Renewable energy (RE) applications in agriculture, such as solar irrigation, agrivoltaics, floating solar, biogas systems, and rural microgrids, can enhance resilience, reduce emissions, and support rural livelihoods. However, agricultural and land governance policies designed to protect food production and tenure can constrain renewable deployment, creating trade-offs between food security and energy transition goals. This study integrates international lessons with a stakeholder-based assessment of six Philippine agriculture and land governance policies. Based on a structured survey of 36 experts from academia, industry, government, and civil society, results show that the Department of Agriculture and Department of Energy - Renewable Energy Program for the Agri-Fishery Sector and the 2021 Department of Agrarian Reform amendment on land conversion are perceived as enabling, while the Agriculture and Fisheries Modernization Act, Comprehensive Agrarian Reform Law, Comprehensive Agrarian Reform Program Extension with Reforms, and the 2002 conversion rules are viewed as restrictive. The findings suggest that policies prioritizing agricultural land protection and exclusive land use may be less flexible for RE integration, while more adaptive and coordinated frameworks may better support dual-use systems and decentralized deployment. Divergent perceptions reflect competing priorities of food security, tenure protection, and investment feasibility. These findings also reveal significant heterogeneity across stakeholder groups, with government respondents generally supporting existing regulatory frameworks, while NGOs and other stakeholders emphasize the need for reform and greater flexibility. The paper identifies reform pathways for synergistic approach focused on recognizing agrivoltaics within agricultural zoning, streamlining permitting procedures, and linking solar irrigation to groundwater and equity safeguards to better align food, land, and energy policy.

The integration of artificial intelligence (AI) is critically transforming the renewable energy sector. This review synthesizes AI's role in optimizing solar and wind energy systems, focusing on power forecasting, system optimization, and predictive maintenance. The research goal was to systematically analyze how diverse AI techniques enhance these critical aspects. Key findings indicate AI's capacity to substantially improve short-term solar irradiance and wind power forecasts (e.g., via SARIMAX, long short-term memory (LSTM), and hybrid deep learning models), dynamically manage energy flow in smart grids and microgrids, optimize maximum power point tracking (MPPT) in photovoltaic (PV) systems, and enable proactive maintenance through anomaly detection in wind turbines using IoT-integrated AI. Key conclusions reveal that AI significantly enhances the efficiency, reliability, and economic viability of solar photovoltaic and wind power generation, offering superior adaptability and predictive capabilities over traditional methods. While AI is important for the global transition to cleaner energy, persistent challenges related to data quality and availability, model interpretability, and cybersecurity must be addressed to fully unlock its potential in practical renewable energy applications.

Solar energy deployment increasingly competes with prime agricultural lands, creating conflicts between energy goals and food security. To resolve these competing demands, our study identified where agrivoltaic systems—combining solar energy and agricultural production on the same land—should be strategically deployed across the Philippines. Using geospatial analysis which integrates terrain suitability, solar photovoltaic (PV) potential, and crop compatibility with shade-tolerant crops, we identified 10.09 million has of cropland suitable for agrivoltaics, representing 81.8% of the nation's agricultural land. Regions in the Mindanao island emerged as premier agrivoltaic deployment zones, combining maximum crop compatibility (15 shade-tolerant crops), high solar PV potential (683-687 MW), and substantial suitable areas (587,000-715,000 has). These findings provide actionable recommendations for strategic agrivoltaic deployment that advances both food security and renewable energy goals in the Philippines simultaneously.

Renewable energy systems play a vital role in achieving global decarbonization goals but face growing exposure to climate-induced hazards such as heatwaves, typhoons, floods, and wildfires. These extreme events pose a threat to long-term energy security by compromising performance, damaging infrastructure, and increasing outage. This review assesses the resilience of renewable energy infrastructure by employing data-driven siting strategies, sophisticated control, and adaptive design. It emphasizes the importance of grid-forming inverters, fault-tolerant turbine control, and battery storage management in ensuring operational stability during periods of duress. Additionally, it investigates practitioner-oriented tools, such as parametric bill of materials (BOM) templates, one-page design protocols, hazard-measure-cost-benefit tables, and value-of-resilience (VoR) calculators, that simplify decision-making and quantify resilience benefits. The integration of these methods promotes cost efficiency and engineering robustness. Empirical performance validation under actual hazard conditions and standardized resilience metrics for low-inertia systems are the remaining research gaps. In conclusion, the integration of resilience principles into the design and governance of renewable energy systems guarantees the development of climate-ready, equitable, and dependable infrastructures that serve as the foundation for a sustainable low-carbon future.

The increasing frequency and intensity of natural hazards highlight the limitations of traditional disaster monitoring systems that rely on static sensors and delayed reporting. Advances in the Internet of Things and computer vision now enable distributed, real-time observation across multiple hazards. IoT networks provide fine-scale measurements of environmental and structural parameters, while vision systems using cameras, drones, and satellite imagery deliver spatial verification and automated impact assessment through artificial intelligence. When integrated, these technologies improve detection accuracy, shorten response times, and strengthen situational awareness. This review synthesizes recent global developments across three dimensions: technology, resilience, and governance. The analysis examines hybrid architectures that merge IoT and vision systems, evaluates continuity strategies such as renewable-powered microgrids and UAV-based communications, and identifies governance challenges involving interoperability, privacy, and institutional coordination. A layered conceptual framework is proposed to link sensing, analytics, alerting, and policy mechanisms. Findings reveal persistent gaps in endurance during extended outages, the need for generalizable multihazard fusion models, and the importance of ethical data governance. The synthesis provides guidance for integrating IoT-vision systems into resilient, scalable, and inclusive early warning infrastructures aligned with global sustainability and disaster-risk-reduction goals.