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Predicting carbon dioxide emissions in the United States of America using machine learning algorithms

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Abstract

Carbon dioxide (CO2) emissions result from human activities like burning fossil fuels. CO2 is a greenhouse gas, contributing to global warming and climate change. Efforts to reduce CO2 emissions include transitioning to renewable energy. Monitoring and reducing CO2 emissions are crucial for mitigating climate change. Strategies include energy efficiency and renewable energy adoption. In the past few decades, several nations have experienced air pollution and environmental difficulties because of carbon dioxide (CO2) emissions. One of the most crucial methods for regulating and maximizing CO2 emission reductions is precise forecasting. Four machine learning algorithms with high forecasting precision and low data requirements were developed in this study to estimate CO2 emissions in the United States (US). Data from a dataset covering the years 1973/01 to 2022/07 that included information on different energy sources that had an impact on CO2 emissions were examined. Then, four algorithms performed the CO2 emissions forecast from the layer recurrent neural network with 10 nodes (L-RNN), a feed-forward neural network with 10 nodes (FFNN), a convolutional neural network with two layers with 10 and 5 filters (CNN1), and convolutional neural network with two layers and with 50 and 25 filters (CNN2) models. Each algorithm’s forecast accuracy was assessed using eight indicators. The three preprocessing techniques used are (1) without any processing techniques, (2) processed using max–min normalization technique, and (3) processed using max–min normalization technique and decomposed by variation mode decomposition (VMD) technique with 7 intrinsic mode functions and 1000 iterations. The latter with L-RNN algorithm gave a high accuracy between the forecasting and actual values. The results of CO2 emissions from 2011/05 to 2022/07 have been forecasted, and the L-RNN algorithm had the highest forecast accuracy. The L-RNN model has the lowest value of 1.187028078, 135.5668592, and 11.64331822 for MAPE, MSE, and RMSE, respectively. The L-RNN model provides precise and timely forecasts that can help formulate plans to reduce carbon emissions and contribute to a more sustainable future. Moreover, the results of this investigation can improve our comprehension of the dynamics of carbon dioxide emissions, resulting in better-informed environmental policies and initiatives targeted at lowering carbon emissions.

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Data availability

The data are not shared but the data will be available if requested by the journal.

Abbreviations

AI :

Artificial Intelligence

ML :

Machine learning

CO 2 :

Carbon dioxide

L-RNN :

Layer recurrent neural network

FFNN :

Feed-forward neural network

DL :

Deep learning

RFM :

Random Forest Model

KNN :

K-Nearest Neighbors

GBR :

Gradient Boosting regression

DTM :

Decision Tree Model

ANN :

Artificial Neural Networks

(RMSE):

The Root Mean Square Error

(MSE):

The Mean Square Error

SVM :

Support Victim Machine

HMM :

Hidden Markov Model

CNN :

Convolutional neural network

VMD :

Variation mode decomposition

LSTM :

Long Short-Term Memory

COA :

Cuckoo Search Algorithm

LSSVM :

Least Square Support Victim Machine

PSO :

Particle Swarm Optimization

AIC:

Akaike Information Criterion

BIC :

Bayesian Information Criterion

(MED):

Median Absolute Error

(\({R}^{2}\)):

The Determination Coefficient

MAE :

Mean absolute error

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Funding

This work was supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University (IMSIU) (grant number IMSIU-RPP2023014) and Humanities and Social Sciences Foundation of the Ministry of 569 Education of China (Grant number 22YJA790030), China University of Geosciences, Wuhan, China.

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Authors and Affiliations

  1. School of Public Administration, China University of Geosciences, Lumo Road 388, Wuhan, 430074, People’s Republic of China

    Bosah Philip Chukwunonso & Li Shixiang

  2. School of Automation, China University of Geoscience, Wuhan, 430074, China

    Ibrahim AL-Wesabi

  3. College of Engineering, Electrical Engineering Department, King Saud University, Riyadh, Saudi Arabia

    Khalil AlSharabi & Abdullah M. Al-Shaalan

  4. Electrical Engineering Department, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia

    Abdullrahman A. Al-Shamma’a & Hassan M. Hussein Farh

  5. Computer Science Department, Imam Mohammad Ibn Saud Islamic University (IMSIU), 11564, Riyadh, Saudi Arabia

    Fahman Saeed

  6. School of Engineering, Western Carolina University, Cullowhee, NC, 28723, USA

    Tarek Kandil

Authors
  1. Bosah Philip Chukwunonso
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  2. Ibrahim AL-Wesabi
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  3. Li Shixiang
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  4. Khalil AlSharabi
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  9. Abdullah M. Al-Shaalan
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Contributions

Study conception and design- Bosah Philip Chukwunonso, Ibrahim AL-Wesabi, Khalil AlSharabi. Material preparation- Abdullrahman A. Al-Shamma’a, Shixiang Li, Ibrahim AL-Wesabi. Data collection and analysis- Bosah Philip Chukwunonso, Shixiang Li, Ibrahim AL-Wesabi and Abdullah M. Al-Shaalan. The first draft of the manuscript- Bosah Philip Chukwunonso, Shixiang Li, Hassan M. Hussein Farh. All authors read and approved the final manuscript.

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Correspondence to Ibrahim AL-Wesabi.

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Chukwunonso, B.P., AL-Wesabi, I., Shixiang, L. et al. Predicting carbon dioxide emissions in the United States of America using machine learning algorithms. Environ Sci Pollut Res (2024). https://doi.org/10.1007/s11356-024-33460-1

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