Search Results (1,151)

Environmental issues such as climate change and cumulative emissions have intensified debate on the effectiveness of public environmental protection expenditure. Traditional Pigouvian taxes and subsidies mainly target instantaneous flow externalities. However, it is difficult to resolve dynamic stock externalities that accumulate over many years, such as climate change and cumulative greenhouse gas emissions, through taxation policies alone. This situation requires the government to make direct environmental protection expenditure to support the ecosystem’s natural assimilation capacity and reduce the rate at which pollution accumulates. This study specifically examines the relationship between stock externalities and environmental protection expenditure within the context of the Turkish economy, which is highly industrialised and under pressure to comply with international environmental commitments such as the European Green Deal and the Paris Climate Agreement. In the study’s empirical analysis phase, long-term relationships between macroeconomic variables and pollution stocks were tested using Fourier cointegration methods and econometric time series analyses. The analysis revealed a long-term co-movement (cointegration) relationship between the series, indicating that public environmental protection expenditures and industrial emissions move in the same direction over the long term. FMOLS and DOLS estimates indicate that environmental protection expenditures are positively associated with industrial emissions in the long run (FMOLS coefficient = 2.1125; DOLS coefficient = 2.0375), whereas renewable energy consumption exerts a negative effect on emissions (FMOLS coefficient = −1.4391; DOLS coefficient = −1.4967). These results suggest that environmental protection expenditure in Türkiye is not independent of current production and industrialisation dynamics, and that its emission-reducing effects are influenced by technological transformation processes. Full article

The sizing of rooftop solar energy systems is commonly based on the most visible load or on generic end-use allocations, leading to an inadequate distribution of the limited rooftop area between heat and electricity. This study formalizes the energy audit within a three-level deterministic framework that selects and sizes solar technologies by energy vector: demand is first decomposed by vector; the technology for the thermal vector is then selected through a levelized cost of heat selection ratio $\psi$, while the photovoltaic system of the electrical vector is sized for self-consumption; and the rooftop area is finally allocated among vectors according to marginal value per unit area. In a 75-bed university residence in Cienfuegos, Cuba, air conditioning is the dominant energy end-use in terms of installed power (accounting for $77\%$ of the connected load), whereas the thermal vector dominates annual energy consumption (domestic hot water: 127,440 versus 76,818 kWh/year for electricity; thermal-to-electric ratio $1.66$). Solar thermal technology has been selected for the thermal vector ($0.018$ versus $0.088$ USD/kWh_th; $\psi =0.21$, a robust value according to the sensitivity analysis), and the marginal value (≈111 versus ≈32 USD/(m²·year)) allocates 104 m² to solar thermal collectors and 134 m² to photovoltaic energy, thereby reversing the original design that prioritized photovoltaic energy. The resulting portfolio achieves an annual solar fraction close to $100\%$ in both vectors on an energy balance basis, avoids $86.5$ t of operational CO₂ emissions per year, and combines a simple payback of $1.1$ years (solar thermal) with a net present value of 55,327 USD and an internal rate of return of $28\%$ (photovoltaics). The sizing decision is shown to be robust to the choice of statistical design criterion (median, mean, P90, maximum), and none of the three framework decisions is reversed under $\pm 30\%$ parameter variations. By replacing the subjective weightings of multi-criteria methods with observable economic criteria, the framework provides a replicable and auditable design protocol. Full article

Algal cultivation has attracted significant attention due to CO₂ biocapture and potential for biofuel generation. Enormous generation of waste polymer often poses an environmental problem due to non-biodegradability. This study comprehensively analyses the thermal degradation characteristics of blue–green alga, Leptolyngbya subtilis JUCHE1 (LS) and waste textile polyester (WTP) and their mixtures (LS1P3 (1:3); LS1P1 (1:1); LS3P1 (3:1)) during co-pyrolysis. The interaction between LS and WTP during co-pyrolysis has been assessed through the verification of synergism using different blending ratio and through the comparison of the corresponding values of the Comprehensive Pyrolysis Index (CPI). The composite, LS1P3, exhibited the highest synergism and the maximum value of CPI. Isoconversional models (FWO, Starink, Bosewell and Tang) have been used to predict the activation energies (E_a). Thermodynamic parameters, namely, heat of reaction (ΔH), Gibbs free energy change (ΔG) and entropy change (ΔS), have also been determined for all. The average value of E_a for LS1P3 is also the lowest (96.015 kJ/mol) among all composites. The Master plot method identifies that there is a shift of reaction mechanism from phase boundary type (R2 and R3) for LS and WTP to a P2-type acceleratory reaction rate mechanism for LS1P3. The lowest average value of ΔH and the highest values of ΔG and ΔS for LS1P3 co-pyrolysis also support the least consumption of energy and the highest favorability under present conditions. The product yield distribution of co-pyrolysis in the isothermally operated conditions (450 °C) also establishes the superiority of LS1P3. Yields of pyro-oil and pyro-gas are the highest among all composites. The study ensures the future application prospects of co-pyrolysis of LS and WTP as a means for generation of energy resources (pyro-oil and pyro-gas) and chemicals (pyro-char). Full article

Industrial Internet of Things (IIoT) networks use on-demand sensing and wireless power transfer (WPT) for self-sustainable operation. Existing scheduling frameworks are fundamentally limited because they react only after energy levels decline. Consequently, IoT nodes enter charging mode only when their residual energy falls below a threshold, causing energy outages, increased latency, and missed sensing tasks while preventing proactive WPT resource allocation. This paper proposes a Digital Twin (DT)-enabled proactive scheduling framework that transforms IIoT scheduling from reactive to proactive. The key innovation is a closed-loop virtual–real integration in which a DT layer, co-located with the control centre, maintains a Kalman filter predictor to forecast node energy over an H-slot horizon, enabling scheduling decisions before energy shortages occur. Physical layer security (PLS) constraints and DT-based anomaly detection protect against eavesdropping, energy depletion, and false data injection attacks. A multi-objective formulation jointly optimises sensing utility and WPT efficiency while accounting for DT synchronisation overhead and uplink bandwidth consumption. The resulting multi-slot Binary Integer Linear Programmes (BILP) are solved using branch-and-bound with a reliability branching rule, and a fast greedy heuristic is also developed. Simulation results over 50 Monte Carlo iterations show that the proposed framework reduces energy outage events by approximately 70% compared with the reactive baseline, activates less than 50% of available sensing nodes, and schedules less than 60% of energy transmitters for WPT. Ablation studies confirm that DT prediction is the primary contributor to the outage reduction. DT-based anomaly detection achieves a false alarm rate below 3% while maintaining a detection rate above 95%. The proposed framework improves the sustainability, efficiency, and security of IIoT networks with practical computational overhead, making it well suited for Industry 5.0 deployments. Full article

Background/Objectives: Adolescence is a critical developmental stage marked by increased susceptibility to engaging in risky health behaviors, including the use of alcohol, energy drinks (EDs), and tobacco products. Methods: A cross-sectional sample of 1249 individuals aged 10–24 years was analyzed, stratified into two age groups (<16 and >16 years). The study sample comprised volunteers living in Rome, Italy, who were recruited for the ALIMA (ALImentazione Multiculturale negli Adolescenti) study. Categorical variables were analysed using the Pearson chi-square test and p < 0.05 was considered statistically significant. Results: Alcohol consumption was higher among participants aged ≥16 years (62.2%) than among those age <16 years (39.9%), without significant gender difference, with binge drinking also more common, especially among older males. ED use was widespread, more frequent in males, and often associated with alcohol consumption. Smoking prevalence differed by age group, reaching 41.5% among participants over 16 years of age, with higher rates among younger females. Finally, 17.3% reported combined use of alcohol, EDs, and tobacco, with significant gender differences in both age groups. Conclusions: These findings highlight a clear age-related escalation and co-occurrence of substance-use behaviors, with emerging gender differences in specific patterns. The results underscore the need for integrated, age-targeted prevention strategies addressing multiple substance use simultaneously to reduce long-term health risks and promote adolescent well-being. Full article

The common bean (Phaseolus vulgaris L.) is an important food crop in tropical agriculture. However, fertilization and soil management methods for common beans require further investigation to reduce production costs and increase sustainability. Furthermore, cultivation methods can directly affect GHG emissions. Thus, this study evaluates the CO₂ emissions from fuel consumption as a function of soil management and fertilization methods on the common bean crop. The randomized block design was used in a 2 × 3 factorial scheme with six repetitions composed of two fertilization systems (spread and furrow) and three soil management systems: convention-al tillage—CT, minimum tillage—MT, and no-tillage—NT. The productive performance of common beans varies according to fertilization methods and soil management. Field capacity in the (CT) was impaired due to the various mechanized soil preparation operations with 0.30 and 0.32 ha h⁻¹, without a significant effect from the fertilization method. CT system resulted in higher CO₂ emissions of 175.74 kg ha⁻¹ and 165.50 kg ha⁻¹; thus, in soil conservation management, these same values were up to 58% lower, with the lowest rates for NT. Crop yield in the MT system presented the best result compared to the CT and NT, with an appropriate cost–benefit ratio for bean production in tropical crops. Full article

Ethanol, a cleaner fuel alternative in diesel engines, is used in certain proportions to modify engine performance and improve emissions in diesel engines. In this study, a series of experiments were conducted to determine the effect of different ethanol concentrations on engine performance and emissions in a single-cylinder, water-cooled partial homogenous charge compression ignition (HCCI) diesel engine. Partial HCCI combustion was achieved by port injecting ethanol to form a homogeneous ethanol–air mixture that entered the cylinder, followed by direct diesel injection near the end of the compression stroke to initiate combustion. Experiments were primarily conducted with diesel fuel to achieve performance and emission values of conventional diesel engines. Subsequently, tests were conducted for ethanol contents of 5%, 10%, 15%, and 20%, respectively, under different load and engine speed conditions. Analysis of the experimental results revealed that ethanol fumigation improved combustion and engine performance under all operating conditions. Compared with neat diesel fuel, the E20 blend increased the maximum cylinder pressure by approximately 10% and the peak heat release rate by nearly 38% under full-load conditions. Furthermore, ethanol fumigation improved brake thermal efficiency and reduced brake-specific fuel consumption. In terms of emissions, CO and CO₂ emissions decreased by approximately 40% and 18%, respectively, whereas NOx and HC emissions increased by nearly 25% and 57%, respectively, with increasing ethanol content. Among the investigated fuels, E20 provided the most favorable overall balance between combustion enhancement, engine performance, and emission characteristics. Full article

High-efficiency industrial motors are increasingly important for reducing energy consumption, operating costs, and indirect carbon emissions. This study presents a comparative evaluation of IE2 and IE4 efficiency class induction motors with the same rated power and frame size through finite element analysis and prototype testing. Two-dimensional transient electromagnetic models were developed in ANSYS Maxwell to investigate magnetic flux distribution, torque behavior, losses, and steady-state performance, and the numerical results were experimentally validated according to IEC 60034-2-1 procedures. The results show that the IE4 motor provides a more balanced magnetic flux distribution, lower local saturation tendency, reduced torque ripple, and lower total losses than the IE2 motor. Experimental measurements confirmed the numerical predictions with good agreement, particularly at the rated operating point. In addition to higher efficiency, the IE4 motor exhibited stronger starting and breakdown torque characteristics, indicating superior load-handling capability. An economic assessment based on a representative duty cycle showed that the relative additional cost of the IE4 motor can be recovered within approximately 0.81 years, while lower annual electricity consumption also reduces indirect CO₂ emissions. Furthermore, the IE4 prototype operated at a lower thermal steady-state temperature, supporting longer insulation life and improved long-term reliability. Overall, the findings demonstrate that replacing conventional IE2 motors with IE4 alternatives is not merely an efficiency upgrade, but also a technically robust, economically justified, and environmentally effective strategy for sustainable industrial systems. Full article

Turbofan engine performance is critically sensitive to operating conditions, yet comprehensive frameworks that simultaneously assess exergo-economic, exergo-environmental, and lifecycle cost performance across realistic flight envelopes remain limited, particularly for Gulf-region climates. In this study, we present a comprehensive analysis of the exergo-economic, exergo-environmental, and lifecycle costings of five different configurations of two-spool and triple-spool turbofan engines. The analysis was carried out for a wide range of four operating conditions, namely ambient temperature, flight altitude, Mach number, and % relative humidity, with emphasis on the climate conditions likely to be found in the Gulf region. The computational models developed were validated against published data to confirm their reliability. It was found that fuel consumption was the most significant contributor to total lifecycle ownership cost, between 60 and 75% of hourly operating cost over a 20-year service period. Ambient temperature, Mach number, and Cruise altitude represented the most significant drivers of long-term economic performance, with % relative humidity having little effect. Exergo-economic analysis showed that the major cost mechanisms changed dramatically with operating conditions. Exergy destruction and component inefficiencies determined the costs at Takeoff, with capital investment being the dominant factor when cruising. Increase in both or either ambient temperature and altitude was shown to reduce cost rates but simultaneously reduced thermo-economic efficiency via higher specific exergy costs. However, increase in Mach number enhances both exergy output and cost-effectiveness, confirming that specific exergy cost is a more reliable indicator of true system performance than cost rate alone. The two-spool configurations show superior specific CO₂ emissions, with Case 3 recording the lowest emissions at Takeoff and Case 2 at Cruise. For exergy-based environmental indicators, Case 3 performs best at both Takeoff and Cruise, achieving the lowest environmental destruction coefficient and index, as well as the highest environmental benign index among all five configurations. These findings provide actionable guidance for engine selection, operational optimization, and sustainable propulsion system design. Full article

Battery cell manufacturing is highly energy intensive, with clean and dry rooms being among the largest consumers of electricity and thermal energy. Due to the moisture sensitivity of most advanced cathode materials (e.g., NMC 811) and sulfide-based solid-state materials, production environments must operate at extremely low humidity, requiring energy-intensive HVAC systems to remove moisture introduced mainly by workers and infiltration. To reduce energy consumption, a detailed understanding of the airflow patterns in the room is essential. Because of complex flow patterns (exhaust air demands, energy dissipation), tracer gas techniques using CO₂ as a marker provide an operation-integrated method for determining local air age. The studies presented in this paper apply tracer gas techniques for the first time to a room in which air is almost completely recirculated at high air change rates of approximately 27 h⁻¹, with the supply air being conditioned by removing all process-relevant contaminants such as moisture and particles. Measurements in a separate flow box show successful air age calculations that agree with simplified CFD simulations. For the clean and dry room, the empirical variable relative exposure (REX) was introduced. The measurements indicate an inhomogeneous air distribution inside the room, accompanied with short-circuit flows, partial displacement flow, and mixing, and therefore have the potential to provide a cost-effective first-hand insight into the prevailing airflow patterns. Nevertheless, the presented measurement technique must be further optimized and validated for rooms with air recirculation and high air change rates. Full article

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

Sustainable pavement maintenance increasingly requires coordinated management of infrastructure condition, renewable-energy availability, carbon emissions, financial resources, and operational capacity. This study proposes a renewable-energy resource management framework for low-carbon network-level pavement maintenance using simulation-based pavement-energy modeling and multi-agent deep reinforcement learning. The proposed framework develops an AnyLogic-based pavement-energy simulation environment in which road sections, deterioration states, work zones, maintenance crews, equipment resources, photovoltaic generation, battery storage, grid support, diesel backup, carbon tracking, and budget consumption are represented within one integrated decision environment. To support adaptive maintenance control, pavement sections are modeled as interacting agents, while road connectivity, dispatch dependency, traffic interaction, and maintenance-route relationships are encoded through graph structures. A graph-based multi-agent deep reinforcement learning model, named Graph-MAPPO, is then used as the decision controller. The model integrates multi-head graph attention for spatial dependency learning, GRU-based temporal memory for deterioration-history representation, finite-element-assisted structural-risk indicators for hidden damage characterization, and constraint-aware action masking to prevent infeasible decisions under budget, carbon, energy, crew, and equipment constraints. Two calibrated datasets were generated to support the framework: a pavement network and maintenance dataset containing 4437 records and 55 features, and a renewable energy-carbon-budget dataset containing 9875 records and 38 features. The decision controller jointly selects the pavement section, treatment type, intervention timing, crew, equipment, and energy mode. Results from 20 experimental configurations show that the balanced Graph-MAPPO policy improves average $PCI$ from 69.4 to 78.9, achieves an $RSL$ gain of 6.8 years, reduces emissions to 58.3 tCO₂e, maintains a renewable-energy share of 74.6%, and limits the constraint-violation rate to 1.8%. Under high renewable-energy availability, the framework achieves the best overall performance, with an average $PCI$ of 80.2, renewable-energy share of 84.6%, emissions of 50.8 tCO₂e, and reward of 0.90. These findings demonstrate that integrating pavement-energy simulation, renewable-energy resource allocation, carbon-aware maintenance planning, structural-risk awareness, and multi-agent decision control can support more adaptive, low-carbon, and resource-efficient pavement maintenance management. Full article

In the context of growing attention to environmental sustainability, emission reduction efforts increasingly involve all sectors, including agriculture. European “Stage” regulations (from Stage I in 2002 to Stage V in 2019) have progressively reduced regulated pollutants such as hydrocarbons (HC), nitrogen oxides (NO_x), particulate matter (PM), and carbon monoxide (CO). However, carbon dioxide (CO₂) emissions from agricultural tractors are not currently subject to specific legislation. This study assesses CO₂ emissions through their direct relationship with fuel consumption. Hourly and specific CO₂ emissions (g/kWh) were estimated using power and fuel consumption data from 877 tractors tested under OECD Code 2 procedures from the 1960s to the present. The same tractors were analyzed under two operating conditions: power take-off (PTO) dynamometer bench tests and drawbar tests, considering maximum power and rated engine speed. The four testing conditions were compared to assess differences in delivered power, fuel consumption, and CO₂ emissions. Fuel consumption was modeled through linear regression using power as the independent variable, while specific fuel consumption and fuel productivity were estimated using a nonlinear regression approach. The comparison between test conditions shows a reduction in delivered power of 21.2% when moving from the PTO dynamometer test at maximum power to the drawbar test at rated engine speed, accompanied by an 18.9% increase in specific CO₂ emissions. These findings indicate that operating conditions significantly influence tractor carbon emissions and suggest that assessments accounting for traction-related losses provide a more realistic estimate of tractor environmental performance than PTO dynamometer tests alone. The proposed approach may support the development of carbon-oriented mitigation strategies and future greenhouse gas reduction policies for agricultural mechanization. Full article

Currently, high-speed gasoline engines are increasingly focusing on miniaturization and efficiency. Compared with turbocharging, mechanical supercharging is undoubtedly the more suitable technical approach for small, high-speed gasoline engines. To clarify the influence of the proposed supercharging approach on power, thermal efficiency and emissions within a broad range of engine speeds, this study designed two supercharging schemes (with different supercharger/crankshaft transmission ratios), and conducted bench tests comparing with the original engine. The results showed that the boost effect was more pronounced under medium load conditions. At full load, the high-speed supercharging scheme (94.5/86 ratio) on average improved torque by 10.8%, while the low-speed boost mode (86/61 ratio) only took effect after 5500 rpm. But at 60% load, 94.5/86 and 86/61, respectively, improved torque by 24.4% and 11.7%; thermal efficiencies of both supercharging schemes were almost the same and higher than that of the original operation by 0.8%; thus, the specific fuel consumption was reduced, on average, by ~9.5%. After boosting, the ignition phase was delayed due to the knock limit, but the high cylinder temperature promoted the recovery of the combustion rate in the later stage. In terms of emissions, NO_x increased by 28% with the 94.5/86 scheme, while it decreased very slightly with the 86/61 scheme. CO rose by 3.7% under the 94.5/86 scheme, while it almost did not change under the 86/61 scheme operation, and HC increased by 13% and decreased by 21%, respectively, under the high and low boosting schemes. In conclusion, our proposed supercharging approach improved power and thermal efficiency and afforded a compromise emission effect. This study has revealed the performance trade-off rules of different boosting modes, which can provide important theoretical and technical support for the mechanical supercharging modification of high-speed gasoline engines. Full article

Vapor compression refrigeration systems account for a substantial share of global electricity consumption in residential and commercial applications, with environmental impacts arising from both energy use and refrigerant leakage. Refrigerant charge optimization offers an economical means of improving system performance without hardware modifications. Oil-free linear compressors mitigate lubricant-induced degradation of heat transfer, yet the combined influence of charge amount on the coefficient of performance (COP) and total equivalent warming impact (TEWI) has not been thoroughly quantified. An experimental investigation was conducted on a vapor compression refrigeration system equipped with an oil-free linear compressor using R134a. The experiments covered refrigerant charges of 220–330 g, piston strokes of 9–12 mm, and pressure ratios of 2.0–3.5. Component-level refrigerant distribution and system performance characteristics were analyzed systematically. The condenser holds 74.7% of the total refrigerant charge at the optimal charge of 280 g. Rising charge reduces superheat and increases subcooling, both of which serve as practical indicators of the charge level. The mass flow rate, cooling capacity, and COP all exhibit characteristic non-monotonic trends. The maximum COP of 4.67 and the maximum cooling capacity of 472.7 W are both achieved at 280 g, which is identified as the optimal operating condition. The oil-free design eliminates lubricant interference and yields a clearly condenser-dominated refrigerant distribution. The TEWI increases by only 3.6% when the charge is raised to 330 g, and this slight environmental drawback is offset by the gain in energy efficiency. A distinct COP reduction is observed at a charge of 220 g. The charge of 280 g achieves the best balance between energy efficiency and lifecycle CO₂ emissions. This work provides quantitative guidance for charge selection in oil-free linear compressor refrigeration systems. Full article