Machine learning-based time series models for effective CO2 emission prediction in India

China, India, and the USA are the countries with the highest energy consumption and CO 2 emissions globally. As per the report of datacommons.org , CO 2 emission in India is 1.80 metric tons per capita, which is harmful to living beings, so this paper presents India’s detrimental CO 2 emission effec...

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Veröffentlicht in:	Environmental science and pollution research international Jg. 30; H. 55; S. 116601 - 116616
Hauptverfasser:	Kumari, Surbhi, Singh, Sunil Kumar
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2023 Springer Nature B.V
Schlagworte:	algorithms Aquatic Pollution Atmospheric Protection/Air Quality Control/Air Pollution Autoregressive models Business metrics Carbon dioxide Carbon dioxide emissions China Comparative studies comparative study Deep learning Earth and Environmental Science Ecotoxicology Emission energy Energy consumption Environment Environmental Chemistry Environmental Health GIS Applied to Soil-Agricultural Health for Environmental Sustainability India Learning algorithms Long short-term memory Machine learning Mathematical models neural networks Performance measurement prediction Predictions Regression analysis Regression models Statistical analysis Statistical models Time series time series analysis Waste Water Technology Water Management Water Pollution Control India China Holt-Winters LSTM Linear regression Time series forecasting Random forest regressor Air pollution emissions
ISSN:	1614-7499, 0944-1344, 1614-7499
Online-Zugang:	Volltext
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Zusammenfassung:	China, India, and the USA are the countries with the highest energy consumption and CO 2 emissions globally. As per the report of datacommons.org , CO 2 emission in India is 1.80 metric tons per capita, which is harmful to living beings, so this paper presents India’s detrimental CO 2 emission effect with the prediction of CO 2 emission for the next 10 years based on univariate time-series data from 1980 to 2019. We have used three statistical models; autoregressive-integrated moving average (ARIMA) model, seasonal autoregressive-integrated moving average with exogenous factors (SARIMAX) model, and the Holt-Winters model, two machine learning models, i.e., linear regression and random forest model and a deep learning-based long short-term memory (LSTM) model. This paper brings together a variety of models and allows us to work on data prediction. The performance analysis shows that LSTM, SARIMAX, and Holt-Winters are the three most accurate models among the six models based on nine performance metrics. Results conclude that LSTM is the best model for CO 2 emission prediction with the 3.101% MAPE value, 60.635 RMSE value, 28.898 MedAE value, and along with other performance metrics. A comparative study also concludes the same. Therefore, the deep learning-based LSTM model is suggested as one of the most appropriate models for CO 2 emission prediction.
Bibliographie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1614-7499 0944-1344 1614-7499
DOI:	10.1007/s11356-022-21723-8