The metaverse is reshaping interaction, learning, and community-building in immersive virtual environments. While interest in metaverse adoption is growing, most research has focused on technological predictors and has overlooked the experiential dimensions that are central to sustained engagement in these spaces. This gap limits understanding of how users develop and maintain meaningful virtual existence in the metaverse. Therefore, this study develops the Metaverse Experience and Technology Acceptance (META) model by integrating the principles of the Technology Acceptance Model (TAM) and Embodied Social Presence Theory (ESPT). Structural equation modeling (SEM) was used to analyze data collected from 924 students with metaverse experience. The META model demonstrates strong explanatory power in accounting for both technology acceptance and user experience in virtual worlds. Moreover, the findings indicate that adoption of the metaverse as a digital university extends beyond the functional focus of TAM to include the immersive, social, and embodied elements emphasized in ESPT. By bridging technological and experiential determinants, the META model advances theoretical understanding and offers actionable insights for creating metaverse environments that promote conducive digital existence.

This review examines the integration of climate-smart aquaculture (CSAq) as a strategy to enhance the resilience and sustainability of global aquaculture and coastal agriculture in the face of climate change. CSAq encompasses innovations such as integrated multi-trophic aquaculture (IMTA), genetic advancements, renewable energy integration, and optimized water management, all aimed at minimizing environmental impacts while maintaining productivity. As climate change introduces threats like ocean acidification, temperature fluctuations, and extreme weather events, CSAq offers adaptive solutions critical for preserving marine ecosystems, reducing greenhouse gas emissions, and sustaining food security. The review emphasizes that the successful adoption of CSAq is contingent upon supportive policies, cross-sectoral collaboration, and socio-economic considerations, including gender inclusivity and community involvement. As aquaculture's role in food security continues to grow, CSAq provides a pathway for mitigating climate impacts while promoting sustainable development. This review underscores the necessity of climate-smart approaches for building resilient food systems that can adapt to a changing climate and sustain livelihoods in vulnerable coastal regions.

Achieving a just and accelerated renewable energy (RE) transition in the Philippines requires not only technological innovation but also coherent and cross-sectoral policy alignment. Non-energy policies can facilitate or hinder the RE development. Non-energy policies, particularly those governing land use, permitting, and environmental regulation, and other significantly shape the feasibility of RE deployment. However, the analyses and evidences on implications of the non-energy policies on RE development are scarce, especially in the context of developing countries. This study provides a comprehensive, stakeholder-informed assessment of 43 national-level policy instruments across five domains in the Philippines: Energy Policy and Regulation, Climate Change and Sustainability, Environmental and Natural Resource Conservation, Agriculture and Rural Development, and Land Use and Property Rights. In this study, using a modified Sustainable Development Goals (SDG) interaction framework, stakeholders from academia, government, industry, and non-governmental organizations evaluated each policy's influence on RE development using a seven-point scale. Weighted average (WA) scores were computed to determine whether policies act as enablers or constraints. Results show that energy and climate policies are strongly supportive due to clear mandates and institutional coordination, whereas land governance and agrarian reform policies are viewed as restrictive because of procedural uncertainty and tenure risks. Environmental policies are generally enabling but raise permitting concerns. Divergent stakeholder perceptions underscore the need for inclusive and transparent governance. The study concludes that accelerating the RE transition will depend on harmonizing institutional mandates, reforming land-use frameworks, enabling decentralized systems, and strengthening technical and governance capacity across all sectors.

Artificial Intelligence (AI) is reshaping higher education by streamlining tasks, supporting personalized learning, and enhancing student engagement. Yet, these benefits coexist with risks of dependence, reduced critical thinking, and inequities-a duality termed the relief paradox. This study examined the perceptions of 138 IT students at FEU Institute of Technology to explore how they experience both the relief and paradoxical burdens of AI in education, with attention to Sustainable Development Goal 4 (SDG 4) on inclusive and equitable quality education. Using a descriptiveanalytic design, validated survey instruments, and both descriptive and inferential statistics, results showed that students generally recognized AI's potential to reduce workload and improve inclusivity, while also expressing concern over ethical issues and overdependence. The study underscores the need for balanced integration of AI in education, offering recommendations for curriculum, policy, and capacity-building to ensure responsible adoption.

Despite the potential for e-commerce to boost productivity and market access for farmers, adoption remains low, particularly in rural areas of developing countries. This study addresses the research gap by predicting farmers' adoption of digital platforms in the National Capital Region, Philippines, using the Theory of Planned Behavior (TPB) and the Technology Acceptance Model (TAM). Based on a survey of 615 farmers and analysis with various machine learning models, with XGBoost as the top performer, the study found that perceived usefulness, trust, and price value are the most significant factors influencing adoption. Social influence and ease of use also play important roles. The findings provide guidance for policymakers and platform developers, highlighting the need to improve digital literacy, build trust, and ensure affordability to accelerate the digital transformation of the agricultural sector.

The growing demand for sustainable electricity in emerging economies necessitates hybrid systems that leverage local renewable resources while remaining economically viable. This study optimizes and evaluates photovoltaic–biogas (PV–BG) hybrid systems for Rosso, Mauritania, through a techno-economic and environmental framework. HOMER Pro was used for baseline modeling, while Grey Wolf Optimizer (GWO) and Whale Optimization Algorithm (WOA) refined both on-grid and off-grid designs. The optimal on-grid configuration—801 kW PV, 100 kW BG generator, and 408 kW converter—achieved a Levelized Cost of Energy (LCOE) of $0.041/kWh, Net Present Cost (NPC) of $1.89 M, Payback Period (PP) of 6.6 years, Internal Rate of Return (IRR) of 14%, and Return on Investment (ROI) of 11%. GWO and WOA further reduced LCOE to $0.038/kWh and $0.036/kWh and NPC to $1.81 M and $1.77 M, shortening PP to 6.4 and 6.1 years. Environmental analysis showed an annual offset of 1,220 tCO2 and a 100% renewable fraction. The results provide a scalable framework for hybrid energy planning, supporting policy development and investment strategies toward low-carbon power systems.

This study examines the Canvas’ usability in Learning Management System (LMS) from the perspectives of students and teachers, focusing on experiences across Progressive Web App (PWA) and native mobile platforms. A task-based usability testing approach was employed, combining quantitative measures of task completion and time with qualitative insights from observations and participant feedback. Findings indicate that both platforms supported high task completion, though clear differences emerged in efficiency and feature accessibility. Teachers achieved a 91.7% completion rate on the mobile app compared to 100% on the PWA. The mobile app was faster for grading and assignment creation, while the PWA provided broader feature coverage, particularly for analytics, though some users reported navigation difficulties. For students, performance differences were more pronounced: average task completion time on the PWA was 1.24 minutes compared to 5.72 minutes on the mobile app. Tasks such as replying to announcements and checking grades were completed up to ten times faster on the PWA. Overall, the mobile app demonstrated greater stability and efficiency for routine functions, whereas the PWA offered extended functionality and cross-platform access but with tradeoffs in responsiveness and interface clarity. These results highlight the role of platform choice in shaping user experience and suggest directions for optimizing Canvas LMS for both teaching and learning contexts. By advancing usability in digital learning platforms, this research contributes to Sustainable Development Goal (SDG) 4: Quality Education, while also supporting SDG 9: Industry, Innovation, and Infrastructure through insights on mobile technology design, and SDG 10: Reduced Inequalities by emphasizing accessibility across diverse devices and connectivity conditions.

Upcycling is a sustainable solution that mitigates environmental changes, focusing on climate action, by extending the lifecycle of products and reducing wastage. This study investigates Filipino consumers’ behavioral intention to use an upcycling e-marketplace, highlighting the intersection of sustainability, consumer psychology, and digital platforms. Despite growing interest in circular economy models, adoption drivers in this domain remain underexplored and are rarely modeled with predictive analytics. To address this gap, the study collected data from 500 Filipino participants capturing environmental knowledge and concern, perceived ease of use and usefulness, attitude toward use, perceived behavioral control, subjective norms, user demand, and intention, then analyzed using multiple machine learning algorithms, namely, Decision Tree, Random Forest, Gradient Boosting, XGBoost, K-Nearest Neighbors, and Support Vector Machine. Among these, the Decision Tree model demonstrated the best balance of predictive accuracy (95%), precision (96%), and recall (91%), suggesting strong classification capability. Analysis of the importance of features revealed that Perceived Usefulness, Attitude Toward Use, and Subjective Norms were the most influential predictors of adoption, outweighing traditional environmental concerns. These findings underscore the importance of designing upcycling platforms that emphasize practical value, user convenience, and social validation. The study concludes that sustainable behavior is more likely when aligned with personal benefits and peer influence, rather than relying solely on environmental appeals.

Marine ecosystems face unprecedented threats from pollution, overfishing, and climate change, creating an urgent need for conservation initiatives. While consumer awareness of ocean degradation is increasing, there remains a persistent gap between environmental concern and actual purchasing behavior toward sustainable products. This study aims to examine public readiness to adopt OceanGuardian, a sustainable e-commerce platform for marine conservation products, by integrating behavioral, technological, and environmental perspectives. Using a modified Extended Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework, survey data from 600 respondents were analyzed with machine learning models, including Support Vector Machines, Random Forests, and XGBoost, to identify key determinants of consumer intention and use behavior. Results indicate that social influence, performance expectancy, affordability, and habit formation significantly predict adoption, with Support Vector Machines achieving the highest predictive accuracy (92.5%). The findings highlight the potential of artificial intelligence to enhance consumer behavior analysis while recognizing challenges such as economic barriers and consumer skepticism. The study offers theoretical contributions by extending UTAUT2 with environmental factors and provides practical insights for policymakers and businesses to design strategies that foster sustainable shopping and strengthen marine conservation efforts.

This paper discusses that the Digital marketplaces play a crucial role in connecting startups with potential investors, yet their adoption success depends on understanding the key factors influencing user intention. Predicting adoption behaviors accurately can help improve engagement and ensure platform sustainability. The study applies the Unified Theory of Acceptance and Use of Technology (UTAUT) framework to identify key adoption factors including Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions (FC), Trust (TR), and Government Support (GS).and this has been widely applied to study technology adoption, limited research integrates this framework with machine learning models to predict adoption intention in e-marketplaces for startups. This study aims to develop machine learning-based prediction models for StartSmart an e-marketplace linking startups and investors and identify the most influential factors affecting adoption intention based on the UTAUT framework. Data from 542 respondents were analyzed using six machine learning techniques: Decision Trees (DT), Random Forests (RF), Gradient Boosting (GRB), XGBoost (XGB), K-Nearest Neighbors (KNN), and Support Vector Machines (SVM).Results indicate that DT achieved the highest accuracy (0.93) and precision (0.94), while RF obtained the highest AUC-ROC score (0.98). Analysis of feature importance revealed that PE and EE were the most significant predictors of adoption, followed by TR and GS. These findings provide valuable insights for platform developers to prioritize usability and performance improvements, and for policymakers to strengthen trust and government support. The study also highlights the potential of combining UTAUT with machine learning to enhance predictive accuracy in digital adoption research.