The Prediction of Carbon Dioxide Emissions from Past Data Based on Algorithms
DOI:
https://doi.org/10.54097/f041e741Keywords:
Carbon dioxide (CO2) emissions; greenhouse gases; Machine learning.Abstract
Carbon dioxide is one of the most notorious greenhouse gases, which has been largely emitted especially in recent years. Therefore, it is crucial to predict the emission trends in order to propose corresponding measures to reduce it. Thanks to the invention and development of machine learning schemes, it is feasible to predict the trends accurately. With this in mind, this study explores the crucial task of predicting carbon dioxide (CO2) emissions based on past data. Leading authorities and organizations have relied on predictive models to anticipate future emission levels. As the implementation of models and algorithms becomes increasingly common, this project seeks to explore different approaches to this topic and summarizes their common discoveries, as well as discussions on possible variations. According to the analysis, the pros and cons of the different models are clarified. Overall, these results shed light on guiding further exploration of carbon dioxide (CO2) emissions predictions.
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References
United States Environmental Protection Agency, “Greenhouse gases: What are the trends in greenhouse gas emissions and concentrations and their impacts on human health and the environment?” Report on the Environment, Retrieved from: https://www.epa.gov/report-environment/greenhouse-gases#:~:text= Carbon%20dioxide%20is%20widely%20reported,warming%20associated%20with%20human%20activities.
United Nations Framework Convention on Climate Change. Science: why is there a need to act? Introduction to Science in the UNFCCC, Retrieved from: https://unfccc.int/topics/science/the-big-picture/introduction-science?gclid=Cj0KCQjwx5qoBhDyARIsAPbMagCA9jiBrbvY4x-tN7DXYbYSoPBjXcNszX4VHtzWMsawlMtpGDwEDrAaAt1EEALw_wcB.
Yin F. Prediction of carbon dioxide emissions in China using a novel grey model with multi-parameter combination optimization. Journal of Cleaner Production, 2023, 404: 136889.
Gou X, Zeng B, Ying G. Application of the novel four-parameter discrete optimized grey model to forecast the wastewater discharged in Chongqing China. Engineering Applications of Artificial Intelligence, 2022, 107: 104522.
Huo T, Xu L, Feng W, et al. Dynamic scenario simulations of carbon emission peak in China's city-scale urban residential building sector through 2050. Energy Policy, 2021, 159: 112612.
Mardani A, Liao H, Nilashi M, et al. A multi-stage method to predict carbon dioxide emissions using dimensionality reduction, clustering, and machine learning techniques. Journal of Cleaner Production, 2020, 275: 122942.
Sterligov V V. Prediction of carbon dioxide emissions from fuel combustion. IOP Conference Series: Materials Science and Engineering, 2020, 1: 866.
Shivammiglani09. Difference between Statistical Model and Machine Learning. Class method vs Static method in Python, Geeks for Geeks, Retrieved from: https://www.geeksforgeeks.org/difference-between-statistical-model-and-machine-learning/.
Wabomba M S, Mutwiri M P, Fredrick M. Modeling and forecasting Kenyan GDP using autoregressive integrated moving average (ARIMA) models. Science Journal of Applied Mathematics and Statistics, 2016, 4(2): 64-73.
Geoffroy O, Saint-Martin D, Olivié D J L, et al. Transient climate response in a two-layer energy-balance model. Part I: Analytical solution and parameter calibration using CMIP5 AOGCM experiments. Journal of climate, 2013, 26(6): 1841-1857.
Intergovernmental Panel on Climate Change, Working Group I: The Scientific Basis. Retrieved from: https://archive.ipcc.ch/ipccreports/tar/wg1/378.htm.
Cao Y, Yin K, Li X, et al. Forecasting CO2 emissions from Chinese marine fleets using multivariable trend interaction grey model. Applied Soft Computing, 2021, 104: 107220.
Duan H, Luo X. A novel multivariable grey prediction model and its application in forecasting coal consumption. ISA transactions,2022, 120: 110-127.
Deng J. Introduction to grey system theory. The Journal of grey system 1989, 1(1): 1-24.
Meng Y, Hossain N. Predicting CO2 Emission Footprint Using AI through Machine Learning. Atmosphere, 2022, 13(11): 1871.
Mardani A, Van Fan Y, Nilashi M, et al. A two-stage methodology based on ensemble Adaptive Neuro-Fuzzy Inference System to predict carbon dioxide emissions. Journal of cleaner production, 2019, 231: 446-461.
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