A Neural Network Decision-Based System for Evaluating Public Acceptance in Advancing Energy Sustainability Among Young College-Educated Professionals Within the Capital of the Philippines

The Philippine nuclear power plant reopening is facing significant challenges because of public concerns regarding issues related to environmental effects, safety, and trust in management. Additionally, discussions about clean energy solutions pose a major challenge and continue to be a main point of attention. This research investigated the public’s acceptance of nuclear power plants as a sustainable energy option in the values-beliefs-norms theory as a framework, analyzed using machine learning algorithms. As the country seeks sustainable energy solutions to reduce reliance on fossil fuels, nuclear power is seen to emerge as a potential option despite challenges of public skepticism. This research employed a survey with 434 participants limited to young college-educated professionals living in the capital of the Philippines to represent the population to assess how different values, ecological worldviews, and norms influence acceptance. The analysis using neural network and long short-term memory (LSTM) revealed significant accuracy in the model, with R2 values around 90%. The LSTM produced a root mean square error of 1.2618, effectively predicting public sentiment. Key factors influencing opinion included egoistic values, personal norms, and environmental awareness. This study emphasized the importance of transparent communication and public engagement in addressing skepticism and fostering support for sustainable energy solutions. The findings could also improve people’s awareness of nuclear energy’s significance in the Philippines and offer important perspectives for promoting public support for nuclear power.