Predicting economic activity using atmospheric nitrogen dioxide (NO2) satellite data: Evidence from local economic indicators in Japan

Abstract

Accurate and timely measurement of subnational economic activity is crucial for policymakers during economic crises, natural disasters and pandemics such as COVID-19. The availability of such measurement enables policymakers to identify affected regions quickly, allocate emergency resources efficiently, and target fiscal interventions. Satellite-based indicators such as nighttime lights data can be used for such purposes. Nighttime lights data are now widely used to measure economic activity, yet recent studies have highlighted several limitations, including saturation in densely populated areas, omission of daytime activity, inconsistencies among satellite sensors, and measurement errors in regions without electrification. To address these issues, this study evaluates nitrogen dioxide (NO₂) as an alternative satellite-based indicator of regional economic activity in Japan. NO₂, primarily emitted from combustion processes in transportation and industry, provides a direct measure of economic production that complements nighttime lights data. Using two-way fixed-effects panel regressions, we examine the relationship between NO₂ concentrations and prefectural gross domestic product across multiple sectors. At a spatial resolution of 0.25 degrees (0.25°), NO₂ concentrations exhibit statistically and economically significant associations with gross domestic product across most sectors, with particularly strong relationships in energy-intensive industries. However, when higher-resolution data (0.1 degrees (0.1°)) are used, most coefficients lose statistical significance, and some reverse sign in ways that contradict theoretical expectations. These results highlight both the advantages of using NO₂ over nighttime lights data for measuring subnational economic activity and the importance of appropriate spatial scale. Our findings suggest that moderate-resolution satellite data may more accurately capture regional economic patterns than finer-resolution alternatives, provided the data are properly calibrated.