Search Results (148)

Climate change and the transition toward sustainable development have increased interest in the institutional and psychological factors shaping sustainable consumption behavior. This study investigates how perceived government regulation, perceived corporate environmental responsibility, sustainable production practices, perceived digital environmental transparency, and eco-anxiety jointly influence sustainable consumption behavior among eco-conscious producer-consumers in Azerbaijan. Data were obtained from 425 eco-conscious producer-consumers through a structured questionnaire, and the proposed research model was empirically examined using Structural Equation Modeling. The findings indicate that perceived government regulation positively influences perceived corporate environmental responsibility, which subsequently enhances sustainable production practices. However, sustainable production practices do not exert a significant direct effect on sustainable consumption behavior. The results further demonstrate that perceived digital environmental transparency partially mediates the relationship between corporate environmental responsibility and sustainable consumption behavior, while eco-anxiety positively moderates the relationship between perceived government regulation and sustainable consumption behavior. These findings suggest that self-reported sustainable consumption behavior is shaped by individuals’ perceptions of institutional and organizational sustainability signals, perceived transparency of sustainability communication, and psychological responses to environmental challenges. The study contributes to the sustainability literature by providing evidence from eco-conscious producer-consumers in an emerging sustainability-transition context and highlights the importance of integrating governance, corporate responsibility, digital transparency, and environmental concern within a unified analytical framework. Full article

Brands increasingly rely on digital peer communication to promote environmentally friendly products. Whether electronic word-of-mouth (EWOM) actually shifts green purchase intention (GPI), though, is far from settled. Some studies have found no reliable word-of-mouth effect; others have examined green trust and anticipated guilt separately, leaving the joint operation of these parallel mediators poorly specified. This is especially true in Bali, Indonesia, where environmental pressures, heavy social media use, and Tri Hita Karana values coexist. Working under the stimulus–organism–response (SOR) framework, this study tests a dual-mediation model that routes EWOM to GPI through anticipated guilt and green trust among eco-conscious social media users. Survey responses from 393 participants were analysed with partial least squares structural equation modelling (PLS-SEM). EWOM related positively to GPI, directly and through both mediators. Anticipated guilt and green trust each partially mediated the effect, so the mediation was partial rather than full. The EWOM-to-guilt path was the stronger of the two. The EWOM-to-trust path was weaker, yet both mediators connected to GPI at similar strength. This study contributes a dual-pathway SOR specification by incorporating anticipated guilt into digital peer communication within a tourism-heavy cultural setting. Interpreted with caution, the pattern indicates that credible EWOM aimed at greener, more responsible consumption may operate through two mechanisms simultaneously, validating trust and prompting moral anticipation among digitally exposed, eco-conscious consumers. However, this interpretation still needs experimental and longitudinal testing before these relationships can be treated as causal. Full article

This study investigates the relationships among military expenditure, economic growth, energy consumption, and carbon dioxide (CO₂) emissions using panel data for eight Central and Eastern European countries over the period 2000–2023. The analysis employs the Pooled Mean Group Autoregressive Distributed Lag (PMG-ARDL) estimator to examine both the short-run and long-run dynamics. To ensure the robustness of the findings, the CS-ARDL, Common Correlated Effects Mean Group (CCEMG), and panel FMOLS estimators are additionally applied. The results of the Pedroni, Kao, and Johansen–Fisher panel cointegration tests provide robust evidence of cointegration, confirming the existence of a stable long-run equilibrium relationship among the variables. The long-run estimates indicate that military expenditure and economic growth contribute to lower CO₂ emissions, whereas higher energy consumption increases environmental degradation. In the short run, military expenditure has no statistically significant effect on CO₂ emissions, suggesting that its environmental implications emerge only over a longer time horizon. These findings imply that strategically directed investments in the defense sector, particularly those promoting environmentally friendly technologies, energy efficiency, and technological innovation, can support the alignment of national security objectives with environmental sustainability. The results provide important policy implications for Central and Eastern European countries, highlighting that coordinated policies promoting cleaner energy use, sustainable economic growth, and environmentally conscious defense modernisation can contribute to long-term environmental and economic sustainability. Full article

Tribology is a growing field concerning reductions in the environmental footprint of tribological systems while increasing their operational efficiency by minimizing friction, wear and lubrication in an environmentally conscious manner. This review aims to discuss in detail sustainable lubrication strategies, with particular emphasis on surface modification and coating-based strategies for advanced tribological applications. The surface engineering techniques covered in this study are critically reviewed with regard to their ability to improve wear resistance, reduce friction and increase the durability of components, such as laser surface texturing (LST), nitriding, plasma treatment and advanced coating technologies such as DLC-, TiN-, CrN- and PVD-based coatings. Another key focus is on the synergy of sustainable lubricants and engineered surface coatings. The tribochemical compatibility of eco-friendly lubricants, bio-lubricants, ionic liquids and advanced coated surfaces is explained in detail, because they are essential for the formation of stable tribofilms, lubricant retention, reductions in surface degradation, and the minimization of tribological losses. They are an important component of environmental footprint, energy consumption and the thermal stability and service life of tribological components. Recent developments in coating-assisted green tribology, sustainable tribochemistry and circular sustainability concepts of the design of tribological systems are also included in the review. In addition, the review briefly outlines the use of Life Cycle Assessment (LCA) as a potential tool for the future evaluation of the environmental sustainability of tribological systems. The proposed expanded set of Green Tribology principles offers useful guidance for the implementation of tribology-based solutions toward more sustainable engineering systems and responsible resource utilization. Full article

Consumer behavior evolves unexpectedly, and understanding new perspectives according to market trends is a key factor in identifying the most commonly recurring consumption demands. In this context, this study aimed to examine the associations among green consumption orientation, consumer ethical beliefs and green purchasing intention. Based on a quantitative and explanatory study with the participation of 411 Peruvian consumers, this study demonstrated that green consumption orientation has a direct and negative association with consumers’ ethical beliefs, while green consumption orientation has a direct and positive association with green purchasing intention. Finally, green purchasing intention has a direct and negative association with the ethical beliefs of Peruvian consumers. These findings reveal a reconfiguration of the ethical framework of Peruvian consumers, where green consumption orientation acts as a catalyst for the intention to purchase eco-friendly products, but at the same time generates tensions with traditional ethical beliefs. This phenomenon suggests that consumers are developing new value systems that prioritize environmental sustainability over conventional ethical considerations, which represents both a challenge and an opportunity for companies seeking to position themselves in the green market. Full article

Background: Shifting toward more plant-based diets is promoted for health, environmental, and ethical reasons. However, the nutritional consequences of reducing animal-based foods, particularly across age groups with specific dietary needs, remain insufficiently understood. Methods: In this simulation study, we used dietary intake data from 3570 participants in the Dutch National Food Consumption Survey (2019–2021) to evaluate how replacing animal-based foods with plant-based alternatives affects the intake of protein, essential amino acids (EAAs), vitamins, and minerals across different age groups. Two substitution scenarios were modeled: a nutritionally conscious and a less conscious plant-based dietary pattern. Results: Total protein intake decreased in both scenarios, with the strongest reductions observed in elderly individuals (71–79 years). Vitamin intake, particularly B vitamins and vitamin A, declined in most age groups, and vitamin D remained chronically low. Mineral intake also decreased, notably for calcium, iron, iodine, selenium, and zinc, especially among women and adolescents. Conclusions: These results underscore the importance of dietary planning and targeted fortification when promoting plant-based eating patterns. Future research should refine bioavailability estimates and evaluate the long-term health effects of such dietary transitions across life stages. Full article

Healthy and sustainable food consumption implies not only making conscious food choices but also limiting food waste. Interventions targeting choices are effective only if the healthier food selected is not wasted on the plate. The aims of the study were: to quantify plate and service waste in hospital canteens, characterize plate waste (PW), and compare PW before and after an intervention aimed at promoting healthy and sustainable food choices. A descriptive study was conducted in three Italian hospital canteens. Trays of users who gave consent were photographed (N = 1624). PW was quantified visually. Energy, nutrients, and environmental indicators were estimated using portion sizes, recipes provided by the canteens, food composition, and environmental databases. Both plate and service waste varied substantially across canteens. Post-intervention PW was 4.7% (C1: 6.1%, C2: 2.0%, C3: 2.6%) of the served food, corresponding on average to 33.1 g/tray, 43.6 kcal/tray, 69.6 g CO₂ eq./tray and 61.8 LH₂O/tray. Side dishes contributed most to the total PW. The canteen where PW decreased significantly compared to pre-intervention (C2: −48.9 g/tray in median among wasters) was the one with the least improvement in food choices. These findings highlight the importance of considering waste when implementing food choice interventions. Full article

Smart cities increasingly rely on distributed sensing and edge intelligence to support urban planning, mobility management, environmental monitoring, and critical infrastructure operation. However, large-scale urban Internet-of-Things deployments are constrained by heterogeneous device capabilities, limited energy availability, variable communication conditions, and data-governance requirements. Federated learning offers a data-locality-preserving alternative to centralized model training, but conventional federated learning strategies often assume full, random, or fixed client participation, which can lead to unnecessary energy consumption, communication overhead, or client starvation in resource-constrained urban environments. This paper proposes an Energy- and Communication-Aware Federated Learning strategy, termed ECA-FL, for smart city sensing systems. The main novelty of the work lies in the joint use of residual device energy and communication conditions to guide adaptive client participation and local training effort, providing a tunable resource–performance trade-off rather than an accuracy-maximizing strategy alone. The framework is evaluated through a controlled simulation-based study using a synthetic multi-class urban sensing proxy task distributed across 100 federated clients under strongly non-IID conditions. Compared with full-participation FedAvg, ECA-FL reduces cumulative energy consumption by 82.9% and communication overhead by 64.7%, while maintaining a final accuracy of 0.8124 compared with 0.8319 for FedAvg-full. Compared with rigid fixed-participation strategies, ECA-FL avoids severe learning degradation by adapting participation dynamically instead of excluding clients according to a static rule. A sensitivity analysis further shows that the trade-off parameter controls the balance between learning performance and resource conservation, allowing the framework to be adjusted according to different deployment priorities. The results support the hypothesis that adaptive energy- and communication-aware participation can substantially reduce operational cost while preserving acceptable learning performance within the adopted simulation setting. The study provides practical design insights for sustainable, communication-conscious, and data-locality-preserving federated learning in smart city sensing infrastructures. Full article

Green prepared dishes are an emerging food-consumption format that links convenience, food safety, and sustainable consumption. In this study, “green” denotes a sustainability-oriented product profile involving food-safety assurance, resource-conscious packaging or sourcing, and waste-reduction potential, rather than formal organic certification. However, existing studies have mainly relied on linear behavioral models and have paid limited attention to nonlinear and asymmetric consumer decision mechanisms. This study integrates the stimulus–organism–response framework with explainable machine learning to predict consumers’ sustainable purchase intention toward green prepared dishes. Based on 805 valid questionnaires collected in Jilin Province, China, predictors were organized into three dimensions: environmental and health cognition, socioeconomic and infrastructural conditions, and sustainable behavioral propensity. The sample represents a regional online consumer profile in Jilin Province rather than a national probability sample. Six classifiers were trained using SMOTE–Tomek resampling and Optuna-based hyperparameter optimization. XGBoost achieved the best predictive performance, with an F1-score of 0.894, an AUC of 0.934, and an MCC of 0.702. Unlike conventional black-box machine learning, the SHAP-based interpretation translated ensemble predictions into transparent feature-level and case-level explanations. Accordingly, the model interpretations are framed as predictive associations rather than causal mechanisms. The study reveals an asymmetric decision pattern in which core behavioral willingness functions as a non-compensatory barrier, while channel convenience, delivery efficiency, and after-sales support facilitate purchase intention among consumers who already show high behavioral readiness. The findings suggest that green prepared-dish strategies should prioritize trust-based advocacy and word-of-mouth, reliable channel design, low-risk trial experiences, and collaborative food-safety governance rather than relying only on short-term traffic acquisition. Full article

Corporate social responsibility (CSR) has grown in importance as a means for companies to engage with customers who are increasingly environmentally and socially conscious. This study examines how CSR affects sustainable consumer buying tendencies, emphasizing the mediating role of consumer happiness and corporate image. Scientists contend that customers are more inclined to support businesses whose values align with CSR programs that foster positive feelings and trust. Therefore, a conceptual model was developed by following cognitive consistency theory. Data from 504 customers in Pakistan, an expanding market where awareness of sustainability issues is continually rising, were gathered to test this. The results demonstrate that CSR has a significant and favorable influence on consumer purchasing preferences, as assessed using partial least squares structural equation modeling (PLS-SEM). Crucially, the proposed relationship is not only direct: CSR improves consumer happiness and corporate image, leading to better purchase decisions. By emphasizing the emotional and perceptual processes involved, these findings provide a better understanding of how CSR influences consumer behavior. The study demonstrates how CSR can encourage more conscientious consumption habits from a sustainability standpoint, supporting Sustainable Development Goal 12 (Responsible Consumption and Production). Findings suggest that well-thought-out CSR programs may truly affect how and why customers make purchase decisions, especially in emerging countries, going beyond reputation-building. Full article

Extending the service life of structural components through sustainable replacement is a key strategy for reducing material consumption and environmental impact in the cycling industry. This study evaluates the potential substitution of bicycle frame components, combining fused deposition modeling (FDM) with externally applied fiber tape reinforcement. Preliminary experimental validation was conducted on coupon specimens to assess the mechanical and environmental viability of the proposed material system. Two thermoplastic substrates, a bio-based green co-polyester (GreenTEC PRO, GT) and polycarbonate (PC), were printed at three orientations and reinforced with unidirectional carbon fiber (CF) or flax fiber (Flax) tapes. The results show that fiber position was the dominant factor governing both ultimate flexural strength (UFS) and elastic modulus (EF), accounting for over 74% and 81% of total variability, respectively. Carbon fiber reinforcement increased mean UFS from 60.6 MPa to 142.9 MPa, with peak values of 236.4 MPa, while flax fiber provided a statistically significant intermediate reinforcement, reaching up to 108.9 MPa. The bio-based GT substrate performed comparably to PC across all configurations, demonstrating that sustainability goals need not compromise structural performance. Bilateral fiber placement and 90° printing orientation consistently yielded the best mechanical response. These findings support the hybrid FDM/prepreg approach as a viable, tooling-free, and environmentally conscious strategy for the replacement of bicycle frame components. Full article

Achieving sustainable design requires balancing environmental performance, resource efficiency, functional feasibility, and aesthetic acceptance throughout the product life cycle. However, traditional design approaches often struggle to quantitatively integrate subjective aesthetic evaluation with objective sustainability indicators such as energy consumption, carbon emissions, and material recyclability. To address this challenge, this study proposes a semantic-guided multi-objective optimization framework for sustainable design that integrates cross-modal aesthetic evaluation with life cycle environmental performance assessment. The proposed framework employs a Contrastive Language–Image Pre-training (CLIP)-based semantic evaluation mechanism to translate abstract sustainability and aesthetic concepts into quantifiable design features, enabling consistent assessment across diverse design solutions. These semantic features are further optimized using a multi-objective evolutionary optimization strategy to simultaneously minimize energy consumption and carbon emissions while maximizing material recovery and design quality. Life cycle environmental indicators derived from OpenLCA datasets are incorporated into the optimization process to ensure practical sustainability relevance. The experimental results demonstrate that the proposed framework achieves a superior performance compared with benchmark optimization methods. Specifically, carbon emission equivalents are reduced to as low as 12.3 kg CO₂e, material recovery rates exceed 92%, and total computational energy consumption is reduced by more than 40% relative to comparative models. In addition, the framework shows strong stability and convergence efficiency while maintaining a high aesthetic evaluation accuracy in high-quality design ranges. The findings indicate that the proposed approach provides an effective pathway for integrating aesthetic value with environmental responsibility in sustainable design practice. This framework supports low-carbon and resource-efficient product development and offers practical insights for sustainable manufacturing, circular design, and environmentally conscious innovation. Full article

Generative Artificial Intelligence (GenAI) has rapidly spread worldwide, driving structural changes and redefining approaches to knowledge. This trend has introduced significant challenges, particularly within higher education, where its adoption and acceptance are crucial for pedagogical transformation. However, the increasing integration of GenAI also raises pressing questions related to sustainability, encompassing both its environmental impact (e.g., energy consumption and carbon footprint of AI models) and social and ethical implications (e.g., responsible use, equity, and digital inclusion). This study investigates the factors influencing the adoption and acceptance of GenAI among higher education students, considering these sustainability dimensions. Using an adapted version of the UTAUT2 (Unified Theory of Acceptance and Use of Technology) model, the research analysed data from 229 students, collected in 2025, employing the Partial Least Squares method. By integrating the sustainability perspective, this work seeks to offer an understanding of the challenges and opportunities that GenAI presents for a more equitable and ecologically conscious educational future. The study demonstrates that habit and performance expectancy are the primary drivers of GenAI adoption among students, suggesting that its integration into higher education should prioritize functional value and ethical habit-building over social or hedonic factors. Full article

This research addresses the urgent need to reduce carbon emissions in the footwear manufacturing industry by utilizing digital twin technology with project management frameworks. It focuses on identifying critical emission sources across the entire life cycle of shoe production from (i) material sourcing, (ii) manufacturing, and (iii) transportation, to (iv) end-of-life disposal. By data collection, infusing project management, and integrating digital twin approaches, the study offers a dynamic, data-driven method to simulate, monitor, and optimize carbon reduction strategies in real time. An extensive literature review and industry data analysis informs the assessment of carbon emissions and energy consumption patterns. Based on these insights, a tailored project management approach is followed to analyze the feasibility of the footwear sector to adopt sustainable practices such as renewable energy adoption, eco-friendly material sourcing, and closed-loop production systems. Validation was conducted using plant simulation software to model emissions scenarios and evaluate the effectiveness of proposed interventions. Case studies from leading brands, including Nike, Adidas, and Puma, were examined for Scope 1, 2 and 3, to extract the best practices and strategic insights. The research underscores the importance of combining digital tools with sustainability goals to create an environmentally conscious manufacturing ecosystem, highlights the role of policymakers in incentivizing green practices, and emphasizes collaborative industry efforts to accelerate change. The paper concludes by highlighting that digital twin systems provide effective, scalable solutions for reducing carbon emissions in footwear manufacturing. Full article

Construction site safety remains a critical global challenge, demanding urgent attention. Existing surveillance systems struggle to balance multi-object detection accuracy, real-time efficiency, and environmental robustness under strict edge constraints. This paper presents MMDet-Edge, an edge-optimized unified detection framework that addresses these competing demands via three synergistic innovations. First, an adaptive feature fusion architecture with a learnable spatial–channel attention mechanism resolves cross-scale conflicts, boosting small-object average precision (AP) by 9.3%. Second, a hardware-conscious neural architecture search (HC-NAS) strategy co-optimizes sparsity patterns and quantization sensitivity, achieving a state-of-the-art performance of 89.4% mAP@0.5 at only 1.8 W power consumption—surpassing contemporary edge detectors by 6.3% mAP under equivalent power budgets. Third, by incorporating OSHA fatality statistics into a novel risk-weighted evaluation paradigm, we reduce high-consequence false negatives by 34%. Comprehensive evaluations on a purpose-built benchmark and cross-dataset tests demonstrate MMDet-Edge’s superiority. It outperforms a wide range of state-of-the-art models. Validated across three active construction sites, the system enables real-time detection of five safety-critical targets (personnel, helmets, flames, smoke, vests) under extreme conditions, including >60% occlusion and >100 lux illumination variance. Our field deployments demonstrated a 22% reduction in safety incidents compared to conventional systems, establishing a new architectural paradigm for safety-critical edge AI through principled hardware–algorithm co-design. Full article