Linking Socioeconomic Status and Emissions: The Predictive Power of the International Wealth Index for NO2 column densities

Ensuring accountability in emissions reductions is critical as many nations struggle to meet their Nationally Determined Contributions (NDCs) under the Paris Agreement. Traditional greenhouse gas (GHG) monitoring approaches are hindered by the long atmospheric lifetimes of key gases like carbon dioxide (CO2) and methane (CH4). In contrast, nitrogen dioxide (NO2) is a short-lived pollutant with high spatial and temporal variability, making it an effective proxy for tracking anthropogenic emissions. This study introduces a novel predictive framework integrating satellite-derived NO2 data with socioeconomic indicators, specifically the International Wealth Index (IWI). Using machine learning techniques, we establish IWI as a reliable predictor of NO2 column densities, demonstrating that socioeconomic development patterns significantly influence emission trends. Our convolutional neural network (CNN) model achieves an average predictive accuracy (R2 = 0.56) across the African continent, allowing for anticipatory analysis of emission hotspots. Forecasts for 2030 reveal substantial disparities between projected NO2 levels and NDC commitments, highlighting regions at risk of non-compliance. These findings emphasize the potential of integrating socioeconomic and environmental data to improve emissions monitoring, inform policy decisions, and enhance accountability in global climate action.