Prediction of CO2 emissions using deep learning hybrid approach: A Case Study in Indian Context

Abstract

Carbon Dioxide (CO2), which accounts for 1% of the atmospheric gases but responsible for about 81% of the total emissions, is a major component of Green House Gas (GHG) Emissions. Produced naturally (decomposition, respiration and ocean drive) or released as a consequence of human activities (burning of fossil fuels, cement production, automotive exhausts etc.), CO2has played a critical role in global warming. The concern over CO2is the significant change in its level over a short period of time. The net result is melting of polar ice caps or rising annual global temperature. India, a developing nation and an emerging economy, is the fourth largest producer of Carbon Dioxide emissions following China, United States of America and the European Union. India has now overtaken Russia to become the third largest producer of electricity but it still relies on coal as the biggest source of electricity. Awaken by the adversities of Carbon Dioxide Emissions, India has signed the Paris Agreement and pledged to reduce the Carbon Dioxide levels to 30-35% of the level in the year 2005. This agreement will start from the year 2020. The research is aimed at predicting the CO2levels in the year 2020 to have a better understanding of the challenge posed to Government of India. The prediction technique used is a deep learning hybrid model of Convolution Neural Network and Long Short-Term Memory Network (CNN-LSTM).

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