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

Linking Socioeconomic Status and Emissions: The Predictive Power of the International Wealth Index for NO2 column densities | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Linking Socioeconomic Status and Emissions: The Predictive Power of the International Wealth Index for NO 2 column densities Francesco Grieco, Adel Daoud, Mohammad Kakooei This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6347584/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract 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. Earth and environmental sciences/Environmental sciences/Environmental impact Earth and environmental sciences/Environmental social sciences/Environmental impact International Wealth Index (IWI) NO2 monitoring Machine learning in environmental policy Nationally Determined Contributions (NDCs) Full Text Additional Declarations There is NO Competing Interest. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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