Exploring the relationship between air quality and happiness in South Korea using artificial neural networks

Abstract

This study investigates the relationship between air quality and subjective happiness across South Korean districts using artificial neural network (ANN)-based modeling. By aggregating the Korean National Assembly Futures Institute’s happiness survey (2020–2021) data with the Korean Ministry of Environment’s air quality data, among others, six major air pollutants were examined for their potential associations with the happiness ladder at the minuscule city level throughout South Korea. Complex non-linear patterns were observed. Among the pollutants, PM2.5 exhibited the most consistent negative association with the happiness ladder. The robust modeling and training strategies provide insights into the intricate relationships between air quality factors and the individual happiness ladder. The analysis effectively captures subtle relationships under fixed socioeconomic and happiness-related conditions, highlighting varying confidence intervals across multiple scenarios. These findings underscore the potential of ANN-based modeling in assessing the environmental factors of subjective happiness. Despite limitations related to the spatiotemporal scale of the annual happiness survey, this study contributes to the methods by applying deep learning techniques to infer the relationship between air quality and happiness, providing evidence that may inform environmental policymaking and urban sustainability strategies.