A long short-term memory approach for CO2 emission estimation

Jialin Wu

doi:10.1117/12.3005940

24 October 2023 A long short-term memory approach for CO₂ emission estimation

Jialin Wu

Proceedings Volume 12804, Second International Conference on Sustainable Technology and Management (ICSTM 2023); 1280428 (2023) https://doi.org/10.1117/12.3005940
Event: 2nd International Conference on Sustainable Technology and Management (ICSTM2023), 2023, Dongguan, China

Abstract

The massive greenhouse gas emissions, such as carbon dioxide (CO₂) emissions, is not only a reflection of economic development and population expansion, but also the result of human over-exploitation. CO₂ emission is a complex indicator, and the development of CO₂ emission varies among countries with different development conditions. Some developed countries have achieved carbon peaking, while most developing countries still have to experience the process of rising CO₂ emissions. The statistics and prediction of CO₂ emissions in the United States cannot only obtain the relationship between economy, population, and CO₂ emissions, but also provide some help for controlling CO2 emissions. Machine learning has played an important role in the fields of statistics and forecasting and is suitable for predicting CO2 emissions. This paper uses the Long Short-Term Memory (LSTM) model for CO₂ emission prediction. Adjust the model using Adam optimization and normalize the training data. Using year, population, and gross domestic product (GDP) as independent variables, projections of CO₂ emissions in the United States for 2022-2035 are made. Finally, the prediction curve was obtained, and the result evaluation was carried out. The CO₂ change trend reflected by the model is relatively accurate, and there are still many areas for improvement.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jialin Wu "A long short-term memory approach for CO₂ emission estimation", Proc. SPIE 12804, Second International Conference on Sustainable Technology and Management (ICSTM 2023), 1280428 (24 October 2023); https://doi.org/10.1117/12.3005940

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KEYWORDS

Carbon dioxide

Data modeling

Machine learning

Education and training

Atmospheric modeling

Mathematical optimization

Reflection

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