1,721,012 research outputs found
Integrating AI to Assess Community Roles in Environmental Safeguarding During Mining: Implications for ESIA in SSA
Full text linkThis study investigates the role of local communities in environmental safeguarding during mining operations in Sub-Saharan Africa (SSA) and its implications for Environmental and Social Impact Assessments (ESIAs). While mining drives economic development, it often imposes environmental and social costs on local populations. The study critiques existing ESIA frameworks for privileging top-down, technocratic models that marginalize community voices. Using a systematic scoping review of 62 peer-reviewed empirical studies published since 2010, the research analyzes community participation and safeguarding practices through thematic coding and AI-powered tools like natural language processing. The findings underscore that local communities possess unique monitoring capacities, contextual knowledge, and culturally grounded environmental ethics that can enhance ESIA efficacy. These communities often respond more effectively than regulatory authorities to environmental infractions. The study also identifies structural barriers such as tokenistic participation, poverty, and policy exclusion that undermine meaningful engagement. It recommends embedding community-driven perspectives within ESIA processes by strengthening collaborative frameworks, recognizing indigenous knowledge systems, and leveraging AI to ensure inclusive and transparent evaluations. Furthermore, it argues for a shift toward participatory governance models that empower communities as co-regulators of environmental standards. By reframing ESIA as a dynamic socio-environmental negotiation, the study offers practical insights for policy reform, corporate responsibility, and sustainable development in SSA’s mining sectors
Enhancing Wetland Restoration through Machine Learning-Based Decision Support Systems
No full textResearchers are increasingly employing Machine Learning (ML) and Deep Learning (DL) algorithms to address complex geo-environmental challenges, particularly in predicting risk, susceptibility, and vulnerability to environmental changes. These advanced computational models have shown significant promise in various applications, ranging from natural disaster prediction to environmental monitoring. Despite their growing usage, very few studies have leveraged Machine Learning-Based Decision Support Systems (MLBDSS) to restore the health status of wetland habitats. To our knowledge, there are no comparative analyses between Machine Learning models and traditional Decision Support Systems (DSS) in this specific context. Wetlands play a crucial role in supporting biodiversity, including fish and wildlife populations, while also contributing to improved water quality and providing essential ecosystem services to nearby communities. These services include flood control, carbon sequestration, and water filtration, which are vital for both ecological and human well-being. However, over the past decades, wetland areas, particularly in coastal regions, have faced significant degradation due to anthropogenic pressures, resulting in a substantial reduction of these critical benefits. This ongoing loss poses serious ecological and socio-economic challenges that require immediate and effective intervention. Current wetland assessment and mitigation frameworks often encounter limitations in their practical implementation, despite regulatory advancements aimed at promoting wetland conservation. These shortcomings can lead to delayed project approvals, increased costs, and further loss of valuable ecosystem services. Integrating ML and DSS models into wetland management strategies could provide innovative solutions to overcome these challenges by improving predictive accuracy, optimizing restoration efforts, and enhancing decision-making processes. The development of hybrid models combining ML and DSS approaches may offer a more holistic framework for addressing wetland loss, ultimately contributing to sustainable habitat restoration and conservation efforts
Technofixing the Future in Mining Industry: Ethical Side Effects of Using AI and Big Data to Meet the SDGs
No full textRecent advances in artificial intelligence (AI), big data, and non- geostationary satellite (NGSO; LEO/MEO) services promise faster , safer , and “ greener ” mining , but also raise ethical and governance risks . This study interrogates the technofix narrative. Objectives were to map NGSO+AI applications across the mining value chain ; assess technical , operational , environmental , and economic performance; examine governance , data rights , and justice implications ; evaluate capacity and procurement models ( with an East African lens ); and distill actionable guidance . Following a PRISMA-2020 protocol , a mixed-methods review ( database inception –12 Aug 2025) of peer- reviewed and grey literature was undertaken with duplicate screening and appraisal (JBI, RoB 2/ROBINS-I, AACODS; GRADE/ CERQual ). Over 80 empirical studies and initiatives were synthesized ; random-effects meta- analysis was used where outcomes were comparable, alongside realist narrative synthesis . NGSO connectivity reduced latency (LEO: tens of ms; MEO: ~100–200 ms) and high-revisit EO (SAR/ optical ) improved surface-change detection ; operational gains ( uptime , reporting ) were noted but with low – moderate certainty given short follow -up and sponsorship . Governance lagged capability : data ownership and portability were unclear , third-party audit access rare, and community participation uneven ; ethical risks included bias , privacy , and cultural impacts . East African pilots showed technical promise amid institutional gaps. NGSO+AI can advance SDG- aligned mining only when coupled to binding data rights , independent assurance , participatory pathways , open interfaces, and local capacity ; otherwise tools risk performative compliance rather than accountable , just outcomes
Systematic Review on the Application of Nanotechnology and Artificial Intelligence in Agricultural Economics
Full text linkThe convergence of nanotechnology and artificial intelligence (AI) represents a transformative force in agricultural economics, offering innovative solutions to longstanding challenges such as productivity inefficiencies, environmental degradation, and unsustainable resource use. This study presents a systematic literature review (SLR) aimed at synthesising theoretical frameworks, applications, and economic implications associated with these technologies in agriculture. A structured search strategy was developed using Boolean operators to combine key terms related to nanotechnology, AI, and machine learning. Comprehensive searches were conducted across six academic databases—Springer, IEEE Xplore, ACM, Science Direct, Wiley, and Google Scholar—complemented by manual and snowballing techniques. From an initial pool of 840 records, 55 studies met the inclusion criteria after rigorous screening and eligibility assessment. Findings indicate that nanotechnology enhances nutrient delivery, pest control, and crop monitoring through nanosensors and nano-fertilisers, while AI facilitates data-driven decision-making, yield prediction, and resource optimisation in precision farming. Despite promising results, challenges such as high initial investment, technological complexity, and limited access for smallholder farmers remain significant. The review concludes that the integration of nanotechnology and AI can improve agricultural efficiency, economic viability, and environmental sustainability. However, targeted investments, capacity-building, and interdisciplinary collaboration are essential to bridge the gap between innovation and implementation in developing economies
Enhancing Urban Green Spaces: AI-Driven Insights for Biodiversity Conservation and Ecosystem Services
Full text linkUrban green spaces (UGS) enhance biodiversity and provide essential ecosystem services like air purification, climate regulation, water management, and recreation. Despite their importance, UGS are often overlooked in urban planning, limiting their potential for resilience and sustainability. This study examines biodiversity in UGS and their capacity to deliver ecosystem services using field surveys, GIS mapping, stakeholder interviews, and AI-driven analytics. AI-based image recognition and remote sensing automate species identification and assess vegetation health, improving biodiversity assessments. Machine learning models analyze spatial and environmental data to predict UGS contributions to mitigating heat islands, air pollution, and stormwater runoff. Findings show that UGS serve as biodiversity hotspots, hosting diverse flora and fauna. Ecosystem service provision varies based on green space type, size, and management. AI-driven insights reveal key biodiversity factors like vegetation composition, spatial configurations, and human activities, offering data-driven recommendations for urban planning. Integrating AI into urban ecology supports evidence-based decision-making, urging policymakers and communities to optimize UGS management for biodiversity and human well-being
Challenges in Sub-Saharan Africa’s Food Systems and the Potential Role of AI
No full textSub-Saharan Africa (SSA) faces persistent food insecurity due to low agricultural productivity, limited access to modern technologies, and growing climate variability. This study explores the transformative potential of Artificial Intelligence (AI) to enhance food systems across SSA. The objective is to assess how AI applications—such as machine learning, remote sensing, and big data analytics—can address systemic inefficiencies in cereal crop production, with a focus on barley, millet, and sorghum. Using a systematic review approach aligned with PRISMA guidelines, literature from 2015–2025 was analyzed across multiple databases to identify empirical studies and models related to AI in SSA agriculture. Results reveal that AI can significantly improve crop monitoring, yield forecasting, and resource optimization. However, adoption barriers such as inadequate infrastructure, financial constraints, and the digital divide persist. The study concludes that while AI holds significant promise, its success in SSA depends on inclusive policies, capacity building, and localized data governance. It recommends interdisciplinary research, investment in rural digital infrastructure, and participatory innovation frameworks to empower smallholder farmers and ensure equitable AI deployment. This review provides a roadmap for integrating AI into SSA food systems to enhance resilience, productivity, and food security
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
A comprehensive exploration of nongeostationary satellite systems in the mining industry: emphasizing AI , ethical considerations, and communication strategies
No full textNon-geostationary satellite (NGSO) constellations—particularly LEO/MEO—are transforming mining by providing low-latency connectivity and taskable Earth observation to remote, infrastructure-poor sites. Objectives include mapping NGSO applications across exploration, planning, and operations; assessing AI's role in tasking, routing, and analytics; and examining governance and ESG implications, with a focus on Africa and East Africa. Methods involved a PRISMA-aligned systematic review (protocol registered) synthesising primary and secondary evidence on NGSO-enabled EO and communications in mining. A random-effects meta-analysis was planned if three or more comparable studies reported the same outcome; otherwise, a structured narrative synthesis with predefined subgroups (LEO vs MEO, EO vs backhaul, open-pit vs underground, Africa vs elsewhere) was used. Results and discussion showed that across more than 30 use cases, NGSO backhaul and EO tasking consistently reduced time to insight for pit progression, tailings surveillance, and asset tracking; simulations indicated routing improvements of approximately 10% on tree topologies and 30% on mesh networks at N=500, demonstrating tangible latency and capacity benefits for safety-critical workflows. Continuity was enhanced through multi-sensor PNT (GNSS/inertial/vision plus radio localisation ) and hierarchical link adaptation that rapidly re- parameterises under noise, weather, or interference. AI added value by improving tasking and congestion control in edge and cloud inference, though it required cascaded models, compression, and uncertainty gating to meet compute and bandwidth constraints. Governance themes—such as data protection, transparency, and community benefit—were recurring enablers of adoption. Conclusion: When combined with resilient positioning, adaptive operations, and credible ESG safeguards, NGSO combined with AI can significantly enhance mining efficiency, safety, and sustainability; priorities include standardised KPIs, transparent cost models, and long-term pilot deployments
Systematic Review on the Application of Nanotechnology and Artificial Intelligence in Agricultural Economics
No full textThe convergence of nanotechnology and artificial intelligence (AI) represents a transformative force in agricultural economics, offering innovative solutions to longstanding challenges such as productivity inefficiencies, environmental degradation, and unsustainable resource use. This study presents a systematic literature review (SLR) aimed at synthesising theoretical frameworks, applications, and economic implications associated with these technologies in agriculture. A structured search strategy was developed using Boolean operators to combine key terms related to nanotechnology, AI, and machine learning. Comprehensive searches were conducted across six academic databases—Springer, IEEE Xplore, ACM, Science Direct, Wiley, and Google Scholar—complemented by manual and snowballing techniques. From an initial pool of 840 records, 55 studies met the inclusion criteria after rigorous screening and eligibility assessment. Findings indicate that nanotechnology enhances nutrient delivery, pest control, and crop monitoring through nanosensors and nano-fertilisers, while AI facilitates data-driven decision-making, yield prediction, and resource optimisation in precision farming. Despite promising results, challenges such as high initial investment, technological complexity, and limited access for smallholder farmers remain significant. The review concludes that the integration of nanotechnology and AI can improve agricultural efficiency, economic viability, and environmental sustainability. However, targeted investments, capacity-building, and interdisciplinary collaboration are essential to bridge the gap between innovation and implementation in developing economies
- …
