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Can satellite data on air pollution predict industrial production?

Author

Listed:
  • Jean-Charles Bricongne
  • Baptiste Meunier
  • Thomas Pical

Abstract

The Covid-19 crisis has highlighted innovative high-frequency dataset allowing to measure in real-time the economic impact. In this vein, we explore how satellite data measuring the concentration of nitrogen dioxide (NO2, a pollutant emitted mainly by industrial activity) in the troposphere can help predict industrial production. We first show how such data must be adjusted for meteorological patterns which can alter data quality and pollutant emissions. We use machine learning techniques to better account for non-linearities and interactions between variables. We then find evidence that nowcasting performances for monthly industrial production are significantly improved when relying on daily NO2 data compared to benchmark models based on PMIs and auto-regressive (AR) terms. We also find evidence of heterogeneities suggesting that the contribution of daily pollution data is particularly important during “crisis” episodes and that the elasticity of NO2 pollution to industrial production for a country depends on the share of manufacturing in the value added. Available daily, free-to-use, granular and covering all countries including those with limited statistics, this paper illustrates the potential of satellite-based data for air pollution in enhancing the real-time monitoring of economic activity.

Suggested Citation

  • Jean-Charles Bricongne & Baptiste Meunier & Thomas Pical, 2021. "Can satellite data on air pollution predict industrial production?," Working papers 847, Banque de France.
    • Handle: RePEc:bfr:banfra:847
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    Citations

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    Cited by:

    1. Bricongne, Jean-Charles & Meunier, Baptiste & Pouget, Sylvain, 2023. "Web-scraping housing prices in real-time: The Covid-19 crisis in the UK," Journal of Housing Economics, Elsevier, vol. 59(PB).
    2. Caroline Jardet & Baptiste Meunier, 2022. "Nowcasting world GDP growth with high‐frequency data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(6), pages 1181-1200, September.
    3. Mantas Lukauskas & Vaida Pilinkienė & Jurgita Bruneckienė & Alina Stundžienė & Andrius Grybauskas & Tomas Ruzgas, 2022. "Economic Activity Forecasting Based on the Sentiment Analysis of News," Mathematics, MDPI, vol. 10(19), pages 1-22, September.
    4. Luca Tiozzo Pezzoli & Elisa Tosetti, 2022. "Seismonomics: Listening to the heartbeat of the economy," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(S2), pages 288-309, December.
    5. Alexandre Aspremont & Simon Ben Arous & Jean-Charles Bricongne & Benjamin Lietti & Baptiste Meunier, 2023. "Satellites Turn “Concrete”: Tracking Cement with Satellite Data and Neural Networks," Working papers 916, Banque de France.
    6. Jean-Charles Bricongne & Baptiste Meunier & Raquel Caldeira, 2024. "Should Central Banks Care About Text Mining? A Literature Review," Working papers 950, Banque de France.

    More about this item

    Keywords

    Data Science; Big Data; Satellite Data; Nowcasting; Machine Learning; Industrial Production;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • E23 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Production
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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