Search Results (3,157)

Ammonia (NH₃) is a compelling carbon-free hydrogen carrier. Its catalytic decomposition to produce a hydrogen/nitrogen (H₂/N₂) gas stream is central to the “NH₃-H₂” clean energy cycle, provided that residual NH₃ is removed to fuel-cell-grade purity downstream. This review integrates advances from the past five years across four major catalytic NH₃ decomposition pathways, encompassing conventional thermocatalysis, plasma-catalytic, photo(thermal), and electrically driven catalysis, within a unified mechanistic and practical framework, distinguishing it from existing single-pathway reviews. Noble metal catalysts, particularly Ru-based systems, achieve superior low-temperature activity through support engineering, promoter effects, and active-site construction. However, our analysis reveals that non-noble metal (Fe, Co, Ni) catalysts and their alloys, nitrides, and carbides have made substantial progress, with certain Co-based and bimetallic systems approaching Ru-level performance via interfacial oxygen vacancy engineering and electronic structure modulation. Emerging non-thermal routes effectively overcome thermodynamic barriers, enabling operation at temperatures 200–300 °C below conventional thermal requirements, though each faces distinct challenges in energy efficiency, stability, and scalability. Key challenges remaining across all pathways to practical implementation, including residual NH₃ removal and H₂ purification, catalyst deactivation and stability, heat management and energy efficiency, start-up/shut-down dynamics, as well as system integration and economics, are critically assessed. This review provides theoretical guidance and practical recommendations for developing scalable, low-temperature NH₃ decomposition technologies. Full article

The transition from centralized fossil fuel-based power systems toward decentralized smart grids with a high penetration of renewable energy sources (RES) introduces substantial challenges in monitoring, control, coordination, and management. These challenges are particularly evident at the active power grid periphery, defined in this work as the decentralized edge layer of modern power systems comprising low-voltage distribution networks, distributed energy resources (DERs), prosumers, energy storage systems, electric vehicles (EVs), and localized intelligent control entities operating near the consumer side of the grid. This review systematically examines the role of multi-agent systems (MASs) in addressing these emerging challenges. A total of 160 articles, drawn predominantly from top-tier Q1 journals and published up to March 2026, were systematically analyzed to evaluate recent methodological advances, identify persistent research gaps, and compare existing problem formulations and mathematical techniques. The review covers MAS-based applications including distributed energy management, voltage and frequency regulation, demand-side management, microgrid coordination, EV charging coordination, resilience enhancement, and cyber-physical supervisory control. The findings indicate that although MASs offer enhanced scalability, flexibility, resilience, and decentralized decision-making capabilities, existing approaches continue to face significant limitations associated with communication latency, cybersecurity vulnerabilities, interoperability constraints, heterogeneous agent dynamics, and limited real-time experimental validation. Furthermore, this review proposes six emerging research hypotheses targeting underexplored domains, presents a methodological decision flowchart for MAS implementation and selection, and discusses future research directions involving the integration of digital twins, blockchain technologies, edge intelligence, and advanced communication architectures with MAS frameworks. Full article

Wildfires in California increasingly threaten communities and ecosystems. However, comprehensive estimation of fire dynamics and fuel structure remains limited. Recent advances in Uncrewed Aerial Vehicle (UAV) technology and high-spatial-resolution mapping have provided increasingly important tools for estimating wildfire fuel-height loss across fuel types. This study used a one-year Uncrewed Aerial Vehicle (UAV) time series to quantify fuel-height loss and vegetation regrowth associated with a prescribed upslope canyon fire near Salinas, California, USA. Multispectral, infrared, and visible UAV imagery collected before, during, and after burning was used to generate orthomosaic, digital surface models (DSMs), fuel-type classifications, and surface-volume estimates. To enable reliable pre- and post-fire comparison, ground control points and tie points were used to train linear regression calibrations that corrected angular discrepancies and elevation offsets among time-series DSMs. Calibrated DSMs were then integrated with ecological field measurements to map fuel-height consumption and post-fire recovery at the individual-plant scale. UAV-derived fuel-height change was associated with in situ twig-diameter measurements, which provide field-based indicators of fire effects in chaparral vegetation, while the maximum recorded temperature explained only a small proportion of variation in fuel-height loss. This workflow can support integrated fire ecology and remote-sensing studies by providing repeatable measurements of post-fire changes in vegetation structure. Full article

Oil and gas industry operations are critical and laborious. Recent advances in artificial intelligence and machine learning (ML) have opened up a variety of Industry 4.0 applications in the oil and gas sector. This paper introduces the architecture of an automation system for identifying the fuel types in the downstream sectors. In this research, a real-time image dataset of fuel samples is collected and annotated with the corresponding class labels, i.e., petrol and diesel. The three core modules of the architecture are pre-processing, feature extraction and classification. In pre-processing, the input images are rescaled for spatial normalization. Then, discrete wavelet transform (DWT) is applied to extract approximate, vertical, horizontal and diagonal subimages. In the feature extraction module, the features from the DWT subimages are extracted to exploit the textural properties of the input images. This work is an ablation study of various individual image features and their combinations. During classification, the extracted features are modeled using six different ML models to provide a detailed study of the image features for identifying fuel types. To fine tune the ML models, the hyperparameters are adjusted using grid search and randomized search approaches. The results show that the extreme gradient boosting (XGB) model fine tuned with randomized search is the most effective classification model with an accuracy of 97.6% on the fuel classification task. Full article

To achieve dual-carbon goals and advance the sustainable development of the automotive industry, China’s Dual-Credit Policy serves as the core long-term mechanism for the low-carbon transition of the automotive industry. Given the coexistence of fuel vehicles (FVs) and new energy vehicles (NEVs) in China, existing research often overemphasizes production output while neglecting energy consumption control, and focuses predominantly on NEVs at the expense of FV optimization. To address these gaps, this paper treats FV fuel consumption and NEV energy efficiency as core endogenous decision variables. We construct profit-maximizing optimal production decision models for both types of manufacturers under the Dual-Credit Policy. Through mathematical derivation, numerical simulations, and empirical tests using actual industrial parameters, this study verifies the existence and uniqueness of optimal solutions. It clarifies the influence mechanisms of policy and market factors on corporate energy decisions and identifies the rules of strategy dominance. The findings reveal that the optimal fuel consumption decisions of FV manufacturers exhibit distinct piecewise patterns and critical threshold effects. Specifically, credit prices, NEV quotas, and fuel consumption standards determine the dominance of compliant (low-consumption) versus non-compliant (high-consumption) strategies. Furthermore, the policy exerts a significant market-oriented positive incentive on the energy efficiency upgrading of NEV manufacturers, with credit prices, market demand, and R&D costs acting as core constraints. Notably, the transition-guiding effect of the policy has clear effective boundaries, and its efficacy highly depends on the alignment between parameter design and market conditions. This research provides theoretical support for manufacturers to formulate energy-optimized production decisions and offers actionable references for the continuous optimization of the Dual-Credit Policy system and the sustainable low-carbon transformation of China’s automotive sector. Full article

Surface congestion and taxiing delays at high-density airports increasingly constrain aviation sustainability, as ground-phase fuel consumption and emissions constitute a significant share of total airport emissions. Existing studies typically decouple air traffic flow management from ground resource scheduling, hindering coordinated optimization of punctuality, environmental benefits, and resource utilization. This paper proposes a multi-objective optimization method for runway-taxiway systems oriented toward air–ground collaborative decision-making, integrating Calculated Take-Off Time (CTOT) compliance constraints. A tri-objective mixed-integer programming model is formulated to minimize CTOT deviation, total taxiing time, and runway workload imbalance. A hybrid intelligent algorithm, SSA-SCA-NSGA-II, is designed with a bidirectional elite feedback mechanism to address this NP-hard problem. Validation uses real operational data of 58 departure flights during a peak period at Beijing Daxing International Airport. The results demonstrate that the proposed method achieves effective trade-offs on the Pareto front: CTOT compliance rate increased from 77.6% to 89.7–96.6%; total taxiing time decreased from 692 min to 551–635 min; and dual-runway utilization imbalance declined from 5.2% to 1.7–3.8%. These improvements translate into quantifiable sustainability gains: fuel consumption is reduced by 1425–3525 kg and CO₂ emissions by 4503–11,139 kg per peak hour, alongside a 19-percentage point improvement in punctuality that lowers passenger delay costs and reduces controller coordination workload. By simultaneously advancing environmental sustainability (carbon footprint reduction), economic sustainability (fuel and operational cost savings), and social sustainability (service punctuality and labor efficiency), the framework provides a measurable, monitorable, and policy-relevant decision-support tool for green airport surface operations aligned with sustainable development goals (SDGs). Full article

Advanced biofuels represent a key option for transport decarbonization, particularly in sectors where electrification is constrained by technical and economic barriers. Their compatibility with existing vehicle fleets and fuel distribution infrastructure enables rapid deployment without the need for major capital investments. In local public transport, biodiesel (FAME), hydrotreated vegetable oil (HVO), and biomethane are mature solutions capable of delivering greenhouse gas emission reductions of 60–90% compared with fossil fuels. Among these, HVO is particularly promising, as an extensive body of literature has consistently shown its potential to significantly reduce engine-out emissions, especially particulate matter (PM) and nitrogen oxides (NO_x). This study reports the results of an experimental campaign carried out on a diesel-powered local public transport bus equipped with a Euro III engine and lacking particulate matter and NO_x after-treatment systems. Emissions were measured using a portable emissions measurement system (PEMS) under real driving conditions, operating the vehicle with neat diesel, a 15% HVO blend, and a 70% HVO blend. Tests were conducted over urban and extra-urban routes. The results show that NO_x emissions decrease proportionally with increasing HVO content, with high-blend ratios (HVO70) yielding estimated reductions of approximately 13–18%, and up to 23% under carefully controlled and comparable urban driving conditions. Based on these findings and the existing literature, HVO proves to be a useful instrument to meet 2025–2030 climate and air quality targets (particularly NO_x and PM emission reductions), alongside electrification and modal shift measures, if used in public transport fleets. Full article

Under the background of the green transformation and energy conservation in the aviation field, hydrogen-fueled aero-engines are the primary direction for achieving sustainable aviation power development. However, the unique thermophysical properties of hydrogen fuel induce extreme thermal load challenges to engine thermal management. Based on the requirements of energy conservation and digital-intelligent technologies, this paper reviewed the recent research progress, important challenges, and future development directions in the thermal management field for hydrogen-fueled aero-engines, and filled the gaps in existing related reviews. (1) As for the liquid hydrogen thermal properties and thermal management requirements, the unique thermal physical properties of liquid hydrogen can easily cause fluctuations in heat load, large temperature differences, and material compatibility issues such as hydrogen embrittlement during storage, transportation, and combustion. The application of thermal barrier coatings, the design of targeted cooling structures, and the regulation of heat loss in the pipeline of the hydrogen supply system require particular attention. (2) As for the technical architecture and optimization of thermal management, the optimization of the high-pressure side manifolds in the cooled cooling air heat exchanger increases the flow uniformity by 18.8% and reduces the weight by 22.5%. The intercooled recuperated engine with the optimum area ratio reduces specific fuel consumption by 5.3% compared to the baseline engine in cruise. However, the system-level optimization research of the above widely recognized solutions is relatively limited in terms of coordinating the energy flow of engines. The baseline engine employed the method of system integration optimization to achieve a 2.99% increase in thrust and a 6.78% reduction in fuel consumption. (3) As for the thermal management modeling and simulation, the intelligent optimization method based on computational fluid dynamics reduces the pressure loss coefficient of the vane-integrated heat exchanger by 36%. Nevertheless, the multiphysics coupling model confronts a contradiction between computational cost and accuracy. (4) As for the comprehensive evaluation method, the advanced configuration of the hydrogen-fueled aero-engine can approximately reduce specific fuel consumption by 68.5% and NOx emission by 12.7% under the same maximum thrust condition. The hydrogen consumption of the proton exchange membrane fuel cells system model compared with the baseline system, optimized by the multi-objective optimization algorithm, has decreased by 15%, while the thermal uniformity has improved by 20–30%. However, the current evaluation system mostly focuses on a single dimension, lacking the analysis of nonlinear coupling among multiple factors and a closed-loop mechanism for evaluation, optimization, and verification. Future research should focus on the matching model of liquid hydrogen’s thermophysical properties and full flight conditions, global multi-energy flows optimization methods, multidimensional collaborative numerical simulation, multiphysics coupling models, and multidimensional comprehensive evaluation systems, to provide closed-loop theoretical support for the efficient, intelligent, and reliable thermal management system for hydrogen-fueled aero-engines. Full article

The Lantana genus, especially L. camara, has emerged as a potential yet underutilized lignocellulosic feedstock for various catalytic thermochemical conversion products and advanced carbon materials. This study reviews recent developments in the valorization of Lantana biomass to generate biofuels, bio-oil, syngas, and engineered biochar materials through pyrolysis, gasification, hydrothermal processing, and integrated biorefinery processes, in a critical manner. Particular focus will be on nanocomposite-modified, metal-doped biochar with catalytic elements such as ZSM-5, Fe₃O₄, TiO₂, and Ni-, Co-, and Zn-based oxides to enhance deoxygenation, catalytic cracking, tar reforming, pollutant remediation, and energy storage. Recent developments in catalyst synthesis techniques, such as impregnation, hydrothermal deposition, and in situ functionalization, are reviewed, along with characterization methods including BET, XRD, SEM/TEM, Raman spectroscopy, and XPS. The review further examines the impact of pore structure, surface chemistry, the presence of redox-active centers, and catalyst stability on product selectivity, syngas quality, and upgrading bio-oil performance. The effects of biochar on microbial immobilization, anaerobic digestion, and integrated biochemical conversion are discussed in detail, excluding thermochemical effects. The challenges of catalyst deactivation, biomass heterogeneities, scalability, techno-economic viability, and decentralized biomass logistics are also discussed. In summary, the development and implementation of catalytic reaction engineering, the design of nanocomposite biochar, and circular bioeconomy strategies have great potential to facilitate the conversion of invasive Lantana biomass into renewable fuels, multifunctional carbon materials, and environmentally friendly bioeconomy products. Full article

Climate action is an alarming issue, and the world is concerned about sustainable solutions. Governance quality, financial technology and advanced-resource endowments have a pivotal role in climate action, yet the literature lacks evidence on their linkages with carbon emissions. The present study covers this gap in the literature for the Gulf Cooperation Council countries, as they offer an ideal study setting given their extreme vulnerability to climate change, their reliance on fossil fuels, the strong efforts to address climate change issues, and their prioritization of transforming the financial sector through financial technology as a means of resolving climate issues. Results show that governance quality and financial technology curb carbon emissions. Specifically, financial technology reduces CO₂ emissions by approximately 36.4% in the FMOLS estimation, while governance quality contributes negatively and significantly to CO₂ emissions across most specifications. Further, the interaction term of financial technology and advanced-resource endowments is statistically significant with a negative coefficient, hereby providing a supportive role in reducing CO₂ emissions. Hence, advanced-resource endowments play a moderating role, transforming the effectiveness of financial technology in reducing carbon emissions. The findings are robust for quantile regressions and alternative measures of environmental degradation and have strong policy implications for the Gulf Cooperation Council countries. Full article

The National Electric Vehicle Infrastructure (NEVI) Program is a major federal initiative to expand electric vehicle (EV) charging infrastructure and support transportation electrification in the United States. This study examines the evolution of NEVI implementation across New York, New Jersey, and Pennsylvania between 2022 and 2026. A qualitative comparative longitudinal approach was used to analyze 23 official documents, including NEVI deployment plans, annual implementation updates, Federal Highway Administration guidance, and Joint Office of Energy and Transportation resources. The findings show that implementation evolved beyond compliance with the Alternative Fuel Corridor toward broader transportation electrification, characterized by adaptive governance, infrastructure scalability, and operational resilience. New York demonstrated the most advanced implementation through extensive interagency coordination, infrastructure integration, and long-term planning. New Jersey emphasized metropolitan charging accessibility, adaptive planning, and alignment with statewide zero-emission vehicle objectives. Pennsylvania followed a more gradual implementation trajectory shaped by phased deployment, regional accessibility priorities, and procurement-related challenges. The study demonstrates that implementation trajectories differed despite a common federal framework and contributes to the literature by providing a comparative longitudinal perspective on how governance and institutional adaptation influence large-scale EV infrastructure deployment. Full article

In the last 10 years, the global green bond market has reached an estimated value of USD 6.8 trillion. However, credibility concerns persist due to greenwashing risks and issues regarding the reporting system. The current measurement, reporting, and verification systems (MRV) have high uncertainty levels of 10–30%, and so they contribute to information asymmetries and fuel investor skepticism when allocating capital to green bond instruments. The scope of this study is to develop an integrated management approach that links air quality and greenhouse gas monitoring with financial incentives throughout the lifecycle of green bonds. The central contribution is a four-phase lifecycle model covering issuance, allocation, monitoring, and impact reporting, which systematically identifies where greenwashing risks and verification gaps arise across the investment cycle. Methodologically, the study combines qualitative content analysis, a novel Disclosure Quality Score (DQS) instrument, based on the Regulation (EU) 2023/2631, four documentary case studies, and an advanced verification framework. The content analysis shows that regulatory and market-performance studies dominate the literature, while integrated lifecycle verification frameworks remain less explored. The DQS uses eight indicators, applied to a matched sample of green bonds, in accordance with the European Green Bond Standard (EuGB) and the ICMA Green Bond Principles (GBP). The results demonstrate that bonds issued under the EuGB present higher disclosure quality (mean DQS = 15.4/16) compared to GBP-aligned bonds (mean DQS = 11.4/16). Case studies show strong issuance-stage disclosure, but weak post-issuance verification. The framework enables lifecycle-wide accountability by reducing information asymmetry. The proposed lifecycle framework and DQS instrument offer a replicable model for improving disclosure quality and ESG performance standards, with direct implications for sustainable investment screening and ESG fund selection. Overall, the findings show that improving green bond credibility requires moving beyond issuance-focused disclosure toward lifecycle-wide verification. Full article

The development of a marine ammonia–natural gas co-combustion engine aims to achieve high power with low carbon and low nitrogen oxide (NO_x) emissions. Using AVL Fire software, a numerical model of a marine dual-fuel engine with a large cylinder diameter and diesel ignition for ammonia and natural gas co-combustion was constructed. The effects of ammonia injection timing (AI) and natural gas injection timing (NGI) on the combustion process, as well as on NO_x and carbon dioxide (CO₂) emissions, were investigated. The results indicate that, under full-load, low-speed engine conditions, when the energy fractions of three fuels are constant, the heat release rate and pressure are more sensitive to AI. Advancing the injection timing of NH₃ and CH₄ can achieve higher indicated mean effective pressure and indicated thermal efficiency. Under various ammonia and natural gas injection strategies, the marine engine meets the NO_x emission requirements of International Maritime Organization’s Tier III. Furthermore, advancing AI or NGI reduces greenhouse gas emissions. Specifically, when ammonia is injected at a high pressure before the top dead center (TDC), advancing injection by every 2 degrees of the crank angle (°CA) reduces equivalent CO₂ emissions by 1.3%. Similarly, when natural gas is injected at a high pressure before the TDC, advancing injection by every 2 °CA reduces equivalent CO₂ emissions by 1.7%. Full article

Taxing multinational corporations raises significant government revenue to support progress towards the Sustainable Development Goals (SDGs).This study examines Philips, a multinational corporation that publicly reports country-by-country data on revenue, profits, taxes, employees and tangible assets, using the Government Revenue and Development Estimations (GRADE) econometric model to estimate the development impact of its corporate income tax contributions. Tax payments do not always reflect actual economic activity in host countries due to profit shifting. This study also assesses the degree to which reported profits align with economic activity. Results indicate Philips’ tax payments make a meaningful positive contribution to sustainable development: government revenue equivalent to these payments enables over 1100 additional children to attend school daily and advances SDG progress on basic water (8100 people), sanitation (13,400 people), clean fuels (28,000 people) and electricity (1700 people). However, analysis reveals some misalignment between reported profits and economic activity across countries, suggesting unrealised potential in Philips’ development contribution. Modelling a reallocation of taxing rights to host countries where economic activity occurs using unitary tax with formulary apportionment indicates that annual tax payments may average $78 million higher in constant 2015 USD, potentially enabling 900 more children to attend school and expanding access to basic water (6500 people), sanitation (9600 people), clean fuels (22,600 people) and electricity (7400 people). These findings highlight the value of transparent country-by-country reporting as a foundation for evidence-based tax policy reform and the significant development gains from Philips’ tax payments and greater gains if profits were better aligned with economic activity. Full article

Electrochemical CO₂ reduction to ethanol is a promising route for circular carbon fuel and chemical production, but practical implementation remains limited by coupled membrane, catalyst, transport, and system integration constraints. This Communication reassesses anion-exchange membranes (AEMs) and bipolar membranes (BPMs) for CO₂-to-ethanol electroreduction by integrating recent 2024–2026 advances with foundational membrane and CO₂RR literature. The central argument is that membrane selection is not a passive separation choice; instead, it actively controls local pH, charge carriers, CO₂ availability, carbonate formation, water activity, proton/cation delivery, product crossover, and downstream techno-economic assessment (TEA) and life-cycle assessment (LCA) burdens. AEM operation can create alkaline cathodic microenvironments that favor C–C coupling, but bicarbonate/carbonate formation imposes carbon-loss, salt-management, and CO₂-recovery penalties. BPM operation can improve pH separation and carbon management through water dissociation and bicarbonate acidification, but its viability depends on water-dissociation efficiency, co-ion exclusion, junction stability, hydration management, and voltage control. Recent ethanol-selective catalyst studies further show that copper oxidation state, grain boundaries, subsurface dopants, ionomers, interfacial wettability, and dynamic operation interact strongly with membrane-imposed microenvironments. This Communication proposes a membrane-centered decision framework linking AEM/BPM selection with ethanol selectivity, single-pass carbon utilization, energy efficiency, durability, TEA/LCA boundaries, and future reactor design. Full article