necessity of merging economic valuation with spatially informed and behaviorally conscious policy tools, illustrating the potential of WTP as a funding mechanism for sustainable and circular waste management in coastal tourism regions.
This study aims to analyze the traffic noise levels at three locations in Makassar City and to compare them with the established noise quality standards. Measurements were conducted over a one-week period at specific times using a sound level meter, a vehicle speed measurement device, and a counting application to classify vehicle types into heavy vehicles (HV), light vehicles (LV), and motorcycles (MC). The observation sites included an educational area, a hospital area, and a residential area. Correlation analysis using Statistical Package for the Social Sciences (SPSS) was employed to examine the relationships between HV, LV, MC, and vehicle speed with the equivalent continuous sound level (Leq). The results indicated that noise levels at all three locations exceeded the standard threshold of 55 decibels (dB). The correlation analysis showed significant relationships between Leq and HV (0.834), LV (0.782), MC (0.787), and vehicle speed (-0.680). The effective contribution to noise was highest for HV (40.44%), followed by MC (13.35%), LV (12.68%), vehicle speed (10.38%), and other factors (23.15%), including human activity, construction noise, road surface type, road gradient, and surrounding environmental conditions. Recommended mitigation measures include restricting the operating hours and rerouting of HV in sensitive areas, as well as enforcing noise emission testing and regulations on illegal exhaust modifications.
In mobility-aware scenarios such as vehicular networks, mobile augmented reality (AR)/virtual reality (VR) services, and other latency-sensitive Multi-access Edge Computing (MEC) applications, continuous user movement leads to frequent migrations of service function chains (SFCs). Traditional approaches typically rely on global deployment comparisons, which fail to accurately identify the specific virtual network functions (VNFs) that require migration and their optimal target nodes. This limitation often results in redundant migrations, inefficient resource utilization, and an increased risk of service disruption, thus hindering the balance between latency assurance and resource efficiency. To overcome these limitations, this paper proposed a graph-enhanced deep reinforcement learning–based adaptive migration optimization (DRL-GAMO) framework. By integrating the topological representation capability of graph neural networks (GNNs) with the decision-making efficiency of deep reinforcement learning (DRL), DRL-GAMO established a topology–resource–decision mapping that jointly optimized VNF selection and determination of target nodes. This pre-migration decision process effectively reduced redundant operations and directed migration behaviors toward resource-efficient strategies. The designed reward function minimized migration overhead under service-level agreement (SLA) latency constraints and penalized downtime to maintain service continuity. Simulation results demonstrated that DRL-GAMO achieved stable service latency, lower resource consumption, and shorter migration time while reducing migration volume by more than 40% compared with DRL-ADMO, thereby improving the migration success rate and validating its effectiveness in MEC environments.
The Public Utility Vehicle Modernization Program (PUVMP) is a key national reform in the Philippines’ mass transportation subsector. However, its application at the local level, island-provinces, has received limited attention. This study addresses that gap by evaluating Guimaras province’s Local Public Transport Route Plan (LPTRP). A questionnaire survey and transport modeling were used to assess travel behavior, accessibility, and network performance. Results show that many essential facilities, such as schools and health centers, are not adequately served by formal PUV routes. As a result, residents rely on informal modes that are often unsafe and expensive. The analysis also revealed issues with route overlap and inefficient area coverage. To address these local concerns, the study recommends redesigning routes, establishing transfer hubs, and adopting coordinated fleet management. These strategies aim to improve safety, accessibility, and system reliability for commuters. Overall, the findings offer a model for context-sensitive public transport planning in rural and island settings across the Philippines.
Climate change poses giant, demanding situations to geotechnical systems, affecting soil behavior, slope stability, basis performance, and the resilience of coastal infrastructure through interacting thermal, hydrological, and mechanical strategies. This examination evaluates both determined and projected impacts of weather exchange drivers, along with growing worldwide temperatures, altered precipitation styles, permafrost thaw, sea-level upward push, and freeze–thaw cycles, on geotechnical structures. The evaluation makes use of the Climate Change Dataset (2000–2024) together with tests from the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6). Descriptive statistics, correlation evaluation, and regression modeling had been applied to quantify the relationships amongst CO$_2$ emissions, worldwide temperature anomalies, and sea-level upward push. The outcomes imply robust, superb correlations between anthropogenic CO$_2$ emissions and global temperature will increase, which can be intently associated with accelerating sea-level upward thrust. Scenario-based total projections underneath business-as-usual, moderate mitigation, and aggressive mitigation pathways display that persisted high emissions significantly intensify weather-pushed geotechnical dangers. In comparison, competitive mitigation techniques can considerably lessen the projected temperature increase and associated sea-level upward thrust. The evaluation emphasizes the need of linked thermal-hydraulic-mechanical (THM) techniques, specifically in permafrost areas, moisture-sensitive soils, and coastal regions that are undergoing erosion and subsidence. Additionally, rainfall-added landslides and infrastructural instability are exacerbated by using the growing frequency and depth of extreme precipitation sports. In order to beautify infrastructure resilience, a number of version techniques, climate-conscious geotechnical formats, ground development techniques, geosynthetic reinforcement, and wonderful monitoring systems are advised based totally on the findings. The evaluation also highlights the need of changing geotechnical layout codes to comprise multi-threat modeling techniques, lengthy-term observational statistics, and harsh weather conditions. This takes a look at provides a complete framework for evaluating weather alternative effects on geotechnical structures and permits the development of resilient and sustainable infrastructure in climate conversion via combining historical weather information, statistical analysis, and kingdom-of-affairs-based simulations.
Increased agricultural production could improve household income but often generates adverse environmental impacts, including soil degradation, rising temperatures, and drought, thereby contributing to climate change. This study aims to optimize income and carbon emissions in the trade of rice, corn, and cattle commodities in the Indonesia–Timor Leste border region and to assess the performance of integrated sustainable trade among farmers, traders, and processing industries. A Multi-Objective Linear Programming (MOLP) model and Partial Least Squares Structural Equation Modeling (PLS-SEM) were employed for analysis. The findings indicated that increased trade activities could improve economic outcomes while maintaining emissions within manageable limits. Farmer income is projected to increase by IDR 5.779 billion per production season, with improved cost efficiency at approximately IDR 64,000 per acre and maximum emissions of 356,561 tons CO₂e. Traders’ income is expected to increase by IDR 8.526 billion, with maximum emissions of 2,443.241 tons CO₂e and average transport costs of IDR 4,600 per kilometer. Carbon emissions at the farm level primarily stem from inefficient use of fertilizer and land burning, while emissions at the trader level are driven by transport capacity and travel distance. Although processing industries have established direct relationships with farmers, most farmers remain dependent on traders for market access. Strengthening the capacity of processing industries in the border region is therefore considered essential for maximizing farmers’ income.
With the rapid expansion of high-speed railway networks and the continuous growth in urban travel demand, the efficiency of first/last-mile connections at transport hubs has become a critical factor constraining the performance of integrated transportation systems. Demand-responsive customized bus services dynamically match passenger demand with available capacity, providing a feasible solution for improving travel flexibility. However, in practical applications, the rational design of customized bus networks remains challenging due to heterogeneous spatial demand distributions, vehicle capacity limitations, and various operational constraints. This study proposes an integrated methodological framework for customized bus network design that combines three key components: stop identification, route optimization, and simulation-based validation. First, a hybrid clustering approach that integrates density-based clustering with centroid partitioning is employed to extract potential stop locations from passenger origin–destination data. A capacity-constrained mechanism is further introduced to regulate clustering results, ensuring that stop sizes are compatible with vehicle carrying capacity. Based on the identified stops, the network design problem is formulated as a vehicle routing problem with time window constraints, where operational cost, passenger travel time cost, and environmental impact are jointly considered as optimization objectives. A genetic algorithm is adopted to solve the model. A case study involving a feeder service between a high-speed rail station and the urban core business district is conducted, and the proposed framework is validated through simulation using the AnyLogic platform. The results demonstrate that the proposed method improves vehicle utilization and route efficiency while maintaining service quality and system stability. This research provides a practical technical pathway and decision support for the intelligent design and operation of demand-responsive customized bus services.
The physical quality of seeds is a critical determinant of sorting efficiency and crop productivity, yet conventional assessment approaches are often labor-intensive, invasive, and time-consuming. To address these limitations, computer vision-based methods have been increasingly adopted; however, most existing techniques rely primarily on reflected visible light, thereby capturing only surface-level features and limiting the detection of internal defects. In this study, a low-cost imaging system integrating both reflection and transmission of visible light was developed to enhance the characterization of maize seed translucency. By enabling simultaneous acquisition of information from the two principal faces of white maize seeds, a more comprehensive representation of both external morphology and internal structural variations was achieved. A comparative analysis was conducted between the conventional reflection-based method and the proposed imaging approach, with correlation coefficients between seed faces determined as 0.62 and 0.84, respectively, indicating a substantial improvement in feature consistency and information richness. A dedicated dataset was subsequently constructed using both imaging techniques and employed to train a YOLOv5s-based detection model over 200 epochs. The classification performance demonstrated a marked enhancement, with the proposed method achieving an accuracy of 93.07%, compared to 81.5% obtained using the conventional approach. Furthermore, real-time detection capability was validated through the implementation of the optimized imaging system, in which improved inference stability and robustness were achieved under practical operating conditions. The results indicate that the integration of transmission with reflection imaging provides a cost-effective and reliable solution for non-destructive seed quality assessment, offering significant potential for scalable deployment in agricultural sorting systems.
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