Producción Científica

The study explored rural livelihood sustainability in South Asia and Africa through a bibliometric analysis and systematic review approach. The purpose of the study was to identify the trend of scholarly discourse, the dominant themes that have attracted scholarly interest over the past decade, and to shed light on the emerging opportunities for enhancing rural livelihood sustainability. The study carefully selected 139 articles sourced from Scopus, Web of Science, Google Scholar, and other institutional websites. Our findings revealed noticeable growth in scholarly output over the past decade and identified some scholars with noteworthy contributions. Notably, it was found that the impact of climate change, drought, poverty, and food security on rural livelihoods has largely dominated the scholarly investigations over the period. However, the potentials of pyrolis, biochar, and dryland forestry for rural livelihood sustainability were found to be underexplored. In light of these insights, we recommend the scholarly community develop much interest in less explored areas that equally hold great potential for enhancing rural livelihoods. We also recommend that rural livelihood programming by policymakers must seek the integration of indigenous knowledge and be tailored to strengthen the resilience of rural households against the perils of climate change and weather variability.

Recent years have seen an increasing interest in 3D concrete printing (3DCP); however, its sustainability challenges, particularly regarding environmental concerns, require close attention. To mitigate sustainability-related problems of technology, new realm of research has emerged: sustainable 3DCP. However, this field currently lacks a classification framework for sustainability practices and approaches. To fill this gap, current study aims to classify strategies in 3DCP to enhance environmental sustainability and offer recommendations for further research. To achieve this goal, first, a bibliometric analysis, a computer-aided statistical method for reviewing the literature, is employed. This analysis will explore, measure, and map existing research to identify current trends and the state-of-the-art in the field. Subsequently, a thematic analysis of the keywords categorises prior sustainability strategies. Finally, benchmarking concrete technology offers some recommendations for mitigating sustainability concerns and assessing the benefits of applied strategies. The results show that research in the realm of sustainable 3DCP began in 2017 and has a significant growth rate of 1.5 times per year. Besides, strategies to improve the sustainability performance of 3DCP pursue four main targets: reducing carbon footprint and raw material consumption, increasing durability characteristics, and enhancing thermal performance.

This study provides a comprehensive bibliometric and in-depth analysis of artificial intelligence (AI) and machine learning (ML) applications in environmental monitoring, based on 4762 publications from 1991 to 2024. The research highlights a notable increase in publications and citations since 2010, with China, the United States, and India emerging as leading contributors. Key areas of research include air and water quality monitoring, climate change modeling, biodiversity assessment, and disaster management. The integration of AI with emerging technologies, such as the Internet of Things (IoT) and remote sensing, has significantly expanded real-time environmental monitoring capabilities and data-driven decision-making. In-depth analysis reveals advancements in AI/ML methodologies, including novel algorithms for soil mapping, land-cover classification, flood susceptibility modeling, and remote sensing image analysis. Notable applications include enhanced air quality predictions, water quality assessments, climate impact forecasting, and automated wildlife monitoring using AI-driven image recognition. Challenges such as the “black-box” nature of AI models, the need for high-quality data in resource-constrained regions, and the complexity of real-time disaster management are also addressed. The study highlights ongoing efforts to develop explainable AI (XAI) models, which aim to improve model transparency and trust in critical environmental applications. Future research directions emphasize improving data quality and availability, fostering interdisciplinary collaborations across environmental and computer sciences, and addressing ethical considerations in AI-driven environmental management. These findings underscore the transformative potential of AI and ML technologies for sustainable environmental management, offering valuable insights for researchers and policymakers in addressing global environmental challenges.

This study addresses environmental concerns related to construction and demolition waste (CDW), which constitutes 35–40% of global waste. It explores the potential use of recycled fine aggregates (RFA) from concrete demolition waste as a sustainable alternative to natural fine aggregates in 3D printed mortar (3DPM). By conducting a systematic literature review (SLR) and utilizing VOSviewer for bibliometric analysis, the research assesses the mechanical properties, flowability, extrudability, and buildability of RFAs in 3DPM. The analysis also highlights key trends in keywords and research distribution across different countries. The findings reveal that while RFAs in 3DPM slightly reduce compressive and flexural strengths compared to natural aggregates, they significantly contribute to environmental sustainability by reducing landfill waste and conserving natural resources. The study underscores the importance of further research to optimize RFA-based mixtures for wider application in 3D printing technologies.