Search Results (16,043)

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

Carbon storage dynamics in dryland and semi-arid ecosystems remain a major uncertainty in global carbon cycle assessments, particularly in regions like the Yellow River Basin (YRB). Using the Arid Ecosystem Model (AEM), we simulated the spatiotemporal evolution of four major carbon pools—total carbon (TOTC), vegetation carbon (VEGC), soil organic carbon (SOC), and litter carbon (LTRC)—from 1981 to 2060 under factorial climate scenarios. During 1981–2020, TOTC increased by 0.09 Pg C (+3.54%), driven by gains in VEGC (+0.03 Pg C, +21.43%) and SOC (+0.06 Pg C, +2.78%). LTRC showed minimal net change but was highly sensitive to interannual variability. From 2021 to 2060, under the high-emission SSP5 scenario, TOTC is projected to increase by 0.114 Pg C (+4.81%), with VEGC contributing most of the gain (+23.87%). CO₂_only simulations showed similar increases, underscoring the dominant role of CO₂ fertilization. In contrast, warming and precipitation alone produced weaker and more variable effects. Spatially, upper YRB regions are expected to maintain strong sink capacity, while the Loess Plateau and central-western subregions remain vulnerable to warming and moisture decline. LTRC exhibited the highest variability across scenarios (−18% to +22%), highlighting its role as a sensitive indicator of sink stability. These findings emphasize the need to account for nonlinear climate–carbon interactions and regional heterogeneity. Region-specific, adaptive strategies that integrate ecological restoration and climate adaptation will be critical to enhancing carbon sinks and supporting China’s carbon neutrality targets in the Yellow River Basin. Full article

This paper proposes an optimal operational framework for enhancing the economic, technical, and environmental performance of a renewable energy-based microgrid. The proposed system integrates photovoltaic (PV) generation, wind turbines (WTs), battery energy storage systems (BESSs), diesel generators (DGs), and utility grid interaction. Three multi-objective optimization algorithms, namely Multi-Objective Particle Swarm Optimization (MOPSO), Multi-Objective Genetic Algorithm (MOGA), and Multi-Objective Celestial Orbit Optimization (MOCOO), are employed to minimize the total operating cost and grid dependency. The obtained results demonstrate that MOPSO achieves the best techno-economic performance with a minimum operating microgrid cost of 2.2 M$/year and a low grid dependency ratio of 0.0333. The operational analysis confirms that the proposed renewable-priority scheduling strategy significantly reduces operational emissions and reliance on the utility grid through coordinated BESS charging/discharging and efficiency-aware DG dispatch. The microgrid (MG) achieves zero-emission operation during operating periods dominated by renewable generation. Furthermore, the DG operates within an efficiency range of 36.8–39.3%, improving fuel utilization and reducing unnecessary emissions. The battery degradation analysis indicates high lifetime cycle capability under shallow depth-of-discharge operation, demonstrating improved long-term operational sustainability. Overall, the proposed framework provides a reliable and economically balanced solution for sustainable microgrid energy management. Full article

The study investigated and quantified the feasibility of using waste derived from shea tree fruit shells (Vitellaria paradoxa) as an organic multifunctional additive for building ceramic bodies, focusing on its influence on thermal behavior, pore formation, and mechanical performance. The scope of the research included sieve analysis, chemical analysis (WDXRF), phase composition analysis (XRD), thermal analysis coupled with evolved gas analysis (DTA–TG–EGA), and the evaluation of the physical and mechanical properties of the obtained ceramic materials. The analyses demonstrated that the shea waste was characterized by a high content of organic matter, a loss in ignition of 93.84%, and a calorific value of 19.421 kJ/g. The incorporation of biomass resulted in increased porosity and reduced apparent density of the ceramic materials. The relative porosity increased from 27.00% for the reference sample to 34.98% for the sample containing 30% shea waste. Simultaneously, the compressive strength decreased from 23.67 MPa to 10.10 MPa, while the flexural strength decreased from 8.96 MPa to 4.76 MPa. Partial replacement of conventional mineral additives and, in particular, partial substitution of fossil-derived kiln fuel demand with high-calorific biomass enabled a reduction in overall CO₂ emissions associated with ceramic production. This includes both process-related emissions from raw material decomposition and fuel-related emissions generated in the tunnel kiln. In addition, a reduced contribution of carbon originating from inorganic mineral sources (including carbonates) to total emissions covered by emission trading systems (ETSs) was observed. Despite the reduction in mechanical parameters, samples containing up to 20% shea waste retained properties suitable for application in the production of ceramic building materials. Full article

The paper examines the macroeconomic and environmental outcomes associated with green financing across 22 emerging markets and developing economies from 2002 to 2024. Driven by the increased policy focus on climate finance as a two-fold tool of sustainability and development, the analysis assesses whether green financing is an economic growth driver. A multi-model structure is used (fixed effects, non-linear (quadratic), threshold, dynamic (lagged), and first-difference specifications) to achieve strength and eliminate model-specific bias. The findings show that green financing exhibits a weak positive association with economic growth in baseline and regime specifications. Still, this relationship is not robust across dynamic and first-difference models. Moreover, there is no indication of non-linearity or a threshold effect (a Green Laffer Curve). Patterns that indicate a weak positive relationship are cross-sectional and not robust to panel estimation; they are therefore aggregation-biased. Conversely, green financing has a low negative correlation with CO₂ emissions, indicating partial environmental efficiency. The results show that climate finance is limited in scale and inefficiently structured, which limits its macroeconomic impact. In general, the paper concludes that green finance, although environmentally applicable, is not sufficient as it currently stands to spur economic growth in emerging economies. Full article

Partial substitution of ordinary Portland cement (OPC) with supplementary cementitious materials is a key strategy for reducing the clinker factor and associated CO₂ emissions from cement production. This study investigates the feasibility of incorporating untreated electric arc furnace steel slag (EAF-SS), collected from a steel plant in Kenitra, Morocco, as a partial replacement of OPC in Portland cement. The material was characterized using X-ray diffraction (XRD), X-ray fluorescence (XRF), and particle size distribution (PSD) analysis. Cement blends containing 2–15 wt.% EAF-SS as a replacement of OPC were prepared and tested in accordance with EN standards to evaluate consistency, setting time, density, porosity, and compressive and flexural strengths at 2, 7, and 28 days. Increasing EAF-SS content from 2% to 15% slightly delayed the initial setting time by 3–17 min and reduced early-age compressive strength from 36 MPa for OPC to 26 MPa for the 15% blend at 2 days. At 28 days, mixtures containing 2–5% EAF-SS achieved compressive strengths of 42–52 MPa, satisfying class 42.5R requirements, whereas higher replacement levels (10–15%) reduced strength to 36–39 MPa. Flexural strength decreased from 7.5 MPa for OPC to 5.7 MPa for the 15% blend at 2 days and to 7.3 MPa at 28 days, while density decreased by 2–4% and total porosity increased from 12% to 18% with increasing slag content. Drying shrinkage decreased slightly with increasing EAF-SS content, from 630 µm/m for OPC to 560 µm/m for BC15 at 28 days, suggesting a modest beneficial effect on dimensional stability. The investigated slag exhibited an Fe₂O₃ content of ~57 wt.%, substantially higher than values commonly reported for many European and Chinese EAF slags. Accordingly, the novelty of the present work lies not simply in the geographical origin of the material, but in the standardized experimental assessment of a compositionally atypical, untreated, Fe-rich EAF steel slag used directly as a partial replacement of OPC in Portland cement. The study is intended as a first performance-oriented evaluation of this Moroccan by-product under EN-based testing conditions, rather than as a complete mechanistic or environmental assessment. These findings support the feasibility of low-level EAF-SS incorporation in blended cement and indicate a potential contribution to clinker factor reduction and associated CO₂ savings under the assumptions adopted in this study. However, the environmental benefit assessment remains preliminary and should be confirmed by full life-cycle and leaching analyses. Full article

Co-firing biomass with coal in existing circulating fluidized bed (CFB) boilers is a promising strategy for reducing net CO₂ emissions and utilizing renewable energy. However, the impact of biomass-blending ratio on the complexity of multiphase flow, combustion characteristics, and pollutant formation inside a full-scale CFB boiler is not fully understood yet. This study developed Eulerian–Lagrangian Multiphase Particle-In-Cell (MP-PIC) model and this model was employed to simulate the co-combustion of coal and rice husk in a 72 MW industrial-scale CFB boiler. The pyrolysis kinetics of the coal and biomass were first measured experimentally via thermogravimetric analyzer and integrated into the model via source terms. The experimentally validated model was further used to investigate the effects of biomass-blending ratio (0–40 wt.%) on hydrodynamics, temperature distribution, and gaseous pollutant emissions (NO, SO₂). Results indicated that biomass addition has a negligible impact on the overall particle flow patterns and particle volume fraction distribution in the boiler. However, it significantly lowered the average furnace temperature due to the lower calorific value of biomass. A blending ratio of 40 wt.% biomass yielded the most substantial reduction in pollutant emissions at the outlet, with decreases of 51.99% for CO₂, 15.70% for NO, and 49.50% for SO₂ compared to single coal combustion. This study presents the MP-PIC model as an efficient numerical framework for optimizing co-firing operations and shows that a high ratio of biomass co-firing (40 wt.%) is technically feasible and environmentally advantageous in existing CFB boilers. Full article

Accurate carbon price forecasting is essential for emission trading risk management and low-carbon investment decisions. In existing decomposition-prediction frameworks, secondary decomposition targets are typically selected based on statistical complexity rather than domain-informed causality, and standard Long Short-Term Memory (LSTM)-Transformer architectures discard the cell state that encodes long-term temporal memory. These limitations are particularly pronounced where energy-driven causal structures and regime-switching volatility coexist. This study proposes Causal State-driven Network (CaStNet), an intelligent forecasting framework with two core innovations. A Policy-Causality-guided Residual Secondary Decomposition (PCRSD) module replaces entropy-based criteria with Granger causality to select intrinsic mode functions (IMFs) exhibiting significant energy-carbon causal linkages for targeted variational mode decomposition (VMD). A Cell-State-Driven Dual-function Attention (CSDA) mechanism repurposes the LSTM cell state for simultaneously injecting long-term memory into the Transformer and employing the cell-state differential velocity as a volatility proxy to adaptively regulate Top-k attention sparsity. The Artificial Lemming Algorithm (ALA) globally co-optimizes decomposition dimensions and attention boundaries. A Shapley Additive exPlanations (SHAP)–Local Interpretable Model-agnostic Explanations (LIME) interpretability analysis reveals horizon-dependent driver transitions from short-term autoregressive momentum to long-term energy fundamentals, uncovering threshold nonlinearities in energy-carbon transmission channels. Validation on the Shanghai market (2013–2025) achieves point-forecast RMSE = 0.8326 and R² = 0.9777, outperforming all twelve benchmark models. Cross-market testing on the Hubei market yields R² = 0.9487, and expanding-window five-fold cross-validation on the Shanghai dataset yields mean R² = 0.9704, jointly confirming generalization robustness. Full article

Rural greywater management is a critical global challenge due to the lack of centralized treatment in dispersed communities. This study aimed to characterize the pollution characteristics and bacterial community structure of samples from four greywater collection tanks in eastern China using high-throughput sequencing and absolute quantification of the 16S rRNA gene. Pollution characteristics showed spatial heterogeneity: chemical oxygen demand ranged from 19.8 to 272.5 mg/L, total nitrogen from 8.6 to 16.4 mg/L, and dissolved oxygen from 1.3 to 5.3 mg/L. Dissolved greenhouse gases also varied, with N₂O reaching 103.6 ppmv and CH₄ up to 50.4 ppmv. Based on the estimated absolute abundance of 16S rRNA gene copies, we found that the bacterial communities were dominated by Pseudomonadota, Actinomycetota, Bacteroidota, and Bacillota. Key genera such as Acinetobacter, Pseudomonas, and unclassified Enterobacteriaceae were positively correlated with nitrate, suggesting their potential association with denitrification and potential N₂O production. The methanotrophic genus Methyloparacoccus was enriched in a tank with high dissolved organic carbon. Co-occurrence network analysis revealed that core taxa like unclassified Paracoccaceae and Limnohabitans function as module hubs, maintaining community stability. These findings reveal associations between bacterial taxa, pollutant transformation, and greenhouse gas emissions in rural greywater and provide fundamental insights to support the development of low-carbon, resource-oriented treatment technologies. Full article

Used engine oil (UEO) is a hazardous waste stream that poses significant environmental risks when improperly managed. However, its high heating value makes it a promising candidate for energy recovery. In Kuwait, rising vehicle ownership has led to increasing quantities of UEO, while the power sector remains heavily dependent on conventional fossil fuels. Although extensive research has examined UEO treatment methods and combustion characteristics, limited attention has been given to its integration into utility-scale power-generation systems. This study presents a national-scale techno-economic and environmental assessment of using UEO as a supplementary fuel for electricity generation in Kuwait. East Doha Power Station was selected as a representative case study to evaluate fuel-substitution potential and the practicality of integrating UEO into existing power-generation infrastructure. Historical vehicle-registration data were used to estimate UEO generation, and future availability was projected through 2035 based on vehicle-growth trends. The corresponding thermal energy potential, equivalent electricity generation, fuel-displacement capacity, economic benefits, and environmental impacts were subsequently evaluated. The results indicate that annual UEO generation is projected to increase from approximately 181,800 tonnes/year in 2024 to 303,300 tonnes/year in 2035. This quantity corresponds to about 12,126 TJ/year of recoverable thermal energy and an equivalent electricity-generation potential of approximately 1.1 TWh/year (4000 TJ/year), assuming a power-plant efficiency of 33%. The recovered UEO could displace approximately 311,000 tonnes/year of heavy oil or 287,000 tonnes/year of crude oil, with estimated net annual fuel-cost savings of approximately 28–30 million KD. Based on literature-reported emission factors, UEO utilization could reduce combustion-related CO₂ emissions by up to 19.0% and NO_x emissions by up to 45.5% compared with heavy oil. Sensitivity analysis further confirmed the robustness of the findings under a range of recovery and operating conditions. To the best of the authors’ knowledge, this study represents the first comprehensive national-scale assessment of the potential use of UEO for utility-scale power generation in Kuwait. The findings indicate that UEO has the potential to serve as a strategic secondary energy resource that supports waste reduction, fuel conservation, economic savings, and circular-economy objectives. However, practical implementation will require appropriate collection and treatment infrastructure together with further technical validation, pilot-scale demonstration, and regulatory evaluation. Full article

Raising indoor temperature setpoints is widely promoted as a practical way to reduce cooling-related energy demand in commercial buildings, yet its net carbon impact becomes uncertain once behavioral rebound effects are considered. This study develops an integrated carbon-accounting framework to evaluate the climate implications of summer indoor temperature increases of 1–3 °C in U.S. mercantile buildings. The framework combines operational energy savings from reduced cooling demand with consumption-driven emissions arising from longer customer dwell times and increased consumer spending under improved thermal comfort conditions. Carbon outcomes are quantified using sector-level electricity data and the USEEIO emission factor for retail trade. The results reveal a clear imbalance: operational carbon savings range from 0.21 to 0.64 Mt CO₂, whereas consumption-driven emissions range from 3.37 to 21.90 Mt CO₂, yielding a consistently positive net carbon impact of 3.16–21.26 Mt CO₂ across all scenarios. A break-even analysis indicates that only 1.30–3.89 billion USD in additional spending is sufficient to offset the operational savings. The findings remained robust across alternative behavioral and carbon-accounting specifications; a 10,000-iteration Monte Carlo analysis produced positive net carbon impacts in every simulation (median 8.54 Mt CO₂; P(NCI > 0) = 1.00). Overall, the results suggest that temperature-based efficiency measures may overstate their climate benefits when behavioral responses are ignored, highlighting the importance of incorporating rebound effects into building energy assessments and commercial climate policy. Full article

The production of building ceramics is an energy-intensive part of the industry, causing high CO₂ emission per production volume. In addition to the combustion of fossil fuels, CO₂ is emitted as a byproduct of calcium carbonate decomposition, a compound present in clay raw materials. In this paper, a method for reducing emissions by lowering the firing temperature of ceramics, thereby preventing the complete decarbonation of carbonate minerals, is presented. Thermal research has shown that lowering the firing temperature to 750 °C resulted in a 55% calcium carbonate decomposition and a reduction in CO₂ emissions by over 30 kg for every ton of clay used. At this temperature, sintering shrinkage mechanisms were not observed, which resulted in a reduction in the strength of the materials by almost 25% compared to samples fired at 900 °C. An attempt was made to compensate for the negative effects of lowering the firing temperature by adding ground glass cullet, which brought only partially positive results: an increase in flexural strength, but no change in compressive strength. Microscopic observations and phase composition studies indicate that lowering the firing temperature causes changes in the proportions of calcium compounds: increased amounts of calcite, and decreased amounts of silicates and calcium aluminosilicates. Full article

The post-COVID-19 tourism recovery exposes a structural divergence between economic resilience and environmental sustainability. Traditional tourism planning metrics consistently fail to diagnose how macroeconomic growth dynamics decouple from environmental pressures, leaving policymakers without empirical tools to identify structural vulnerabilities or prevent carbon-intensive recoupling during post-crisis transitions. This study integrates macroeconomic, environmental, and digital data across a global panel to map actionable pathways for sustainable tourism transitions. Employing a multi-stage methodology, the analysis first utilizes K-Means clustering (n = 80) to isolate the structural fixed effects of baseline destination archetypes driving a K-shaped recovery. Second, using a synchronized environmental panel (n = 41), a Decoupling Index evaluates eco-efficiency elasticity to test the alignment between tourism value recovery and aviation-induced CO₂ emissions. Third, regression analysis of an elite digital cohort (n = 18) measures dynamic exogenous catalysts, revealing that digital attractiveness, proxied by the global digital nomad market share, is a significantly stronger accelerator of recovery (β = 55.59, p = 0.019) than traditional physical air connectivity (β = −46.48, p = 0.036). Synthesizing these insights, a 2 × 2 Strategic Decision Matrix (n = 41) classifies destinations into Sustainable Leaders, Mass-Market Traps, Value Pivoters, and Vulnerable Laggards. The empirical results demonstrate that pre-pandemic structures do not deterministically dictate recovery (p > 0.05, Partial η² ≤ 0.077), yet rapid financial recovery often masks deep atmospheric vulnerabilities, with specific absolute decoupling leaders achieving exceptional value expansion alongside strict carbon contraction (e.g., Saudi Arabia, DE = −0.35; El Salvador, DE = −0.26). This framework provides a data-driven roadmap for policymakers to utilize “soft” digital infrastructure to transition from carbon-intensive, volume-dependent models toward value-optimized, low-emission ecosystems. Full article

Reducing embodied greenhouse gas emissions in the initial design phase is essential for attaining low-carbon buildings, as the highest potential for reduction exists prior to the finalization of construction decisions. While Building Information Modelling (BIM) and Life Cycle Assessment (LCA) have been progressively integrated for embodied carbon evaluation, current frameworks are predominantly deterministic, offer minimal uncertainty quantification, and seldom utilize machine-learning-assisted optimization to facilitate design decision-making. This paper presents an uncertainty-aware BIM–LCA methodology to solve these shortcomings, integrating automated quantity takeoff, probabilistic carbon assessment, and explainable machine-learning optimization. The proposed methodology integrates IFC-based BIM models, Bills of Quantities (BoQs), and regional life cycle inventory databases to conduct a cradle-to-grave embodied carbon assessment. Quantities produced from BIM were checked against BoQ data, and the uncertainty related to material quantities and emission factors was assessed by Monte Carlo simulation. A machine-learning surrogate model was created with 1200 design samples to facilitate swift optimization, and SHapley Additive exPlanations (SHAPs) were utilized to determine the most significant design factors. A mid-rise residential structure in Chongqing, China, encompassing a gross floor area of 9750.03 m², was used as a case study. The baseline Global Warming Potential (GWP) was calculated as 514.29 ± 30.09 kgCO₂e/m² (A1–A5), with product-stage emissions (A1–A3) accounting for roughly 89.28% of total embodied carbon, predominantly from concrete and steel. Enhanced BIM maturity lowered uncertainty by roughly 20%. Optimization resulted in a 38.13% decrease in embodied carbon, reducing GWP to 318.21 kgCO₂e/m². SHAP research identified the percentage of material reuse and concrete composition as the primary factors influencing carbon reduction. The suggested framework offers a clear and replicable decision-support mechanism for low-carbon building design that accounts for uncertainty. Full article

CO₂/N₂-enhanced coalbed methane recovery (ECBM) offers a potential route to improve coalbed methane production, reduce CH₄ emissions, and couple gas drainage with low-carbon coal development. However, the relative roles of adsorption-controlled replacement and pressure-driven displacement under deep stress conditions remain insufficiently resolved. Here, CO₂ and N₂ injection experiments were conducted under different vertical stresses to quantify the evolution of gas flow, breakthrough time, increase in coal gas content, replacement–displacement ratios, and injection efficiency. Increasing stress compressed the pore–fracture network, reduced gas transport capacity, and delayed breakthrough of the injected gas. CO₂, because of its strong adsorption affinity, remained dominated by replacement throughout the injection process. Higher stress enhanced CO₂ retention in coal and therefore its potential storage capacity, but it also weakened sustained CH₄ recovery by restricting transport. In contrast, N₂, which adsorbs weakly, rapidly shifted to displacement-dominated recovery after breakthrough. Although high stress delayed the formation of connected displacement pathways, N₂ maintained high injection efficiency. These results show that stress controls the dominant ECBM mechanism by regulating adsorption retention, seepage transport, and displacement outflow. The findings provide a mechanistic basis for selecting injection gases and designing low-carbon ECBM strategies in deep coal seams. Full article