Search Results (3,966)

Textile membrane systems are increasingly used in sports halls because of their low structural weight, rapid assembly, and ability to span large areas. Their operational performance, however, is strongly affected by local climate conditions, envelope configuration and the limited thermal inertia of membrane materials. This study presents a comparative EnergyPlus-based assessment of textile membrane sports halls in six representative climate contexts: Helsinki, Berlin, Niš, Barcelona, Dawadmi and Bangkok. A conventional masonry hall was used as the reference case and compared with a single-layer PVC-coated polyester membrane system and double-layer membrane systems with air gaps of 0.4, 0.5 and 0.6 m, including mechanically ventilated air-cavity variants. The assessment combines four performance indicators: annual operational energy demand, carbon emissions, indicative global cost and thermal comfort expressed through Fanger’s Predicted Percentage of Dissatisfied (PPD) index. The results show that the dominant energy demand is climate-dependent, with heating prevailing in cold climates and cooling becoming decisive in hot-arid and hot-humid climates. Double-layer cases usually show lower operational energy demand and lower associated carbon dioxide emissions than the single-layer membrane case. This improvement, however, is not uniform; it depends on the climatic setting and on the width of the air gap. The comfort results lead to a similar but more limited conclusion. Although PPD is reduced in the double-layer configurations, the values remain above conventional comfort acceptance levels in all tested cases. The double-layer membrane should therefore be understood as a measure that reduces thermal dissatisfaction, not as a complete comfort solution. The economic assessment indicates that membrane systems have substantially lower initial capital costs than masonry construction, while their long-term performance depends on operational energy costs, membrane replacement assumptions and the selected analysis horizon. The study provides a climate-specific comparative framework for early-stage envelope selection in textile membrane sports halls, emphasizing that energy demand, carbon emissions, cost and thermal comfort should be considered together rather than as separate outputs. Full article

Rapid economic development has led to a growing reliance on private car commuting, making the mitigation of carbon dioxide (CO₂) pollution along road environments critical for the health of nearby residents. Road greening serves as an ecological barrier between traffic emissions and adjacent residential areas, and its effectiveness in reducing local CO₂ pollution has been widely studied. However, the influence of different spatial morphologies of road greening on the distribution of CO₂ around buildings remains underexplored. In this study, we developed a numerical simulation model to investigate CO₂ dispersion on building surfaces under various road greening spatial configurations. Simulation results indicate that a “tree–shrub–grass” composite configuration significantly reduces CO₂ concentrations around buildings. These findings provide practical guidance for optimizing vegetation spatial layouts in high-density road networks and contribute to the global pursuit of carbon peak and carbon neutrality goals. Full article

This study examines the determinants of carbon dioxide (CO₂) emissions across 25 European Union countries over the period 2000–2021, with particular emphasis on the roles of environmental taxation and green innovation in shaping environmental sustainability. The analysis is grounded in ecological modernization theory, endogenous growth theory, and the Environmental Kuznets Curve hypothesis, which collectively explain the long-run and dynamic interactions between environmental policy, economic activity, structural transformation, and environmental outcomes. To ensure robust empirical inference, this study applies a comprehensive econometric framework that accounts for cross-sectional dependence, heterogeneity, non-stationarity, cointegration, and endogeneity. The empirical strategy begins with Pesaran cross-sectional dependence tests and slope heterogeneity diagnostics, followed by second-generation panel unit root tests (Pesaran CADF/CIPS) and Westerlund cointegration tests to establish the existence of long-run equilibrium relationships among the variables. Long-run coefficients are estimated using Fully Modified Ordinary Least Squares (FMOLS), Dynamic Ordinary Least Squares (DOLS), Canonical Cointegrating Regression (CCR), and Common Correlated Effects Mean Group (CCEMG) estimators. In addition, the Panel Autoregressive Distributed Lag (ARDL) model is employed to capture both short-run dynamics and long-run adjustment processes, while the System Generalized Method of Moments (System GMM) estimator addresses potential endogeneity, reverse causality, omitted variable bias, and dynamic persistence in CO₂ emissions. The empirical results indicate that environmental taxation has a positive and statistically significant association with CO₂ emissions, suggesting that current fiscal environmental policies in EU-25 countries may not yet be sufficiently effective in discouraging pollution-intensive activities. In contrast, green innovation is found to significantly reduce CO₂ emissions, underscoring the critical role of innovation-driven environmental investment and technological progress in improving environmental quality. Economic growth, exports, and urbanization are associated with higher emissions, while imports contribute to emission reductions, reflecting differences between domestic production-based effects and trade-related structural adjustments. The System GMM results further confirm the persistence of CO₂ emissions over time and validate the robustness of the long-run relationships identified by alternative estimators. Likewise, the CCEMG and Panel ARDL results support the stability and consistency of the findings under conditions of cross-sectional dependence and heterogeneous country dynamics. Taken together, the results highlight the importance of integrating environmental taxation with green innovation policies, innovation-driven investment, and sustainable trade policies to achieve long-term emission reductions in the European Union. This study contributes to the environmental economics literature by providing robust empirical evidence using second-generation panel econometric techniques that explicitly address cross-sectional dependence, heterogeneity, and endogeneity in the analysis of environmental sustainability. Full article

Incorporation of carbon-11 radiotracers for positron emission tomography (PET) imaging requires close coordination between cyclotron operation, radiochemistry production, quality control, and clinical administration. A persistent challenge exists is the minimization of the carbon-12 isotopologue mass of the radiotracer, which reduces molar activity and can compromise PET image quality. This challenge can be particularly acute at facilities where cyclotron operation and carbon-11 radiochemistry are realized by separate organizations with distinct operational priorities. Here, we describe how the Radiochemistry Group at New York University Grossman School of Medicine and Siemens Healthineers have developed an integrated operational framework for consistent, high-quality carbon-11 production within an academic–industry partnership. Cyclotron target maintenance and conditioning protocols, remote chemistry module maintenance schedules, a validated radio-HPLC method (UV LOD = 0.9 µg/mL, UV LOQ = 3.0 µg/mL) for trending methyl iodide isotopologue mass, and structured inter-team communication protocols are presented in this manuscript. Quality analysis demonstrates molar activities consistently exceeding the recommended minimum of 40 GBq/µmol for reversibly binding radiotracers used in human PET studies. This work is intended as a practical resource for radiochemists, cyclotron engineers, and facility managers working to establish or improve institutional carbon-11 programs. Full article

The assessment of humanity’s ecological footprint has become increasingly critical in contemporary discourse due to growing environmental challenges. This study examines the economic evaluation of the ecological footprint with a particular focus on forest ecosystem services and food productivity. Using harmonized secondary data from FAOSTAT, EUROSTAT, the World Bank, and IPBES, the analysis covers selected developed and emerging economies, including the European Union, the United States, China, Brazil, and other representative countries. This study investigates the macroeconomic implications of natural capital degradation by applying a panel data econometric model to European Union countries over the period 2010–2023. Moving beyond descriptive approaches, the research formulates and tests three hypotheses linking biodiversity, environmental pressure, and green transition variables to economic performance. Using harmonized data from Eurostat and Statista, the study employs a fixed-effects regression framework to estimate the impact of biodiversity indicators, greenhouse gas emissions, renewable energy share, and environmental protection expenditures on GDP per capita. The results demonstrate that biodiversity preservation and resource efficiency are positively associated with economic performance, while environmental degradation—proxied by greenhouse gas emissions—exerts a statistically significant negative effect. Additionally, the findings confirm that investments in renewable energy and environmental protection contribute to long-term economic stability. By providing a transparent data structure, explicit variable operationalization, and reproducible econometric specification, the study offers an original empirical contribution to ecological economics and addresses the limitations of prior literature that relied primarily on descriptive synthesis. Full article

This paper presents an exploratory operational assessment of the carbon footprint of an LNG tanker using real operational data collected by a continuous emission monitoring system over a ten-month period of vessel operation. The analysis included carbon dioxide (CO₂) and methane (CH₄) emissions from the main engines and diesel generators, the calculation of CO₂-equivalent using the GWP₁₀₀ and GWP₂₀ global warming potential factors, and a comparison with a hypothetical heavy fuel oil (HFO) operating scenario. The methodology is based on a Tier III approach, that is, on real operational data, which allows a more realistic assessment of emissions than approaches based on standard emission factors. The results show that CO₂ emissions make up the largest share of total emissions, but including methane emissions significantly increases the ship’s overall climate impact. Total methane slip was 3.62%, with diesel generators exhibiting higher slip than the main engines. When GWP₂₀ was applied, total emissions expressed as CO₂-equivalent were, in some periods, comparable to or higher than those estimated for the HFO scenario, despite lower direct CO₂ emissions. The emission distribution indicated that the main engines dominated CO₂ emissions, while methane emissions were more evenly distributed between the main engines and the auxiliary generators, with generators making a significant contribution to total CO₂-equivalent emissions due to their higher methane slip. The results confirm that any assessment of the climate performance of LNG-fueled operation must include methane emissions and should be based on real operational data; otherwise, the overall climate impact may be underestimated. Full article

Deep gas emissions in volcanic and tectonic environments are commonly interpreted as the surface expression of localized deep emitters. This representation is adequate for first-order description, but it is not physically complete. Deep degassing is more appropriately represented as a coupled source–storage–pathway system in which volatile generation, compressible accumulation, phase change, hydraulic communication, and permeability evolution are dynamically linked. Starting from phase-wise mass conservation in deformable porous–fractured media, reduced equations for gas migration, pore-pressure diffusion, and thermo-poro-mechanical coupling are derived, showing how the distinction between gas-mass transport and pressure propagation provides a unified framework for volcanic and tectonic degassing. Deep pressure gradients are shown to arise from the competition between volatile supply and pathway leakance, while episodic discharge can occur when permeability evolves under effective stress, sealing, and failure. A minimal analytical source–storage–pathway model is further derived, yielding explicit criteria for valve onset, source charging and discharge times, and the distinction between pressure-led and mass-led responses. The framework is then applied to the published Campi Flegrei carbon dioxide (${\mathrm{CO}}_2$) diffuse total output record, providing a real-data illustration of slow storage loading and rapid transient discharge. The analysis considers magmatic exsolution, hydrothermal mediation, metamorphic devolatilization, advective–diffusive near-surface filtering, and the inverse problem through which surface fluxes and gas compositions are used to infer deep source properties. The formulation links magmatic degassing, hydrothermal pressurization, tectonic fluid ascent, and fault-valve behavior within a common continuum-physics perspective and identifies the constitutive assumptions that most strongly control interpretation. Full article

Municipal solid waste (MSW) landfills are major sources of greenhouse gas (GHG) emissions, particularly methane (CH₄), which possesses a significantly higher global warming potential than carbon dioxide (CO₂). This study evaluates methane emission and energy recovery potential from the Okhla landfill site, South Delhi, India, using the Landfill Gas Emissions Model (LandGEM). Site-specific model parameters suitable for Indian landfill conditions (k = 0.032 year⁻¹ and L₀ = 70 m³ Mg⁻¹) were incorporated to improve prediction accuracy. The results showed that methane generation initiated in 1997 and is expected to continue until 2068. Peak methane emission of approximately 17.15 million m³ year⁻¹ was observed in 2020 due to rapid degradation of the biodegradable organic fraction, especially food waste. The corresponding peak total landfill gas (LFG) and CO₂ emissions were approximately 35.43 million m³ year⁻¹ and 17.71 million m³ year⁻¹, respectively. A strong correlation (R² = 0.9557) between cumulative waste deposition and methane generation confirmed model reliability. The estimated maximum energy recovery potential was approximately 46.19 million kWh year⁻¹. The study further discusses the applicability of the LandGEM under non-engineered landfill conditions commonly observed in developing countries. Overall, the findings emphasize the importance of methane recovery for greenhouse gas mitigation, sustainable waste management, and renewable energy generation in urban landfill systems. Full article

This study presents a comparative analysis of energy consumption and gaseous emissions from a compressed natural gas (CNG)-fueled vehicle under real driving emissions (RDE) conditions and values predicted using machine learning (ML) models developed from chassis dynamometer data. The analyzed components included energy consumption (EC) as well as carbon dioxide (CO₂), carbon monoxide (CO), total hydrocarbons (HC), methane (CH₄), and nitrogen oxides (NO_X). The models were trained using a limited set of easily accessible predictors, namely vehicle speed and acceleration. A hybrid modelling approach was proposed, combining laboratory data with validation under real-world conditions. Additionally, road load power (P_rl) was introduced as a novel predictor representing vehicle operating load. The results demonstrate that the models effectively capture emission trends, with the highest agreement obtained for CO, CO₂. The inclusion of P_rl improved prediction accuracy, which increased from approximately 64% to 71% for CO and from 57% to 61% for HC. For CO₂, the model achieved about 80–82% agreement with RDE measurements, with analogous levels obtained for EC. A key advantage of the proposed methodology is its reliance on a limited number of input variables, which enhances practical applicability while maintaining satisfactory accuracy. Furthermore, the use of precise laboratory data improves model robustness, and the approach enables the estimation of methane (CH₄), which is typically not measured by standard portable emissions measurement systems (PEMSs). The results confirm the effectiveness of the hybrid ML framework and highlight the importance of incorporating load-related parameters in real-world emissions and energy consumption modeling. Full article

Against the backdrop of global green and low-carbon energy structural transition, renewable energy represented by photovoltaic power has emerged as a critical strategy for safeguarding energy security and mitigating climate change. As typical energy-intensive infrastructures, wastewater treatment plants (WWTPs) suffer from excessive energy consumption and substantial carbon emissions. In this study, a distributed photovoltaic power generation system is deployed at WWTPs to alleviate on-site power demand, and its economic and environmental benefits are quantitatively analyzed via PVsyst software simulation. The simulation results indicate that the overall system efficiency reaches 83.3%, with an annual average power generation capacity of 825,500 kW·h. Annually, the proposed system can save 275.17 tons of standard coal, and correspondingly reduce carbon dioxide emissions by 687.92 tons, sulfur dioxide emissions by 20.64 tons and nitrogen oxide emissions by 10.32 tons, thereby realizing synergistic enhancement of economic and environmental performances. This work offers a feasible engineering reference for promoting the modernized transformation of WWTPs toward energy self-sufficiency and low-carbon operational modes. Full article

The application of urea in agricultural practices leads to carbon dioxide (CO₂) emissions through hydrolysis. Urea, when applied to soil, reacts with water and undergoes hydrolysis, releasing ammonia (NH₃) and CO₂. This reaction is facilitated by soil enzymes such as urease. The released NH₃ can further undergo nitrification, producing nitrate (NO₃⁻) and nitrous oxide (N₂O). While CO₂ from urea hydrolysis is relatively small compared to other sources, cumulative emissions from agricultural activities contribute significantly to climate change and agriculture’s carbon footprint. A straightforward calculation model (CO₂ = A × 0.73) was employed to approximate CO₂ emissions in various countries based on annual urea usage. In this model, China led emissions with 40,483 Gg yr⁻¹, followed by India (26,031 Gg yr⁻¹) and the USA (12,032 Gg yr⁻¹). Out of total annual emissions (94,763 Gg), China contributed 43%, India 27%, the USA 13%, the EU 8%, Pakistan 5%, and Indonesia 4%. China’s CO₂ emissions from urea were 16% higher than India, 30% higher than the USA, 35% higher than the EU, 38% higher than Pakistan, and 39% higher than Indonesia. As expected from the deterministic IPCC formula (CO₂ = Urea × 0.73), the relationship between urea consumption and CO₂ emissions is linear with a slope of 0.73. Linear regression shows that for every 1000-ton increase in urea consumption, CO₂ emissions increase by 730 tons (0.73 Gg) (R² = 0.99, p < 0.001). Pakistan’s urea consumption grew at an average annual rate of 2.2% from 2015 to 2023, with corresponding CO₂ emissions increasing from 4015 to 4788 Gg yr⁻¹ (total increase of 20% over eight years). Optimizing fertilizer application rates, timing, and methods to enhance nutrient uptake efficiency, along with sustainable agricultural practices (organic matter management, conservation tillage, and precision agriculture), can help mitigate environmental impacts. This study emphasizes implementing sustainable agricultural practices and integrated nutrient management to minimize CO₂ emissions from urea application, enabling agricultural systems to contribute to climate change mitigation and reduced carbon footprints. Full article

Given the combined limitations of carbon peaking and carbon neutrality goals, the economic cost of industrial emission reduction in China has become increasingly prominent and regionally differentiated. This study evaluates the shadow prices of CO₂ within the Chinese sector and examines the spatiotemporal evolution of carbon abatement costs across provinces, as well as the underlying influencing mechanisms. To capture the evolution of marginal abatement costs (MAC), we use two non-parametric frameworks based on provincial panel data from 2010 to 2022: slack-based measure (SBM), and the directional distance function (DDF) that accounts for unwanted outcomes. In addition, a fixed effects model with regional and temporal effects was constructed to determine the key determinants of marginal carbon reduction costs. Empirical evidence suggests that: (1) From 2010 to 2022, China’s industrial carbon abatement marginal cost has clearly increased, indicating that emission reduction has gradually shifted from a low-cost stage driven by efficiency improvement to a high-cost stage relying on structural adjustment and advanced technologies. (2) Carbon abatement costs exhibit significant provincial heterogeneity by a small number of high-cost provinces (mainly in developed regions) and a majority of low-cost regions. (3) The industrial carbon emission reduction cost curves in some provinces of China have obvious similar evolution paths, and some areas also show a lagging phenomenon. (4) Carbon emission intensity is the dominant factor influencing abatement costs and presents a significant U-shaped relationship, while urbanization increases cost pressure and trade openness helps reduce abatement costs through structural optimization and technology spillovers. Full article

Air pollution is primarily caused by human activities such as industrial emissions, road traffic, waste incineration, and fossil fuel power plants. Pollution refers to the presence of harmful substances in the air, such as nitrogen dioxide (NO₂), sulfur dioxide (SO₂), ozone (O₃), carbon monoxide (CO), and other environmental pollutants. Some pollutants pose health risks even at low doses. Given the critical importance of air quality, monitoring air pollution has become an urgent and essential subject. Air quality monitoring relies on accurate data, so changeable environments and sensor issues make using interval diagnostic techniques for addressing uncertainty in systems interesting. In this article, we focus on three key aspects to achieve precise and efficient results: (1) the use of an accurate fault detection method that accounts for data uncertainty while maintaining model symmetry, (2) the implementation of a reliable detection index invariant to symmetric sensor behaviors, and (3) the combination of both to improve fault localization accuracy. This paper presented a fault detection and localization framework designed for uncertain and nonlinear monitoring environments. A novel fault-sensitive detection index was developed and integrated into an elimination-based localization strategy within a reduced-rank interval kernel PCA (RR-IKPCA) model. By exploiting information contained in modified residual subspaces and explicitly accounting for measurement uncertainty, the proposed approach enhances fault sensitivity while preserving robust localization capability, as validated on the AIRLOR air quality monitoring network. Full article

Reducing carbon dioxide (CO₂) emissions from buildings is essential for climate change mitigation, with universities representing major energy consumers. This study develops a hybrid data-driven framework combining machine learning and simplified emission factor rescaling to predict campus-wide CO₂ emissions. Nine machine learning models were comparatively evaluated under both cross-sectional and temporal validation settings. Among all evaluated models, the Artificial Neural Network (ANN) demonstrated the most reliable predictive performance, achieving the best balance between prediction accuracy and generalization capability. Although the proposed physics-informed LSBoost_PI framework aimed to integrate physical priors with machine learning through residual correction, it did not improve predictive generalization under the limited sample conditions of the dataset. Time-series cross-validation further confirmed the ANN model’s temporal forecasting capability (RMSE = 2.13 ton/year, R² = 0.985). To support trustworthy and interpretable machine learning, feature importance analysis identified CO₂ intensity indicators (CO₂/kWh and CO₂/TEP) as the dominant drivers of emissions. The study also conducted an emission reduction assessment, revealing that a limited number of high-energy buildings dominate overall campus emissions. These findings provide actionable insights for campus-scale energy management, supporting targeted energy efficiency improvements and renewable energy integration strategies in high-emission buildings. Full article

Life expectancy is a key indicator of public health and sustainable development in Gulf Cooperation Council (GCC) countries, where rapid economic growth, urbanization, and fossil-fuel dependence create environmental and health challenges. This study examines the determinants of life expectancy in six Gulf Cooperation Council countries from 2000 to 2023, focusing on death rates, renewable energy consumption, gross domestic product (GDP) per capita growth, government health expenditure, and carbon dioxide (CO₂) emissions. The empirical strategy combines cross-sectional dependence and slope heterogeneity tests, second-generation panel unit root tests, panel cointegration analysis, and a dynamic System Generalized Method of Moments (System GMM) estimator, with Driscoll–Kraay fixed-effects estimates used for robustness. The results show that higher death rates significantly reduce life expectancy, whereas renewable energy consumption and government health expenditure improve longevity. GDP per capita growth has a modest positive effect, while CO₂ emissions negatively affect life expectancy, confirming the adverse public health consequences of environmental degradation. Robustness checks support the reliability of the main findings. Overall, the evidence highlights the need for integrated policies that combine clean energy transition, stronger environmental regulation, preventive healthcare investment, and sustainable urban development to improve long-term health outcomes in resource-dependent economies in the region. Full article