Search Results (1,510)

In response to the International Maritime Organization’s strengthened regulations on carbon emissions, the introduction of novel eco-friendly technologies for ship operators has become necessary. In this context, various energy saving devices such as wind-assisted propulsion systems (e.g., wing/rotor sails), propeller-rudder efficiency enhancers (e.g., pre-swirl stators or ducted propellers), and the gate rudder system have been proposed. Among various energy-saving technologies, the air lubrication system has been widely investigated as an effective means of reducing hull frictional resistance through air injection beneath the hull. The performance of air lubrication systems can be evaluated through experimental testing or computational fluid dynamics (CFD) simulations. However, accurately simulating air lubrication systems in CFD remains challenging. Therefore, this study aims to quantitatively evaluate the influence of numerical parameters on the CFD implementation of air lubrication systems. To evaluate these influences, CFD simulations employing the unsteady Reynolds-averaged Navier–Stokes (URANS) method were conducted to investigate air layer formation and sweep angle on a flat plate. The numerical predictions were systematically compared with experimental results by varying key numerical parameters. These quantitative estimations of the effects of numerical variables are expected to serve as a useful benchmark for CFD simulations of air lubrication systems. Full article

Accurate perception of dynamic carbon intensity is a prerequisite for low-carbon demand-side response. However, traditional grid-average carbon factors lack the spatio-temporal granularity required for real-time regulation. To address this, this paper proposes a “Prediction-Optimization” closed-loop framework for electric vehicle (EV) fleets. First, a hybrid forecasting model (VMD-BSLO-CTL) is constructed. By integrating Variational Mode Decomposition (VMD) with a CNN-Transformer-LSTM network optimized by the Blood-Sucking Leech Optimizer (BSLO), the model effectively captures multi-scale features. Validation on the UK National Grid dataset demonstrates its superior robustness against prediction horizon extension compared to state-of-the-art baselines. Second, a multi-objective Model Predictive Control (MPC) strategy is developed to guide EV charging. Applied to a real-world station-level scenario, the strategy navigates the trade-offs between user economy and grid stability. Simulation results show that the proposed framework simultaneously reduces economic costs by 4.17% and carbon emissions by 8.82%, while lowering the peak-valley difference by 6.46% and load variance by 11.34%. Finally, a cloud-edge collaborative deployment scheme indicates the engineering potential of the proposed approach for next-generation low-carbon energy management. Full article

As a critical carbon offset mechanism, China’s Certified Emission Reduction (CCER) plays a pivotal role in achieving the “dual carbon” targets. With the relaunch of its trading market, refining the CCER valuation framework has become imperative. This study develops a multidimensional CCER valuation methodology based on both the income and market approaches. Under the income approach, two probabilistic models—discrete and continuous emission distribution frameworks—are proposed to quantify CCER value. Under the market approach, a Geometric Brownian Motion (GBM) model and a Long Short-Term Memory (LSTM) neural network model are constructed to capture nonlinear temporal dynamics in CCER pricing. Through a systematic comparative analysis of the outputs and methodologies of these models, this study identifies optimal pricing strategies to enhance CCER valuation. Results reveal significant disparities among models in predictive accuracy, computational efficiency, and adaptability to market dynamics. Each model exhibits distinct strengths and limitations, necessitating scenario-specific selection based on data availability, application context, and timeliness requirements to strike a balance between precision and efficiency. These findings offer both theoretical and practical insights to support the development of the CCER market. Full article

Compressed air systems are widely used in industry, and air leaks that occur over time lead to significant and unnecessary energy losses. This study aims to quantify the energy-saving potential of compressed air leaks in a manufacturing plant and to develop machine learning (ML) regression models for sustainable leak management. A total of 230 leak points were identified by measuring three periods using an ultrasonic device. Using the measured acoustic emission level (dB) and probe distance (x) as inputs, the leak flow rate, annual energy-saving potential, cost loss, and carbon footprint were calculated. As a result of the repairs, energy consumption improved by 8% compared to the initial state. Three regression models were compared to predict leak flow: Linear Regression, Bagging Regression Trees, and Multivariate Adaptive Regression Splines. Among the models evaluated, the Bagging Regression Trees model demonstrated the best prediction performance, achieving an R² value of 0.846, a mean squared error (MSE) of 389.85 (L/min²), and a mean absolute error (MAE) of 12.13 L/min in the independent test set. Compared to previous regression-based approaches, the proposed ML method contributes to sustainable production strategies by linking leakage prediction to energy performance indicators. Full article

The convergence of Artificial Intelligence (AI) and the Internet of Things (IoT), known as Artificial Intelligence of Things (AIoT), has emerged as a transformative paradigm for enabling intelligent, data-driven, and context-aware decision-making in urban environments to reduce the carbon footprint of mobility and industry. This review examines the conceptual foundations, and state-of-the-art developments of AIoT, with a particular emphasis on its applications in smart cities and its relevance to climate change management. AIoT integrates sensing, connectivity, and intelligent analytics to provide optimized solutions in transportation systems, energy management, waste collection, and environmental monitoring, directly influencing urban sustainability. Beyond urban efficiency, AIoT can play a critical role in addressing the global challenges and management of climate change by (a) precise measurements and autonomously remote monitoring; (b) real-time optimization in renewable energy distribution; and (c) developing prediction models for early warning of climate disasters. This paper performs a literature review and bibliometric analysis to identify the current landscape of AIoT research in smart city contexts. Over 1885 articles from Web of Sciences and over 1854 from Scopus databases, published between 1993 and January 2026, were analyzed. The results reveal a strong and accelerating growth in research activity, with publication output doubling in the most recent two years compared to 2023. Waste management and air quality monitoring have emerged as leading application domains, where AIoT-based optimization and predictive models demonstrate measurable improvements in operational efficiency and environmental impact. Altogether, these support faster and more effective decisions for reducing greenhouse gas emissions and ensuring the sustainable use of resources. The reviewed studies reveal rapid advancements in edge intelligence, federated learning, and secure data sharing through the integration of AIoT with blockchain technologies. However, significant challenges remain regarding scalability, interoperability, privacy, ethical governance, and the effective translation of research outcomes into policy and citizen-oriented tools such as climate applications, insurance models, and disaster alert systems. By synthesizing current research trends, this article highlights the potential of AIoT to support sustainable, resilient, and citizen-centric smart city ecosystems while identifying both critical gaps and promising directions for future investigations. Full article

The emerging urbanization and the extensive increase of the transportation sector are responsible for the significant increase in carbon dioxide emissions. Therefore, replacing traditional cars with Electric Vehicles (EVs) is a promising solution, offering a clearer alternative. EVs are becoming more and more well-known and are being quickly used worldwide. However, the exponential rise in EV sales has also raised a number of issues, which are becoming important and demanding. These challenges include the need of driving security, the battery degradation, the inadequate infrastructure for charging EVs, and the uneven energy distribution. In order for EVs to reach their full potential, intelligent systems and innovative technologies need to be introduced in the field of EVs. This is where business intelligence (BI) can be employed, along with artificial intelligence (AI), data analytics, and machine learning. In this paper, we provide a comprehensive survey on the use of BI strategies in the EV transportation sector. We first introduce the EVs and charging station technologies. Then, research works on the application of BI and data analysis techniques in EV technology are reviewed to further understand the challenges and open issues for the research and industry community. Moreover, related works on accident analysis, battery health prediction, charging station analysis, intelligent infrastructure, locating charging stations analysis, and autonomous driving are investigated. This survey systematically reviews 75 peer-reviewed studies published between 2020 and 2025. Finally, we discuss the fundamental limitations and the future open challenges in the aforementioned topics. Full article

To identify the emission reduction potential and policy synergies of Tokyo’s road passenger and urban road freight transport under the “carbon neutrality target,” this paper constructs an assessment framework for megacities. First, based on macroeconomic socioeconomic variables (population, GDP, road length, and employment), regression equations are used to predict traffic turnover for different modes of transport from 2021 to 2050. Then, the prediction results are imported into the LEAP (Long-range Energy Alternatives Planning) model. By adjusting three policy levers—vehicle technology substitution (ZEV: EV/FCEV), energy intensity improvement, and upstream electricity and hydrogen supply decarbonization—a “single-factor vs. multi-factor (policy synergy)” scenario matrix is designed for comparison. The results show that the emission reduction potential of a single measure is limited; upstream decarbonization yields the greatest independent emission reduction effect, while the emission reduction effect of deploying zero-emission vehicles and improving energy efficiency alone is small. In the most ambitious composite scenario, emissions will decrease by approximately 83% by 2050 compared to the baseline scenario, with cumulative emissions decreasing by over 35%. Emissions from rail and taxis will approach zero, while buses and freight will remain the primary residual sources. This indicates that achieving net zero emissions in the transportation sector requires not only accelerated ZEV penetration but also the simultaneous decarbonization of electricity and hydrogen, as well as policy timing design oriented towards fleet replacement cycles. The integrated modeling and scenario analysis presented in this paper provide quantifiable evidence for the formulation of a medium- to long-term emissions reduction roadmap and the optimization of policy mix in Tokyo’s transportation sector. Full article

The rapid integration of renewable energy sources and increasing complexity of modern power grids demand advanced forecasting tools capable of simultaneously predicting multiple interconnected variables. While time series foundation models (TSFMs) have demonstrated remarkable zero-shot forecasting capabilities across diverse domains, their application in power grid operations remains limited due to complex coupling relationships between load, price, emissions, and renewable generation. This paper proposes GridFM, a novel physics-informed foundation model specifically designed for multi-task energy forecasting in power systems. GridFM introduces four key innovations: (1) a FreqMixer adaptation layer that transforms pre-trained foundation model representations to power-grid-specific patterns through frequency domain mixing without modifying base weights; (2) a physics-informed constraint module embedding power balance equations and zonal grid topology using graph neural networks; (3) a multi-task learning framework enabling joint forecasting of load demand, locational-based marginal prices (LBMP), carbon emissions, and renewable generation with uncertainty-weighted loss functions; and (4) an explainability module utilizing SHAP values and attention visualization for interpretable predictions. We validate GridFM using over 10 years of real-time data from the New York Independent System Operator (NYISO) at 5 min resolution, comprising more than 10 million data points across 11 load zones. Comprehensive experiments demonstrate that GridFM achieves state-of-the-art performance with an 18.5% improvement in load forecasting MAPE (achieving 2.14%), a 23.2% improvement in price forecasting (achieving 7.8% MAPE), and a 21.7% improvement in emission prediction compared to existing TSFMs including Chronos, TimesFM, and Moirai-MoE. Ablation studies confirm the contribution of each proposed component. The physics-informed constraints reduce physically inconsistent predictions by 67%, while the multi-task framework improves individual task performance by exploiting inter-variable correlations. The proposed model provides interpretable predictions supporting the Climate Leadership and Community Protection Act (CLCPA) 2030/2040 compliance objectives, enabling grid operators to make informed decisions for sustainable energy transition and carbon reduction strategies. Full article

As China’s urbanization rate continues to rise, the scale of high-rise residences also grows, emerging as one of the main sources of building energy consumption and carbon emissions. It is therefore crucial to conduct energy-efficient design tailored to local climate and resource endowments during the schematic design phase. At the same time, consideration should also be given to its impact on economic efficiency and environmental comfort, so as to achieve synergistic optimization of energy, carbon emissions, and economic and environmental performance. This paper focuses on typical high-rise residences in three cities across China’s northwestern region, each with distinct climatic conditions and solar energy resources. The optimization objectives include building energy consumption intensity (BEI), useful daylight illuminance (UDI), life cycle carbon emissions (LCCO2), and life cycle cost (LCC). The optimization variables include 13 design parameters: building orientation, window–wall ratio, horizontal overhang sun visor length, bedroom width and depth, insulation layer thickness of the non-transparent building envelope, and window type. First, a parametric model of a high-rise residence was created on the Rhino–Grasshopper platform. Through LHS sample extraction, performance simulation, and calculation, a sample dataset was generated that included objective values and design parameter values. Secondly, an SVM prediction model was constructed based on the sample data, which was used as the fitness function of MOPSO to construct a multi-objective optimization model for high-rise residences in different cities. Through iterative operations, the Pareto optimal solution set was obtained, followed by an analysis of the optimization potential of objective performances and the sensitivity of design parameters across different cities. Furthermore, the TOPSIS multi-attribute decision-making method was adopted to screen optimal design patterns for high-rise residences that meet different requirements. After verifying the objective balance of the comprehensive optimal design patterns, the influence of climate differences on objective values and design parameter values was explored, and parametric models of the final design schemes were generated. The results indicate that differences in climatic conditions and solar energy resources can affect the optimal objective values and design variable settings for typical high-rise residences. This paper proposes a building optimization design framework that integrates parametric design, machine learning, and multi-objective optimization, and that explores the impact of climate differences on optimization results, providing a reference for determining design parameters for climate-adaptive high-rise residences. Full article

Coastal shipping plays a critical role in meeting maritime decarbonization targets under the International Maritime Organization’s (IMO) Carbon Intensity Indicator (CII) and the European Union Emissions Trading System (EU ETS); however, operators currently lack robust tools to forecast route-specific carbon intensity and evaluate the causal benefits of fuel switching. This study developed a distribution-free causal forecasting framework for voyage-level Carbon Dioxide (CO₂) intensity using an enriched panel of 1440 real-world voyages across four Nigerian coastal routes (2022–2024). We employed a physics-informed monotonic Light Gradient Boosting Machine (LightGBM) model trained under a strict leave-one-route-out (LORO) protocol, integrated with split-conformal prediction for uncertainty quantification and Causal Forests for estimating heterogeneous treatment effects. The model predicted emission intensity on completely unseen corridors with a Mean Absolute Error (MAE) of 40.7 kg CO₂/nm, while 90% conformal prediction intervals achieved 100% empirical coverage. While the global average effect of switching from heavy fuel oil to diesel was negligible (≈−0.07 kg CO₂/nm), Causal Forests revealed significant heterogeneity, with effects ranging from −74 g to +29 g CO₂/nm depending on route conditions. Economically, targeted diesel use becomes viable only when carbon prices exceed ~100 USD/tCO₂. These findings demonstrate that effective coastal decarbonization requires moving beyond static baselines to uncertainty-aware planning and targeted, route-specific fuel strategies rather than uniform fleet-wide policies. Full article

Carbon capture and storage (CCS) technologies are an important step to mitigate CO₂ emissions. This study focuses on CO₂ capture from biomass combustion in fluidized bed boilers using a vacuum pressure swing adsorption (VPSA) process. A pilot-scale VPSA unit was used to evaluate the dynamic adsorption behavior of zeolite 13X and clinoptilolite under realistic operating conditions. Moreover, a simplified one-dimensional isothermal mathematical model of a fixed-bed adsorption column was developed to simulate breakthrough curves to validate whether the model reproduces the observed experimental trends. Experimental results confirmed that fresh zeolite 13X exhibited the highest CO₂ adsorption capacity, while clinoptilolite showed moderate uptake. For both sorbents, a decrease in derived adsorption capacity was observed after prior use. The developed mathematical model successfully reproduced the experimental breakthrough curves, achieving coefficients of determination (R²) up to 0.99 and percentage fit (%Fit) values close to 94% for fresh sorbents, while lower correlations were observed for used sorbents. The model reliably captured the breakthrough curves, validating its applicability for process prediction. These results highlight the effectiveness of combining experimental measurements with modeling to assess sorbent performance and guide further optimization of VPSA processes under realistic flue gas conditions. Full article

Accurately predicting pollutant emission factors (EFs) from woody biomass fuels remains challenging because small-scale combustion tests are fuel-specific, time-consuming, and highly sensitive to operating conditions. This study combines controlled laboratory combustion experiments with a physics-informed artificial neural network (ANN–PINN) to estimate the emission factors of particulate matter (${\mathrm{EF}}_{PM}$), carbon monoxide (${\mathrm{EF}}_{CO}$), and nitrogen oxides (${\mathrm{EF}}_{NOx}$) using only laboratory-scale fuel characterization. Three pelletized woody biomass, Pinus radiata, Acacia dealbata, and Nothofagus obliqua, were analyzed through ultimate and proximate composition, lignin content, and TGA-derived parameters and tested in a residential pellet stove under identical control setpoints, resulting in a narrow and well-defined operating regime. A medium-depth ANN–PINN was constructed by integrating mechanistic constraints, monotonicity based on known emission trends and a weak carbon balance penalty, into a feed-forward neural network trained and evaluated using Leave-One-Out Cross-Validation. The model accurately reproduced the experimental behavior of ${\mathrm{EF}}_{CO}$ and captured structured variability in ${\mathrm{EF}}_{PM}$, while the limited nitrogen variability of the fuels restricted generalization for ${\mathrm{EF}}_{NOx}$. Sensitivities derived via automatic differentiation revealed physically coherent relationships, demonstrating that PM emissions depend jointly on fuel chemistry and aero-thermal conditions, CO emissions are dominated by mixing and temperature, and ${\mathrm{NO}}_x$ formation is primarily governed by fuel-bound nitrogen. When applied to external biomass fuels characterized independently in the literature, the ANN–PINN produced physically plausible predictions, highlighting its potential as a rapid, low-cost screening tool for assessing new biomass feedstocks and supporting cleaner residential heating technologies. The integrated experimental–PINN framework provides a physically consistent and data-efficient alternative to classical empirical correlations and purely data-driven ANN models. Full article

This study provides a multi-faceted and detailed perspective on the relationships between logistics performance, environmental degradation, and economic growth in 38 OECD countries, using each as an individual target variable. In the Analysis section, the relationship between logistics and environment is examined within a broader context, taking economic indicators into account. This examination utilizes the machine learning algorithms Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM). For each algorithm, the dataset is split into training and testing sets using three different ratios: 90:10, 80:20, and 70:30. A comprehensive performance evaluation is conducted on each of these splits by applying 5-fold and 10-fold cross-validation (CV). Considering economic indicators, the analysis section examines how the logistics-environment relationship is shaped in a broader context using the machine learning algorithms RF, XGBoost, and LightGBM. MSE, MAE, RMSE, MAPE, and R² metrics are utilized to evaluate model performance, while MDA and SHAP are employed to assess feature importance. Furthermore, a bee swarm plot is leveraged for visualizing the results. The XGBoost algorithm can successfully predict carbon dioxide (CO₂) emissions from transport and economic growth with high accuracy. However, the logistics performance model achieves high performance only with the LightGBM algorithm using a 90% train, 10% test split, and 5-fold CV setup. Based on the variable importance levels of the best-performing algorithm for each of the three target variables separately, the prediction of logistics performance is largely dependent on the economic growth predictor, and secondly, on the trade openness predictor. In predicting CO₂ emissions from transport, economic growth is identified as the most effective predictor, while logistics performance and trade openness contribute the least to the prediction. The findings also reveal that transport-related emissions and environmental indicators are prominent in the prediction of economic growth, whereas logistics performance and trade openness play a supportive, yet secondary role. Full article

Tropical buildings face the coupled effects of four-high environmental factors, which accelerate thermal–humidity degradation, increase operational energy demands, and diminish building health attributes. This paper systematically integrates global research advancements to establish a theoretical framework for Tropical Low-Carbon Healthy Building Enclosures (TLHBEs) by linking materials, structures, and buildings across scales. It identifies three key scientific questions: (1) Establishing a multi-scale parametric design model that couples materials, structures, and architecture. (2) Elucidating experimental and simulated multi-scale equivalent relationships under the coupled effects of temperature, humidity, radiation, and salinity. (3) Design multi-objective optimization strategies balancing energy efficiency, comfort, indoor air quality, and carbon emissions. Based on this, a technical implementation pathway is proposed, integrating multi-scale unified parametric design, multi-physics testing and simulation, machine learning, and intelligent optimization technologies. This aims to achieve multi-scale parametric design, data–model fusion, interpretable decision-making, and robust performance prediction under tropical climatic conditions, providing a systematic technical solution to address the key scientific questions. This framework not only provides scientific guidance and engineering references for designing, retrofitting, and evaluating low-carbon healthy buildings in tropical regions but also aligns with China’s dual carbon goals and healthy building development strategies. Full article

Nitrogen (N) deposition poses a multi-pronged threat to the carbon (C)-regulating services of moss understories. For forest C-cycle modeling under increasing N deposition, failure to mechanistically incorporate the moss-mediated processes risks severely overestimating the C sink potential of global forests. To explore whether and how N input affects the moss-mediated CH₄ and carbon dioxide (CO₂) fluxes, a five-year field measurement was performed in the N manipulation experimental plots treated with 22.5 and 45 kg N ha⁻¹ yr⁻¹ as ammonium chloride for nine years under a well-drained temperate forest in northeastern China. In the presence of mosses, the average annual CH₄ uptake and CO₂ emission in all N-treated plots ranged from 0.96 to 1.48 kg C-CH₄ ha⁻¹ yr⁻¹ and from 4.04 to 4.41 Mg C-CO₂ ha⁻¹ yr⁻¹, respectively, with a minimum in the high-N-treated plots, which were smaller than those in the control (1.29–1.83 kg C-CH₄ ha⁻¹ yr⁻¹ and 4.82–6.51 Mg C-CO₂ ha⁻¹ yr⁻¹). However, no significant differences in annual cumulative CO₂ and CH₄ fluxes across all treatments occurred without moss cover. Based on the differences in C fluxes with and without mosses, the average annual moss-mediated CH₄ uptake and CO₂ emission in the control were 0.77 kg C-CH₄ ha⁻¹ yr⁻¹ and 2.40 Mg C-CO₂ ha⁻¹ yr⁻¹, respectively, which were larger than those in the two N treatments. The N effects on annual moss-mediated C fluxes varied with annual meteorological conditions. Soil pH, available N and C contents, and microbial activity inferred from δ¹³C shifts in respired CO₂ were identified as the main driving factors controlling the moss-mediated CH₄ and CO₂ fluxes. The results highlighted that this inhibitory effect of increasing N deposition on moss-mediated C fluxes in the context of climate change should be reasonably taken into account in model studies to accurately predict C fluxes under well-drained forest ecosystems. Full article