Search Results (3,935)

The increasing interest in carbon-free fuels has positioned ammonia as a promising energy carrier for Internal Combustion Engines (ICEs), particularly in hard-to-abate sectors such as Heavy-Duty (HD) transport and maritime applications. However, its low reactivity, narrow flammability limits, and intrinsic nitrogen content pose significant challenges for stable combustion and emissions control. This work presents a predictive Quasi-Dimensional (QD) combustion model applied to simulate ammonia-fueled engines operating under both Spark Ignition (SI) and Reactivity Controlled Compression Ignition (RCCI) modes. The proposed framework couples a turbulent premixed combustion sub-model with a diffusive combustion sub-model, including a dedicated fuel-NO_x mechanism to capture nitrogen oxide formation pathways associated with fuel-bound nitrogen. The model accounts for key physical and chemical processes governing combustion, such as ignition delay, mixture stratification, and heat release dynamics, while maintaining computational efficiency suitable for parametric studies. The model is validated against experimental data from a Single-Cylinder Engine (SCE) over a wide range of operating conditions, including variations in equivalence ratio, spark timing, Ammonia Energy Fraction (AEF), and injection strategy. Results demonstrate good agreement in terms of in-cylinder pressure evolution, Apparent Heat Release Rate (AHRR), and NO_x emissions, with peak-pressure errors below 4 bar and peak-pressure locations predicted within 2 crank angle degrees. Notably, the dedicated fuel-NO_x sub-model substantially improves emission predictions, revealing that fuel-bound nitrogen is the dominant source of NO_x in ammonia combustion. Overall, the proposed QD model represents a robust and efficient tool for the analysis and optimization of ammonia-fueled engines, supporting the development of low-carbon combustion strategies for future energy systems. Full article

The cement industry is one of the most challenging sectors to decarbonize due to the coexistence of high-temperature thermal demand and process-related emissions from limestone calcination. This study presents an energy and emissions assessment of cement manufacturing based on representative mass and energy balances derived from literature benchmarks and industrial operating data. Typical cement production requires 2.8–3.6 GJ of thermal energy and 80–120 kWh of electricity per tonne of final product, resulting in total emission in the range 500–850 kg CO₂/t cement, of which 55–65% originate from clinker calcination. Moving from this baseline, possible decarbonization pathways are evaluated, including energy efficiency improvements, clinker substitution through supplementary cementitious materials use of alternative fuels, electrification, hydrogen utilization and carbon capture technologies. The analysis shows that energy efficiency measures provide relatively limited reductions (10–30 kg CO₂/t cement), while alternative fuels and clinker substitution can achieve larger but still partial benefits. Hydrogen emerges as a promising option for decarbonizing the combustion-related share of emissions, with a potential reduction ranging from 50 to 200 kg CO₂/t cement, particularly when integrated with oxy-fuel combustion systems. Deep decarbonization ultimately requires carbon capture and storage (CCS), the only technology capable of addressing the substantial process emissions inherent to clinker production and use of hydrogen can be relevant too. Full article

To advance its green and low-carbon transition, China has implemented the Low-Carbon City Pilot Policy (LCCPP) as a strategic initiative. A critical question remains unanswered on whether this policy effectively improves urban carbon productivity and facilitates the decoupling of economic growth from carbon emissions. Using the phased expansion of the LCCPP across Chinese cities as a quasi-natural experiment, this study constructs a multi-period difference-in-differences (DID) model based on a panel dataset of 276 prefecture-level and above cities from 2007 to 2023. The empirical results show that the LCCPP significantly enhances urban carbon productivity in pilot cities relative to non-pilot counterparts. This finding remains robust across a series of sensitivity checks. Mechanism analysis provides suggestive evidence that the policy may operate through two primary channels: fostering green technological innovation and promoting economic agglomeration. Heterogeneity analysis indicates that the policy’s positive effects are more marked in developed cities, innovation-active cities, high-carbon-emission cities, and environmentally prioritized cities. Furthermore, moderation analysis demonstrates that stricter environmental regulations strengthen the policy’s effectiveness. These findings provide empirical evidence for optimizing the design of low-carbon policies and offer actionable insights for tailoring green urban transformation strategies to local conditions. Full article

This study provides the main performance estimates for new concepts, using flexible gasification operation modes, adaptable to prevailing market conditions, for the production of bio-synthetic natural gas (bio-SNG) and biochar from biogenic residues and waste, such as bark, straw, and Solid Recovered Fuel (SRF). Dedicated integrated process models were developed in Aspen Plus based on and validated against data from experimental campaigns in a gasification and gas cleaning pilot plant. Simulation runs show that the proposed concepts convert biomass to bio-SNG 10% more efficiently than the reference case, mainly due to the considerably reduced oxygen demand at the Autothermal Reformer (ATR) enabled by the improved catalyst. The co-production mode schemes showed promising results in terms of overall plant efficiency, at 76.5–78.2%, and total carbon utilisation, at 41–55.3%. The hybrid cases require an electrolyser with a power capacity almost 70% of the biomass thermal input to the gasifier, resulting in a total electricity consumption of up to 0.769 kWhe/kWh of biofuel. In return, they achieve over 50% utilisation of the carbon contained in the feedstock for biofuel production and a 70.1–76.5% total plant energy efficiency. Efficient biofuel and biochar production unlock negative emission potential, further strengthening the value of these flexible concepts. Full article

Combined cooling, heating, and power (CCHP) tri-generation systems can greatly improve comprehensive energy utilization efficiency thanks to their energy-cascade utilization concept. However, traditional fossil-fuel-based CCHP systems still suffer from intensive carbon emissions, hindering their further development in the current low-carbon scenario. To solve this issue, a new low-carbon CCHP system (LC-CCHP) integrating a calcium-looping, sorption-enhanced steam methane reforming (CL-SE-SMR) unit, a lithium bromide absorption chiller, and a hydrogen gas turbine is proposed in this work, and the corresponding system model is built to evaluate its performance. The proposed system features an innovative architecture that integrates carbon capture directly into the reforming process, which simultaneously enables a high hydrogen yield and low carbon-capture penalty. Moreover, instead of the widely used thermodynamic equilibrium assumption, a detailed kinetic model is employed for the CL-SE-SMR unit, which provides more realistic predictions and greater reference value for practical engineering applications. Then, multi-objective optimization is conducted using a particle swarm optimization algorithm to identify the optimal operating conditions. It is found that the proposed system performs best at a steam-to-carbon molar ratio of 4.37, a calcium-to-carbon mass ratio of 6.23, an air-equivalency molar ratio of 1.39 for a hydrogen gas turbine and a reaction temperature of 600 °C for SE-SMR. Under these operating conditions, the system can achieve a carbon-capture rate of 89.2%, an exergy efficiency of 45.7%, an energy efficiency of 95.4%, and a levelized cost of exergy of 0.109 $/kWh. Full article

This research concentrates on the electric vehicle (EV) industry in China and Japan. China is the largest and Japan the third-largest auto production country after the U.S. This study explores the current and future transition to battery electric vehicles and hybrid electric vehicles in Japan and China. Three Japanese auto companies, Toyota, Honda, and Nissan, and BYD from China are studied in this research. Toyota, Honda, and Nissan are actively pursuing the development of hybrid electric vehicles in Japan. Meanwhile, the research examines the Chinese EV company BYD, which is a major global competitor in the EV industry. This study compares the companies in terms of their strategies, strengths, weaknesses, and export destinations and delineates their competitive strategies and outlooks. In addition, the study examines the elements of the supply chain needed for building EVs, such as lithium, nickel, and cobalt. Furthermore, this research discusses some of the issues with EVs, such as the challenges related to the production and recycling of batteries and the implications as far as green and sustainable practices regarding EVs in the selected countries are concerned. The final part of this research explores how the production of EVs can affect the global reduction of carbon emissions. The findings of this study indicate that the transition to EVs depends on the structural position as far as the supply chain, the manufacturing of electric batteries, charging stations, and the size of the operations are concerned. The results indicate that BYD is in a stronger position in terms of the infrastructure necessary for the production of EVs. Meanwhile, Japanese auto companies are focused on hybrid EVs due to infrastructure related to EV batteries, supply sources, and charging stations. In addition, this research provides informative insights into the future of electric vehicles in the global market. The study offers recommendations for a comprehensive approach that integrates national policies, technological innovation, and the environmental impact of the transition to electric vehicles on a global scale. Full article

Last-mile delivery systems are under greater pressure to deliver cost-efficient, reliable, environmentally friendly, and resilient services amid operational challenges. Distance/cost is the usual optimization criterion for traditional vehicle routing methods, and factors related to disruptions, such as the delay frequency, delay severity, and delivery failure risk, are often treated separately or neglected. This study proposes a green-resilient last-mile delivery optimization framework that integrates operational costs, delivery delays, carbon emissions, and operational risk within a single multi-objective decision model. The proposed framework models the capacitated vehicle routing problem with time windows, accounting for vehicle capacity, service time commitments, fuel consumption, emission-level estimates, working hour limits, and lateness penalties and incorporating a disruption-based operational risk score. The risk score is based on the delay frequency, delay severity, and failure probability and can inform routing decisions based on efficiency and resilience. The framework is tested with a case study of urban last-mile delivery and compared with several benchmark scenarios: the current operational plan, a distance-based vehicle routing problem (VRP), a cost-based VRP, a green VRP, and a delay-aware vehicle routing problem with time windows (VRPTW). The results reveal balanced improvements in key performance indicators, in line with the proposed framework. It reduces the total distance by 35.11%, total operational cost by 34.01%, fuel consumption by 10.46%, CO₂ emissions by 9.34%, estimated late orders by 93.45%, and total delay minutes by 80.10%, and there are no working hour violations compared to the current case. Other sensitivity, weight, and ablation analyses illustrate the trade-offs among cost/service reliability/environmental goals and risk exposures. The results show that operational risk can be incorporated into the green last-mile routing problem to facilitate more comprehensive—and thus more robust and sustainable—delivery planning in the context of disruptions in urban environments. Full article

Improving the operational energy and carbon performance of hotel buildings is an important environmental dimension of sustainable building design. This study develops a reproducible EnergyPlus-based framework that combines parametric simulation, interpretable sensitivity analysis, machine learning surrogates, and carrier-resolved operational carbon accounting for Beijing hotel buildings. From 20,000 Latin hypercube candidates, input-side physical and functional screening retained 4640 successful EnergyPlus simulations. The simulated EUI mean was 140.6 kWh/(m²·a), 14.3% above the published Beijing hotel mean; surrogate performance is therefore interpreted as fidelity to the simulator rather than direct measured-building prediction. SRC with bootstrap uncertainty and a SHAP cross-check identified the main domestic-hot-water, building-form, and HVAC drivers. Of 17 models, Poly3-RidgeCV achieved the highest held-out fidelity (R² = 0.9976; RMSE = 1.72 kWh/(m²·a)). Baseline OCEI averaged 48.20 kgCO₂e/(m²·a); EUI and OCEI were strongly correlated (r = 0.954) but not interchangeable, with 71.8% overlap between the top-10% low-EUI and top-10% low-OCEI cases. Emission-factor scenarios showed robust but non-static energy-carbon coupling. The framework supports early-stage comparison of energy-efficient alternatives with comparatively lower operational carbon emissions within the stated accounting boundary, contributing to sustainable hotel design without constituting a whole-life or measured-building sustainability assessment. Full article

Transport decarbonization requires infrastructure that can use time-resolved carbon information without overstating the representativeness of short proof-of-method runs. This study introduces Adaptive Energy Stations (AESs), multi-fuel transport-energy nodes that integrate marginal grid-emission signals, fuel life-cycle carbon intensities, wholesale electricity prices, and vehicle operating constraints into a station-level dispatch optimization. The implemented case is a one-week winter proof-of-method for CAISO/CAISO_NORTH using 168 hourly service events over 1–8 January 2026 Pacific time, archived WattTime marginal operating emissions, CAISO locational marginal prices, eGRID CAMX annual-average factors, and declared vehicle and fuel-pathway parameters. In the audited CAISO scenario, the attached dispatch outputs report a reduction from 181.76 to 123.38 g CO₂e/km relative to the specified static baseline, corresponding to a 32.12% reduction for the one-week winter service-event stream. The populated dispatch trace shows that the carbon-priority AES plug-in hybrid electric vehicle (PHEV) run selected cellulosic E85 for all 168 events and selected no electric events; this result is interpreted as an operational scenario result for the archived week, not as an annual fleet-average, smart-charging benefit, or deployment forecast. The revised analysis explicitly separates implemented CAISO evidence from ERCOT, MISO-MROW, and ISO–NE extension sensitivities, which remain hypothetical until equivalent marginal-emissions, price, and service-event data are supplied. Battery-production amortization is treated as a separate sensitivity because it can change battery electric vehicle (BEV)–cellulosic E85 equivalence conclusions: at 50–100 kg CO₂e/kWh over 240,000 km, a 75 kWh BEV pack contributes 15.6–31.3 g CO₂e/km and a 14 kWh PHEV pack contributes 2.9–5.8 g CO₂e/km. Practical-equivalence claims are therefore conditional on the declared boundary, equivalence margin, and production-emissions treatment. Full deployment requires validated marginal-emission access, transparent dispatch-audit outputs, supply-chain verification, user-behavior characterization, cost sensitivity analysis, and cybersecurity safeguards. Full article

Smart and sustainable construction requires methods that jointly manage construction processes, carbon emissions, energy supply, and decision-making. This study develops a renewable energy (RE)-aware carbon digital twin using Siemens Tecnomatix Plant Simulation, version 2022 (Siemens Digital Industries Software, Plano, TX, USA), hereafter referred to as STPS, and artificial intelligence (AI)-based multi-objective optimization. The framework integrates activity records, equipment-demand profiles, renewable-generation data, battery-storage states, emission factors, cost parameters, schedule indicators, and reinforcement learning (RL) state–action–reward records within a discrete-event simulation environment. Construction activities are represented through event-driven source, queue, buffer, processor, resource-pool, transporter, event-controller, table-file, and sink objects, enabling predecessor validation, resource allocation, processing, completion tracking, and key performance indicator (KPI) updates. The energy layer coordinates equipment demand, solar photovoltaic (PV) generation, battery charging and discharging, grid electricity, diesel backup, RE share, battery state of charge (SOC), and emissions. The AI-control layer observes carbon, cost, delay, queue length, utilization, idle time, renewable share, and SOC; filters infeasible actions; evaluates corrective interventions; and ranks policies under carbon, delay, and SOC constraints. Three scenarios are evaluated: diesel–grid baseline operation, rule-based solar-battery operation, and AI-controlled renewable-aware operation. The AI-controlled scenario achieved 16,850 kg carbon dioxide equivalent (kg CO₂e), a cost of 2.28 million United States dollars (M USD), a delay of 4.2 h, an 81.6% RE share, 86.7% resource utilization, and 8.6% idle time. Relative to the diesel–grid baseline, it reduced emissions by 32.2%, cost by 18.0%, delay by 66.7%, and diesel-equivalent energy by 97.7%, while increasing RE share by 69.3 percentage points. Reward-weight sensitivity results show that the balanced policy preserves low-carbon performance across stakeholder priorities. The findings demonstrate that integrating digital twins, RE dispatch, and AI-based decision control can support data-driven, optimized low-carbon construction management. Full article

Urban instant-delivery platforms increasingly require efficient, punctual, and low-carbon delivery services. Unmanned aerial vehicles (UAVs) can reduce dependence on congested ground traffic in meal delivery and further improve overall delivery efficiency through coordinated operations with ground riders at rendezvous points. However, existing studies mainly focus on ground-based routing or simplify UAV-assisted delivery as a single-objective problem, limiting their ability to balance cost, completion time, carbon emissions, and service quality. To address this limitation, this paper investigates a collaborative UAV–rider meal delivery routing problem and formulates a multi-objective optimization model integrating restaurant pickup, UAV transfer, rider last-mile delivery, and soft customer time windows. An Enhanced NSGA-II algorithm is then developed, where hybrid initialization improves solution quality and diversity, adaptive operators balance exploration and exploitation, local search refines route structures, structural repair maintains feasibility, and diversity preservation supports a well-distributed Pareto front. Comparative experiments against NSGA-II, MOPSO, NSGA-III, MOEA/D, MODE, and IMODE, together with scalability, ablation, sensitivity, and case analyses, show that E-NSGA-II provides stronger Pareto-front approximation. The results support its use as a decision-support method for service-aware and low-carbon UAV–rider meal delivery, while also revealing additional computational cost. Full article

This paper presents a hybrid physics–AI framework for real-time emission monitoring in industrial boiler systems within an IIoT-enabled Industry 4.0 environment. The proposed framework integrates physics-based emission estimation with AI-based anomaly detection within a unified operational technology and information technology (OT–IT) architecture to support continuous environmental monitoring. Process data, including fuel oil consumption, oxygen concentration, temperature, and pressure, are acquired from an industrial boiler through a Siemens programmable logic controller (PLC) using an Open Platform Communications Unified Architecture (OPC UA) communication layer. The acquired measurements are processed at the edge analytics level to estimate the emission rates of carbon monoxide (CO), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), and particulate matter (PM) using stoichiometric combustion models based on fuel composition and flue gas characteristics. An autoencoder-based anomaly detection model is employed to identify abnormal operating conditions by monitoring the reconstruction error against a predefined threshold. The framework is validated using a PLC-based quasi-real-time prototype that replays one year of historical industrial boiler operating data. The emission estimation results show close agreement with reference engineering calculations, with relative errors below 0.1% across the evaluated operating conditions. The anomaly detection model achieved an F1-score of 96.14% and an AUC of 0.981. An edge monitoring dashboard provides real-time visualization of process variables, estimated emissions, and alarm status, while cloud connectivity supports remote monitoring and long-term data analytics. Overall, the proposed framework demonstrates how existing industrial process data can be utilized to transform conventional offline emission estimation into a continuous OT–IT monitoring service for legacy industrial environments. Full article

Against global dual-carbon targets, urban residential central heating dominates building energy use and carbon emissions. Conventional LSTM forecasting requires manual hyperparameter adjustment and easily falls into local optima; micro-community carbon prediction also lacks accurate energy models and policy-based multi-scenario analysis for targeted low-carbon renovation. This study adopts the 2018–2023 hourly heating data of a community in H Province. It builds a preprocessing workflow with boxplot-Isolation Forest anomaly detection and MissForest filling, then constructs an SSA-LSTM hybrid model optimized by Sparrow Search Algorithm to predict heat and power loads precisely. Combined with carbon accounting and three policy scenarios, it evaluates carbon peak timing and emission reduction potential of heating renovations. Results show that SSA-LSTM attains 2.48% MAPE for heat and 3.20% for power, surpassing LSTM and BP. Only moderate and ideal renovation scenarios realize carbon peaks in the 2023–2024 heating period, with cumulative cuts of 138.19 t and 254.2 t by 2031–2032; household heat meters deliver 28% of total reductions. The framework offers quantitative support for community heating operation, renovation evaluation and carbon quota management. Full article

The present work proposes a holistic techno-economic framework for the production of green hydrogen and synthetic methane from a dedicated 18.8 MW renewable park (13.8 MW wind and 5 MW photovoltaic) in southern Italy. A Mixed-Integer Linear Programme optimises the hourly dispatch of the electrolyser, the battery, and the bi-directional grid connection over a full-year horizon (8760 h) while meeting an annual target of 600,000 kg_H2. Two downstream pathways are compared: compressed-hydrogen transport by tube trailer at 200, 400, and 600 km, and on-site catalytic methanation via the Sabatier reaction. The assessment is deliberately restricted to the cost side, so that the two carriers are compared on a robust, price-agnostic levelised cost basis. Operated in an import-free mode to guarantee hydrogen with zero operational carbon emissions, the optimisation co-determines a cost-optimal battery capacity of 18 MWh. The levelised cost of hydrogen (LCOH) ranges between 9.55 and 10.27 €/kg_H2 at the gate, rising to 11.17–12.33 €/kg_H2 with transport, whereas the levelised cost of synthetic methane is 5.67 €/kg_CH4. On an energy basis, hydrogen is more competitive (0.335–0.370 €/kWh) compared to methane (0.409 €/kWh). A structured sensitivity analysis shows that a grid connection of 10 MW or more renders the battery unnecessary and lowers the gate LCOH to 8.26 €/kg, and ranks the renewable CAPEX and the discount rate as the dominant cost drivers. The produced hydrogen is essentially carbon-free at the gate, with the only residual emissions (0.014–0.041 kgCO₂/kWh) arising from its diesel trucking. Full article

Soil and groundwater contamination across multiple plots in industrial parks is an important environmental management challenge. Because plots often differ in contamination characteristics and remediation requirements, remediation decision-making needs to generate sustainability-oriented alternatives that account for explicit management preferences and trade-offs among carbon emissions, remediation duration, and cost. Although technology screening for individual contaminated plots has been widely investigated, how to use plot-level screening results to support the selection of multi-plot remediation alternatives under multiple objectives remains insufficiently addressed. This study developed a preference-guided multi-objective decision framework for selecting remediation alternatives in contaminated industrial parks. The framework first generates cross-plot remediation alternatives by assigning one feasible technology to each plot and removes alternatives that violate technological compatibility or project-level engineering constraints. It then identifies Pareto non-dominated alternatives using NSGA-II, retains preference-consistent alternatives through LO-based filtering, and selects the final recommendation using ideal-point distance comparison. The framework was applied to a five-plot contaminated industrial park in northern China. Compared with the LO and NSGA-II benchmarks, the hybrid NSGA-II–LO model selected less imbalanced alternatives that retained the preferred-objective advantage while improving the balance among the remaining objectives. The results show that management priorities can substantially affect remediation technology allocation across plots and that preference screening can support the adjustment of remediation alternatives when low-carbon targets, remediation schedules, or budget constraints change. This study provides an operational decision framework for translating plot-level technology screening into sustainability-oriented multi-plot remediation decision-making. Full article