Search Results (87)

The circular economy (CE) is increasingly recognized as a strategic approach that enables firms to address environmental challenges while enhancing competitiveness and long-term value creation. However, evidence regarding its impact on business performance remains fragmented across sectors, performance dimensions, and organizational contexts. This study presents a systematic literature review conducted in accordance with the PRISMA 2020 guidelines to examine how CE practices influence business performance. The review synthesizes evidence from 79 peer-reviewed publications published between 2015 and 2025. The findings identify five major channels through which CE practices affect business performance: (1) economic, environmental, and social performance, (2) operational and supply chain performance, (3) competitive advantage and strategic positioning, (4) financial and environmental performance, and (5) barriers and performance in SMEs. Across these dimensions, CE practices are frequently associated with improved resource efficiency, cost reduction, innovation capacity, supply chain resilience, and enhanced environmental outcomes, including waste reduction and lower emissions. The review suggests that the performance effects of CE are contingent upon contextual factors such as firm size, ownership structure, industry characteristics, regulatory environment, and digital capabilities. While large firms often benefit from greater resources and organizational capacity, SMEs face significant barriers related to finance, technology, and governance, although these can be mitigated through collaboration networks and digitalization. The study contributes to the Strategic Environmental Management literature by indicating that CE practices may function not only as environmental initiatives but also as strategic capabilities that support competitiveness, resilience, and sustainability transitions. The findings provide implications for managers seeking to integrate circularity into business strategy and for policymakers designing institutional conditions that enable circular business transformation. Full article

This study investigates whether the optimal utilization of the biomass potential contained in municipal solid waste (MSW) can support the implementation of circular economy (CE) principles and contribute to climate policy objectives, particularly the reduction in greenhouse gas (GHG) emissions in the waste management sector. The analysis evaluates whether waste-to-energy recovery can support the objectives of the European Green Deal, including a 55% reduction in GHG emissions by 2035 and the achievement of climate neutrality by 2050. The assessment was conducted for two MSW streams generated in a Polish municipality: separately collected biowaste and residual MSW remaining after meeting European reuse and recycling targets. The study summarizes the results of detailed experimental investigations of the physicochemical and fuel properties of these waste streams. Proven and commercially available energy recovery technologies, including anaerobic digestion (AD) of biowaste and incineration of residual waste, were analyzed. GHG emissions were assessed using a life cycle assessment (LCA) approach, taking into account both direct emissions and avoided emissions resulting from the substitution of conventional energy and fertilizer production. The experimental results revealed significant variability in the biodegradability and energy potential of individual biowaste fractions, with the highest biogas yields observed for kitchen waste. Residual waste exhibited a considerable calorific value and a significant share of renewable energy due to its biomass content. The results indicate that the share of renewable energy in electricity generated from waste is expected to increase from 46.1% in 2025 to 49.9% in 2040. In relation to the total electricity demand of the analyzed city, energy recovered from waste accounts for 1.8 ± 0.3% in 2025 and 1.3 ± 0.2% in 2040. Scenario-based modeling demonstrated that the target system, maximizing energy recovery from both biowaste and residual waste, achieves a consistently negative GHG emission balance throughout the analyzed period (2025–2040), ranging from −72 ± 15 kg CO₂-eq/ton in 2025, through the most favorable value of −81 ± 17 kg CO₂-eq/ton in 2035, to −57 ± 12 kg CO₂-eq/ton in 2040, expressed per ton of total managed biowaste and residual waste. Full article

Conducted electromagnetic interference (EMI) in multilevel power converters is governed by nonlinear interactions among passive filter components, operating conditions, and resonance-sensitive spectral behavior, making analytical prediction and trial-and-error tuning insufficient for systematic compliance-oriented design. This study presents an experimentally grounded, data-driven framework for predicting and optimizing conducted EMI in an IGBT-based, SVPWM-controlled three-level neutral-point-clamped (NPC) inverter equipped with an active harmonic filter. A dataset of 1000 conducted-emission measurements was constructed from 250 filter parameter combinations evaluated under four operating scenarios: constant-current average, constant-current peak, standby average, and standby peak, over the 10 kHz–30 MHz range. Four surrogate architectures were trained and evaluated: a multilayer perceptron (ANN), a convolutional neural network (CNN), a deep neural network (DNN), and a physics-informed neural network (PINN). Model reliability was assessed through nested cross-validation, standard 5-fold cross-validation, Monte Carlo resampling, and SHAP-based interpretability analysis. Among the tested architectures, the CNN achieved the most consistent predictive performance and stability, whereas the PINN provided smoother and more physically disciplined spectral reconstructions in several load-related conditions. The trained surrogates were embedded in a Python 3.11-based graphical user interface and further employed within a compliance-oriented optimization framework to identify filter parameter sets capable of satisfying legal conducted-emission limits. Experimental verification confirmed that surrogate-guided optimized designs achieved positive worst-case legal margins between 7.26 and 11.50 dBµV. Relative to the best measured pre-optimization combination, which still exhibited a worst-case margin of −3.7 dBµV, the best experimentally validated optimized design improved the worst-case legal margin by 15.20 dBµV. These results demonstrate that experimentally trained surrogate models can support not only high-resolution EMI prediction but also regulation-aware filter design and practical engineering decision making. Full article

This study focuses on optimizing hybrid photovoltaic (PV)–wind–lithium-ion battery systems, aiming to balance lifecycle cost (LCC) minimization and power supply reliability (measured by loss of power supply probability, LPSP). A multi-algorithm optimization framework was constructed to compare the performance of Particle Swarm Optimization (PSO), Moth–Flame Optimization (MFO), Grey Wolf Optimization (GWO), and Hybrid Optimizer of PSO and GWO Merits (PSO-GWO) for off-grid power supply; additionally, a PSO-GWO was proposed to address multi-objective demands of economy, environment, and reliability for remote grid-connected power supply. Combined with system architecture design, energy management strategies, and component availability analysis, the PSO-GWO reduced 25-year LCC to $2.024 million, LPSP to 0.05, and cost of energy (COE) to $0.06254/kWh. PSO-GWO further optimized carbon emissions (CEs, operational carbon emissions only) to 2750 tons/year (14.1% lower than PSO) while maintaining LCC at $1.981 million and LPSP at 0.01. Thirty independent runs of each algorithm were conducted for statistical validation, and sensitivity analysis verified the algorithms’ robustness to PV efficiency, battery cost, wind speed fluctuations, battery price volatility, and carbon tax changes. The study also expanded the analysis to multiple climatic scenarios, providing an economical, reliable, low-carbon solution with strong generalizability. Full article

This study presents a comprehensive comparative analysis of the performance, combustion, and emission characteristics of a single-cylinder, four-stroke spark-ignition engine fueled by commercial gasoline and raw biogas enhanced with cerium oxide (CeO₂) nanoparticles. Raw biogas containing 58% methane was tested without carbon dioxide removal to reflect practical rural applications, while CeO₂ nanoparticles were ultrasonically dispersed in the fuel to promote homogeneous suspension and catalytic activity. Experiments were conducted under wide-open and part-throttle conditions across a range of engine speeds, with simultaneous measurement of brake thermal efficiency, brake-specific fuel consumption, volumetric efficiency, in-cylinder pressure, heat release rate, combustion phasing, and regulated emissions. The results showed that while gasoline consistently outperformed biogas in torque and power due to its higher heating value and flame speed, the addition of CeO₂ significantly reduced the performance gap. For the biogas mode, CeO₂ addition increased brake thermal efficiency by up to 5%, lowered brake-specific fuel consumption by up to 8%, and shifted the start of main combustion to earlier crank angles, indicating faster and more complete combustion, particularly at high loads where higher temperatures activate CeO₂’s catalytic behavior. Emission analysis revealed that CeO₂-blended biogas reduced carbon monoxide emissions by approximately 25% and unburned hydrocarbons by up to 55% compared with gasoline, while nitrogen oxide emissions were consistently 15–22% lower. These reductions were observed across both wide-open and part-throttle conditions, confirming improved combustion completeness and lower peak flame temperatures. These improvements are attributed to CeO₂’s oxygen-storage capability, catalytic oxidation activity, and enhanced thermal conductivity, which collectively strengthen combustion completeness and cyclic stability. The findings demonstrate that nanoparticle-enhanced biogas can substantially improve the environmental and operational viability of spark-ignition engines, offering a practical pathway for integrating renewable gaseous fuels into existing transportation systems. Full article

Construction and demolition waste (CDW) represents a critical environmental challenge in the building sector, with global generation exceeding 3.57 billion tonnes annually. The circular economy (CE) framework offers a transformative pathway through selective deconstruction and material recovery, yet implementation faces significant barriers including information asymmetry, supply chain fragmentation, and regulatory uncertainty. This study conducts a systematic literature review using the Context–Mechanism–Outcome (CMO) framework to analyze how computational methods, specifically Digital Twins (DT), Building Information Modeling (BIM), Internet of Things (IoT), blockchain, artificial intelligence, and robotics, act as enablers for resilience in CDW management. Following PRISMA 2020 guidelines and realist synthesis principles, we analyzed 42 high-quality empirical studies from Web of Science and Scopus (2015–2025). Our analysis identifies seven primary mechanisms: traceability (M1), simulation (M2), classification (M3), tracking (M4), collaboration (M5), analytics (M6) and robotics (M7). These mechanisms interact with four critical contexts (information asymmetry, supply chain fragmentation, economic uncertainty, operational risks) to generate outcomes at two levels: resilience capabilities (visibility, monitoring, collaboration, flexibility, anticipation) and performance indicators (recovery rates, cost reduction, CO₂ emissions mitigation, occupational safety). Key findings from the CMO analysis reveal that blockchain-enabled traceability increases material recovery rates by 15–25%, DT simulation reduces deconstruction costs by 20–30%, and computer vision automation improves sorting accuracy to 85–95%. The study contributes middle-range theories explaining how digital technologies enable circular transitions under specific contextual conditions, offering actionable strategic implications for researchers, project managers, technology developers, and policymakers committed to advancing computational economics in sustainable construction. Full article

With the growing global emphasis on nuclear reactor decommissioning, reliable thermal neutron detection has become increasingly important for ensuring critical safety and for the identification of fuel debris and radioactive waste. In this context, this study developed and characterized a Ce-doped CaF₂/⁶LiF (Ce:CaF₂/LiF) eutectic scintillator for thermal neutron detection with Ce concentrations ranging from 0.5 to 10 mol%. The eutectic samples were grown by the melt-solidification method, and their crystalline properties were evaluated using inductively coupled plasma mass spectrometry, X-ray diffraction, scanning electron microscopy, and field-emission electron probe microanalysis. Radioluminescence, photoluminescence, transmittance, scintillation decay, and pulse-height measurements were conducted to assess their scintillation performance. Structural characterization revealed a well-defined eutectic microstructure together with several Ce-rich phases. The results of the effective neutron sensitivity demonstrated that the Ce concentration was effectively optimized based on the effective neutron sensitivity: the sample with 1 mol% Ce exhibited the highest neutron sensitivity (approximately 1.5 times that of a Ce:LiCaAlF₆ single crystal) and a 1.6-times higher neutron-induced light yield, while maintaining a fast effective decay time of 400 ns. These findings suggest that the Ce:CaF₂/LiF eutectic is a promising candidate for high-performance thermal-neutron scintillators for applications in nuclear decommissioning. Full article

Background: The aim of the present systematic review (SR) is to compile evidence regarding the use of radiomic-based machine learning (ML) models for predicting human papillomavirus (HPV) status in oropharyngeal squamous cell carcinoma (OPSCC) patients and to assess their reliability, methodological frameworks, and clinical applicability. The SR was conducted following PRISMA 2020 guidelines and registered in PROSPERO (CRD42025640065). Methods: Using the PICOS framework, the review question was defined as follows: “Can radiomic-based ML models accurately predict HPV status in OPSCC?” Electronic databases (Cochrane, Embase, IEEE Xplore, BVS, PubMed, Scopus, Web of Science) and gray literature (arXiv, Google Scholar and ProQuest) were searched. Retrospective cohort studies assessing radiomics for HPV prediction were included. Risk of bias (RoB) was evaluated using Prediction model Risk Of Bias ASsessment Tool (PROBAST), and data were synthesized based on imaging modality, architecture type/learning modalities, and the presence of external validation. Meta-analysis was performed for externally validated models using MetaBayesDTA and RStudio. Results: Twenty-four studies including 8627 patients were analyzed. Imaging modalities included computed tomography (CT), magnetic resonance imaging (MRI), contrast-enhanced computed tomography (CE-CT), and ¹⁸F-fluorodeoxyglucose positron emission tomography (¹⁸F-FDG PET). Logistic regression, random forest, eXtreme Gradient Boosting (XGBoost), and convolutional neural networks (CNNs) were commonly used. Most datasets were imbalanced with a predominance of HPV+ cases. Only eight studies reported external validation results. AUROC values ranged between 0.59 and 0.87 in the internal validation and between 0.48 and 0.91 in the external validation results. RoB was high in most studies, mainly due to reliance on p16-only HPV testing, insufficient events, or inadequate handling of class imbalance. Deep Learning (DL) models achieved moderate performance with considerable heterogeneity (sensitivity: 0.61; specificity: 0.65). In contrast, traditional models provided higher, more consistent performance (sensitivity: 0.72; specificity: 0.77). Conclusions: Radiomic-based ML models show potential for HPV status prediction in OPSCC, but methodological heterogeneity and a high RoB limit current clinical applicability. Full article

The aviation industry requires a series of actions that will transform its current status, aiming for sustainable operations. Aviation’s end-of-life stream is a pivotal lever for circularity, yet current dismantling and recycling practices leave significant value unrealized. Circular Economy could be considered as a transformational approach to the aviation industry and address its environmental and economic challenges, meeting sustainability principles. This study conducts a PRISMA-guided qualitative systematic review across academic and industry sources to synthesize regulations, technologies, and economics of aircraft decommissioning. It aims to quantify material recovery potential and environmental gains at the aircraft level and assess technology readiness and cost drivers for metals, polymers, and composites. Findings indicate that optimized decommissioning enables high-value part reuse and substantial material recovery (notably aluminum), with associated lifecycle greenhouse-gas avoidance at the aircraft scale. However, high costs, weak regulations, and limited recycling technologies hinder adoption. Results show that optimized dismantling and certified part-reuse pathways can recover up to 85–90% of total aircraft mass, with potential CO₂-emission avoidance of 25–35 t per narrow-body aircraft compared with landfill disposal. Metal recycling technologies (TRL 8–9) already achieve high yields, whereas polymer and composite recycling remain limited (TRL 5–6) by purity and certification barriers. A comparative assessment of EU, US, and Asia–Pacific regulations identifies enforcement and infrastructure gaps hindering implementation. The study introduces an integrated CE roadmap for aviation comprising (i) standards-aligned design-for-disassembly and digital traceability, (ii) accredited MRO-to-reuse networks, and (iii) performance-based policy incentives. Full article

The increasing atmospheric CO₂ concentration is one of the major environmental challenges, necessitating not only emission reduction but also effective carbon utilization. Non-thermal plasma-catalytic CO₂ conversion offers an efficient pathway under mild conditions by synergistically combining plasma activation with catalytic surface reactions. In this study, mesoporous ceria catalysts were synthesized by different methods and characterized using N₂ adsorption–desorption, SEM, XRD, XPS, CO₂-TPD, and XRF techniques. The materials exhibited distinct textural and electronic properties, including variations in surface area, pore structure, and basicity. Plasma-catalytic CO₂ dissociation experiments were conducted in a dielectric barrier discharge reactor at near-room temperature. Among the synthesized catalysts, Ce(mp)-4 demonstrated the highest CO₂ conversion of 32.3% at a 5 kV input voltage and superior energy efficiency, which can be attributed to its meso-macroporous structure that promotes microdischarge formation and enhances CO₂ adsorption–desorption dynamics. CO was the only product obtained, with near-100% selectivity. Catalyst stability testing showed no deactivation while spent catalyst characterization indicated carbon-containing species. The findings in this study highlight the critical role of tailored pore structure and basic-site distribution in optimizing plasma-catalytic CO₂ dissociation performance, offering a promising strategy for energy-efficient CO₂ utilization. Full article

Although fast-paced ongoing industrial growth, on the one hand, enhances the lifestyle of the population, on the other hand, it affects human health and the environment as a result of the discharge of pollutants. To address this, designing a novel and effective photocatalyst is necessary to mitigate increasing environmental pollutants. In the present work, we aim to synthesize a single-phase high-entropy zirconate pyrochlore oxide (Ce_0.2Pr_0.2Zn_0.2Nd_0.2Tb_0.2)₂Zr₂O₇ using a modified Pechini method. The physicochemical properties of the prepared nanoparticles were investigated using X-ray diffraction, UV-visible spectroscopy, field emission scanning electron microscopy, and X-ray photoelectron spectroscopy. The photocatalytic properties were examined using cationic dye (methylene blue), anionic dye (Congo red), and Cr(VI). Photocatalytic degradation experiments demonstrate exceptional efficiency in the removal of persistent organic pollutants. The photocatalytic results indicate that the prepared high-entropy (Ce_0.2Pr_0.2Zn_0.2Nd_0.2Tb_0.2)₂Zr₂O₇ zirconate pyrochlore oxide could effectively degrade dyes and reduce Cr(VI). Radical trapping experiments indicate that the degradation of dyes was driven by the hydroxyl radicals, superoxide radicals, and holes. Furthermore, the position of the valence band and conduction band promoted efficient photocatalytic reaction kinetics. The prepared photocatalyst remains structurally stable and can be reused three times without losing activity. Full article

The great city of Mayapan has experienced a technological change in pottery making, and our results confirm a shift in the raw materials and possibly the potters’ knowledge about them. The dynamics of change during the Postclassic period in the Maya area are reflected in the material changes used to make pottery. A comprehensive analysis was conducted on a collection of 248 pottery items from the archaeological site of Mayapán in Yucatán, Mexico, dating from the Middle Preclassic to Postclassic periods (700 BC–1500 CE). Non-invasive methods were used for the entire pottery set, including X-ray fluorescence (XRF) and fiber-optic reflectance spectroscopy (FORS). Additionally, for a representative subset, minimally invasive techniques such as inductively coupled plasma optical emission spectrometry (ICP-OES) and laser-induced breakdown spectroscopy (LIBS) were employed. The resulting data enabled the identification of materials used in the pottery’s manufacture. The elemental composition of the objects was determined, revealing correlations between elements such as Si with Al that yield a $R^2$ factor of 0.94. The results indicate the presence of smectite clays, carbonates, and iron oxides. The results show that a higher proportion of carbonates was found in the pieces from the Postclassic period compared to those from the Preclassic period, which may be associated with a change in the manufacturing process. Likewise, the Postclassic pieces are distinguished by a greater contribution of the Mg-OH signal, unlike the Preclassic and Classic, which show a greater contribution of the Al-OH group. The implications for the technological knowledge of the potters suggest the use of different technologies across various periods and material changes driven by shifts in political and economic relations in the city and the northern plains. Full article

This study examines the effects of injection timing and cerium oxide (CeO₂) nanoparticle (NP) size on NOx emissions and brake thermal efficiency (BTE) in a compression ignition engine, contributing to Sustainable Development Goals 7 and 13. Experiments were conducted at four load conditions (25–100%) using NP sizes of 10 nm, 30 nm, and 80 nm. An artificial neural network integrated with multi-objective particle swarm optimization (ANN-PSO) was employed to identify optimal operating parameters. The optimized configurations improved BTE and reduced NOx emissions across all loads; for example, at 75% load, BTE increased from 30.38% (average) to 32.13% (optimum), while simultaneously reducing the NOx emissions from 1322 ppm (average) to 1272 ppm (optimum). Analysis of variance (ANOVA) confirmed load as the most significant factor (p < 0.001), followed by injection timing and NP size. The model predictions closely matched experimental results, validating the optimization approach. The optimization suggests an interpolated optimal NP size of approximately 45 nm, highlighting the potential for further exploration. This integrated experimental and computational approach offers a promising framework for improving combustion efficiency and reducing emissions, thereby advancing cleaner and more sustainable fuel technologies. Full article

The fisheries industry faces increasing sustainability challenges from environmental, economic, and social perspectives, which directly affect fishing vessels as its primary infrastructure. This study conducted a systematic literature review following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines to evaluate technological innovations that improve the sustainability of fishing vessels. Comprehensive searches were performed in Scopus, Web of Science, ScienceDirect, and IEEE Xplore, covering the period 2020–2024. The searches identified 756 articles, of which 105 met the predefined eligibility criteria after screening titles, abstracts, and full texts. Each innovation was categorised and analysed based on its functional vessel domain, contribution to environmental, economic, and social sustainability, maturity level using the Technology Readiness Levels (TRLs) framework, and relevance to Circular Economy (CE) principles. The results indicate that most innovations focus on environmental sustainability, particularly on emission reduction and energy efficiency. Social sustainability remains under-addressed, especially in terms of labour conditions and gender equality. CE principles are present in some initiatives but are not yet fully integrated into vessel design or operation. Most innovations are at medium TRL stages, with adoption limited by financial, infrastructural, and institutional barriers, especially in small-scale fisheries. Future research should address these gaps by enhancing CE integration and promoting a more balanced attention across all three sustainability dimensions. Full article

Marine applications often involve metallic materials, including steel, that must endure harsh conditions such as cavitation erosion (CE). This study investigates the CE behavior of 42CrMo4 steel, both in its original state and after pre-corrosion in a 3.5% NaCl solution for 120 days, simulating a simplified marine environment. Cavitation testing was conducted using an ultrasonic vibratory setup with a stationary sample, at intervals of 10 and 30 min, with a total testing time of 150 min. Mass loss (ML), mass loss rate (MLR), mean depth of erosion (MDE), and level of degradation (LoD) were calculated, while surface roughness (Rz) was measured using a TR200 tester. Surface changes were analyzed through field emission scanning electron microscopy (FESEM) and image analysis techniques. Morphological parameters such as the number of pits, average diameter, and total pit area were used to quantify surface damage. Results showed that pre-corroded samples exhibited a significantly higher erosion rate than non-corroded ones. Pre-corrosion introduced microcracks and surface defects that served as initiation sites for cavitation damage. These imperfections increased surface roughness and created favorable conditions for pit formation, leading to faster and deeper material loss. Image and FESEM analyses confirmed the presence of larger and deeper pits in pre-corroded samples compared to the smaller and shallower pits in non-corroded specimens. This study highlights the impact of pre-corrosion on the cavitation resistance of 42CrMo4 steel and demonstrates the effectiveness of combining mass loss data with morphological and surface analyses for evaluating cavitation damage under marine-like conditions. Full article