Search Results (3,562)

This study examines the association between tourism intensity, the free movement of people, and water quality outcomes across the European Union (EU-27) over the period 2012–2024. By integrating open-access datasets from Eurostat, the European Environment Agency (EEA), and the EXIOBASE input–output framework, the analysis estimates the direct (blue), indirect, and grey components of the tourism-related water footprint and explores their relationship with bathing water quality indicators using panel econometric models. The results indicate that tourism activity increased substantially during the study period, while the share of bathing waters classified as “excellent” also improved. The findings further show that the gray water footprint is strongly associated with variations in water quality, whereas higher wastewater treatment coverage is positively associated with improved environmental outcomes. These results highlight the importance of wastewater management and governance capacity in moderating the relationship between tourism and water quality across diverse European contexts. We find that tourism activity rose by approximately 28% during the study period; yet, through improvements in wastewater treatment infrastructure and governance, the share of bathing waters rated “excellent” also increased. Notably, the grey water footprint emerged as the strongest predictor of water quality deterioration, while wastewater treatment coverage significantly mitigated negative impacts. Comparative case studies of Spain, Greece, Croatia and Romania highlight how institutional and technological capacity are associated with differences in tourism–water relationships across diverse hydro-climatic contexts. Our findings underscore that sustainable tourism in Europe is less a matter of visitor numbers and more a question of effective water management systems. The study supports a policy shift towards integrated water-tourism planning and circular water-use strategies to support more sustainable management of tourism-related environmental pressures. Full article

The construction sector consumes significant amounts of natural resources and contributes substantially to global CO₂ emissions, making it necessary to develop materials with a reduced environmental impact. In this context, the valorization of reusable industrial waste as secondary raw materials represents a strategic direction for applying circular economy principles and for decarbonizing the construction materials industry. The scientific problem addressed in this review is the urgent need to develop construction materials with a reduced environmental footprint, given that the construction sector is a major consumer of natural resources and a significant contributor to global CO₂ emissions. This challenge requires the identification and critical evaluation of sustainable solutions that support decarbonization and the transition toward a circular economy. The main findings indicate that the valorization of industrial waste offers high decarbonization potential: supplementary cementitious materials (SCMs), such as ground granulated blast furnace slag and fly ash, can reduce CO₂ emissions by approximately 20–50%, while alkali-activated binders and geopolymers achieve reductions of 40–80% compared to Portland cement. These materials also enhance durability, extending service life by 10–20% in aggressive environments, although early-age strength may decrease by 10–30%; recycled aggregates derived from construction and demolition waste (CDW) can substitute up to 100% of natural aggregates, while rubber fibers can increase impact resistance by 30–50% and reduce density by 10–20%. However, key limitations relate to waste variability, heavy metal leaching risks (requiring immobilization efficiencies > 90%), and the relatively low technological maturity of many solutions (TRL < 7), leading to the TRL–CO₂ paradox and highlighting the need for standardization and performance-based regulatory frameworks. The synthesized results indicate that the appropriate integration of industrial waste enables a significant reduction in clinker content, lowers associated CO₂ emissions, and decreases primary energy consumption while maintaining physical–mechanical properties and durability characteristics comparable to or in some cases superior to those of traditional materials, if mix design is based on clear performance criteria, stratified according to the type of waste, dosage used, curing regime, binder chemistry, and the target application. Full article

The transition toward sustainable facility management requires cleaning systems that reduce environmental burdens while maintaining high hygienic standards. This study presents a comparative evaluation of a green cleaning protocol (EVA SmartClean), compliant with the Italian Minimum Environmental Criteria (CAM; D.M. 29 January 2021), compared with a conventional cleaning system implemented in a civil facility (Adriatico Guest House, Trieste, Italy; 8260 m²). The assessment integrates a cradle-to-grave Life Cycle Assessment (LCA), conducted in accordance with ISO 14040, ISO 14044, ISO 14067 and PCR 2011:03 for professional cleaning services, with an extensive microbiological surface monitoring campaign performed using RODAC plates and swab sampling. The functional unit was defined as 1 m² of representative surface maintained clean for one year. The green protocol achieved a 47.7% reduction in Global Warming Potential (GWP100 based on IPCC AR6 characterization factors), corresponding to −110 g CO₂e/m²·year and −908 kg CO₂e/year for the entire facility. Major reductions in climate impact were associated with chemical consumption (−82.6%), energy use (−49.5%), and textile waste generation (−92.4%). Microbiological analyses demonstrated that both protocols complied with reference hygiene thresholds, while the green system achieved reductions in total mesophilic counts that were comparable or superior across representative surfaces. The results confirm that environmental optimization in cleaning services can be achieved without compromising microbiological safety, supporting public procurement policies aligned with CAM requirements and Sustainable Development Goals (SDGs 12 and 13). Full article

The global construction industry urgently requires sustainable alternatives to ordinary Portland cement (OPC) to mitigate its immense carbon footprint. Red mud (RM), a highly alkaline bauxite residue, presents tremendous but challenging potential as a supplementary cementitious material. This review systematically bridges the gap between atomic-level interfacial bonding mechanisms and macroscopic engineering performance, highlighting how these properties are significantly dictated by specific RM sources (e.g., Bayer vs. Sintering processes). We first elucidate advanced pretreatment strategies, notably CO₂ mineralization, which synergistically mitigates extreme alkalinity and sequesters carbon. Crucially, the fundamental bonding mechanisms are decoded: beyond physical filling, RM integration induces significant micro-morphological densification via intense aluminosilicate depolymerization—evidenced by the Al[VI] to Al[IV] coordination shift—and the quantitative integration of approximately 40% reactive iron phases into stable Fe-S-H networks. By clearly distinguishing between traditional hydration and clinker-free alkali-activation pathways, we evaluate holistic structural parameters beyond mere 28-day compressive strength (40–67 MPa), explicitly addressing flexural capacity, modulus of elasticity, and volume stability. Environmental assessments confirm exceptional heavy metal immobilization (>95% efficiency, leaching < 0.010 mg/L) and a substantial 50–80% reduction in Global Warming Potential (GWP), provided the environmental burden of alkaline activators is rigorously accounted for. Furthermore, the long-term risk of Alkali–Silica Reaction (ASR) is evaluated as a primary durability concern. Finally, to overcome persistent rheological bottlenecks, this paper highlights transformative future trajectories, particularly data-driven Machine Learning (ML) for complex mix optimization and 3D concrete printing for advanced infrastructure. Ultimately, this review provides a robust theoretical foundation and a pragmatic roadmap for upcycling RM into safe, high-performance, and ultra-low-carbon building materials. Full article

This systematic review examines the application of life cycle assessment (LCA) to evaluate the environmental performance of sparkling wine production across its value chain. Following PRISMA 2020 guidelines, a structured search in Scopus and Web of Science identified 17 relevant studies published between 2015 and 2025. The results show that environmental hotspots are consistently associated with viticultural inputs (fertilizers, pesticides, and fuel use), energy consumption in winery operations, packaging—particularly glass bottle production—and distribution. Carbon footprint values typically range from 0.9 to 1.9 kg CO₂eq per bottle, with packaging accounting for up to 55–60% of total impact. Methodologically, most studies adopt an attributional LCA approach, apply partial system boundaries, and focus primarily on climate change, limiting comparability and completeness. Conversely, sparkling wine-specific stages, such as secondary fermentation and aging, remain underrepresented. Overall, the findings reveal substantial methodological heterogeneity across studies, particularly in functional units, system boundaries, and impact assessment methods. However, processes specific to sparkling wine production remain underrepresented, limiting the accuracy of environmental characterization for these systems. This review highlights the need for harmonized cradle-to-grave LCA frameworks, bottle-based functional units, and broader impact categories to improve the robustness and comparability of LCA applications in sparkling wine production. Full article

This study investigates the development of sustainable composite materials using recycled low-density polyethylene (rLDPE) and high-density polyethylene (rHDPE) in an 80/20 mass ratio, incorporating kraft lignin as a bio-derived additive and polyethylene-graft-maleic anhydride (PE-g-MA) as a compatibilizer. Reactive melt mixing was employed to produce composites with varying lignin loadings (1, 3, 5, and 10 wt%). The structural, thermal, and mechanical properties and segmental dynamics of the materials were thoroughly examined using differential scanning calorimetry (DSC), infrared spectroscopy (IR), X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD), thermogravimetric analysis (TGA), pyrolysis–gas chromatography/mass spectrometry (Py–GC/MS), tensile testing, scanning electron microscopy (SEM), and dielectric relaxation spectroscopy (DRS). The incorporation of lignin exhibited minimal disruption to the polymeric thermal transitions, while it boosted thermal stability, as confirmed by the TGA curves. According to the segmental dynamics findings, the glass transition temperature of the polymeric blend (−35 °C) was increased systematically with the addition of lignin by ~1–20 K. Tensile tests showed that the 1 wt% additive ratio demonstrated the optimal balance of strength and ductility. Morphological observations supported these findings, revealing uniform dispersion at low additive ratio and increased agglomeration at higher ratios. Based on its superior performance, the composite containing 1 wt% lignin was successfully extruded into filament suitable for 3D-printing. This study highlights the synergy of bio-based additives and recycled polymers in engineering high-performance materials, promoting circular economy principles and reduced environmental footprint through upcycling post-consumer waste into functional, valuable products. Full article

The dynamic development of the food delivery sector and the resulting increase in last-mile distribution operations generate the need to simultaneously improve the efficiency of delivery processes and reduce the environmental impacts of urban logistics. In this context, shortening delivery time contributes not only to higher service quality and competitiveness but also to lower energy consumption and carbon dioxide emissions, which are key elements of sustainable urban mobility and logistics. Therefore, the aim of this study is to develop a delivery time optimization algorithm for the food delivery sector using selected machine learning methods, supporting the implementation of sustainable development principles in the operations of transport enterprises. This study presents an integrated approach to modelling delivery time in food distribution as a tool for building the competitive advantage of logistics enterprises under the conditions of implementing sustainable development principles. The study combines a literature review on sustainable last-mile logistics and data-driven optimization with an empirical analysis using traditional methods such as multiple regression and selected machine learning methods: decision trees, the Gradient Boosting Machine (GBM) method, and the XGBoost algorithm. The operational data include parameters related to delivery execution, such as supplier characteristics, vehicle type, order execution date, weather conditions and traffic situation. The developed mathematical models enable high-accuracy prediction of delivery time and the identification of the most important factors affecting both timeliness and potential energy consumption in the delivery process. The comparative assessment of the applied methods makes it possible to indicate the algorithms that provide the best forecast quality and practical usefulness in logistics decision-making. The proposed delivery time optimization algorithm supports data-driven decision-making that leads to shorter delivery times and lower energy intensity and thus to a reduction in the carbon footprint of last-mile operations, simultaneously strengthening the competitiveness and environmental responsibility of logistics enterprises. The results contribute to the development of sustainable urban logistics by linking predictive modelling with the economic, environmental and operational dimensions of efficiency in last-mile transport processes. Overall, this study offers an original, high-quality contribution to sustainable last-mile food delivery by integrating large-scale operational data with advanced machine learning models to deliver practically relevant, highly accurate delivery time predictions for logistics enterprises. Full article

This study addresses the sustainable supply chain network design (SSCND) problem by integrating economic and environmental dimensions through a multi-objective, multi-echelon stochastic mathematical model. The proposed model focuses on simultaneously optimizing total cost, carbon emissions, water footprint, and renewable energy utilization. Strategic solar energy investment alongside facility location and sizing decisions are considered under uncertain conditions. Initially, a multi-product stochastic model is developed and solved utilizing the augmented epsilon constraint (AUGMECON2) method to obtain Pareto-optimal solutions for small-scale instances. For validation purposes, the exact solutions obtained using AUGMECON2 were used as the benchmark for the small-scale instance, while the proposed hybrid NSGA-II algorithm generated near-optimal solutions with deviations of 0.30%, 1.53%, 0.03%, and 0.0006% for total cost, carbon emissions, renewable energy use, and water footprint, respectively. Compared with the cost-oriented solution, the renewable energy-focused solution increased total cost by 76.33% while reducing the water footprint by 6.36% and carbon emissions by 3.57%. For medium- and large-scale instances, where exact solutions became computationally impractical, the hybrid NSGA-II algorithm remained applicable and generated feasible Pareto solutions within 59.05 s and 309.62 s, respectively. Overall, the presented framework provides a scalable decision-support tool for sustainable supply chain planning under uncertainty. Full article

Effective supply chain planning increasingly requires balancing cost-efficiency with environmental responsibility, particularly as organisations face growing pressure to reduce the carbon footprint of logistics operations. This study develops a mixed-integer linear programming model to optimise inventory and transportation decisions in a multi-echelon distribution network comprising a central warehouse, regional warehouses, and retailers. The model integrates a continuous-review (r,Q) replenishment policy, stochastic demand, safety stock requirements, transportation lead times, and stockout behaviour, enabling a detailed representation of operational dynamics under uncertainty and environmental concerns. Unlike most sustainable inventory models—which typically treat environmental impacts and replenishment control separately or rely on simplified service assumptions—this study provides an integrated framework that jointly embeds (r,Q) policies, stochastic demand, stockouts and distance-based CO₂ metrics within a unified optimisation structure. The model advances prior work by explicitly integrating continuous-review (r,Q) replenishment policies with distance-based CO₂ metrics under stochastic demand, a combination rarely addressed in sustainable multi-echelon inventory models. A multi-objective formulation captures the trade-off between economic performance and CO₂ emissions, allowing the identification of Pareto-efficient strategies that reconcile financial and environmental goals. Reducing emissions by over 90% requires an additional cost of only about 4%, demonstrating that substantial emission reductions can be achieved at relatively low additional cost. The findings offer practical insights for managers seeking to design more sustainable and cost-effective distribution policies, highlighting the value of integrated optimisation approaches in contemporary logistics systems. Full article

Agricultural soils play a dual role in the climate system, acting both as carbon sinks and natural sources of greenhouse gas emissions, which may be intensified under unsustainable management. However, the comparative effectiveness of different soil management strategies, particularly organic amendments derived from agro-industrial residues, remains insufficiently clarified. This review aims to critically synthesize current scientific evidence on soil stewardship practices for mitigating greenhouse gas emissions and enhancing soil carbon sequestration. The analysis is based on a structured review of peer-reviewed literature published over the last decade, including field experiments, long-term trials, and LCA studies. Comparative insights are provided across conventional mineral fertilization, organic amendments, and circular fertilization approaches based on agro-industrial by-products. The results indicate that organic amendments such as compost, digestate, and vermicompost generally increase soil organic carbon stocks (up to +40% in long-term systems) and can reduce greenhouse gas emissions and carbon footprint compared with mineral fertilization, although responses vary depending on soil, climate, and management conditions. The review evaluates the effects of different management practices on soil organic carbon dynamics, greenhouse gas fluxes, nutrient use efficiency, and soil biological functioning. Special emphasis is placed on the role of waste-derived fertilizers—such as composts, digestates, and vermicompost—in promoting soil carbon stabilization while reducing the environmental burden associated with synthetic inputs. Evidence consistently indicates that soil stewardship strategies grounded in circular economy principles can lower net carbon footprints, improve soil resilience, and mitigate trade-offs between productivity and climate mitigation. By framing soil management within the context of global warming mitigation, this review highlights the multifunctional role of soils as climate regulators and underscores the potential of agro-industrial waste valorization as a scalable pathway toward climate-smart and low-emission agricultural systems. Full article

Attention has been drawn internationally to the carbon footprint of the healthcare sector, its impact upon climate change and promises that have been made to reduce carbon emissions. Even so, there are, as yet, not many reports about steps that have been taken in the practical setting to bring about the promised reductions. This review is intended to provide some guidance on actions that could prove beneficial. It includes examples of steps that have been undertaken and shown to be viable options in the practical setting and that now need to be implemented more widely. Certain types of medical textiles contribute more substantially to the carbon footprint of healthcare than others. To achieve significant reductions, attention needs to be focused on reducing the environmental impact of hospital and care centre linen, textile filter components of HVAC systems and PPE, such as gowns, drapes and facemasks, rather than on implantable items and specialist medical devices. Policy makers, those officials responsible for procurement and healthcare practitioners all need to become more involved in ensuring that the correct guidance and resulting actions are implemented in a coordinated fashion. Full article

Edible insects have emerged as a sustainable source of high-quality proteins, lipids, and carbohydrates (including chitin), as well as micronutrients such as minerals and vitamins, and diverse bioactive compounds, thereby making them promising ingredients for functional food applications. Their favourable nutritional profile and low environmental footprint make them attractive ingredients for next-generation food systems. However, processing and preservation remain critical challenges, particularly with respect to the stability of bioactive compounds, lipid oxidation, and protein functional properties such as solubility, emulsifying capacity, and water-holding capacity. This review critically examines recent advances in food processing technologies applied to edible insects, including drying, extraction, fermentation, and microencapsulation, with emphasis on their effects on bioactive compound retention and functional performance. The role of processing strategies in enhancing oxidative stability, protein solubility, emulsifying properties, and overall technological applicability is discussed, alongside safety, regulatory, and consumer acceptance considerations. Overall, this review highlights key technological pathways for the effective valorisation of insect-derived ingredients and outlines future directions for their integration into sustainable and functional food products. In contrast to previous reviews, this work provides a comparative and mechanism-oriented analysis of processing methods, highlighting inconsistencies across studies and identifying key technological trade-offs. Particular attention is given to the relationship between processing parameters and the stability of bioactive compounds. Full article

Environmental factors play a crucial role in shaping human development. This study explores an extension of the United Nations Planetary pressure-adjusted Human Development Index ($PHDI$) by incorporating three methodological refinements: (i) a disaggregated analysis of material footprint data; (ii) the inclusion of a local adjustment factor related to fine particulate matter (PM${}_{2.5}$) exposure; and (iii) a variation to the planetary pressure aggregation method for obtaining the $PHD{I}^{*}$ index. The geographical scope encompasses 137 countries across the five permanently inhabited continents (Africa, America, Asia, Europe, and Oceania). The analysis first evaluates how these additional parameters deviate from the standard UN framework, followed by a continental assessment of national performances and their underlying drivers. A revised global ranking is presented, with countries categorised into four development levels based on Jenks Natural Breaks-derived cut-off values. Comparative cartographic visualisations highlight the shifts among the standard indexes and the proposed $PHD{I}^{*}$, illustrating that while some high-development countries—primarily in Europe—maintain their status, the inclusion of environmental aspects change the categories of important countries. These results suggest that accounting for localised environmental stressors and a more detailed material footprint analysis provides a more granular representation of the constraints on human development. Full article

In the context of green energy, the use of lake reeds is becoming an increasingly important factor. Therefore, research into the availability of reeds, determining their area in lakes, predicting the potential biomass volume and calculating the carbon footprint are important. Currently, there have been no significant research results on the availability of reeds and the assessment of the sustainability of reed products in Latvia. However, these aspects are crucial for the development of reed products, as they help to assess their market potential and environmental impact. The main goal of this work is to develop a method for modeling the distribution of lake reeds in order to predict their availability in the future, which would allow assessment of the volume of biomass and its impact on the environment. This research develops an integrated GIS–LCA framework that combines Sentinel-2 satellite data, machine learning-based classification, biomass estimation, and carbon footprint modeling. Using Lake Cirma as a case study, the classification results show that reed stands occupy 2.18–3.51 percent of the lake area in certain years, corresponding to approximately 1158–1861 tons of biomass. The framework enables quantification of harvesting potential while considering ecological constraints that limit annual extraction to approximately 50% of total biomass. The proposed GIS–LCA framework provides a replicable methodology for assessing reed biomass availability and environmental performance across lake ecosystems. It supports evidence-based decision-making for sustainable reed resource management and contributes to the development of low-carbon bioeconomy pathways in line with EU climate and bioeconomy strategies. Full article

The relationship between economic development and environmental degradation in Brazil was studied over the period 1970–2022, using ecological footprint (EF) as an environmental indicator. A contribution to the scientific literature exists here because biomass energy (BIO) has been separated from other types of renewable energy sources, and environmental policy stringency (EPS) and energy depletion (END) have been simultaneously analyzed for their joint impacts on EF in Brazil. In this research, four hypotheses were formulated for the relationships of: GDP, BIO, EPS, RE, and END with EF. The ARDL method was used in this analysis due to the different orders of integration for some of the variables and sample size limitations, both of which make alternative cointegration techniques inappropriate. All four hypotheses were supported in the empirical estimates of this study. In the long run, increases in GDP will result in increased EF, decreases in BIO and EPS will decrease EF, and no long-run relationship exists between RE and EF. However, RE has a short-term rebound effect. Increases in END will increase EF, indicating the environmental costs associated with the extraction and consumption of non-renewable resources. The statistically significant error correction term also supports the idea that there will be a quick adjustment towards the long-run equilibrium. The implications of these results suggest that Brazil continues to operate within a stage of growth driven primarily by scale rather than intensity, yet well-regulated biomass energy and strict environmental regulations provide a pathway for achieving decoupling in alignment with SDG 13 and SDG 15. Full article