Can satellites predict oil demand?

Jean-Charles Bricongne, Baptiste Meunier, Joao Macalos, Julia Milis and Thomas Pical

No 3198, Working Paper Series from European Central Bank

Abstract: We investigate whether satellite observations of nitrogen dioxide (NO₂) – a short-lived pollutant primarily emitted by fossil fuel combustion – can improve the forecasting of oil demand. After retrieving, cleaning, and aggregating daily satellite data, we integrate NO₂ into a range of forecasting models. Across a panel of advanced and emerging economies, we find that including NO₂ significantly enhances nowcasting accuracy relative to benchmark models based on autoregressive terms and traditional predictors such as industrial activity, prices, weather, and vehicle registrations. Accuracy gains are particularly strong during crisis episodes but remain present in more stable times. Non-linear models, especially neural networks, yield the largest improvements, highlighting the non-linear link between energy demand and pollution. By offering a timely, globally consistent, and freely available proxy, satellite-based NO₂ data provide a valuable new tool for real-time monitoring of oil dema JEL Classification: C51, C81, E23, E37

Keywords: big data; energy consumption; machine learning; nowcasting; satellite data (search for similar items in EconPapers)
Date: 2026-02
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Persistent link: https://EconPapers.repec.org/RePEc:ecb:ecbwps:20263198

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