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arXiv · 2407.20057

Reconstructing Global Daily CO2 Emissions via Machine Learning

Tao Li·Lixing Wang·Zihan Qiu·Philippe Ciais·Taochun Sun·Matthew W. Jones·Robbie M. Andrew·Glen P. Peters·Piyu ke·Xiaoting Huang·Robert B. Jackson·Zhu Liu
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Abstract

High temporal resolution CO2 emission data are crucial for understanding the drivers of emission changes, however, current emission dataset is only available on a yearly basis. Here, we extended a global daily CO2 emissions dataset backwards in time to 1970 using machine learning algorithm, which was trained to predict historical daily emissions on national scales based on relationships between daily emission variations and predictors established for the period since 2019. Variation in daily CO2 emissions far exceeded the smoothed seasonal variations. For example, the range of daily CO2 emissions equivalent to 31% of the year average daily emissions in China and 46% of that in India in 2022, respectively. We identified the critical emission-climate temperature (Tc) is 16.5 degree celsius for global average (18.7 degree celsius for China, 14.9 degree celsius for U.S., and 18.4 degree celsius for Japan), in which negative correlation observed between daily CO2 emission and ambient temperature below Tc and a positive correlation above it, demonstrating increased emissions associated with higher ambient temperature. The long-term time series spanning over fifty years of global daily CO2 emissions reveals an increasing trend in emissions due to extreme temperature events, driven by the rising frequency of these occurrences. This work suggests that, due to climate change, greater efforts may be needed to reduce CO2 emissions.

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Tao Li, Lixing Wang, Zihan Qiu, Philippe Ciais, Taochun Sun, Matthew W. Jones, Robbie M. Andrew, Glen P. Peters, Piyu ke, Xiaoting Huang, Robert B. Jackson, Zhu Liu. 2024-07-29. Reconstructing Global Daily CO2 Emissions via Machine Learning. https://arxiv.org/abs/2407.20057

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