Modeling Air Quality Determinants in Indonesia Using Generalized Linear Models for Sustainable Development

Abstract

The Sustainable Development Goals (SDGs), particularly Goal 11 (Sustainable Cities and Communities) and Goal 13 (Climate Action), underscore the interconnectedness between air quality and climate change. Escalating levels of air pollution in both urban and rural regions of Indonesia necessitate a deeper understanding of the factors contributing to air quality degradation. This study employs a generalized linear modeling approach, specifically focusing on ordinal logistic regression, to explore the determinants influencing the Air Quality Index (AQI) across 34 provinces in Indonesia. Key predictors, including motor vehicle density, population density, Greenhouse Gas (GHG) emissions, and forest cover, are analyzed to assess their impact on air quality levels. The findings indicate that the number of motor vehicles and the extent of forest cover are significant predictors of air quality. Elevated motor vehicle density is shown to deteriorate the AQI, while larger forest cover areas are associated with improvements in air quality. These results emphasize the importance of targeted environmental interventions, particularly those aimed at reducing vehicle emissions and preserving forest ecosystems. The study highlights the need for the development and enforcement of policies that promote sustainable urban mobility and forest conservation to mitigate air pollution. By providing a comprehensive statistical framework through ordinal logistic regression, this research offers actionable insights for policymakers. The findings can guide the formulation of effective environmental management strategies, supporting efforts to achieve sustainable development objectives. Moreover, this study demonstrates the relevance of adopting rigorous statistical models to address complex environmental challenges, contributing to the broader discourse on sustainability and climate action.