Search Results (1,255)

Electric vehicles (EVs) are central to low-carbon urban mobility, but range anxiety persists. In real fleet operations, vehicles are rarely discharged to low State-of-Charge (SOC), so the remaining driving range (RDR) labels are incomplete, hindering accurate RDR prediction and analysis of operating conditions. This paper proposes a label completion framework that reconstructs low SOC mileage and a hybrid mileage-factor-oriented residual regressor (MF-CMR) to learn mileage factors under SOC imbalance. Applied to one year of data from eight EVs in Guangzhou, China, the method achieves a mean absolute error of 0.88 and a coefficient of determination of 0.64, yielding completed trip-level RDR labels whose distribution centers around 241.73 km. Using the completed labels, a two-way analysis of variance (ANOVA) with ambient temperature and driving style as factors shows that temperature is the dominant determinant of RDR, while driving style exerts a secondary but substantial effect, with a significant interaction. Together, the label completion framework and the quantified impacts of temperature and driving style enable more reliable RDR estimation from fleet logs, offering a quantitative basis for dispatching policies, charging margins, and eco-driving guidance in EV fleet services involving long distance trips or low SOC deep discharge scenarios. Full article

This study introduces an integrated Life Cycle Assessment–Multi-Criteria Decision Analysis–Nash Equilibrium (LCA–MCDA–NE) framework to assess the feasibility of hydrogen energy storage (HES) in Philippine island grids. It starts with a cradle-to-gate LCA of hydrogen production across various electricity mix scenarios, from diesel-dominated Small Power Utilities Group (SPUG) systems to high-renewable configurations, quantifying greenhouse gas emissions. These impacts are normalized and integrated into an MCDA framework that considers four stakeholder perspectives: Regulatory (PRF), Developer (DF), Scientific (SF), and Local Social (LSF). Attribute utilities for Maintainability, Energy Efficiency, Geographic–Climatic Suitability, and Regulatory Compliance inform a 2 × 2 strategic game where net utility gain (Δ) and switching costs (C₁, C₂) influence adoption behavior. The findings indicate that the baseline Nash Equilibrium favors non-adoption due to limited utility gains and high switching barriers. However, enhancements in Maintainability and reduced costs can shift this equilibrium toward adoption. The LCA results show that meaningful decarbonization occurs only when low-carbon generation exceeds 60% of the electricity mix. This integrated framework highlights that successful HES deployment in remote grids relies on stakeholder coordination, reduced risks, and access to low-carbon electricity, offering a replicable model for emerging economies. Full article

This study is an economic evaluation of a composting facility in southeastern Spain (applying Life Cycle Costing), a key region in European horticulture with a significant availability of agricultural biomass. Composting helps reduce dependence on inorganic fertilizers, aligning with European policies that promote the transition toward organic fertilization practices. In addition, compost enhances soil health, increases soil organic carbon, and supports climate change mitigation. Despite its agronomic and environmental benefits, and the large availability of biomass in this region, there is a notable lack of literature addressing the economic costs of composting, which is the first step in assessing the sustainability of a production process. The proposed facility (production: 9000 tonnes of compost per year) utilizes pruning residues and manure to produce high-quality organic amendments. The analysis includes infrastructure, equipment, and every operational input. Likewise, the analysis also provides socio-economic indicators such as employment generation and contribution to the regional economy. Three scenarios were evaluated based on the pruning–shredding location: at the plant, at the farm with mobile equipment, and at the farm with conventional machinery. The most cost-effective option was shredding at the farm using mobile equipment, reducing the unit cost to EUR 65.19 per tonne due to the transport of a smaller volume of prunings and, therefore, lower fuel consumption. The plant also demonstrates high productivity per square metre and generates stable employment in rural areas. Overall, the findings highlight composting as a viable and competitive strategy within circular and low-carbon agricultural systems. Full article

Achieving sustainable growth in the global economy and promoting low-carbon development can be achieved by concluding trade agreements that advance trade liberalisation progressively. The study looks at how far environmental rulers go in trade deals between different countries, by examining what these agreements actually say. Combining this analysis with trade-embedded carbon data from 35 sub-sectors across 60 countries from 2009 to 2023, the effect of the depth of environmental rulers in trade deals on trade-embedded carbon is the focus of this empirical study and its underlying mechanisms. Research findings indicate that strengthening environmental clauses significantly reduces carbon emissions embedded in trade. This result remained consistent after undergoing a series of robustness tests and employing instrumental variable methods to address endogeneity issues. Mechanism tests reveal that the carbon reduction effect of environmental clauses can be achieved through two channels: green technology cooperation between countries and increased carbon productivity. Heterogeneity tests indicate that provisions in trade agreements that are more environmentally focused can have a greater effect on reducing embedded carbon in non-technology-intensive areas and pollution-intensive sectors, particularly for developing countries. Provisions relating to the environment in bilateral trade agreements demonstrate greater effectiveness in curbing trade-embedded carbon. This paper concludes that a more in-depth knowledge of the way environmental provisions are created in trade agreements, an accurate assessment of the impact, effectiveness and applicable scenarios of these provisions, and the promotion of targeted policy measures for future provisions relating to the environment and trade agreements and the global transition to green, low-carbon trade, will provide policy references and development guidance. Full article

Although greenhouse gas emissions have significantly reduced, the European Union still faces a major challenge in meeting its 2050 net-zero goal set under the European Green Deal. Focusing on the impacts of population, economic output, and carbon intensity of economy, this study employs Index Decomposition Analysis to estimate the reductions in carbon intensity needed to reach this target. The findings show that the extent of the technical effort required for decarbonization is much influenced by economic expansion. Under a 3% annual Gross Domestic Product growth scenario, the EU’s carbon intensity of economy must decline by 11.8% per year, which is a particularly demanding rate given the already low baseline. The decomposition also quantifies the technological challenge: under high growth, up to 5867 MtCO₂ in reductions would be needed by 2050 (compared with 1990), with Carbon Capture and Storage (CCS) contributing only 10–15%. In contrast, in zero- or negative-growth scenarios, required reductions fall to 4923–4594 MtCO₂, with CCS accounting for up to 50–90%. These results show that decarbonization in EU industrial sectors requires systemic transformations and strategic CCS deployment. A balanced approach, limiting economic growth and increasing innovation, appears essential to achieve the climate neutrality target. Full article

This study evaluates the environmental outcomes of integrating anaerobic digestion (AD) with pyrolysis (Py) and hydrothermal carbonization (HTC) to treat cattle slurry and grass silage in an Irish agricultural context. A consequential life cycle assessment (CLCA) was carried out for six scenarios based on 1 t of feedstock (0.4:0.6 cattle slurry/grass silage on a VS basis): two standalone AD systems (producing bioelectricity and biomethane) and four integrated AD–Py/HTC systems with different product utilisation pathways. Across all impact categories, the integrated systems performed better than standalone AD. This improvement is mainly due to the surplus bioenergy (electricity, biomethane, hydrocarbon fuel, hydrochar) that replaces marginal fossil energy (hard coal, natural gas and heavy fuel oil), together with the displacement of mineral NPK fertilisers by digestate-derived biochar and HTC process water. Among the configurations, the AD–HTC bioelectricity scenario (S4) achieved the best overall performance, driven by higher hydrochar yields, a favourable heating value, and a lower pretreatment energy demand compared with Py-based options. Across the integrated scenarios, climate change, freshwater eutrophication, and fossil depletion impacts were reduced by up to 84%, 86%, and 99%, respectively, relative to the fossil-based reference system, while avoiding digestate and fertiliser application reduced terrestrial acidification by up to 74%. Overall, the results show that the cascading utilisation of digestate via AD–Py/HTC can simultaneously enhance bioenergy production and nutrient recycling, providing a robust pathway for low-emission management of agricultural residues. These findings are directly relevant to Ireland’s renewable energy and circular economy targets and are transferable to other livestock-intensive regions seeking to valorise slurry and grass-based residues as low-carbon energy and biofertiliser resources. Full article

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

In today’s increasingly serious environmental problems, a growing number of enterprises are upgrading remanufacturing as an important corporate strategy. This paper compares two third-party remanufacturing models: the entrusting and Authorizing Models, and introduces two different levels of consumer low-carbon preferences: medium and high. By establishing game equations, we find the equilibrium solution of each model. The results reveal that in the basic model, OEM tends to choose the Authorizing Model when consumers have a pronounced quality bias against remanufactured products. Contrary to intuition, TRM always prefers the Entrusting Model. In scenarios where consumers possess medium low-carbon preferences, OEM tends to choose the Authorizing Model when consumers have a high bias against the quality of the remanufactured products or a low bias against the carbon emissions of the new products. Conversely, OEM tends to choose the entrusting remanufacturing model under the opposite conditions. In scenarios where consumers express high low-carbon preferences, the situation becomes the complete opposite. When consumers exhibit a low bias against remanufactured products’ quality or a high bias against carbon emissions from new products, OEM tends to choose the Authorizing Model. Conversely, OEM prefers the Entrusting Model when consumers’ biases differ. In addition, the consumer surplus and social welfare of the Entrusting Model are higher than those of the Authorizing Model, regardless of the research scenario. Full article

A Life cycle assessment (LCA) is widely used to evaluate the carbon reduction potential of polycrystalline silicon photovoltaic systems. However, in existing LCA methods, most studies use static attenuation models and fixed lifecycle boundary frameworks. Therefore, this study proposes a dynamic LCA framework that considers the attenuation rate changes in photovoltaic systems and the energy gain during the recovery phase. The innovation of this method lies in its ability to more accurately reflect the carbon emissions and energy recovery period (EPBT) of photovoltaic systems under different operating and attenuation scenarios. In addition, this article expands the application scope of the LCA by introducing new boundary conditions, providing a new perspective for the lifecycle assessment of photovoltaic systems. A practical carbon emission calculation model was established using the full lifecycle data within this boundary, and the quantitative relationship between the EPBT and power generation was derived. A three-dimensional dynamic coupling model was developed to integrate these three key parameters and continuously characterize the dynamic behavior of the system throughout its entire lifecycle. This model explicitly addresses the attenuation of photovoltaic modules in three scenarios: low (1%), baseline (3%), and high (5%) attenuation rates. The results show that under low attenuation, the average EPBT is 4.14 years, which extends to 6.5 years under high attenuation and only 2.37 years under low attenuation. Sensitivity analysis confirmed the effectiveness of the model in representing the dynamic evolution of photovoltaic systems, providing a theoretical basis for subsequent environmental performance evaluations. Full article

To improve traditional cement manufacturing, which generates a large amount of greenhouse gases, blending calcareous green algae and fly ash as cement replacement materials is expected to achieve nearly zero carbon emissions. As a calcareous green alga, Halimeda macroloba is a significant producer of biogenic calcium carbonate (CaCO₃), sequestering approximately 440 kg of carbon dioxide (CO₂) per 1000 kg of CaCO₃, with CaCO₃ production reported in relation to algal biomass. To assess the new low-carbon/low-waste cement production process, the proposed scenarios (2 and 3) are validated via Python-based modeling (Python 3.12) and Aspen Plus^® simulation (Aspen V14). The core technology is the pre-calcination of algae-derived CaCO₃ and fly ash from coal combustion, which are added to a rotary kiln to enhance the proportions of tricalcium silicate (C₃S) and dicalcium silicate (C₂S) for forming the desired silicate phases in clinker. Through the lifecycle assessment (LCA) of all scenarios using SimaPro^® (SimaPro 10.2.0.3), the proposed Scenario 2 achieves the GWP at approximately 0.906 kg CO₂-eq/kg clinker, lower than the conventional cement production process (Scenario 1) by 47%. If coal combustion is replaced by natural gas combustion, the fly ash additive is reduced by 74.5% in the cement replacement materials, but the proposed Scenario 3 achieves the GWP at approximately 0.753 kg CO₂-eq/kg clinker, lower than Scenario 2 by 16.9%. Moreover, the LCA indicators show that Scenario 3 has lower environmental impacts on human health, ecosystem, and resources than Scenario 1 by 24.5%, 60.0% and 68.6%, respectively. Full article

Decarbonization of compression-ignition engines requires evaluation of carbon-free and low-carbon fuel alternatives. Ammonia (${NH}_3$) offers zero direct carbon emissions but faces combustion challenges including low flame speed (7 $\mathrm{c}\mathrm{m}/\mathrm{s}$) and high auto-ignition temperature (657 °$\mathrm{C}$). Methanol provides improved reactivity and bound oxygen content that can enhance ignition characteristics. This computational study investigates diesel–ammonia–methanol ternary fuel blends using validated three-dimensional CFD simulations (ANSYS Forte 2023 R2; ANSYS, Inc., Canonsburg, PA, USA) with merged chemical kinetic mechanisms (247 species, 2431 reactions). The model was validated against experimental in-cylinder pressure data with deviations below 5% on a single-cylinder diesel engine (510 cm³, 17.5:1 compression ratio, 1500 rpm). Ammonia energy ratios were systematically varied (10–50%) with methanol substitution levels (0–90%). Fuel preheating at 530 K was employed for high-alcohol compositions exhibiting ignition failure at standard temperature. Results demonstrate that peak cylinder pressures of 130–145 bar are achievable at 10–30% ammonia with M30K–M60K configurations, comparable to baseline diesel (140 bar). Indicated thermal efficiency reaches 38–42% at 30% ammonia-representing 5–8 percentage point improvements over diesel baseline (31%)-but declines to 30–32% at 50% ammonia due to fundamental combustion limitations. ${CO}_2$ reductions scale approximately linearly with ammonia content: 35–55% at 30% ammonia and 75–78% at 50% ammonia. ${NO}_X$ emissions demonstrate 30–60% reductions at efficiency-optimal configurations. Multi-objective optimization analysis identifies the A30M60K configuration (30% ammonia, 60% methanol, 530 K preheating) as optimal, achieving 42% thermal efficiency, 58% ${CO}_2$ reduction, 51% ${NO}_X$ reduction, and 11% power enhancement versus diesel. This configuration occupies the Pareto frontier “knee point” with cross-scenario robustness. Full article

The utilization of sustainable feedstocks offers significant opportunities for innovation in sustainable and efficient processing technologies, targeting a vacuum residue upgrade industry projected to be valued at around USD 26 billion in 2024. This review examines advances in catalytic strategies for upgrading waste-derived products (plastics, tires) and biomass, in addition to heavy oil feedstocks. Particular emphasis is placed on hydrogen addition pathways, specifically, residue hydroconversion facilitated by dispersed nanocatalysts and waste co-processing methodologies. Beyond nanoscale catalyst design and reaction performance, this work also addresses refinery-level sustainability impacts. The advanced catalytic conversion of heavy oil residue demonstrates superior conversion efficiency, significant coke suppression, and improved carbon utilization, while life cycle and illustrative techno-economic comparisons indicate greenhouse gas reductions and a net economic gain of approximately USD 2–3 per barrel relative to conventional refining under scenarios assuming decarbonized hydrogen production. Co-processing of plastics, tires, and biomass with heavy oil feedstocks is highlighted as a practical and effective approach. Together, these findings outline a rational catalytic pathway toward optimized refining systems. Within the framework of the circular carbon economy, these catalytic processes enable enhanced feedstock utilization, integration of low-carbon hydrogen, and coupling with carbon-capture technologies. Full article

With the advancement of the “dual carbon” goal, the power system is accelerating its transition towards a clean and low-carbon structure, with a continuous increase in the penetration rate of renewable energy generation (REG). However, the volatility and uncertainty of REG output pose severe challenges to power grid operation. Traditional distribution networks face immense pressure in terms of scheduling flexibility and power supply reliability. Active distribution networks (ADNs), by integrating energy storage systems (ESSs), soft open points (SOPs), and demand response (DR), have become key to enhancing the system’s adaptability to high-penetration renewable energy. This work proposes a DR-aware scheduling strategy for ESS-integrated flexible distribution networks, constructing a bi-level optimization model: the upper-level introduces a price-based DR mechanism, comprehensively considering net load fluctuation, user satisfaction with electricity purchase cost, and power consumption comfort; the lower-level coordinates SOP and ESS scheduling to achieve the dual goals of grid stability and economic efficiency. The non-dominated sorting genetic algorithm III (NSGA-III) is adopted to solve the model, and case verification is conducted on the standard 33-node system. The results show that the proposed method not only improves the economic efficiency of grid operation but also effectively reduces net load fluctuation (peak–valley difference decreases from 2.020 MW to 1.377 MW, a reduction of 31.8%) and enhances voltage stability (voltage deviation drops from 0.254 p.u. to 0.082 p.u., a reduction of 67.7%). This demonstrates the effectiveness of the scheduling strategy in scenarios with renewable energy integration, providing a theoretical basis for the optimal operation of ADNs. Full article

The accelerating global transition to electric mobility demands data-driven infrastructure planning that balances technical, economic, and spatial considerations. This study develops a scenario-based demand and economic modeling framework to estimate electric vehicle (EV) charging infrastructure needs across Abu Dhabi’s urban and rural regions through 2050. Two adoption pathways, Progressive and Thriving, were constructed to capture contrasting policy and technological trajectories consistent with the UAE’s Net Zero 2050 targets. The model integrates regional travel behavior, energy consumption (0.23–0.26 kWh/km), and differentiated charging patterns to project EV penetration, charging demand, and economic feasibility. Results indicate that EV stocks may reach 750,000 (Progressive) and 1.1 million (Thriving) by 2050. The Thriving scenario, while demanding greater capital investment (≈108 million AED), yields higher utilization, improved spatial equity (Gini = 0.27), and stronger long-term returns compared to the Progressive case. Only 17.6% of communities currently meet infrastructure readiness thresholds, emphasizing the need for coordinated grid expansion and equitable deployment strategies. Findings provide a quantitative basis for balancing economic efficiency, spatial equity, and policy ambition in the design of sustainable EV charging networks for emerging low-carbon cities. Full article

This study develops a field-based techno-economic model and decision framework for a CO₂-enhanced oil recovery and storage project under joint market uncertainty. Historical drilling and completion expenditures calibrate investment cost functions, and three years of production data are fitted with segmented hyperbolic Arps curves to forecast 20-year oil output. Markov-chain models jointly generate internally consistent pathways for crude oil, ETA, and purchased CO₂ prices, which are embedded in a Monte Carlo valuation. The framework outputs probability distributions of NPV and deferral option value; under the mid scenario, their mean values are USD 18.1M and USD 2.0M, respectively. PRCC-based global sensitivity analysis identifies the dominant value drivers as oil price, CO₂ price, utilization factor, oil density, pipeline length, and injection volume. Techno-economic boundary maps in the joint oil and CO₂ price space then delineate feasible regions and break-even thresholds for key design parameters. Results indicate that CCUS-EOR viability cannot be inferred from oil price or any single cost factor alone, but requires coordinated consideration of subsurface constraints, engineering configuration, and multi-market dynamics, including the value of waiting in unfavorable regimes, contributing to low-carbon development and sustainable energy transition objectives. Full article