The global transition to sustainable energy systems presents significant challenges for developing countries in optimizing energy allocation while balancing economic constraints and renewable energy targets. This study proposes a multi-objective optimization model that minimizes total allocation costs while maximizing renewable energy distribution across industrial sectors. The model integrates Feed-in Tariff rates and Renewable Energy Portfolio requirements to evaluate policy intervention impacts. Furthermore, the proposed computational framework addresses critical gaps in the domain literature by incorporating policy considerations and emerging renewable energy technologies alongside established sources. A case study of Philippine energy infrastructure was conducted, simulating nine scenarios across 2025-, 2030-, and 2040-time horizons. Scenarios examined various policy combinations and their effects on achieving 35% and 50% renewable energy targets. The optimization model successfully achieved targeted values for each scenario, with Pareto analyses revealing expected trade-offs between higher renewable energy shares and increased costs. The results demonstrate that ongoing renewable energy projects possess sufficient capacity to support the renewable energy target, emphasizing stakeholder support importance. Policy interventions consistently showed positive impacts in reducing allocation costs and maximizing renewable energy allocation, though implementation requires government subsidization and additional support mechanisms. This data-driven approach provides a robust analytical tool for developing countries pursuing sustainable energy transitions while maintaining economic viability and industrial competitiveness.

Emergency Response Vehicles (ERV) are one of the most vulnerable road users because of the speed that they travel at. The emerging technology, Vehicle-to-Everything (V2X), offers intelligent solutions to prioritizing ERVs through vehicle traffic. The related work showed that across all the simulated ERV Prioritization methods, using V2X technology improved travel time. However, there have been no studies that exhaustively compare and compound such methods. This work observes the performance of prioritization methods under different levels of V2X integration and traffic congestion. In addition to travel time improvement, road safety and traffic disturbance were also measured. The results show that V2X-based ERV Prioritization yields up to 1.6 times faster trip and safer journey with up to 210s2 reduction in TI-TTC as it reduces obstructions on the road. Increasing the prioritization distance from 30 meters with traffic light priority to the entire route with Route Blocking also increased the performance for travel time and road safety. However, having too large of a prioritization distance, as with the case of Route Blocking with the infinite priority distance, negatively affects NPV travel times as they experience more waiting time, leading to more congested system. This work introduces the framework to characterize the performance of ERV prioritization methods specific to the road network to find the optimal implementation in accordance to road and network conditions. This framework was applied to the Manhattan Grid and East Ave., Quezon City. It was found that while Clearance Distance method proved to be the most optimal for Manhattan Grid, the Adaptive Preemption was the optimal method for East Ave.

This dissertation develops a set-theoretic and algebraic formalization of interoperability and security in information exchanges, with a focus on health information systems. Interoperability is defined as a relation between systems across four levels—technical, syntactic, semantic, and pragmatic—each with distinct requirements and constraints. By treating interoperability as a mathematical construct, the study identifies key algebraic properties such as reflexivity, monotonicity, symmetry, and transitivity, and demonstrates how these properties can hold or fail under different circumstances. The model is then extended to incorporate security protocols, including access control, consent, authentication, and encryption, thereby producing a definition of secure interoperability that integrates both functional and protective dimensions of information exchange. The research further examines how architectural paradigms—centralized, federated, distributed, and point-to-point—affect the realization of secure interoperability. Centralized models strengthen reflexivity and monotonicity but concentrate risks, while federated models enable autonomy and resilience but require strong governance to preserve semantic alignment. Distributed architectures maximize flexibility yet complicate trust negotiation and security enforcement. These theoretical insights are grounded in algebraic proofs, which show how interoperability and security properties vary across contexts and levels, providing a robust framework for analyzing real-world exchanges. The formal model is applied to two Philippine use cases: the national Philippine Health Information Exchange (PHIE), implemented through an Enterprise Service Bus, and the Smarter and Integrated Local Health Information System (SMILHIS), an LGU-led federated platform. These cases illustrate how the model explains practical interoperability challenges, validates security protocols, and anticipates trade-offs in governance and infrastructure. By bridging formal methods with applied governance and institutional realities, this dissertation contributes a novel mathematical framework for secure interoperability and offers actionable insights for the design of sustainable, trustworthy health information systems in the Philippines and comparable settings.

In warm and humid tropical regions, balancing thermal comfort and energy efficiency is a challenge due to high cooling demands. Strategies to reduce energy use and integrate renewable energy into buildings have focused on achieving self-sufficiency. The United Nations Sustainable Development Goals (SDGs) 7 and 13 call for access to clean energy and climate change mitigation. Solar photovoltaic (PV) technology offers a solution for energy-intensive urban areas.

This study assessed the potential of facade PV systems to offset cooling energy demand in tropical buildings through simulations with LiDAR-derived 3D models, solar ray tracing, and thermal load modeling. Buildings in Quezon City, Philippines were modeled to estimate solar irradiation, PV energy output, and cooling load reductions. Results showed that the case-study buildings received high solar irradiance, peaking at approximately 1,037 W/m² under clear-sky conditions. East-west facades achieved the highest annual yields of up to 86 kWh/m2. Although irradiation on these surfaces was about 15% lower than on rooftops, their large areas significantly increased buildings' overall solar potential, with some configurations offsetting a portion of cooling electricity demand. Shading from vertical reduced cooling loads by up to 7.3%, greatest on east and west walls. Techno-economic analysis showed several setups to be viable, with LCOE of 6.2–6.6 PHP/kWh, payback periods of 5.5–10.2 years, and positive NPV, supporting vertical solar PV as a complementary solution in dense tropical environments.

In global estimates of marine plastic debris, the Philippines consistently ranks among the world’s top contributors. Yet, spatially refined models grounded in sufficient empirical evidence for understanding the spatiotemporal dynamics of marine plastic debris remain scarce. This gap stems from the limited utility of global models for localized applications, as well as the broader absence of integrated frameworks that reflect the country’s archipelagic complexity and socio-environmental heterogeneity. Addressing this gap requires deeper insight into how plastic debris originates from point sources in both land and sea, moves through geomorphic pathways shaped by structural and dynamic factors, and eventually accumulates in nearshore ecological habitats. This study bridged this gap by developing a data-driven framework and applying an ecological lens to characterize macroplastic accumulation in the Philippines. This framework integrated empirical count data from the PlastiCount Pilipinas Portal with socio-economic, environmental, and spatial indicators. Ordination and clustering techniques were used to identify ecological gradients and site groupings, while correlation and network analyses uncovered associations in the multivariate structure. Findings revealed associations between macroplastic loads and key drivers, including socio-economic and urbanization metrics, and hydrological and geomorphological features, which inform the source-pathway-sink dynamics of macroplastic accumulation. This framework offers an interpretable approach that emphasizes context-specific drivers of plastic transport. By grounding the analysis in national-scale datasets, the study contributes toward the development of adaptive monitoring strategies and targeted interventions suited to the Philippine archipelagic setting.