A data-driven modeling of public acceptance for fusion energy: analyzing public perceptions through future-centric empirical segmenting framework

Abstract

Abstract As fusion energy approaches commercial viability, establishing strong public acceptance has become as critical as achieving technical success. This study develops a data-driven framework to analyze public perception of fusion technology, complementing traditional surveys toward a data-driven clustering approach. Drawing on “Future Orientation Theory”, we designed an exploratory profiling framework that categorizes public attitudes based on three axes: social direction, vision feasibility, and participation intent. A comprehensive survey of 1,000 South Koreans was analyzed using this integrated segmenting framework to identify eight distinct conceptual profiles, which were then empirically mapped onto three data-driven clusters. The analysis reveals that “Strategist” and “Visionary” groups demonstrate significantly higher acceptance rates (over 80%) compared to “Skeptic” group (approximately 37%). Furthermore, a strong correlation exists between understanding of advanced technology and positive fusion perception, suggesting that acceptance is significantly associated with active attitude toward future social changes. Finally, we propose an “Intelligent Engagement Roadmap” that provides optimized communication strategies for each profile. This study demonstrates that integrating social-perception data into the engineering design process is essential for the sustainable implementation of fusion energy systems.