Search Results (2,841)

The transportation sector is a major consumer of primary energy and is a significant contributor to greenhouse gas emissions. Sustainable transportation requires identifying and quantifying factors influencing transport-related CO₂ emissions. This research aims to establish an adaptable, precise, and transparent forecasting structure for transport CO₂ emissions of the United States. For this reason, we proposed a multi-stage method that incorporates explainable Machine Learning (ML) and Feature Selection (FS), guaranteeing interpretability in comparison to conventional black-box models. Due to high multicollinearity among 24 initial variables, hierarchical feature clustering and multi-step FS were applied, resulting in five key predictors: Total Primary Energy Imports (TPEI), Total Fossil Fuels Consumed (FFT), Annual Vehicle Miles Traveled (AVMT), Air Passengers-Domestic and International (APDI), and Unemployment Rate (UR). Four ML methods—Support Vector Regression, eXtreme Gradient Boosting, ElasticNet, and Multilayer Perceptron—were employed, with ElasticNet outperforming the others with RMSE = 45.53, MAE = 30.6, and MAPE = 0.016. SHAP analysis revealed AVMT, FFT, and APDI as the top contributors to CO₂ emissions. This framework aids policymakers in making informed decisions and setting precise investments. Full article

The Fit for 55 package introduced by the European Union aims to achieve a 55% reduction in greenhouse gas emissions by 2030. In parallel, increasingly stringent exhaust gas regulations have intensified research into alternative fuels. Ethanol presents a promising option due to its compatibility with gasoline, higher octane rating, and lower exhaust emissions compared to conventional gasoline. Additionally, ethanol can be derived from agricultural waste, further enhancing its sustainability. This study examines the impact of two ethanol–gasoline blends (E10, E20) on emissions and performance in a turbocharged gasoline direct injection (TGDI) spark-ignition (SI) engine. The investigation is conducted using three-dimensional computational fluid dynamics (3D CFD) simulations to minimize development time and costs. This paper details the model development process and presents the initial results. The boundary conditions for the simulations are derived from one-dimensional (1D) simulations, which have been validated against experimental data. Subsequently, the simulated performance and emissions results are compared with experimental measurements. The E10 simulations correlated well with experimental measurements, with the largest deviation in cylinder pressure being an RMSE of 1.42. In terms of emissions, HC was underpredicted, while CO was overpredicted compared to the experimental data. For E20, the IMEP was slightly higher at some operating points; however, the deviations were negligible. Regarding emissions, HC and CO emissions were higher with E20, whereas NOx and CO₂ emissions were lower. Full article

Background: Increasing regulatory pressure for maritime decarbonization (e.g., IMO CII, FuelEU) drives adoption of low-carbon fuels and prompts reassessment of regional ports’ competitiveness. This study aims to evaluate the economic and environmental viability of rerouting deep-sea container services to regional ports in a decarbonizing world. Methods: A scenario-based analysis is used to evaluate total costs and CO₂ emissions across the entire container shipping supply chain, incorporating deep-sea shipping, port operations, feeder services, and inland rail/road transport. The Port of Liverpool serves as the primary case study for rerouting Asia–Europe services from major ports. Results: Analysis indicates Liverpool’s competitiveness improves with shipping lines’ slow steaming, growth in hinterland shipment volume, reductions in the emission factors of alternative low-carbon fuels, and an increased modal shift to rail matching that of competitor ports (e.g., Southampton). A dual-port strategy, rerouting services to call at both Liverpool and Southampton, shows potential for both economic and environmental benefits. Conclusions: The study concludes that rerouting deep-sea services to regional ports can offer cost and emission advantages under specific operational and market conditions. Findings on factors and conditions influencing competitiveness and the dual-port strategy provide insights for shippers, ports, shipping lines, logistics agents, and policymakers navigating maritime decarbonization. Full article

The present research features an experimental comparative design and the objective of this work was to determine the susceptibility to microbiological contamination in fatty acid methyl esters (FAMEs) and the FAME–water interface of residual and refined lard, large volume simulating storage conditions as fuel supply chain, and to identify the microorganisms developed. The plates were seeded according to ASTM E-1259 and the instructions provided by the manufacturer of the Bushnell Haas agar. Microbiological growth was observed at the FAME–water interface of FAME obtained from residual lard. Using the MALDI-TOF mass spectrometry technique, Pseudomonas aeruginosa and Streptomyces violaceoruber bacteria were identified in the residual lard FAMEs, with the latter being previously reported in FAMEs. The implications of microorganism development on the physicochemical quality of FAMEs are significant, as it leads to an increase in the acid index, which may negatively impact metals by inducing corrosion. The refined lard FAMEs did not show any development of microorganisms. The present research concluded that residual lard tends to be more prone to microbiological attack if the conditions of water and temperature affect microbial growth. The findings will contribute to the knowledge base for a safer introduction of FAMEs into the biofuel matrix. Full article

Catalytic upgrading of pyrolysis oils is an important step for producing replacement hydrocarbon-rich liquid biofuels from biomass and can help to advance pyrolysis technology. Catalysts play a pivotal role in influencing the selectivity of chemical reactions leading to the formation of main compounds in the final upgraded liquid products. The present work involved a systematic study of solvent-free catalytic reactions of cyclohexanone in the presence of hydrogen gas at 160 °C for 3 h in a batch reactor. Cyclohexanone can be produced from biomass through the selective hydrogenation of lignin-derived phenolics. Three types of catalysts comprising undoped NbOPO₄, 10 wt% NiO/NbOPO₄, and 30 wt% NiO/NbOPO₄ were studied. Undoped NbOPO₄ promoted both aldol condensation and the dehydration of cyclohexanol, producing fused ring aromatic hydrocarbons and hard char. With 30 wt% NiO/NbOPO₄, extensive competitive hydrogenation of cyclohexanone to cyclohexanol was observed, along with the formation of C₆ cyclic hydrocarbons. When compared to NbOPO₄ and 30 wt% NiO/NbOPO₄, the use of 10 wt% NiO/NbOPO₄ produced superior selectivity towards bi-cycloalkanones (i.e., C₁₂) at cyclohexanone conversion of 66.8 ± 1.82%. Overall, the 10 wt% NiO/NbOPO₄ catalyst exhibited the best performance towards the production of precursor compounds that can be further hydrodeoxygenated into energy-dense aviation fuel hydrocarbons. Hence, the presence and loading of NiO was able to tune the activity and selectivity of NbOPO₄, thereby influencing the final products obtained from the same cyclohexanone feedstock. This study underscores the potential of lignin-derived pyrolysis oils as important renewable feedstocks for producing replacement hydrocarbon solvents or feedstocks and high-density sustainable liquid hydrocarbon fuels via sequential and selective catalytic upgrading. Full article

Carbon monoxide (CO) is a major toxic gas emitted from vehicle exhaust, industrial processes, and incomplete fuel combustion, posing serious environmental and health risks. Catalytic oxidation of CO into less harmful CO₂ is an effective strategy to reduce these emissions. In this study, we investigated the catalytic performance of platinum (Pt) single atoms doped on C₃N monolayers with various vacancy defects, including single carbon (C_V) and nitrogen (N_V) vacancies, using density functional theory (DFT) calculations. Our results demonstrate that Pt@N_V-C₃N exhibited the most favorable catalytic properties, with the highest O₂ adsorption energy (−3.07 eV). This performance significantly outperforms Pt atoms doped at other vacancies. It can be attributed to the strong binding between Pt and nitrogen vacancies, which contributes to its excellent resistance to Pt aggregation. CO oxidation on Pt@N_V-C₃N proceeds via the Eley–Rideal (ER2) mechanism with a low activation barrier of 0.41 eV for the rate-determining step, indicating high catalytic efficiency at low temperatures. These findings suggest that Pt@N_V-C₃N is a promising candidate for CO oxidation, contributing to developing cost-effective and environmentally sustainable catalysts. The strong binding of Pt atoms to the nitrogen vacancies prevents aggregation, ensuring the stability and durability of the catalyst. The kinetic modeling further revealed that the ER2 mechanism offers the highest reaction rate constants over a wide temperature range (273–700 K). The low activation energy barrier also facilitates CO oxidation at lower temperatures, addressing critical challenges in automotive and industrial pollution control. This study provides valuable theoretical insights for designing advanced single-atom catalysts for environmental remediation applications. Full article

This research develops a dual physics-based machine learning system to forecast fuel consumption and CO₂ emissions for a 100 m oil tanker across six operational scenarios: Original, Paint, Advanced Propeller, Fin, Bulbous Bow, and Combined. The combination of hydrodynamic calculations with Monte Carlo simulations provides a solid foundation for training machine learning models, particularly in cases where dataset restrictions are present. The XGBoost model demonstrated superior performance compared to Support Vector Regression, Gaussian Process Regression, Random Forest, and Shallow Neural Network models, achieving near-zero prediction errors that closely matched physics-based calculations. The physics-based analysis demonstrated that the Combined scenario, which combines hull coatings with bulbous bow modifications, produced the largest fuel consumption reduction (5.37% at 15 knots), followed by the Advanced Propeller scenario. The results demonstrate that user inputs (e.g., engine power: 870 kW, speed: 12.7 knots) match the Advanced Propeller scenario, followed by Paint, which indicates that advanced propellers or hull coatings would optimize efficiency. The obtained insights help ship operators modify their operational parameters and designers select essential modifications for sustainable operations. The model maintains its strength at low speeds, where fuel consumption is minimal, making it applicable to other oil tankers. The hybrid approach provides a new tool for maritime efficiency analysis, yielding interpretable results that support International Maritime Organization objectives, despite starting with a limited dataset. The model requires additional research to enhance its predictive accuracy using larger datasets and real-time data collection, which will aid in achieving global environmental stewardship. Full article

This study utilises the Open Source Energy Modelling System (OSeMOSYS) in conjunction with the Climate, Land, Energy, and Water systems (CLEWs) framework to investigate Thailand’s energy transition, which is designed to achieve carbon neutrality by 2050. Two scenarios have been devised to evaluate the long-term trade-offs among energy, water, and land systems. Data were sourced from esteemed international organisations (e.g., the IEA, FAO, and OECD) and national agencies and organised into a tailored OSeMOSYS Starter Data Kit for Thailand, comprising a baseline and a carbon neutral trajectory. The baseline scenario, primarily reliant on fossil fuels, is projected to generate annual CO₂ emissions exceeding 400 million tons and water consumption surpassing 85 billion cubic meters by 2025. By the mid-century, the carbon neutral scenario will have approximately 40% lower water use and a 90% reduction in power sector emissions. Under the carbon neutral path, renewable energy takes the front stage; the share of renewable electricity goes from under 20% in the baseline scenario to almost 80% by 2050. This transition and large reforestation initiatives call for consistent investment in solar energy (solar energy expenditures exceeding 20 billion USD annually by 2025). Still, it provides notable co-benefits, including greater resource sustainability and better alignment with international climate targets. The results provide strategic insights aligned with Thailand’s National Energy Plan (NEP) and offer modelling evidence toward achieving international climate goals under COP29. Full article

With the rapid growth of energy demand, the development of efficient energy-conversion technologies (e.g., water splitting, fuel cells, metal-air batteries, etc.) becomes an important way to circumvent the problems of fossil fuel depletion and environmental pollution, which motivates the pursuit of high-performance electrocatalysts with controllable compositions and morphologies. Among them, two-dimensional (2D) Pt-group metallic electrocatalysts show a series of distinctive architectural merits, including a high surface-to-volume ratio, numerous unsaturated metal atoms, an ameliorative electronic structure, and abundant electron/ion transfers channels, thus holding great potential in realizing good selectivity, rapid kinetics, and high efficiency for various energy-conversion devices. Considering that great progress on this topic has been made in recent years, here we present a panoramic review of recent advancements in 2D Pt-group metallic nanocrystals, which covers diverse synthetic methods, structural analysis, and their applications as electrode catalysts for various energy-conversion technologies. At the end, the paper also outlines the research challenges and future opportunities in this emerging area. Full article

Waste-to-energy (WtE) are clean technologies that support a circular economy by providing solutions to managing non-recyclable waste while generating alternative energy sources. Despite the promising benefits, technology adoption is challenged by financing constraints, technical maturity, environmental impacts, supporting policies, and public acceptance. A growing number of studies analyzed the acceptability of WtE and identified the factors affecting the adoption of WtE technologies. This study aims to analyze these research hotspots, technologies, and acceptability factors by combining bibliometric and systematic analyses. An initial search from the Web of Science and Scopus databases identified 817 unique documents, and the refinement resulted in 109 for data analysis. The results present a comprehensive overview of the state-of-the-art, providing researchers a basis for future research directions. Among the WtE technologies in the reviewed literature are incineration, anaerobic digestion, gasification, and pyrolysis, with limited studies about refuse-derived fuel and landfilling with gas recovery. The identified common factors include perceived risks, trust, attitudes, perceived benefits, “Not-In-My-BackYard” (NIMBY), awareness, and knowledge. Moreover, the findings present valuable insights for policymakers, practitioners, and WtE project planners to support WtE adoption while achieving sustainable, circular, and low-carbon economies. Full article

Gasification of municipal solid waste and other biogenic residues (e.g., biomass and biowaste) is increasingly recognized as a promising thermochemical pathway for converting non-recyclable fractions into valuable energy carriers, with applications in electricity generation, district heating, hydrogen production, and synthetic fuels. This paper provides a comprehensive analysis of major gasification technologies, including fixed bed, fluidized bed, entrained flow, plasma, supercritical water, microwave-assisted, high-temperature steam, and rotary kiln systems. Key aspects such as feedstock compatibility, operating parameters, technology readiness level, and integration within circular economy frameworks are critically evaluated. A comparative assessment of incineration and pyrolysis highlights the environmental and energetic advantages of gasification. The valorization pathways for main product (syngas) and by-products (syngas, ash, tar, and biochar) are also explored, emphasizing their reuse in environmental, agricultural, and industrial applications. Despite progress, large-scale adoption in Europe is constrained by economic, legislative, and technical barriers. Future research should prioritize scaling emerging systems, optimizing by-product recovery, and improving integration with carbon capture and circular energy infrastructures. Supported by recent European policy frameworks, gasification is positioned to play a key role in sustainable waste-to-energy strategies, biomass valorization, and the transition to a low-emission economy. Full article

Owing to the need for continuous improvement in building energy performance standards (BEPSs), facilities must adhere to benchmark performances in their quest to achieve net-zero performance. This research explores machine learning models that leverage historical energy data from a cluster of buildings, along with relevant ambient weather data and building characteristics, with the objective of predicting the buildings’ energy performance through the year 2040. Using the forecasted emission results, the portfolio of buildings is analyzed for the incurred carbon non-compliance fees based on their on-site fossil fuel CO_2e emissions to assess and pinpoint facilities with poor energy performance that need to be prioritized for decarbonization. The forecasts from the machine learning algorithms predicted that the portfolio of buildings would incur an annual average penalty of $31.7 million ($1.09/sq. ft.) and ~$348.7 million ($12.03/sq. ft.) over 11 years. To comply with these regulations, the building portfolio would need to reduce on-site fossil fuel CO_2e emissions by an average of 58,246 metric tons (22.10 kg/sq. ft.) annually, totaling 640,708 metric tons (22.10 kg/sq. ft.) over a period of 11 years. This study demonstrates the potential for robust machine learning models to generate accurate forecasts to evaluate carbon compliance and guide prompt action in decarbonizing the built environment. Full article

This study investigated the production of biodiesel from canola oil, the formulation of sustainable ternary fuel blends with diesel and alcohol (ethanol or propanol), and the experimental and machine learning-based modeling of their physical properties, including density and viscosity over a temperature range of 10 °C to 40 °C. Biodiesel was synthesized via alkali-catalyzed transesterification (6:1 methanol-to-oil molar ratio, 0.5 wt % NaOH of oil) and blended with diesel and alcohols (ethanol and propanol) in varying volume ratios. The experimental results revealed that blend density decreased from 0.8622 g/cm³ at 10 °C to 0.8522 g/cm³ at 40 °C for a blend containing ethanol. Similarly, the viscosity showed a significant reduction with temperature, e.g., the blend exhibited a viscosity decline from 8.5 mPa·s at 10 °C to 7.2 mPa·s at 40 °C. Increasing the alcohol or diesel content further reduced density and viscosity due to the lower intrinsic properties of these components. The machine learning models, Gaussian process regression (GPR), support vector regression (SVR), artificial neural networks (ANN), and decision tree regression (DTR), were applied to predict the properties of these blends. GPR demonstrated the best predictive performance for both density and viscosity. These findings confirm the strong potential of GPR for the accurate and reliable prediction of fuel blend properties, supporting the formulation of alternative fuels optimized for diesel engine performance. These aspects contribute new insights into modelling strategies for sustainable fuel formulations. Full article

The generation of fine coal particles during mining and processing presents significant environmental and logistical challenges, particularly in coal-dependent, developing countries like South Africa (SA). This review critically evaluates the technical viability of fine coal briquetting as a sustainable waste-to-energy solution within a SA context, while drawing from global best practices and comparative benchmarks. It examines abundant feedstocks that can be used for valorization strategies, including fine coal and agricultural biomass residues. Furthermore, binder types, manufacturing parameters, and quality optimization strategies that influence briquette performance are assessed. The co-densification of fine coal with biomass offers a means to enhance combustion efficiency, reduce dust emissions, and convert low-value waste into a high-calorific, manageable fuel. Attention is also given to briquette testing standards (i.e., South African Bureau of Standards, ASTM International, and International Organization of Standardization) and end-use applications across domestic, industrial, and off-grid settings. Moreover, the review explores socio-economic implications, including rural job creation, energy poverty alleviation, and the potential role of briquetting in SA’s ‘Just Energy Transition’ (JET). This paper uniquely integrates technical analysis with policy relevance, rural energy needs, and practical challenges specific to South Africa, while offering a structured framework for bio-coal briquetting adoption in developing countries. While technical and economic barriers remain, such as binder costs and feedstock variability, the integration of briquetting into circular economy frameworks represents a promising path toward cleaner, decentralized energy and coal waste valorization. Full article

Rare (only 10) observations, made in the southern topside ionosphere during 2015–2016, demonstrate the amplification of westward subauroral polarization streams (SAPS) up to 3000 m/s near the Harang region. The observed amplified SAPS flows were streaming antisunward after midnight and sunward at midnight, where the dusk convection cell intruded dawnward. One SAPS event illustrates the elevated electron temperature (Te; ~5500 K) and the stable auroral red arc developed over Rothera. Three inner-magnetosphere SAPS events depict the Harang region’s earthward edge within the plasmasheet’s earthward edge, where the outward SAPS electric (E) field (within the downward Region 2 currents) and inward convection E field (within the upward Region 2 currents) converged. Under isotropic or weak anisotropic conditions, the hot zone was fueled by the interaction of auroral kilometric radiation waves and electron diamagnetic currents. Generated for the conjugate topside ionosphere, the SAMI3 simulations reproduced the westward SAPS flow in the deep electron density trough, where Te became elevated, and the dawnward-intruding westward convection flows. We conclude that the near-midnight westward SAPS flow became amplified because of the favorable conditions created near the Harang region by the convection E field reaching subauroral latitudes and the positive feedback mechanisms in the SAPS channel. Full article