This study examines how residents evaluate local government within Japan’s Liveable Well-Being City Indicator (LWCI) framework, with a focus on public service delivery and the application of machine learning (ML). Using a nationwide survey of 66,085 respondents, two dimensions of local government evaluation (Service Accessibility and Government Responsiveness) were examined using correlation analysis and Random Forest classification models. The findings indicate that differences between the two evaluation dimensions are better understood through patterns of relative predictive importance among shared factors than entirely distinct sets of variables. Service Accessibility was more strongly associated with functional and usability-related service factors, whereas Government Responsiveness was associated with a broader range of service-related and community-level conditions. The Random Forest models further identified differences in the relative importance of predictors across the two outcomes, reinforcing the distinction between their broader predictive patterns. These findings demonstrate that integrating ML with well-being assessments complements conventional statistical analysis by providing additional insight into the comparative predictive structure of policy-related factors.
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