Search Results (1,353)

The urgent need to mitigate rising global CO₂ emissions demands the development of efficient carbon capture technologies. This study addresses the persistent challenge of sintering-induced performance degradation in CaO-based sorbents during high-temperature CO₂ capture. A novel solvent/nonsolvent synthetic strategy to fabricate CaO/CaAl-layered double oxide (LDO) composites was developed, where CaAl-LDO serves as a nanostructural stabilizer. The CaAl-LDO precursor enables atomic-level dispersion of components, which upon calcination forms a Ca₁₂Al₁₄O₃₃ “rigid scaffold” that spatially confines CaO nanoparticles and effectively mitigates sintering. Thermogravimetric analysis results demonstrate exceptional cyclic stability; the composite achieves an initial CO₂ uptake of 14.5 mmol/g (81.5% of theoretical capacity) and retains 87% of its capacity after 30 cycles. This performance significantly outperforms pure CaO and CaO/MgAl-LDO composites. Physicochemical characterization confirms that structural confinement preserves mesoporous channels, ensuring efficient CO₂ diffusion. This work establishes a scalable, instrumentally simple route to high-performance sorbents, offering an efficient solution for carbon capture in energy-intensive industries such as power generation and steel manufacturing. Full article

Accurate forecasting of energy consumption in buildings is essential for achieving energy efficiency and reducing carbon emissions. However, many existing models rely on limited input variables and overlook the complex influence of indoor environmental quality (IEQ). In this study, we assess the performance of hybrid machine learning ensembles for predicting hourly energy demand in a smart office environment using high-frequency IEQ sensor data. Environmental variables including carbon dioxide concentration (CO₂), particulate matter (PM_2.5), total volatile organic compounds (TVOCs), noise levels, humidity, and temperature were recorded over a four-month period. We evaluated two ensemble configurations combining support vector regression (SVR) with either Random Forest or LightGBM as base learners and Ridge regression as a meta-learner, alongside single-model baselines such as SVR and artificial neural networks (ANN). The SVR combined with Random Forest and Ridge regression demonstrated the highest predictive performance, achieving a mean absolute error (MAE) of 1.20, a mean absolute percentage error (MAPE) of 8.92%, and a coefficient of determination (R²) of 0.82. Feature importance analysis using SHAP values, together with non-parametric statistical testing, identified TVOCs, humidity, and PM_2.5 as the most influential predictors of energy use. These findings highlight the value of integrating high-resolution IEQ data into predictive frameworks and demonstrate that such data can significantly improve forecasting accuracy. This effect is attributed to the direct link between these IEQ variables and the activation of energy-intensive systems; fluctuations in humidity drive HVAC energy use for dehumidification, while elevated pollutant levels (TVOCs, PM_2.5) trigger increased ventilation to maintain indoor air quality, thus raising the total energy load. Full article

With the advance of digital transformation, the assessment of the environmental impact of digital tools and technologies grows more relevant. Considering the inflated expectations of environmental responsibility in higher education, this study analyses how websites of Polish universities conform to sustainable web design criteria. The sustainability audit employed a methodology encompassing carbon emissions measurement, technical website analysis, and SEO evaluation. The author analysed 63 websites of public universities in Poland using seven independent audit tools, including an original AI Custom GPT agent preconfigured in the ChatGPT ecosystem. The results revealed a substantial differentiation in CO₂ emissions and website optimisation, with an average EcoImpact Score of 66.41/100. Nearly every fourth website exhibited a significant carbon footprint and excessive component sizes, which indicates poor asset optimisation and energy-intensive design techniques. The measurements exposed considerable variability in emission intensities and resource intensity among the university websites, suggesting the need for standardised digital sustainability practices. Regulations on the carbon footprint of public institutions’ websites and mobile applications could become vital strategic components for digital climate neutrality. Promoting green hosting, “Green SEO” practices, and sustainability audits could help mitigate the environmental impact of digital technologies and advance sustainable design standards for the public sector. The proposed auditing methodology can effectively support the institutional transition towards sustainable management of digital infrastructure by integrating technical, sustainability, and organisational aspects. Full article

In boron neutron capture therapy (BNCT), the intracellular localization and concentration of boron-10 atoms significantly influence therapeutic efficacy. Although various boronic-acid-targeted fluorescent probes have been developed to evaluate BNCT agents, most of these probes emit at short wavelengths and are, therefore, incompatible with common nuclear-staining reagents such as Hoechst 33342 and 4′,6-diamidino-2-phenylindole (DAPI). While our previously reported probe, BS-631, emitted fluorescence above 500 nm, it exhibited limitations in terms of reaction rate and fluorescence intensity. To address these issues, we developed a boronic-acid-targeted fluorescent probe with a longer emission wavelength, rapid reactivity, and strong fluorescence intensity. Herein, we designed and synthesized BTTQ, a probe based on a 2-(2-hydroxyphenyl)benzothiazole core structure. BTTQ exhibited immediate fluorescence upon reaction with 4-borono-L-phenylalanine (BPA), with an emission wavelength of 567 nm and a sufficiently high fluorescence quantum yield for detection. BTTQ quantitatively detected BPA with high sensitivity (quantification limit of 10.27 µM), suitable for evaluating BNCT agents. In addition, BTTQ exhibited selective fluorescence for BPA over metal cations. Importantly, BTTQ enabled fluorescence microscopic imaging of intracellular BPA distribution in living cells co-stained with Hoechst 33342. These results suggest that BTTQ is a promising fluorescent probe for the evaluation of future BNCT agents. Full article

Understanding the carbon emission efficiency of apple production (APCEE) is critical for promoting green and low-carbon agricultural development. However, the spatiotemporal dynamics and driving factors of APCEE in China remain inadequately explored. This study employs life cycle assessment, super-efficiency slacks-based measures, and a panel Tobit model to evaluate the carbon footprint, APCEE, and its determinants in China’s two major production regions from 2003 to 2022. The results reveal that: (1) Producing one ton of apples in China results in 0.842 t CO₂e emissions. Land carbon intensity and total carbon emissions peaked in 2010 (28.69 t CO₂e/ha) and 2014 (6.52 × 10⁷ t CO₂e), respectively, exhibiting inverted U-shaped trends. Carbon emissions from various production areas show significant differences, with higher pressure on carbon emission reduction in the Loess Plateau region, especially in Gansu Province. (2) The APCEE in China exhibits a W-shaped trend (mean: 0.645), with overall low efficiency loss. The Bohai Bay region outperforms the Loess Plateau and national averages. (3) The structure of the apple industry, degree of agricultural mechanization, and green innovation positively influence APCEE, while the structure of apple cultivation, education level, and agricultural subsidies negatively impact it. Notably, green innovation and agricultural subsidies display lagged effects. Moreover, the drivers of APCEE differ significantly between the two major production regions. These findings provide actionable pathways for the green and low-carbon transformation of China’s apple industry, emphasizing the importance of spatially tailored green policies and technology-driven decarbonization strategies. Full article

The European Union promotes decarbonization in energy-intensive industries like glass manufacturing. Collaboration between industry and researchers focuses on reducing CO₂ emissions through hydrogen (H₂) integration as a natural gas substitute. However, to the best of the authors’ knowledge, no updated real-world case studies are available in the literature that consider the on-site implementation of an electrolyzer for autonomous hydrogen production capable of meeting the needs of a glass manufacturing plant within current technological constraints. This study examines a representative hollow glass plant and develops various decarbonization scenarios through detailed process simulations in Aspen Plus. The models provide consistent mass and energy balances, enabling the quantification of energy demand and key cost drivers associated with H₂ integration. These results form the basis for a scenario-specific techno-economic assessment, including both on-grid and off-grid configurations. Subsequently, the analysis estimates the levelized costs of hydrogen (LCOH) for each scenario and compares them to current and projected benchmarks. The study also highlights ongoing research projects and technological advancements in the transition from natural gas to H₂ in the glass sector. Finally, potential barriers to large-scale implementation are discussed, along with policy and infrastructure recommendations to foster industrial adoption. These findings suggest that hybrid configurations represent the most promising path toward industrial H₂ adoption in glass manufacturing. Full article

One of the fundamental goals of contemporary mobility is to optimize transport processes in urban areas. The solution in this area seems to be the implementation of the idea of sustainable transport systems based on the Smart City concept. The article presents a case study—an assessment of the possibilities of changing mobility habits based on the idea of sustainable urban transport, taking into account the criterion of energy consumption of individual means of transport. The analyses are based on a comparison of selected means of transport occurring in the urban environment according to several key parameters for the optimization and efficiency of transport processes, i.e., cost, time, travel comfort, and impact on the natural environment, while simultaneously linking them to the criterion of energy consumption of individual means of transport. The analyzed parameters currently constitute the most important group of challenges in the area of shaping and planning optimal and sustainable urban transport. The presented research was used to indicate the connections between various areas of optimization of the transport process and the energy efficiency of individual modes of transport. Analyses have shown that the least time-consuming process of urban mobility is associated with the highest level of CO₂ emissions and, at the same time, the highest level of energy efficiency. However, combining public transport with other means of transport can meet most of the transport expectations of city residents, also in terms of energy optimization. The research results presented in the article can contribute to the creation of a strategy for the development of the transport network based on the postulates of increasing the optimization and efficiency of individual means of transport in urban areas. At the same time, recognizing the criterion of energy intensity of means of transport as leading in the development of sustainable urban mobility. Thus, confirming the important role of existing transport systems in the process of shaping and planning sustainable urban mobility in accordance with the idea of Smart City. Full article

This study proposes an integrated, machine learning-based multi-objective optimization framework to evaluate and optimize the utilization of steel slag in road base layers, simultaneously addressing economic costs and environmental impacts. A comprehensive dataset of 482 scenarios was engineered based on literature-informed parameters, encompassing transport distance, processing energy intensity, initial moisture content, gradation adjustments, and regional electricity emission factors. Four advanced tree-based ensemble regression algorithms—Random Forest Regressor (RFR), Extremely Randomized Trees (ERTs), Gradient Boosted Regressor (GBR), and Extreme Gradient Boosting Regressor (XGBR)—were rigorously evaluated. Among these, GBR demonstrated superior predictive performance (R² > 0.95, RMSE < 7.5), effectively capturing complex nonlinear interactions inherent in slag processing and logistics operations. Feature importance analysis via SHapley Additive exPlanations (SHAP) provided interpretative insights, highlighting transport distance and energy intensity as dominant factors affecting unit cost, while moisture content and grid emission factor predominantly influenced CO₂ emissions. Subsequently, the Gradient Boosted Regressor model was integrated into a Non-Dominated Sorting Genetic Algorithm II (NSGA-II) framework to explore optimal trade-offs between cost and emissions. The resulting Pareto front revealed a diverse solution space, with significant nonlinear trade-offs between economic efficiency and environmental performance, clearly identifying strategic inflection points. To facilitate actionable decision-making, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method was applied, identifying an optimal balanced solution characterized by a transport distance of 47 km, energy intensity of 1.21 kWh/ton, moisture content of 6.2%, moderate gradation adjustment, and a grid CO₂ factor of 0.47 kg CO₂/kWh. This scenario offered a substantial reduction (45%) in CO₂ emissions relative to cost-minimized solutions, with a moderate increase (33%) in total cost, presenting a realistic and balanced pathway for sustainable infrastructure practices. Overall, this study introduces a robust, scalable, and interpretable optimization framework, providing valuable methodological advancements for sustainable decision making in infrastructure planning and circular economy initiatives. Full article

The significant energy intensity of high-speed railway necessitates integrating renewable technologies to enhance grid resilience and decarbonize transport. This study establishes a coordinated carbon–green certificate market mechanism for railway power systems and develops a tri-source planning model (grid/solar/energy storage) that comprehensively considers the full lifecycle carbon emissions of these assets while minimizing lifecycle costs and CO₂ emissions. The proposed EDMOA algorithm optimizes storage configurations across multiple operational climatic regimes. Benchmark analysis demonstrates superior economic–environmental synergy, achieving a 23.90% cost reduction (USD 923,152 annual savings) and 24.02% lower emissions (693,452.5 kg CO₂ reduction) versus conventional systems. These results validate the synergistic integration of hybrid power systems with the carbon–green certificate market mechanism as a quantifiable pathway towards decarbonization in rail infrastructure. Full article

The International Maritime Organization (IMO) has set a goal to reach net-zero greenhouse gas emissions from international shipping by or around 2050. The ship energy efficiency framework has played a positive role over the past decade in improving carbon intensity and reducing greenhouse gas emissions by employing the technical and operational energy efficiency metrics as effective appraisal tools. To quantify the ship energy efficiency performance and review the existing energy efficiency framework, this paper analyzed the data for the reporting year of 2023 extracted from the European Union (EU) monitoring, reporting, and verification (MRV) system, and investigated the operational profiles and energy efficiency for the ships calling at EU ports. The results show that the data accumulated in the EU MRV system could provide powerful support for conducting ship energy efficiency appraisals, which could facilitate the formulation of decarbonization policies for global shipping and management decisions for stakeholders. However, data quality, ship operational energy efficiency metrics, and co-existence with the IMO data collection system (DCS) remain issues to be addressed. With the improvement of IMO DCS system and the implementation of IMO Net-Zero Framework, harmonizing the two systems and avoiding duplicated regulation of shipping emissions at the EU and global levels are urgent. Full article

The objective of this analysis was to compare the greenhouse gas (GHG) emissions from contemporary grazing cattle production with bison grazing, both modern and historical. The data sets used in this analysis were derived from existing research and conservation properties located outside of Manhattan, KS (USA), which are home to stocker cattle, cow–calf production (CCS), and grazing bison. For stocker cattle, 10 years of animal production data (2007–2016) from season-long stocking (SLS, grazing 156 d) and intensive early stocking systems (IES; 76 grazing d and 2× stocking density) were used for GHG calculations. Enteric CH₄, manure CH₄, and direct nitrous oxide emissions were estimated using the IPCC tier 2 methodology. Historic bison (HGB) enteric CH₄ estimates were calculated using a stocking density of 0.15 ha/animal and assuming that only 13% of grassland was used by bison each year. Within contemporary systems, IES had the lowest emissions (463.3 kg CO₂-eq./ha/yr), while SLS, CCS, and MGB had the highest estimates (494.7, 493.9, and 595.9 kg CO₂-eq./ha/yr, respectively). HGB had the lowest estimated annual emissions at 295.7 kg CO₂-eq./ha/yr. These results imply that the historic grazing baseline of this grassland system is lower but similar to that of contemporary grazing cattle in the Great Plains region. Full article

Addressing the imperative for energy transition amid depleting fossil fuels, distributed generation (DG) is increasingly integrated into distribution networks (DNs). This integration necessitates low-carbon dispatching solutions that reconcile economic and environmental objectives. To bridge the gap between conventional “electricity perspective” optimization and emerging “carbon perspective” requirements, this research integrated Carbon Emission Flow (CEF) theory to analyze spatiotemporal carbon flow characteristics within DN. Recognizing the limitations of the single-objective approach in balancing multifaceted demands, a multi-objective optimization model was formulated. This model could capture the spatiotemporal dynamics of nodal carbon intensity for low-carbon dispatching while comprehensively incorporating diverse operational economic costs to achieve collaborative low-carbon and economic dispatch in DG-embedded DN. To efficiently solve this complex constrained model, a novel Q-learning enhanced Moth Flame Optimization (QMFO) algorithm was proposed. QMFO synergized the global search capability of the Moth Flame Optimization (MFO) algorithm with the adaptive decision-making of Q-learning, embedding an adaptive exploration strategy to significantly enhance solution efficiency and accuracy for multi-objective problems. Validated on a 16-node three-feeder system, the method co-optimizes switch configurations and DG outputs, achieving dual objectives of loss reduction and carbon emission mitigation while preserving radial topology feasibility. Full article

Green hydrogen will play a crucial role in the future of emission reduction in air traffic in the long-term, as it will completely eliminate CO₂ emissions and significantly reduce other pollutants such as contrails and nitrogen oxides. Hydrogen offers a promising alternative to kerosene for short- and medium-haul flights, particularly through direct combustion and hydrogen fuel cell technology in new aircraft concepts. Against the background of the immense capital-intensive infrastructure adjustments that are required at airports for this purpose and the simultaneously high future hydrogen demand for the shipping industry, this paper analyses the emission savings potential in Europe if airports near seaports would switch to hydrogen-powered flight connections. Full article

This study investigates whether institutional quality amplifies the emissions-reducing effect of energy efficiency in hydrocarbon-dependent economies. Addressing a gap in the energy–environment literature, it tests how governance conditions shape the effectiveness of technical mitigation strategies. Using panel data from six Gulf Cooperation Council (GCC) countries between 2000 and 2023, we estimate a fixed-effects model with interaction terms between energy intensity (as a proxy for efficiency) and institutional quality (proxied by Control of Corruption). The results show that energy efficiency is associated with lower CO₂ emissions, and this relationship is significantly moderated by institutional quality. We also analyze the emissions impact of natural gas consumption and identify a structural shift following the 2014 energy reforms: while gas use was positively associated with emissions before 2014, the post-reform period shows a weaker or reversed effect. Robustness checks using alternative governance indicators—Regulatory Quality and Government Effectiveness—confirm the moderating role of institutions. The study offers new empirical evidence on the energy–institution–environment nexus and introduces a novel interaction-based methodology suited to resource-rich economies undergoing institutional transition. Full article

The stretch film production is highly energy intensive. The components of the technological line are powered by electrical energy, and the heat is used to change the physical state of the raw material (granules). The raw material is poured into FCR (the first calender roller). To solidify the liquid raw material, the calendar must be cooled. The low-temperature heat, treated as waste heat, has dissipated in the atmosphere. Technological innovations were proposed: (a) the raw material comprises raw material (primary) and up to 80% recyclate (waste originating mainly from agriculture), (b) the use of low-temperature waste heat (the cooling of FCR in the process of foil stretch production). A heat recovery line based on two compressor heat pumps (HP, hydraulically coupled) was designed. The waste heat (by low-temperature HP) was transformed into high-temperature heat (by high-temperature HP) and used to prepare the raw material. The proposed technological line enables the management of difficult-to-manage post-production waste (i.e., agriculture and other economic sectors). It reduces energy consumption and raw materials from non-renewable sources (CO₂ and other greenhouse gas emissions are reducing). It implements a closed-loop economy based on renewable energy sources (according to the European Green Deal). Full article