Search Results (11,636)

Island ecosystems, are characterized by isolation, limited land, and tourism-driven economies, face persistent waste management challenges. Spatial constraints and inadequate infrastructure often limit the development of waste recovery and recycling systems, leading to practices such as open dumping or burning that pose serious environmental and health risks. This paper examines how circular economy (CE) principles, reduce, reuse, recycle, can transform waste into a resource and enhance local resilience. A refined definition of “small islands” is introduced, combining UN criteria with a tourism-intensity filter to capture the strong link between visitor flows and solid waste generation. Barriers to CE adoption are classified into institutional, technical, geographical, financial, and social dimensions, and connected to enabling practices in four thematic areas: multi-stakeholder partnerships, recycling and composting innovations, policy and regulatory tools, and community engagement. Comparative case studies from Europe, Asia, Africa, and the Pacific reveal that integrated approaches are more durable than isolated efforts. Successful initiatives blend technology with governance, education, financial mechanisms, and community participation. The analysis highlights that no single model fits all islands; strategies must be locally adapted to be effective and transferable. Overall, the study shows that circular transitions are both feasible and necessary, offering environmental gains, economic value, and alignment with the EU Green Deal and global sustainability goals. Full article

At present, the digital construction of museums has created a novel cultural ecosystem that integrates digital preservation of cultural heritage, intelligent management, immersive experiences, and cloud-based services. However, insufficient synergistic integration of technological applications constrains the comprehensive release of the digital construction’s efficacy, while the absence of cultural assessment dimensions hinders the effective articulation of mechanisms whereby digital technology empowers cultural innovation. These concerns collectively constitute the primary impediments hindering museums from attaining sustainable development. The effectiveness of museum digital construction is fully clarified by combining grounded theory qualitative research methods with cultural cycle theory in this study. The Analytic Network Process (ANP) is used to manage interdependent relationships between factors, and cloud models are used to clarify indicator ambiguity, which allows for accurate assessment of digital construction results, consequently bolstering the sustainability of museum digitalization initiatives. The developed ‘qualitative–quantitative’ collaborative evaluation methodology for museum digital construction includes three sub-objectives: technology embedding, value co-creation, and institutional adaptation, as well as five primary indicators and ten secondary indicators. An empirical analysis of the ‘Smart Jiangxi Museum’ digital construction initiative at the Jiangxi Provincial Museum in China indicates that the project has achieved an ‘excellent’ standard. The findings of a previous qualitative study are effectively supported by this conclusion. This study presents a systematic approach for museum evaluation and gives decision-making guidance for museums to attain sustainable use of cultural resources, promote social knowledge transmission, and facilitate green, low-carbon transformation of operational models in the digital era. Full article

This paper presents an approach to teaching and consolidating skills in the context of sustainability “Prototyping Sustainability–Designing Sustainable IT” (ProS), using the workshop format for participatory and creative learning. The workshop integrates principles from Education for Sustainable Development (ESD), transformative and experiential learning, participatory design, and critical reflection on the digital age to engage participants in critically examining the environmental, economic, and social impacts of digital technologies in the context of Sustainable Development Goals (SDGs). Structured in five modular phases, from self-reflection and knowledge activation to collaborative prototyping and peer evaluation, the workshop offers a hands-on, gamified learning experience centred on real-world sustainability challenges. Learners create user-centred paper-based prototypes for digital products using tactile materials, persona-driven scenarios and knowledge of sustainable product characteristics gained in the workshop. Outcome measurement is supported through pre- and post-workshop surveys, peer voting templates, and paper-based prototype artefacts, enabling rich insight into behavioural intentions and learning gains. The paper discusses the educational value and sustainability relevance of the workshop engaging young people in critically reflecting on the environmental, economic, and social consequences of digitalization. Finally, it highlights challenges and limitations and proposes directions for future research. Full article

Enhancing green development efficiency (GDE) is of great significance in achieving regional green transition. Against the backdrop of rapid advancements in digital technology, digital–real economy integration (DRI) opens a new avenue for enhancing GDE. This research develops a theoretical analytical framework to analyze the influence of DRI on GDE. It employs panel data from 41 cities in China’s Yangtze River Delta urban agglomeration (YRDUA) spanning from 2011 to 2023 to develop a series of econometric models that empirically examine the impact of DRI on GDE and its underlying mechanisms. Research has demonstrated that the degree of DRI varies by region across the YRDUA, with a pattern of decreasing from east to west. Empirical results confirm that DRI development significantly boosts GDE in the YRDUA. Mechanism tests reveal that DRI indirectly enhances GDE through industrial structure optimization, green technological progress, and resource allocation efficiency. Moderation effects indicate that industrial collaborative agglomeration (ICA) significantly amplifies DRI’s positive impact on GDE. Further analysis indicates that the positive impact of DRI on GDE is only significant in low-carbon pilot cities and non-resource-based cities. Moreover, ICA exhibits a single-threshold effect: when regional ICA exceeds 2.0048, DRI’s impact on GDE demonstrates diminishing marginal returns. These findings not only give a realistic roadmap for accomplishing regional green transformation but also offer empirical evidence for policymakers to make scientific policies, adapt to local conditions, and appropriately promote ICA. This approach fully leverages the benefits of DRI, thereby advancing the economy toward sustainable development. Full article

This study presents the optimisation of an alkali-activated binder produced from ground granulated blast furnace slag (GGBFS) and potassium-rich sunflower husk ash (SHA), by varying SHA content, curing regime, and mixing procedure. Both materials are locally available in the Republic of Serbia. The influence of SHA content (15%, 25%, and 35% by mass of GGBFS) and curing conditions (ambient and 65 °C) on hydration products, workability, and compressive strength was examined. The water-to-binder ratio and GGBFS content were kept constant, and a one-part alkali activation approach was employed using untreated SHA. Fourier-transform infrared spectroscopy (FTIR) and thermogravimetric analysis (TGA) were performed on paste samples, after 2, 7 and 28 days of curing, while workability and compressive strength of mortars were measured after 7 and 28 days. Increasing SHA content enhanced the formation of C-S-H and C-A-S-H gels, resulting in a consistent rise in compressive strength, from 26.6 MPa to 36.2 MPa after 7 days and from 46.2 MPa to 55.1 MPa after 28 days of ambient curing. Workability was slightly reduced with increasing SHA content, resulting in flow diameters of 156.04 mm (15% SHA), 154.10 mm (25% SHA) and 152.76 mm (35% SHA). Curing at 65 °C accelerated early strength gain for 33% to 39% but produced lower 28-day strengths than ambient curing. Additionally, for the optimal mix, SHA was also pre-immersed in water for varying durations to assess its effect on workability, compressive strength, and potassium ion leaching. This pretreatment increased compressive strength by up to 14.7%, depending on immersion time, but reduced workability by up to 15.5%. The novelty of the research is reflected in attaining the highest 28-day compressive strength of 55 MPa (for 25% SHA by mass of GGBFS), under ambient curing, without SHA pretreatment or immersion, highlighting the potential for low-energy, sustainable binder systems using agricultural and industrial by-products. Full article

As the response to climate change transitions from passive adaptation to transformation and resilience, the importance of climate resilience has become increasingly evident. The port logistics industry is highly vulnerable to climate change, and the efficacy of climate resilience within the sector must be empirically validated. In this study, we aim to investigate the impact of national climate resilience on container port throughput. To achieve this, we conducted a panel regression analysis using data spanning 13 years (2010–2022) from 83 countries. The findings reveal that, overall, climate resilience positively influences container port throughput, but in Latin America, it showed a negative correlation. This suggests that the relationship between climate resilience and container port throughput varies depending on regional characteristics and factors. Further, climate resilience indicators specific to the port logistics industry should be developed. This study serves as a foundational exploration into climate resilience in the port logistics industry, providing empirical evidence of its critical role. The findings serve as a foundation for sustainable development and policy decision-making. Full article

Dry reforming of methane (DRM) is a promising method for turning two major greenhouse gases, CO₂ and CH₄, into syngas (H₂ + CO). This syngas has the right H₂/CO ratio for making valuable chemicals and liquid fuels. However, there are significant challenges that make it tough to implement commercially. One big issue is that the process requires a lot of energy because it is highly endothermic, needing temperatures over 700 °C. This high heat can quickly deactivate the catalyst due to carbon build-up (coking) and the thermal sintering of metal nanoparticles. Researchers increasingly recognize mechanochemistry—a non-thermal, solid-state technique employing mechanical force to drive chemical transformations—as a sustainable, solvent-free strategy to address these DRM challenges. This mini-review critically assesses the dual role of mechanochemistry in advancing DRM. First, we examine its established role in creating advanced catalysts at lower temperatures. Here, mechanochemical methods help produce well-dispersed nanoparticles, enhance strong interactions between metal and support, and develop bimetallic alloys that resist coke formation and show great stability. Second, we delve into the exciting possibility of using mechanochemistry to directly engage in the DRM reaction at near-ambient temperatures, which marks a major shift from traditional thermocatalysis. Lastly, we discuss the key challenges ahead, like scalability and understanding the mechanisms involved, while also outlining future directions for research to fully harness mechanochemistry for converting greenhouse gases sustainably. Full article

Cost overruns remain one of the most persistent challenges in construction and infrastructure project management, often undermining efficiency, sustainability, and stakeholder trust. With the rise of digital transformation, artificial intelligence (AI) and machine learning (ML) provide new opportunities to enhance predictive decision-making and strengthen project control. This study introduces a digital project management framework that integrates the Pelican Optimization Algorithm (POA) with Light Gradient Boosting Machine (LGBM) to deliver reliable and interpretable cost overrun forecasting. The proposed POA-LightGBM model leverages metaheuristic-driven hyperparameter optimization to improve predictive performance and generalization. A comprehensive evaluation using multiple error metrics Coefficient of Determination (R²), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) demonstrates that POA-LGBM significantly outperformed baseline LGBM and alternative metaheuristic configurations, achieving an average R² of 0.9786. To support transparency in digital project environments, SHapley Additive exPlanations (SHAPs) were employed to identify dominant drivers of cost overruns, including actual project cost, energy consumption, schedule deviation, and material usage. By embedding AI-enabled predictive analytics into digital project management practices, this study contributes to advancing digital transformation in project delivery, offering actionable insights for cost control, risk management, and sustainable infrastructure development. Full article

Iron-rich metallurgical slag is an underutilized precursor in alkali-activated materials (AAMs), despite its abundance and potential in sustainable construction and waste immobilization. This study evaluates a binary AAM system (Aachen GP), comprising 50 wt.% blast furnace slag (BFS) and 50 wt.% iron-rich slag (Fe₂O₃ ≈ 24.6 wt.%), against a BFS-only reference (Ref GP). Characterization included isothermal calorimetry, Fourier Transform Infrared Spectroscopy (FTIR), X-ray diffraction (XRD), thermogravimetric analysis (TGA), Scanning Electron Microscopy with Energy Dispersive X-ray spectroscopy (SEM–EDX), Brunauer–Emmett–Teller (BET) surface area, water permeability, porosity, and compressive strength. Aachen GP showed delayed setting (32.9 h), reduced cumulative heat (∼70 J/g), and lower bound water (4.6% at 28 days), indicating limited gel formation. Compared to Ref GP, it had higher porosity (38.4%), water permeability ($1.42\times {10}^{-10}$ m/s), and BET surface area (12.4 m²/g), but lower 28-day strength (14.4 MPa vs. 43 MPa). Structural analysis revealed unreacted crystalline phases and limited amorphous gel. While Aachen GP meets regulatory strength thresholds (≥8 MPa) for low- to intermediate-level wasteforms in Belgium and Germany, its elevated porosity may impact long-term containment. Further studies on radionuclide leaching and durability under thermal and radiation stress are recommended. Full article

This study provides a bibliometric analysis of African COVID-19 research outputs from 2019 to 2025, exploring trends in original research articles, citation performance, funding patterns, partnership networks, thematic areas, and alignment with the United Nations Sustainable Development Goals (SDGs). Metadata retrieved from Web of Science™ and Scopus were examined utilizing statistical software (GraphPad Prism version 10.2.3), bibliometric mapping, and collaboration network visualization. Africa produced 14,561 original research articles (a global research output of 2.8%), with South Africa and Egypt accounting for 44.72% of original research articles. Research output and citation peaked in the year 2022 and declined from the year 2023, with the domination of medicine and associated health sciences areas, aligned with SDG 3 (Good Health and Well-Being), demonstrating 54.4% of the research outputs. Notwithstanding various funding sources, less correlation occurred between original research articles and funding levels, underscoring the necessity for reinforced institutional capacity. Moreover, intra-African collaboration remained partial, with South Africa being at the forefront of cross-country collaboration. The findings unravel improvement and persevering gaps in African COVID-19 research, underlining the significance of impartial capacity building, diversified into under-represented SDGs, for instance SDG 14 (Life Below Water) and SDG 7 (Clean Energy), and purpose-built policy frameworks to strengthen pandemic preparedness and multidisciplinary resilience. Full article

The tidal mudflat of the South Yellow Sea is characterized by complex sediment environments that preserve rich paleoenvironmental signals, making it an important area for understanding land–sea interactions and promoting sustainable coastal development. Thus, accurate identification of sediment sequences and layer thicknesses becomes crucial for interpreting sediment dynamics and paleoenvironmental reconstruction. While borehole data have elucidated local sediment facies, their spatially discontinuous nature hinders a holistic reconstruction of regional depositional history. To overcome this limitation, ground-penetrating radar (GPR) surveys were conducted across the tidal mudflat of the South Yellow Sea, enabling systematic correlation between radar reflection patterns and sediment architectures. Based on the relationship between the dielectric permittivity and wave velocity, short-time Fourier transform (STFT) was applied to derive the peak-weighted average frequency in the frequency domain for individual soil layers, revealing its dependence on dielectric properties. Sediment interfaces and layer thicknesses were determined using three methods: the radar image waveform method, the Hilbert spectrum instantaneous phase method, and the generalized S-transform time–frequency analysis method. The results indicate the following: (1) GPR enables high-fidelity imaging of subsurface stratigraphy, successfully resolving three distinct radar facies: F1: high-amplitude, horizontal, continuous reflections with parallel waveforms; F2: moderate-to-high-amplitude, sinuous continuous reflections with parallelism; and F3: medium-amplitude, discontinuous chaotic reflections. (2) All three methods effectively characterize subsurface soil stratification, but positioning accuracy decreases systematically with depth. Excluding anomalous errors at one site, the relative error for most layers within the 1 m depth is below 15%, and remains ≤25% at the 1–2 m depth. Beyond the 2 m depth, reliable stratification becomes unattainable due to severe signal attenuation. (3) Comparative analysis demonstrates that the Hilbert spectral instantaneous phase method significantly enhances GPR signals, achieving an optimal performance with positioning errors consistently below 5 cm for most soil layers. The application of this approach along the tidal mudflat of the South Yellow Sea significantly enhances the precision of sediment layer boundary identification. Our analysis systematically interpreted radar facies, demonstrating the effectiveness of the Hilbert spectrum instantaneous phase method in delineating soil stratification. These findings offer reliable technical support for interpreting GPR data in comparable sediment environments. Full article

The Yellow River Basin faces high-intensity coal resource development and severe water scarcity. This makes the treatment and use of mine water a critical factor constraining both coal industry development and ecological security for the region. This study uses kernel density estimation and the Standard Deviational Ellipse model to identify the spatial pattern of mine water production. It also combines bibliometric analysis and field investigations to assess research progress and current practice for mine water treatment and use in the basin. Results show that mine water production displays strong spatial clustering, with the center of gravity shifting northward. Research is moving from an engineering-focused stage to a theory-oriented one, emphasizing systematic optimization and sustainable use. Current practices still struggle with non-standardized data, uneven treatment quality, and incomplete management systems. This research underscores the importance of improving the region’s integrated management of mine water and proposes shifting mine water from an environmental burden to a resource asset. Full article

As a transformative technology, artificial intelligence (AI) is profoundly reshaping corporate innovation practices, creating new opportunities for ambidextrous (exploitative and exploratory) innovation and thereby advancing sustainable competitiveness. Drawing on ambidextrous theory and adopting a resource-based perspective, we employed panel data from Chinese A-share listed firms in the 2014–2023 period to examine the relationship between AI application and corporate exploitative and exploratory innovation performance as well as the mechanism behind AI’s effects in this regard. We further explored the moderating effect of data resources. The findings reveal that AI application significantly enhances both exploitative and exploratory innovation performance. Improvement in corporate research and development efficiency and the optimization of labor structures play mediating roles in the relationship between AI application and both exploitative and exploratory innovation. Moreover, firms’ data resources strengthen the positive impact of AI on exploitative innovation while weakening its effect on exploratory innovation. Overall, this study provides novel insights into how traditional enterprises can leverage AI to foster ambidextrous innovation and achieve sustainable competitive advantages. These findings offer practical guidance for corporate executives and policymakers on strategically implementing AI for innovation management. Full article

In today’s world, preparing future science teachers is vital because they have the power to transform society and build a sustainable world by raising global citizens. In this manner, the purpose of the present study was to develop pre-service science teachers’ competencies in instructional planning for teaching Sustainable Development Goals (SDGs) in K-12 classrooms through Education for Sustainable Development Goals (ESDG). The research group consisted of 20 pre-service science teachers who enrolled in the ‘Sustainable Ecology’ elective course at a public university in Istanbul, Türkiye. A design-based research model was followed in the study. The data were collected from pre-service science teachers through semi-structured interviews before and after the course. In the data analysis, deductive content analysis was used. The findings revealed that ESDG shows promise for developing pre-service science teachers’ competencies in instructional planning for teaching the SDGs in K-12 classrooms. Full article