Sustainability

Predicting the sustainability of national educational systems presents a complex, multifaceted issue due to the intricate connections between education and wider societal, economic, healthcare, and technological sectors. Current educational models tend to be rigid, narrow in focus, and insufficiently responsive to these changing external factors. This research seeks to fill this void by framing education as an ecosystem and creating a methodological framework that merges systems thinking with sophisticated data-driven methods. The study’s aim is to outline, quantify, and analyze the relationships among education-related subsystems to guide the creation of an adaptive, sustainability-focused educational ecosystem. A mixed-methods approach was utilized, incorporating qualitative coding, system mapping, and natural language processing techniques (specifically Word2Vec) to uncover relational patterns within a structured literature set. These findings were integrated with quantitative metrics to assess subsystem efficacy and pinpoint leverage points. The investigation centers on five primary systems in the education ecosystem: Business, Economic, Government, Healthcare, and Sustainability. The Word2Vec analysis identified significant conceptual relationships between these systems, while the quantitative evaluation indicated strong performance across curriculum, policy, and healthcare metrics. Conversely, inclusivity and accreditation displayed weaker outcomes, indicating areas that need focused improvement. The results highlight the benefits of merging systems thinking with NLP-driven relational analysis as a methodological innovation in education research. The study offers evidence-based recommendations for prioritizing factors that can boost system efficacy and create beneficial cross-system ripple effects, aiding in the advancement of adaptive and sustainable educational ecosystems.

This study examines how real photographs and AI-generated photographs shape sustainable engagement and travel intention in tropical island tourism. We used a one-factor between subjects survey experiment with two independent conditions, real images and AI images, with 357 participants in each group. Guided by the SOR framework, we measured perceived authenticity, cognitive destination image, emotional comfort, and perceived information diagnosticity, together with sustainable engagement and travel intention. Structural equation modeling shows that under both visual conditions the four perceptual factors are positively associated with travel intention. In the real photo condition, sustainable engagement partially mediates the effects of all four factors on travel intention. In the AI photo condition, sustainable engagement mediates the effects of cognitive destination image, emotional comfort, and perceived information diagnosticity on travel intention, while the indirect pathway from perceived authenticity to travel intention through sustainable engagement is not significant. These findings support an actionable dual-track visual strategy. Use AI images to expand reach at low ecological cost, then use real images with verifiable cues to strengthen credibility and encourage responsible choices.

The construction sector undeniably has an impact on sustainability in its three dimensions: economic, social, and environmental. In this context, 3D concrete printing (3DCP) has emerged over the last decade as an attractive technology for transforming this sector. It enables the manufacture of construction elements while saving time, reducing waste, and eliminating the need for molds. However, assessments of the environmental performance of implementing this technology are limited, particularly under representative production conditions. This study evaluates the footprint family indicators, carbon footprint (CF), ecological footprint (EF), and water footprint (WF), of different mixtures of 1 m3 of 3D-printed concrete, with 1m of a high printed wall. These mixtures were made with a proportion of fresh solid aggregates; brick and concrete rubble (as demolition waste (CDW) materials) were used as partial replacements for cement. In addition, the environmental impact of using two printing technologies, gantry and robotic arm systems, is analyzed. The results show that materials are the main source of environmental impacts; the replacement of some of the cement reduces CF and EF by up to 20% and 19%, respectively, while preserving printability and buildability, as demonstrated by the stable fabrication of 1 m-high printed wall elements. However, moderate increases in WF were observed, which were associated with the electricity consumption of waste processing. These results confirm the potential for valorizing CDW in 3D printing mixtures. This environmental assessment under full-scale printing conditions supports sustainability-oriented decision-making in the construction industry.