Search Results (6,782)

Against the backdrop of high-quality urbanization in cities, the rapid expansion of metro networks has led to severe spatial mismatches in land use around station areas, which seriously restricts the full exertion of the comprehensive benefits of the transit-oriented development (TOD) model. Taking 139 operational metro stations in Xi’an in 2024 as the research sample, this study constructs a multi-objective land use optimization model with the richness of public services, transportation accessibility and population distribution balance as the three core maximization objectives. A hierarchically adaptive improved NSGA-III algorithm is proposed, with the following four key technical optimizations implemented: multi-dimensional adaptive reference point adjustment, design of real-integer hybrid coding genetic operators, construction of an enhanced multi-criteria environmental selection mechanism, and dynamic regulation of algorithm iteration. Experimental results show that the performance of the improved algorithm is significantly superior to that of the traditional NSGA-III algorithm: the values of the three core objectives are increased by 59.58%, 12.94% and 7.35% respectively compared with the original data; the algorithm achieves stable convergence after 25 iterations, with the convergence efficiency improved by 30%. The obtained Pareto optimal front features good uniformity (U = 0.92) and coverage (C = 0.95), and all the 80 non-dominated solutions meet all constraint conditions, with the solution set highly coupled with the urban functional zoning and spatial planning of Xi’an. This study proposes a zoned, prioritized and phased hierarchical land use optimization strategy for the areas around metro stations in Xi’an. The research findings provide a replicable research framework and methodological reference for the TOD practice and land use optimization of metro station areas in other rapidly urbanizing central cities in China and developing countries worldwide with the characteristic of rapid rail transit expansion. Full article

Decentralized microgrids have been proven to enable socioeconomic growth in developing countries. However, they are long-lasting investments whose profitability is highly uncertain due to unstable local socioeconomic contexts, which may delay the breakeven point, if ever reachable. Over the long term, capacity expansion and non-linear degradation of components also arise. Moreover, policymakers and developers are increasingly focusing on environmental and social considerations, raising the complexity of project development. Accordingly, multi-year planning has been simplified by addressing single challenges independently. In this paper, we propose a comprehensive procedure to efficiently solve stochastic multi-year problems for off-grid microgrids in developing countries, including capacity expansion and the non-linear degradation of battery and renewable assets. The novel procedure combines the efficient A-AUGMECON2 methodology for multi-objective formulation, the iterative decomposition of the non-linearities of the battery, and the inclusion of a two-step capacity expansion. A case study developed for Soroti, Uganda shows that the proposed model is suitable for planning purposes, with savings even beyond 20%. The Pareto frontier highlights the trade-offs among the net present cost, total emissions, and land use, which can support policy and business decision-making under uncertainty. The methodology renders these complex modeling challenges solvable and is scalable to energy system applications. Full article

The accurate and timely identification of urban building footprints is critical for sustainable urban planning and disaster management. Traditional remote sensing methods for this task often face limitations in scalability, accuracy, and adaptability to complex urban morphologies. This paper addresses these challenges by developing and evaluating a novel data-centric framework that synergistically integrates Graph Neural Networks (GNNs) with zero-shot superpixel segmentation derived from the Segment Anything Model (SAM) applied to Sentinel-2 imagery. A cornerstone of our methodology is a rigorous assessment of OpenStreetMap (OSM) data, refined through temporal NDVI stability analysis to generate high-quality ground truth. We propose an optimized UrbanGraphSAGE model, enhanced with spectral data augmentation and trained using a robust loss function with label smoothing to mitigate label noise. In the complex urban landscape of Algiers, Algeria, our approach achieves a Test F1-Score of 0.7131, demonstrating highly competitive performance with standard pixel-based baselines like U-Net while offering significant topological and computational advantages. Specifically, our model operates with merely 19,585 parameters—orders of magnitude fewer than pixel-based CNNs. A rigorous Gold Standard evaluation against manually labeled imagery confirms the model’s high recall (0.8484) and reliability for automated urban monitoring. Full article

Research on hub-and-spoke logistics networks can effectively advance the construction of the Northeast China Sea–Land Grand Corridor. In the context of regional synergistic development, this study investigates the optimization of the logistics network for the Northeast China Land–Sea Grand Corridor. Focusing on 43 prefecture-level cities across Liaoning, Jilin, Heilongjiang, and Inner Mongolia, a hub-and-spoke logistics network optimization model is developed. The model aims to minimize total network costs while satisfying specific network resilience thresholds. It integrates multi-modal transport and incorporates considerations such as economies of scale, node heterogeneity in resilience evaluation, and route redundancy. Based on this, the study employs the entropy weight method to establish a comprehensive evaluation system for regional logistics and economic development levels and applies an improved coupling coordination degree model to assess the synergistic relationship between these two systems. A modified gravity model, with the coupling coordination degree as a moderating coefficient, is constructed to quantify the strength of logistics–economic linkages between cities. Furthermore, social network analysis and a logistics affiliation model are used to identify key hub cities. The results demonstrate that the optimized network significantly enhances transport efficiency, achieves substantial economies of scale and strikes a balance between cost efficiency and system resilience. This research provides a quantitative foundation and practical reference for node layout planning and multi-modal transport organization along the Northeast China Sea–Land Grand Corridor, and its methodological framework can inform logistics network planning in similar regions. Full article

This paper presents a comprehensive life-cycle analysis of distribution transformers, based on realized measurements of the increased power losses as a result of their long-term service under real-world conditions. The study is based on aggregated measured data from extensive fleets of oil-immersed distribution transformers characterized by diverse designs, manufacturing vintages, and service lives. The evolution of no-load losses and short-circuit losses is analyzed as a function of operational duration, structural characteristics, and the specific technologies employed for windings and magnetic core construction. Statistical models describing the variation in these losses are presented, highlighting the limitations of the static assumptions commonly utilized in power distribution network planning. On this basis, an approximation of the time evolution of the transformer’s total power and energy losses is proposed as appropriate for implementation in a life-cycle analysis model. Furthermore, the impacts of thermal loading and abnormal operating conditions—such as unbalanced loads, frequent short circuits, and repeated overheating of the transformer oil—are analyzed as drivers of accelerated transformer aging. These effects are integrated into a unified life-cycle framework, enabling the quantitative assessment of loss variations and their associated operational expenditures (OPEX). A numerical example is provided to evaluate the cost-effectiveness of “repair vs. replacement” scenarios, utilizing a discounted cash flow analysis that incorporates a carbon component. The findings establish a methodological foundation for a broader assessment of technical condition and energy performance, identifying the optimal intervention point for repair or replacement to support decision-making for Distribution System Operators (DSOs) amidst increasing requirements for efficiency and decarbonization. Full article

The increasing integration of renewable energy sources increases the variability and uncertainty of power systems, requiring advanced prediction-based control strategies. This paper proposes an integrated AutoML–MPC framework for a hybrid renewable energy system (HRES) combining solar and wind generation, biomass, battery energy storage, and a hydrogen chain (electrolyzer and fuel cell). Short-term load and generation forecasts are made using H2O AutoML models, and the energy flow allocation is optimized using model-based control (MPC) formalized in the form of mixed-integer linear programming (MILP). The objective function minimizes electricity imports from the grid and the associated ${\mathrm{C}\mathrm{O}}_2$ emissions, subject to technological constraints. The results obtained showed a clear distribution of short-term (battery) and long-term (hydrogen) storage functions in time: during periods of excess generation, the electrolyzer operated close to nominal mode, and in the deficit phase, the fuel cell was activated, reducing the need for grid imports. The battery ensured fast short-term balancing, while the hydrogen system compensated for the longer-term energy shortage. The forecast models were characterized by high accuracy ($R^2>0.98$), which allowed for reliable planning of energy flows over the MPC horizon. The proposed methodology allows for effective coordination of storage technologies of different time scales, maximum use of renewable generation and reducing the system’s dependence on the external grid. Full article

The use of solar photovoltaic technology is among the most promising approaches to achieving SDG7—Affordable and Clean Energy—which seeks to provide modern, reliable, sustainable, and efficient energy for everyone globally, especially in developing areas with high irradiation, where both energy access and decarbonization are major challenges. South Khorasan Province, Iran, is one of the most highly irradiated regions in the world. However, despite the abundance of solar resources, most previous research in Iran on solar potential has focused on technical potential, with little emphasis on actual energy consumption patterns and economic viability. To the best of our knowledge, this is the first demand-driven assessment at the county level and the first national-scale implementation of the MARCOS (Measurement of Alternatives and Ranking according to Compromise Solution) method for selecting solar energy sites in Iran. A spatially explicit integrated framework based on GIS-MARCOS was established for each of the eleven counties of South Khorasan Province, and five benefits were used as criteria (solar irradiance, population, per capita electrical consumption in residential, industrial, and agricultural sectors). Objective weights were calculated using Shannon’s Entropy. The analysis indicates that residential electricity demand emerges as the most influential factor in the prioritization process. Therefore, the counties of Birjand, Qaenat, and Tabas were identified as top priority counties, while counties with high irradiation levels but low demand (for example, Boshruyeh) received the least priority. These results clearly indicate the need to transition from irradiation-based to demand-based planning to minimize transmission losses and maximize the ability to integrate solar-generated electricity into the electric power grid. This proposed methodology provides a transferable decision-support tool for other high-irradiation, demand-heterogeneous regions around the globe. Full article

The precise identification of terminal area arrival procedures is crucial for airspace planning, traffic management, and safety analysis. Traditional methods are limited in automatically detecting specific procedural maneuvers from large amounts of trajectory data. This paper proposes a methodology with knowledge-based filtering to automatically identify three common air traffic control arrival procedures, namely Point Merge System, Vector for Space, and Trombone, from historical trajectory data. After clustering the landing trajectories in the terminal area, we identify the predominant flight patterns. Then, a knowledge-based filtering algorithm, designed based on knowledge of the procedure and geometry criteria, is employed to precisely extract trajectories with different procedure patterns. Experimental results demonstrate that this method effectively identifies the distinct procedural trajectories. An in-depth analysis of the extracted trajectories reveals significant characteristics and differences in their spatial distribution, trajectory structure, and operational efficiency. This work provides data-driven decision support for evaluating terminal area operational performance and arrival procedures. Full article

Rapid urbanisation and climate change are intensifying urban heat stress, posing significant challenges for climate-responsive urban planning. Digital and data-driven approaches, including GIS, remote sensing, microclimate simulation, and artificial intelligence (AI), have advanced urban climate analysis; however, their capacity to support human-centred planning remains insufficiently synthesised. This review analyses 78 peer-reviewed studies (2015–2025) to evaluate how digital methods address urban microclimate and outdoor thermal comfort. The reviewed studies are classified into four methodological groups: spatial data analytics, simulation-based models, parametric and optimisation workflows, and AI-driven or hybrid approaches. The results show that the majority of studies rely on proxy indicators, such as land surface temperature and sky view factor, while physiologically based comfort indices (e.g., PET and UTCI) are applied in a limited proportion of studies and remain largely confined to microscale simulations. A persistent scale mismatch is identified between large-scale analytics and pedestrian-level thermal experience, alongside geographic and climatic biases, particularly in hot-arid regions. Unlike previous reviews, this study integrates digital methodologies, urban microclimate processes, and human-centred thermal comfort within a unified framework. The findings provide actionable insights for planners and designers by supporting the integration of thermal comfort into multi-scale, climate-responsive decision-making. Full article

Forest harvesting transportation planning must balance operational efficiency with environmental sustainability, because timber transportation can cause both soil disturbance and carbon emissions. However, most vehicle routing studies primarily focus on economic objectives such as distance or cost minimization, whereas environmental impacts are often considered separately. The integrated optimization of ecological disturbance and carbon emissions remains limited in forest transportation planning. To address this gap, this study formulates a multi-vehicle routing optimization model for timber transportation that simultaneously minimizes transportation distance, makespan, soil disturbance, and CO₂ emissions within a hierarchical forest road network. An enhanced evolutionary algorithm, Eco-Constrained Lévy-flight Local Search NSGA-II (ECLS-NSGA-II), is proposed to improve convergence and maintain environmentally favorable routing solutions. Simulation experiments comparing ECLS-NSGA-II with NSGA-II, MOPSO, MOEA/D, and WS-GA demonstrate that the proposed method achieves superior performance across all objectives, producing shorter routes, lower completion times, and reduced CO₂ emissions while maintaining minimal ecological disturbance. Additional experiments on randomly generated networks further confirm the robustness of the proposed approach. These results indicate that the proposed framework provides an effective methodological tool for environmentally sustainable timber transportation planning in forest operations. Full article

This study develops a conceptual framework for reimagining Sustainable Development Goal 17 (SDG 17) in Africa through a reinterpretation of the Marshall Plan’s governance logic. The primary focus is to address persistent failures in development partnerships—namely, fragmentation, weak coordination, power asymmetries, and limited institutional capacity—by proposing a structured model of partnership governance. Using a theory-building methodology grounded in historical analysis and documentary evidence, the study applies a systematic adaptation logic in which core governance mechanisms from the Marshall Plan are re-specified to reflect African institutional realities. These mechanisms—coordination, mutual accountability, collective action, state capacity, and trust—are translated into eight operational pillars: co-development, institutional strengthening, structural transformation, regional integration, blended finance, digital public infrastructure, knowledge co-production, and resilience. The framework conceptualizes SDG 17 as a meta-governance system that aligns actors, institutions, and resources across sectors. By moving from historical abstraction to context-sensitive application, the study contributes a coherent, Africa-centered governance model that enhances partnership effectiveness and informs post-2030 development policy. Full article

Due to the high energy demand of buildings, especially educational buildings, it is crucial to improve total building energy consumption. The proposed methodology is the integration of a photovoltaic (PV) system with a smart control plan for educational buildings. The main aim is to improve energy consumption in an educational building (Electrical Engineering Department, Assiut University, Egypt) using photovoltaic integration and a smart control plan to regulate energy and boost indoor comfort without requiring a significant change in the building architecture. This study was conducted in two main phases: field measurements for annual energy consumption in Assiut University over a five-year period from 2009 to 2014, and an analysis of energy consumption for the Electrical Engineering Department. Then, integration of PV panels on the roof to generate electricity was considered, with the calculation of the shading factor and tilt angle to ensure a realistic estimation of energy yield and to improve energy efficiency using smart control plans. The findings indicate that the average annual peak consumption reached about 30 GWh in Assiut University during the academic years 2009 to 2014. The maximum energy consumption for a typical occupied day in the educational building is 47 kWh. An improvement in building energy consumption was achieved using PV, producing 33–35 MWh annually with an effective smart control plan and without installing sensor-based systems. The results of this study will help improve energy consumption for educational buildings in hot arid climates without building modifications. This study highlights that unoccupied periods—when human activity is absent in classrooms and other rooms—account for up to 40% of the scheduled energy consumption. Using PV panels will result in a shading factor of 0.562 from the total roof area. Full article

Global poultry production continues to expand rapidly to meet the growing demand for affordable and high-quality animal protein. However, this growth raises pressing concerns about environmental sustainability, natural resource use, and public health. Although current initiatives, such as improved housing systems, optimized feeding practices, and partial soybean meal substitution, have helped mitigate some impacts, comprehensive integrated solutions remain underexplored. This review synthesizes emerging nutritional and management innovations that enhance the sustainability of poultry production while maintaining profitability. It addresses three central research questions: (1) Which alternative feed ingredients most effectively preserve animal performance while minimizing environmental burdens? (2) How can environmental management practices enhance resource efficiency and waste valorization? (3) What roles do life cycle assessment methodologies and policy frameworks play in advancing sustainable poultry systems? Evidence from 100 peer-reviewed studies, industrial data, and field analyses reveals that incorporating insect meals, algae, and agro-industrial by-products can reduce dependence on soybean meal by 20–40% and improve feed efficiency by 5–12% across various poultry production systems. Furthermore, integrating environmental management strategies, such as manure valorization, efficient water and energy use, and the adoption of renewable energy, substantially reduces greenhouse gas emissions and promotes circular economic principles. Life cycle assessment studies confirm that combined dietary and management interventions yield greater reductions in carbon footprint than isolated measures. Future research should focus on optimizing interactions among feed strategies, environmental management, and policy frameworks through digital technologies, nanomaterial-based feed additives, and region-specific sustainability plans to accelerate the transition toward resilient, climate-smart poultry production systems. Full article

This study investigates a typical valley city, Lanzhou, China, to reveal the nonlinear relationships and interaction mechanisms between gray–green space morphology and seasonal diurnal land surface temperature (LST) using multi-source remote sensing and land use data. A comprehensive morphological indicator system encompassing scale, complexity, connectivity, and structural integrity was constructed through landscape metric screening and the CRITIC objective weighting method, combined with the XGBoost-SHAP explainable machine learning framework. The findings highlight that: (1) Gray–green space impacts on LST exhibit significant seasonal and diurnal variations—daytime LST is predominantly governed by gray space morphology (e.g., fragmentation degree), while nighttime LST is driven by green space morphology (e.g., coverage intensity). (2) Key indicators demonstrate pronounced nonlinear and threshold characteristics: the cooling effect of green space coverage intensity (GCI) saturates beyond 0.25; gray space morphological structure factor (GRMSF) demonstrates cooling potential when exceeding 0.25, mitigating its warming effect. (3) Significant synergistic interaction effects exist between gray and green spaces. Interaction analysis reveals that “high green coverage with low structural connectivity of gray space” produces optimal synergistic cooling effects, representing the most effective spatial configuration for nighttime LST mitigation. This study deepens theoretical and methodological understanding of the complex relationships between spatial morphology and thermal environments, providing quantified, temporally differentiated spatial optimization guidance for climate-adaptive planning in valley cities. Full article

Evacuation planning is increasingly challenged by compound hazards in which interacting threats degrade infrastructure, influence human behavior, and destabilize transportation systems. Although agent-based models and dynamic traffic simulations have advanced substantially, much of the evacuation literature remains hazard-specific, case-bound, or difficult to transfer across regions. In parallel, transportation resilience research shows that multi-hazard effects are often non-additive and that cascading infrastructure failures can amplify disruption beyond directly affected areas, raising important sustainability concerns related to community safety, infrastructure continuity, social equity, and long-term planning capacity. These realities motivate the development of evacuation modeling frameworks that are modular, adaptable, and capable of representing co-evolving behavioral and network processes under compound hazard conditions. This review synthesizes advances in evacuation agent-based modeling, dynamic traffic assignment, hazard-induced network degradation, and compound disaster research to propose an adaptable compound-hazard evacuation framework integrating three interdependent layers: hazard processes, transportation network dynamics, and agent decision-making. The proposed framework is organized around four principles: (1) modular hazard representation, (2) decoupling behavioral decision logic from hazard physics, (3) dynamic network state evolution, and (4) neighborhood-scale performance metrics. To support sustainable and equitable local planning, the framework prioritizes spatially resolved outputs, including neighborhood clearance time, isolation probability, accessibility loss, and shelter demand imbalance. By emphasizing modularity, configurability, and policy-relevant metrics, this review connects methodological advances in evacuation modeling to the broader sustainability goals of resilient infrastructure systems, inclusive disaster risk reduction, and locally informed emergency planning. Full article