Jonathan P. Wong

This study developed a transition blueprint for the potential privatization of the National Power Corporation (NPC) in Romblon, Philippines. Using a convergent mixed-methods design, it integrated perceptions from NPC employees and the ROMELCO Board in non-privatized Romblon with the lived challenges faced by NPC personnel in already privatized regions of Mindoro and Tablas. Findings revealed that Romblon stakeholders perceived privatization positively in terms of accountability (WM=3.72) and its potential to attract investment for modernization (WM=3.87), but expressed uncertainty regarding consumer-level transparency and job security. Qualitative data from privatized areas highlighted significant challenges in regulatory compliance, workforce transition, and financial sustainability. The study concludes that a successful transition requires a context-sensitive approach anchored on robust stakeholder engagement, regulatory harmonization, and strong social safety nets. The resulting blueprint offers evidence-based recommendations for policymakers considering privatization or Public-Private Partnership (PPP) models for NPC in island provinces.

The municipality of Romblon in the Philippines is an island known for its marble industry. The subsurface of the Philippines is known for its limestone. The production of marble into slab, tiles, and novelty items requires heavy equipment to cut rocks and boulders. The finishing of marble requires polishing to smoothen the surface. During the manufacturing process, massive amounts of particulates and slurry are produced, and with a lack of technology and human expertise, the environment can be adversely affected. Hence, this study assessed and monitored the environmental conditions in the municipality of Romblon, particularly the soils and sediments, which were affected due to uncontrolled discharges and particulates deposition. A total of fifty-six soil and twenty-three sediment samples were collected and used to estimate the metal and metalloid (MM) concentrations in the whole area using a neural network-particle swarm optimization inverse distance weighting model (NN-PSO). There were nine MMs; e.g., As, Cr, Ni, Pb, Cu, Ba, Mn, Zn and Fe, with significant concentrations detected in the area in both soils and sediments. The geo-accumulation index was computed to assess the level of contamination in the area, and only the soil exhibited contamination with zinc, while others were still on a safe level. Nemerow’s pollution index (NPI) was calculated for the samples collected, and soil was evaluated and seen to have a light pollution level, while sediment was considered as “clean”. Furthermore, the single ecological risk (Er) index for both soil and sediment samples was considered to be a low pollution risk because all values of Er were less than 40.