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

• Published in: Environmental science and pollution research international Vol. 30; no. 55; pp. 116601 - 116616
• Main Authors: Kumari, Surbhi, Singh, Sunil Kumar
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2023; Springer Nature B.V
• Subjects: 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
• DOI: 10.1007/s11356-022-21723-8

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.