Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Estimating Carbon Dioxide Emissions from Power Plant Water Vapor Plumes Using Satellite Imagery and Machine Learning

Heather D. Couture
* ORCID logo ,
Madison Alvara
ORCID logo ,
Jeremy Freeman
* ,
Aaron Davitt
,
Hannes Koenig
,
Ali Rouzbeh Kargar
,
Joseph O’Connor
,
Isabella Söldner-Rembold
,
André Ferreira
,
Jeyavinoth Jeyaratnam
,
Jordan Lewis
,
Colin McCormick
,
Tiffany Nakano
,
Charmaine Dalisay
,
Christy Lewis
,
Gabriela Volpato
,
Matthew Gray
,
Gavin McCormick
Version 1 : Received: 21 February 2024 / Approved: 23 February 2024 / Online: 24 February 2024 (08:46:49 CET)

A peer-reviewed article of this Preprint also exists.

Couture, H.D.; Alvara, M.; Freeman, J.; Davitt, A.; Koenig, H.; Kargar, A.R.; O’Connor, J.; Söldner-Rembold, I.; Ferreira, A.; Jeyaratnam, J.; Lewis, J.; McCormick, C.; Nakano, T.; Dalisay, C.; Lewis, C.; Volpato, G.; Gray, M.; McCormick, G. Estimating Carbon Dioxide Emissions from Power Plant Water Vapor Plumes Using Satellite Imagery and Machine Learning. Remote Sens. 2024, 16, 1290. Couture, H.D.; Alvara, M.; Freeman, J.; Davitt, A.; Koenig, H.; Kargar, A.R.; O’Connor, J.; Söldner-Rembold, I.; Ferreira, A.; Jeyaratnam, J.; Lewis, J.; McCormick, C.; Nakano, T.; Dalisay, C.; Lewis, C.; Volpato, G.; Gray, M.; McCormick, G. Estimating Carbon Dioxide Emissions from Power Plant Water Vapor Plumes Using Satellite Imagery and Machine Learning. Remote Sens. 2024, 16, 1290.

Abstract

Combustion power plants emit carbon dioxide (CO₂), a major contributor to climate change. Direct emissions measurement is cost-prohibitive globally, while reporting varies in detail, recency, and granularity. To fill this gap and greatly increase the number of power plants worldwide with independent emissions monitoring, we developed and applied machine learning (ML) models using power plant plumes as proxy signals to estimate electric power generation and CO₂ emissions using Landsat 8, Sentinel-2, and PlanetScope imagery. Our ML models estimated power plant activity on each image snapshot, and then an aggregation model predicted plant utilization over a 30-day period. Lastly, emissions factors specific to region, fuel, and plant technology were used to convert estimated electricity generation to CO₂ emissions. Models were trained with reported hourly generation data in the US, Europe, and Australia and were validated with additional generation and emissions data from the US, Europe, Australia, Türkiye, and India. All results with sufficiently large sample sizes indicate that our models outperformed baseline approaches. In conducting external validation to compare modeled versus reported annual generation and emissions in the countries where available, we calculated the root mean square error overall for modeled countries with validation data as respectively 1.75 TWh (236 plants across 17 countries over 4 years) and 2.04 Mt CO₂ (207 plants across 17 countries over 4 years). Ultimately, we applied our ML method to plants that comprise 32% of global power plant CO₂ emissions averaged over 2015-2022. This dataset is the most comprehensive independent and free-of-cost global power plant point-source emissions monitoring system currently known to the authors and is publicly available at climatetrace.org to support global emissions reduction.

Keywords

CO₂ emissions inventories; greenhouse gases; satellite observations; machine learning

Subject

Environmental and Earth Sciences, Pollution

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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