Search Results (2,172)

The growing environmental impacts associated with conventional plastics and textiles have intensified interest in bio-based and circular material alternatives. This study presents a qualitative and structured literature review of the valorization of fruit and nut agricultural residues as sustainable feedstocks for biomaterials and biotextiles, with a strategic focus on Greece. Drawing on international literature, regional agricultural production data, and validated processing technologies, the review synthesizes existing evidence on residue availability, conversion routes, environmental performance, and market trends. The reviewed literature indicates that residues such as grape pomace, olive by-products, citrus peels, and nut shells have been widely reported as suitable sources of cellulose, lignin, and pectin for the development of fibers, films, and composite materials. Findings from published life cycle assessment (LCA) studies suggest potential reductions in water use, greenhouse gas emissions, and land-use intensity compared with conventional cotton and synthetic textiles, although results vary depending on system boundaries and processing conditions. The review further highlights enabling factors, technical limitations, and policy considerations relevant to the Greek context. This study provides a qualitative integrative perspective on the opportunities and constraints associated with agricultural residue valorization, identifying key research gaps and strategic directions for future development within Greece and similar Mediterranean regions. Full article

The stability of Indonesia’s banking sector is closely linked to the effectiveness of capital regulations, particularly as a country that aligns its policies with Basel III standards. Ensuring that banks have adequate capital buffers is crucial for mitigating systemic risk. However, the interaction between regulatory requirements and actual banking behavior in developing countries remains poorly understood. This study aims to evaluate the impact of Indonesia’s capital requirement instruments, including the countercyclical capital buffer (CCyB), the capital conservation buffer (CCB), and the capital surcharge, on credit performance and financial stability across various bank categories. Using a quantitative approach, the analysis utilizes panel data from commercial banks, state-owned banks and regional development banks over several periods, using the panel regression method and Difference-in-Differences (DID) to assess how changes in buffer levels affect credit growth, Non-Performing Loans (NPLs), and the Capital Adequacy Ratio (CAR). The results show that capital buffers have a statistically significant effect on lending behavior: a 1% increase in buffer levels is associated with a measurable decrease in credit expansion across several bank groups, while CCBs exhibit a stronger stabilizing effect than CCyBs. Although these instruments do not eliminate financial uncertainty, they contribute to more prudent risk-taking. This study also revealed that the CCyB rate increases when the financial cycle is in an expansionary phase. Conversely, if the economy slows (as during the pandemic), the CCyB rate can be lowered back to 0% to encourage bank intermediation, thus shaping the bank’s responses to regulation. Several implications of implementing a capital buffer in Indonesia include the benefits of resilience and bank behavior during credit expansion. Overall, this study concludes that aligning regulatory frameworks with real-world banking behavior is crucial for enhancing financial stability in developing countries, such as Indonesia. Full article

Addressing the lack of high-fidelity test cycles for fuel cell light-duty trucks, this paper proposes an automated driving cycle construction method that integrates unsupervised clustering and reinforcement learning. Firstly, based on large-sample real-world driving data, four libraries of typical driving pattern segments are extracted through dimensionality reduction via Principal Component Analysis (PCA) and K-means clustering. Subsequently, the cycle construction process is formulated as a sequential decision-making problem, and a framework based on the Proximal Policy Optimization (PPO) algorithm, incorporating an action masking mechanism, is designed. This framework innovatively injects macro-level time budget allocation as a hard constraint into the agent’s policy space via action masking, while utilizing micro-level Markov transition probabilities as a soft guide. This dual approach drives the agent to learn an optimal segment concatenation strategy, thereby simultaneously ensuring both the macro-level statistical representativeness and the micro-level driving logic coherence of the synthesized cycle. Validation results demonstrate that the cycle constructed by the proposed method achieves an average relative error of only 7.53% in key characteristic parameters, and its joint speed-acceleration distribution exhibits a similarity as high as 0.9886 with the original data, significantly outperforming traditional methods such as the clustering method, the Markov chain method, and standard driving cycles. This study provides an effective tool for generating high-fidelity driving cycles and testing energy management strategies for fuel cell commercial vehicles. Full article

This study aims to show a detailed life cycle assessment (LCA) approach of battery electric vehicles (BEVs) and internal combustion engine vehicles (ICEVs), with an emphasis on determining the electrical carbon intensity at which these vehicles reach life-cycle greenhouse gas (GHG) parity. The analysis was conducted in openLCA v2.0.3 using the Ecoinvent v3.9.1 database under a European use-phase context, with a functional unit of 150,000 km. BEVs were evaluated for two representative lithium-ion battery chemistries (NMC622 and LFP) under three electricity carbon intensity scenarios (50, 400, and 850 g CO₂/kWh), while ICEVs were modeled for both gasoline and diesel pathways. Results show that BEV life-cycle GHG emissions vary between 91 and 221 g CO₂-eq/km across different combinations of electricity mix, battery chemistry, and end-of-life conditions. When isolating electricity carbon intensity as the primary variable under a fixed BEV configuration, emissions increase approximately linearly with grid emission factor. Under average European electricity conditions (400 g CO₂/kWh), BEVs exhibit lower life-cycle GHG emissions than gasoline ICEVs, whereas under coal-intensive electricity conditions (850 g CO₂/kWh) this advantage may be reduced or reversed. The break-even electricity carbon intensity is derived by linear interpolation under a fixed BEV configuration (NMC622, 60 kWh, constant lifetime and EoL conditions), yielding a threshold of approximately 600 g CO₂/kWh. The results further indicate that this threshold is influenced by battery chemistry, production-related emissions, recycling efficiency, and assumed vehicle lifetime. These findings highlight the importance of simultaneous progress in electricity decarbonization and end-of-life recycling to secure the environmental benefits of vehicle electrification, and they provide a threshold-oriented framework for policy-relevant interpretation of comparative vehicle LCA results. Full article

Energy transition is most often examined through the lens of technological development and integration, including renewable energy sources, energy storage systems, and digital energy management solutions. In practice, however, the actual performance of energy systems—understood as both energy efficiency and environmental impact across the life cycle—depends not only on technical parameters but also on decision-making processes, operational practices, and management capabilities. This paper aims to conceptualize energy and environmental awareness as a determinant influencing energy system performance at organizational and system levels. The study is based on a structured review of the literature from energy engineering, life cycle assessment, and energy management, complemented by a comparative analysis of how similar energy technologies are utilized under different decision-making contexts. On this basis, an integrated analytical framework is proposed that combines conventional energy and environmental performance indicators with awareness-related dimensions, including energy knowledge, perception of environmental risk, and managerial competence. The analysis demonstrates that insufficient energy awareness leads to systematic gaps between the technological potential of energy systems and their actual performance, resulting in increased environmental burdens despite high nominal technical efficiency. The proposed framework helps to explain performance variability in energy systems operating under comparable technical conditions and highlights the importance of incorporating managerial and competency-related factors into life cycle assessments and energy transition policies. The paper contributes to the literature by extending energy system evaluation beyond purely technical criteria and offers practical implications for the design of energy systems, industrial energy management, and policy instruments supporting sustainable energy transitions. Full article

Sustainability assessment of textile materials has traditionally relied on origin-based classifications and indicator-driven life cycle assessment (LCA), often treating sustainability as an inherent or material-intrinsic property. However, materials sharing similar biological origins or “bio-based” labels frequently exhibit substantially different sustainability outcomes when processing pathways, composite structures, and end-of-life (EoL) compatibility are taken into account. To address this limitation, this study develops a qualitative, multidimensional analytical framework that conceptualizes textile material sustainability as a pathway-dependent and system-mediated outcome rather than an inherent material attribute. The framework integrates four interrelated dimensions—renewability, process sustainability, EoL options, and material source—derived from a structured review of academic, policy, and technical literature. To demonstrate the analytical scope and internal logic of the framework, a selected set of 65 innovative textile materials was systematically analyzed using a three-tier qualitative coding scheme (favorable, conditional, and unfavorable) under conservative data validation criteria. The analysis shows that sustainability performance is primarily shaped by pathway configurations—particularly processing intensity, binder chemistry, and EoL compatibility—rather than material origin alone and that similar bio-based materials can exhibit fundamentally different sustainability profiles depending on these factors. By reframing sustainability from a material-centered perspective to a pathway-oriented and system-based perspective, the proposed framework provides a structured basis for integrating material innovation, process design, and end-of-life planning in sustainability-oriented textile research and development and establishes a conceptual foundation for future empirical and quantitative extensions. Full article

The progression towards a circular economy in the construction sector has gained attention as a response to rising resource consumption and construction and demolition waste generation, with material reuse playing a central role. In this context, this study analyses the literature on reuse in the construction sector, examining its investigation over time and its relation to European regulatory frameworks and policy strategies. A systematic literature review was conducted using a structured search across the B-on, Scopus, and Web of Science databases. The search targeted peer-reviewed journal articles in English, published between 2008 and 2023, focusing on titles, abstracts, and keywords with predefined terms. A total of 78 articles met the inclusion criteria and were analysed. Research activity has increased in recent years, reflecting growing European policy attention, particularly the Waste Framework Directive, its 2018 amendment, and the Circular Economy Action Plan. Most studies address strategies to promote the circular economy, waste management practices, life cycle assessments, and the identification of barriers and opportunities to reuse. Despite the expanding literature, reuse remains insufficiently addressed. These findings underline the need for more targeted research and stronger integration between policy and practice to support effective reuse in the construction sector. Full article

This article integrates sustainability principles into a three-echelon supply chain for deteriorating items with imperfect quality, consisting of a single vendor, a third-party logistics enterprise (3PL), and a single buyer, with a focus on balancing economic efficiency with environmental responsibility. The vendor is assumed to operate an imperfect production system, resulting in products of imperfect quality. The 3PL undertakes all transportation activities, while the buyer conducts a quality inspection process to detect defective items, which is subject to Type-I and Type-II errors. Aside from that, the inventory model also assesses carbon emissions arising from various operational activities including energy usage during production, warehousing, and disposal processes, and fuel consumption in transportation, for which the above members of the supply chain are accountable. Afterward, carbon management policies such as a carbon tax and carbon cap-and-trade are considered to regulate total supply chain emissions. The objective is to minimize the joint expected total cost by simultaneously optimizing shipment frequencies and the replenishment cycle for the buyer within carbon emission constraints. An iterative solution procedure is developed to address the problem. A numerical example and sensitivity analysis are provided to demonstrate the model’s applicability and to explore the influence of critical parameters. Finally, the study presents managerial insights, along with conclusions and recommendations for future research directions. Full article

This study develops a Life-Cycle Sustainability Framework for circular business models in the context of post-war economic reconstruction and sustainable value chain transformation. Ukraine is used as the main case study due to its post-war reconstruction context and the need for resource-efficient economic recovery strategies. Under conditions of disrupted supply systems, resource constraints, and structural economic change, circular economy principles are conceptualized as strategic mechanisms for enhancing resilience, resource efficiency, and long-term competitiveness rather than solely as environmental policy instruments. Building on a structured hierarchy of circular business models aligned with product life-cycle stages, the framework emphasizes value retention through functional and usage extension beyond material recovery. The framework includes a hierarchical classification of 12 circular business models and a sustainability evaluation approach based on four criteria (K1–K4), which allow for the comparative assessment of circular business models and their combinations across life-cycle stages. Using secondary statistical data and policy review as analytical inputs, the study identifies sectors with high potential for circular transformation and sustainable investment, including agriculture, energy, industry, construction, and logistics. The results indicate that circular business models applied at early life-cycle stages, such as reuse, repair, and remanufacturing, provide the highest potential for reducing resource intensity and improving long-term economic sustainability, while recycling and energy recovery play a supporting role. These findings highlight how life-cycle-oriented circular strategies can support sustainable reconstruction pathways, strengthen international cooperation, and inform policy and managerial decision-making in transitional economic contexts. Full article

Children are largely absent from circular economy and bioeconomy research, limiting opportunities for early development of systems thinking, sustainability competencies, and inclusive knowledge production. This paper presents a qualitative case study of the Horizon 2020 AgroCycle project (2016–2019), examining how primary school children were engaged as co-researchers through a transdisciplinary, participatory model. Analysis draws on project deliverables, educational resources, workshop records, internal reports, and dissemination materials. The study shows how children and adult co-researchers explored waste valorisation, bioresource transformation, and biobased material innovation in Irish schools. Valorisation in the context of the bioeconomy is the process of converting residues from farming, food, forestry and marine sources into high-value products such as biofertilisers, biofuels and biochemicals. It situates AgroCycle within European sustainability policy, highlighting its influence on subsequent initiatives, including Horizon Europe BioBeo and BiOrbic, Research Ireland’s Centre for Bioeconomy. By combining qualitative case study methodology with reflective practitioner analysis, the paper demonstrates how child-centred, transdisciplinary research can enhance sustainability education, support SDG-aligned competencies, and promote inclusive approaches to circular economy and bioeconomy transitions. Full article

Urban districts are increasingly exposed to overlapping heat stress and stormwater loads driven by warming trends, more intense rainfall, and continued growth of impervious surfaces. Pavements occupy a large share of the public right-of-way, so their material and structural design offers a scalable pathway for urban climate adaptation. Yet the literature on porous asphalt remains fragmented, with hydrological performance often assessed using infiltration or permeability metrics in isolation, while thermal studies frequently report surface cooling without consistently tracking the governing water budget or its persistence. To reconcile these disconnected strands, this review synthesizes a conceptual hydro-thermal balance framework in which runoff mitigation and heat moderation are treated as a coupled problem controlled by storage, drainage pathways, and evaporative demand. Within this framing, cooling is primarily water-limited: permeability enables wetting and redistribution, but the magnitude and duration of temperature reduction depend on how much water is retained near the surface and how long it remains available for evaporation, rather than on permeability alone. The review integrates the current understanding of mixture structure and pore connectivity, permeability–storage behavior, moisture availability and evaporation, and the operational factors that govern performance persistence. Laboratory and field evaluation approaches are summarized alongside modeling methods used to interpret coupled hydro-thermal responses under different climates. Practical constraints—including clogging, maintenance requirements, and durability risks under repeated moisture–temperature cycling—are discussed as mechanisms that can progressively suppress both infiltration and water availability, undermining long-term function without performance-based specifications and life-cycle planning. Finally, design and policy implications are outlined for integrating porous asphalt into coordinated heat-and-stormwater strategies, and research priorities are identified to advance standardization, long-term monitoring, and coupled hydro-thermal–mechanical assessment. Full article

Based on the panel data of Chinese A-share listed companies from 2012 to 2023, this paper takes the pilot policy of Pilot Zones for Innovative Application of Artificial Intelligence as an exogenous shock, and adopts a multi-period difference-in-differences (DID) model to systematically examine the causal effect of this policy on the quality and efficiency of enterprise innovation and its mechanism of action. It is found that the Pilot Zones for Innovative Application of Artificial Intelligence significantly improve enterprises’ innovation quality and efficiency. Mechanism tests show that the pilot policy enhances enterprise innovation quality and efficiency by driving digital transformation, eliminating information barriers, and upgrading supply chain collaboration. Heterogeneity analysis confirms that the policy dividends are more fully released in non-state-owned enterprises, high-tech enterprises, labor-intensive and technology-intensive enterprises, as well as enterprises located in cities with a higher degree of marketization. In addition, the life-cycle heterogeneity analysis shows that the pilot policy exerts the strongest and most comprehensive innovation-promoting effect on maturity-stage firms, mainly improves innovation efficiency for decline-stage firms, and does not produce significant effects for growth-stage firms. The findings offer practical insights for policymakers and local governments in refining AI-related innovation policies and pilot-zone implementation, and for enterprise managers in strategically adopting AI to strengthen innovation capability and long-term sustainable development. Full article

In Latin America, sustainable commitments towards decarbonizing hard-to-abate industrial sectors have identified hydrogen (H2) as a key enabler for the energy transition. This study develops a policy analytical framework to enhance the green H2 economy, using Argentina as the central case study. Key insights from this study include identifying often-overlooked social challenges within the H2 economy and proposing the integration of social indicators into policy design, with a particular focus on the territorial dynamics of Patagonia, labor conditions, Indigenous participation, governance, and community impacts. Drawing from Social Life Cycle Assessment (S-LCA) guideline standards and H2 justice approach, this study highlights key social hotspots that existing S-LCA tools overlook due to their lack of specific focus on regional territories and their communities. The analysis combines six social impact categories, namely, human rights, working conditions, health and safety, cultural heritage, governance, and socio-economic repercussions as recommended by the United Nations Environmental Program (UNEP), analyzed at three levels, and complemented by the H2 justice approach for Argentina’s potential green H2 production sector. These policy recommendations aim to foster a more resilient and sustainable development of the green H2 industry. Full article

This study examines whether international food price dynamics provide a reliable signal of undernourishment and human development outcomes relevant to the attainment of SDG 2 (Zero Hunger) by 2030. We apply wavelet coherence analysis to the FAO Food Price Index and the prevalence of undernourishment (SDG Indicator 2.1.1) over 2001–2023, testing statistical significance against an AR(1) red-noise null hypothesis. Hybrid ARIMA–Random Forest models generate probabilistic price forecasts through 2030. Despite strong raw coherence (R² ≈ 0.77), only 7.8% of time–frequency cells achieve statistical significance, indicating that apparent co-movement largely reflects autocorrelation rather than substantive dependence. Where significant coherence emerges, it concentrates at medium-run horizons (3–6 years), consistent with undernourishment as a habitual dietary adequacy measure linked to sustained affordability pressures affecting health, productivity, and human capital formation. Rolling correlation analysis reveals suggestive evidence of a regime change around 2012—from negative to positive correlation—coinciding with a slowdown in progress toward reducing hunger, although the 5-year rolling windows yield only 19 observations, limiting the power of formal structural break tests. Price forecasts exhibit rapidly widening confidence intervals (by ±131 index points by 2030), underscoring fundamental limits to predictability. The annual PoU series comprises only 23 observations, which constrains the estimation of long-run (8–12-year) wavelet cycles; results at those horizons should therefore be interpreted with caution. These findings caution against mechanistic inferences from global price indices to hunger and human development outcomes, redirecting policy emphasis toward domestic transmission channels and nutrition-sensitive safety nets. Full article

This paper presents the design, development, and evaluation of a Reinforcement Learning (RL)–based torque-split controller for the regenerative braking system (RBS) in battery electric vehicles (BEVs). The controller employs a Deep Deterministic Policy Gradient (DDPG) agent to distribute the braking demand between regenerative and frictional braking systems with the aim of maximizing energy recovery while adhering to the physical and operational constraints. To capture the charging limitation of the battery, a State-of-Power (SoP) calculation mechanism is incorporated, providing a time-varying bound on the regenerative charge power. The agent is trained in a MATLAB/Simulink environment representing the digital twin of a BEV drivetrain, and considers a mix of different braking scenarios, i.e., light braking, medium braking, hard braking, and emergency braking. The RL’s reward shaping promotes efficient utilization of the SoP-limited regenerative capability while discouraging constraint violations and aggressive control behavior. Across a range of State-of-Charge (SoC) conditions and driving cycles, including the Worldwide Harmonized Light–Vehicle Test Procedure (WLTP) and synthetic random-rich driving cycle, the RL controller consistently delivers promising performance, yielding energy recovery of up to ~98% of the total braking energy available on WLTP type 3 driving cycle while being able to operate closely to the battery SoP limit. The results demonstrate the proposed controller’s capability for adaptive, constraint-aware energy management in BEVs and underline its potential for future intelligent braking strategies. Full article