Search Results (1,732)

Green hydrogen is a potential enabler of deep decarbonisation for industrial process heat. We assess its role in Aotearoa New Zealand using a bottom-up, least-cost energy-system model based on the integrated MARKAL-EFOM system (TIMES), which includes hydrogen production electrolysis, storage, and delivery of end-use technologies for process heat, as well as alternative low-carbon options. Drawing on detailed data on industrial energy use by sector and temperature band, we simulate pathways to 2050 under varying assumptions for electrolyser and fuel prices, technology efficiencies, electricity decarbonisation and carbon prices. In most scenarios, the least-cost pathway involves widespread electrification of low- and medium-temperature heat, with green hydrogen playing a targeted role where high-temperature requirements and process constraints limit direct electrification. Sensitivity analysis reveals that hydrogen uptake increases under higher carbon prices, lower electrolyser capital expenditure, and when grid connection or peak capacity constraints are binding. These results suggest that policy should prioritise rapid industrial electrification while focusing hydrogen support on hard-to-electrify, high-temperature processes, such as primary metals and mineral products, alongside enabling infrastructure and standards for hydrogen production, transport, and storage. Full article

China’s rural areas possess abundant renewable energy resources, but lack sufficient energy storage facilities. Hydrogen energy storage has been considered a potential green solution. This study, for the first time, constructed a planning model for a rural electric–hydrogen microgrid incorporating hydrogen and electricity storage, and conducted comprehensive technical and economic analysis under different time periods and combinations of technological elements. The levelized cost of electricity (LCOE) was employed as a key indicator, K-means clustering was employed to obtain typical source–load curves, and the curtailment/self-balancing rate was combined for evaluation. Off-grid energy storage schemes, grid-connected/off-grid modes, and hydrogen production methods were compared to determine the optimal solution. The simulation results show the following: in 2025, off-grid mode with alkaline water electrolyzer (AWE) hydrogen production, hydrogen–battery hybrid storage was the most cost effective (LCOE 0.2824 ¥/kWh) due to hydrogen sales profits and battery flexibility, while fuel cells were unfeasible. Grid-connected mode reduced LCOE by 0.008 ¥/kWh vs. off-grid. Currently, AWE’s LCOE is 0.0172 ¥/kWh lower than proton exchange membrane (PEM), but PEM may have a 0.0004 ¥/kWh lower LCOE by 2030, becoming preferred. The results are potential for cost effectiveness, aiding rural energy transition. Full article

The multi-energy flow coordination optimization of the self-sufficient hydrogen energy park is becoming a research focus. However, without explicit consideration of tank car, the optimization remains incomplete, thereby undermining practical applicability. In this paper, a Dynamic Adaptive Grey Wolf Optimization (DA-GWO) algorithm is proposed for self-consistent multi-energy flow coordination optimization, considering hydrogen energy-based tank cars in hydrogen railway energy parks. First, a foundational model of the hydrogen-based railway energy system was constructed that integrates green non-dispatchable units such as wind power and photovoltaics, as well as dispatchable units such as fuel cells, gas boilers, and cogeneration units. Given the diversity and complexity of in-service hydrogen railway tank cars, a probabilistic model of daily charging behaviour was constructed using a Monte Carlo method to simulate real-world operating conditions of tank cars, thereby enhancing the reliability of the hydrogen-powered railway model. Considering the diverse and complex units in the self-consistent hydrogen energy park for hydrogen-powered railways, a DA-GWO algorithm was constructed for the multi-energy flow optimization. Through a self-adaptive parameter adjustment, the algorithm’s global optimization performance is improved. Finally, the model parameters were further adjusted with data from a coastal Chinese city, and the optimization experimental tests were conducted to validate the proposed method. From the results, the proposed method can save at least 6.7% cost compared with the grey wolf optimization method and the PSO (Particle Swarm Optimization) optimization method. Full article

With increasingly stringent requirements for fuel sulfur content in ship emission control areas, traditional manual onboard inspection methods struggle to meet the demands for real-time supervision. This study proposes a hybrid method for monitoring and matching excessive exhaust emissions from ships underway using Automatic Identification System (AIS) data. A ship emission calculation model is applied to obtain the real-time SO₂ emission source strength for each vessel. Then, an improved Gaussian puff model, considering the moving characteristics of ships, is established to calculate time-series SO₂ diffusion concentrations at monitoring points for each ship within the study area. Finally, a matching algorithm for identifying ships with excessive emissions, based on grey relational analysis, is designed. This algorithm matches the computed time-series diffusion concentration of each ship with the monitored concentration, enabling precise traceability of ships using fuel with excessive sulfur content under multi-ship conditions. This study uses the Nanjing Dashengguan Yangtze River Bridge area as the experimental region and employs measured SO₂ data from monitoring points to verify the method’s feasibility and effectiveness. The results demonstrate that this method can effectively identify ships with excessive emissions, providing crucial technical support for the green development of shipping and for the prevention and control of air pollution. Full article

The transition to renewable energy is crucial in order to attain sustainable development, lower greenhouse gas emissions, and secure long-term energy security. This study examines spatial–temporal trends in electricity generation (both renewable and non-renewable) across EU-28 countries using monthly Eurostat data (2008–2025) at the NUTS0 level. Two harmonized Space–Time Cubes (STCs) were constructed for renewable and non-renewable electricity covering the fully comparable 2017–2024 interval, while 2008–2016 data were used for descriptive validation, and 2025 data were used for one-step-ahead forecasting. In this paper, the authors present a novel multi-method approach to energy transition dynamics in Europe, integrating forecasting (ESF), hot-spot detection (EHSA), and clustering (TSC) with the help of a new spatial–temporal modeling framework. The methodology is a step forward in the development of methodological literature, since it regards predictive and exploratory GIS analytics as comparative energy transition evaluation. The paper uses Exponential Smoothing Forecast (ESF) and Emerging Hot Spot Analysis (EHSA) in a GIS-based analysis to uncover the dynamics in the region and the possible production pattern. The ESF also reported strong predictive performance in the form of the mean Root Mean Square Errors (RMSE) of renewable and non-renewable electricity generation of 422.5 GWh and 438.8 GWh, respectively. Of the EU-28 countries, seasonality was statistically significant in 78.6 per cent of locations that relied on hydropower, and 35.7 per cent of locations exhibited structural outliers associated with energy-transition asymmetries. EHSA identified short-lived localized spikes in renewable electricity production in a few Western and Northern European countries: Portugal, Spain, France, Denmark, and Sweden, termed as sporadic renewable hot spots. There were no cases of persistent or increase-based hot spots in any country; therefore, renewable growth is temporally and spatially inhomogeneous in the EU-28. In the case of non-renewable sources, a hot spot was evident in France, with an intermittent hot spot in Spain and sporadic increases over time, but otherwise, there was no statistically significant activity of hot or cold spots in the rest of Europe, indicating structural stagnation in the generation of fossil-based electricity. Time Series Clustering (TSC) determined 10 temporal clusters in the generation of renewable and non-renewable electricity. All renewable clusters were statistically significantly increasing (p < 0.001), with the most substantial increase in Cluster 4 (statistic = 9.95), observed in Poland, Finland, Portugal, and the Netherlands, indicating a transregional phase acceleration of renewable electricity production in northern, western, and eastern Europe. Conversely, all non-renewable clusters showed declining trends (p < 0.001), with Cluster 5 (statistic = −8.58) showing a concerted reduction in the use of fossil-based electricity, in line with EU decarbonization policies. The results contribute to an improved understanding of the spatial dynamics of the European energy transition and its potential to support energy security, reduce fossil fuel dependency, and foster balanced regional development. These insights are crucial to harmonize policy measures with the objectives of the European Green Deal and the United Nations Sustainable Development Goals (especially Goals 7, 11, and 13). Full article

In 2024, synthetic fuels regained attention as potential low-emission alternatives for internal combustion engines (ICEs), particularly in sectors where electrification remains challenging. This paper compares the estimated CO₂ emission factors of fossil-based fuels and synthetic fuels blended with 20% bioethanol under standardized usage conditions. A key finding is that the emission factor of synthetic fuels is highly dependent on the carbon intensity of the electricity used to produce green hydrogen via electrolysis. Using the projected EU electricity mix for 2030, synthetic fuels show no clear advantage over fossil fuels. However, with a cleaner electricity mix expected by 2050, their emission factor becomes significantly lower. From an economic standpoint, the viability of synthetic fuel production largely depends on reducing green hydrogen costs of €1.50–2.00 per kg through technological advancements and large-scale deployment. This analysis offers a realistic perspective on when and how synthetic fuels could contribute to climate objectives and outlines the technical and economic conditions necessary for their environmental and market viability. Full article

Hydrogen is emerging as a key energy carrier in the transition to a low-carbon economy. This study reviews blue and green hydrogen, analysing their production technologies, environmental impacts, economic viability and global deployment trends. Blue hydrogen, derived from natural gas, coal or biomass with carbon capture, utilisation and storage, offers a transitional pathway by reducing emissions relative to unabated fossil routes, but its benefits depend on high CO₂ capture efficiencies and strict methane leakage control. Green hydrogen, produced via renewable-powered electrolysis and advanced thermochemical, photochemical and photoelectrochemical methods, represents the most sustainable long-term solution, though it is currently limited by cost and scale. This comparative assessment shows that green hydrogen’s production emissions, in the range of 0.67 kgCO-eq/kgH to 1.74 kgCO₂-eq/kgH₂, are substantially lower than those of blue hydrogen, in the range of 1.21 kgCO₂-eq/kgH₂ to 4.56 kgCO₂-eq/kgH₂, reinforcing its alignment with climate neutrality goals. Global production remains below 1% from low-emission sources, yet momentum is growing, with renewable-rich regions investing in large-scale electrolysers. A long short-term memory forecast suggests that blue hydrogen will dominate in the short term, but green hydrogen will surpass it around 2042. Together, both pathways are essential, blue hydrogen as a bridging option and green hydrogen as the foundation of a sustainable hydrogen economy. Full article

The Green Internet of Things (GIoTs) has emerged as a transformative paradigm for environmental conservation, enabling autonomous, self-sustaining sensor networks that operate without batteries and with minimal ecological footprint. This approach is especially critical for long-term mangrove monitoring in smart coastal cities, where conventional battery-powered systems are impractical due to frequent, costly, and environmentally disruptive replacements that hinder continuous data collection. This paper presents a self-sustaining GIoT sensing system for mangrove monitoring powered by sedimentary microbial fuel cells (SMFCs), enabling perpetual, battery-less, and zero-emission operation. A spatial dynamic energy management (DPM) strategy is implemented for the efficient integration of a microcontroller unit with a LoRa wireless communication transceiver and the SMFC harvested energy, ensuring a balanced self-sustained approach into a GIoT sensing network. Experimental results demonstrate an average power consumption of 190.45 μW per 14-byte data packet transmission, with each packet containing pH, electrical conductivity and ambient temperature measurements from the mangrove environment. Under the spatial DPM strategy, the network of four sensing nodes exhibited an energy consumption of 1.14 mWh. Given a harvested power density of 15.1 mW/m² from the SMFC, and utilizing a 0.1 F supercapacitor as an energy buffer, the system can support at least six consecutive data transmissions. These findings validate the feasibility of sustainable, low-power GIoT architectures for ecological monitoring. Full article

Accelerating the diffusion of renewable energy requires clear evidence on which determinants enable or hinder deployment across contexts. This study aims to identify the most frequently discussed contemporary determinants of renewable energy deployment. To this end, we conduct a PRISMA-guided systematic review within the SALSA framework, complemented by VOSviewer bibliometric mapping, synthesizing 110 peer-reviewed studies published between 2013 and 2025. We group the most frequently examined determinants into eight domains (economic, environmental, energy, political, regulatory, regional, technological, and social) and summarize the prevalent direction of effect reported in the literature. Economic conditions (e.g., economic growth, financial development, green finance, and trade) and policy/regulation (e.g., institutional quality, instrument stringency, and feed-in and net-billing schemes) emerge as pivotal. Environmental co-benefits (emissions reduction and air quality improvements) and energy system factors (security and energy poverty) are influential, with context-dependent roles for fossil fuel prices and consumption. Regional context (e.g., geopolitical risk) and technological progress (eco-innovation, storage, and grid integration) shape outcomes, while public acceptance, awareness, perceived benefits/costs, and demographics condition uptake. We also document contradictory findings (e.g., foreign direct investment and oil price effects) and gaps (especially social/demographic determinants and causal evaluation of specific policies). Overall, the review offers a coherent synthesis of evidence and an actionable framework of determinants to inform policy design and investment targeting for large-scale diffusion of renewable energy technologies. Full article

Sugarcane bagasse (SCB), a major byproduct of the sugar industry produced in millions of tons annually, is traditionally burned for energy but holds untapped potential for sustainable valorization amid global shifts toward renewable resources and reduced fossil fuel reliance. This review synthesizes recent advancements in SCB applications beyond energy, emphasizing bioenergy, bioplastics, construction materials, and agriculture to advance circular economy principles—addressing a gap in the existing literature by providing a holistic, comparative analysis of processing technologies, including their efficiency, costs, and scalability, which prior reviews have overlooked. Drawing from scientific literature, industry reports, case studies, and datasets, we evaluate SCB’s composition (40–50% cellulose, 25–30% hemicellulose, 20–25% lignin) and processing methods (e.g., pretreatment, hydrolysis, gasification, pyrolysis). Key findings highlight versatile applications: bioethanol production yielding 40–70% GHG reductions per life cycle assessments; pulp/paper substitution reducing water and chemical use; nanocellulose composites for automotive and medical sectors; particleboard and ash-cement in construction cutting deforestation and carbon footprints by ~20%; and biochar/processed feed enhancing crop yields by 25% while amending soil. Unlike previous reviews focused on isolated applications, this work integrates environmental, economic, and regulatory insights, identifying challenges like standardization gaps and proposing pathways for commercialization to drive scalable, green industry transitions. Continued research and policy support are essential for realizing SCB’s role in sustainable development. Full article

Nitrogen (N₂), constituting the majority of Earth’s atmosphere, remains indispensable for biological systems and underpins modern agriculture and industry. Traditionally, the Haber–Bosch process has been essential for synthesizing ammonia (NH₃) from N₂ under high temperature and pressure, but it contributes significantly to global CO₂ emissions. Recently, carbon-free electrocatalytic nitrogen reduction (e-NRR) has emerged as a promising, eco-friendly, and cost-effective approach for green NH₃ production under mild conditions using renewable energy, offering a sustainable alternative to the fossil fuel dependent Haber–Bosch process. This work explores NRR by contrasting the limitations of Haber–Bosch with the advantages of electrocatalysis. Despite progress, electrochemical N₂ reduction to NH₃ production remains challenging due to low activity, poor selectivity, stability, efficiency, and detection issues. Developing efficient e-NRR electrocatalysts is crucial to enhance activity, suppress hydrogen evolution reaction (HER), boost NH₃ yield, and improve Faradaic efficiency. This review highlights the role of layered double hydroxide (LDH) catalysts in e-NRR, summarizing the fundamental process, reaction pathways, and synthesis strategies. Ammonia detection methods, key metrics, and potential contamination issues are compared to inform standard NRR measurement protocols. Lastly, we summarize key findings to synthesize and improve LDH electrocatalysts for NH₃ production and a sustainable, carbon-free N₂ economy. Full article

The emission of greenhouse gases from fossil fuels creates devastating effects on Earth’s atmosphere. Therefore, a clean energy source is required to fulfill the energy demand. Hydrogen is considered an energy vector, and the production of green hydrogen is a promising approach. Photoelectrochemical (PEC) water splitting is the best approach to produced green hydrogen, but the efficiency is low. To produce hydrogen by PEC splitting water, semiconductor photocatalysts have received an enormous amount of academic research in recent years. A new class of co-catalysts based on transition metals has emerged as a powerful tool for reducing charge transfer barriers and enhancing photoelectrochemical (PEC) efficiency. In this study, copper oxide (CuO) and tin oxide ($Sn{O}_2$) multilayer thin films were prepared by thermal evaporation to create an energy gradient between $Sn{O}_2$ and CuO semiconductors for better charge transfer. To improve the crystallinity and reduce the defects, the prepared films were annealed in a tube furnace at 400 °C, 500 °C, and 600 °C in an argon inert gas environment. XRD results showed that $Sn{O}_2$/CuO-600 °C exhibited strong peaks, indicating the transformation from amorphous to polycrystalline. SEM images showed the transformation of smooth dense film to a granular structure by annealing, which is better for charge transfer from electrode to electrolyte. Optical properties showed that the bandgap was decreased by annealing, which might be diffusion of Cu and Sn atoms at the interface. PEC results showed that the $Sn{O}_2$/CuO-600 °C heterostructure exhibits the solar light-to-hydrogen (STH%) conversion efficiency of 0.25%. Full article

The use of hydrogen in the diesel engine automotive can be a viable and sustainable solution through which to reduce carbon-based emissions for the same operating regimens, without major constructive modifications of the engine. The use of hydrogen in the automotive diesel engine constitutes an efficient solution for controlling its operation with low carbon-based emissions and with reduced energetic specific consumption. This way, the requirements of the EC’s Green Deal policy can be met without major costs. Substitution of fossils fuels with hydrogen ensures the reduction of the carbon content of the air–fuel mixture in the engine cylinder, with favourable influences on the processes of the air–fuel mixture formation and combustion, making it possible to reduce carbon-based emissions. The improvement of the combustion process due to hydrogen use leads to the reduction in carbon-based emissions. For the experimental investigations carried on an automotive diesel engine with 1.5 L by displacement, the authors highlighted the following results: a reduction in smoke emissions by over 22%, a reduction in unburned hydrocarbon emissions by over 25%, and a reduction in carbon dioxide by about 20%, even from the use of relatively low cyclic doses of hydrogen at a usual engine load compared with a standard engine. The authors also obtained an 18.5% increase in the level of nitrogen oxides in the partial load engine, even with the use of small amounts of hydrogen added to the intake air; this is a disadvantage, but by applying specific measures, this emission can be reduced. The increase in the homogeneous air–fuel mixture with the use of hydrogen and the combustion processes’ duration time were reduced due to the high combustion flame speed of hydrogen comparative to diesel fuel; this ensures a reduction in energetic specific consumption and an increase in thermal efficiency. Full article

As a pivotal element in the maritime sector’s green transition, fuel-cell-powered ships have attracted increasing attention due to the energy efficiency and stability of their onboard powertrains. Yet, the dynamic coordination and capacity optimization of fuel cells and supercapacitors remain among the most formidable technological challenges. In this study, a hybrid marine power system pairing fuel cells with supercapacitors is devised by integrating robust control with a particle swarm optimization (PSO) algorithm. The results reveal that, under complex operating conditions, robust control effectively mitigates system uncertainties and secures reliable operation of the ship’s energy system. Optimally allocating component capacities via PSO markedly enhances the synergy between the fuel cell and the supercapacitor. Compared with conventional schemes, optimized architecture boosts energy efficiency by 12.5%, shortens response time by 8.4%, and demonstrates clear superiority in robustness and stability. This robust-control-based hybrid configuration therefore delivers outstanding performance and offers compelling guidance for the refined design of marine propulsion systems. Full article

The maritime sector, responsible for approximately 3% of global greenhouse gas (GHG) emissions, is under growing pressure to transition toward climate-neutral operations. Significant progress has been made in developing sustainable fuels and propulsion systems to meet these demands. Although electric propulsion and fuel cells are highlighted as key technologies for achieving net-zero carbon targets, they remain an immature solution for large-scale maritime use, particularly in long-distance shipping. Therefore, modifying internal combustion engines and employing alternative fuels emerge as more feasible transition strategies, especially in short-sea shipping and port applications such as tugboat operations. Among alternative fuels, hydrogen (H₂) and ammonia (NH₃) have emerged as the most prominent fuels in recent years due to their carbon-free nature and compatibility with existing marine compression ignition (CI) engines with only minor modifications. This study explores the viability of hydrogen and ammonia as alternative fuels for CI engines in terms of technological, economic, and environmental aspects. Also, using a life cycle assessment (LCA) framework, this study examines the environmental impacts and feasibility of gray, blue, and green hydrogen and ammonia production pathways. The analysis is conducted from both well-to-tank (WtT) and tank-to-wake (TtW) perspectives. The results demonstrate that green fuel production pathways significantly reduce emissions but lead to higher economic costs, while intermediate blends offer a balanced trade-off between environmental and financial performance. Moreover, the combustion stage analysis indicates that H₂ and NH₃ provide substantial environmental benefits by significantly reducing harmful emissions. Consequently, a Multi-Criteria Decision Making (MCDM) approach is employed to determine the optimal blending strategy, revealing that a 24% hydrogen and 76% marine diesel oil (MDO) energy share yields the most favorable outcome among the evaluated alternatives. Full article