Abstract
The exponential increase in Greenhouse Gases (GHGs) emission due to the oxidation of fossil fuels is an alarming ecological problem all over the world. Such harmful emissions affect the air quality, thereby creating serious concern to the human health, natural life and agriculture. Consequently, the intercontinental community has developed air quality standards to observe and control pollution rates worldwide. Among the GHGs, carbon-di-oxide (CO2) plays a major role in polluting the air heavily, and hence the estimation and control of CO2 emission has become the need of the hour. The main objective of this research is to study, estimate and forecast CO2 emission in India from various sources of energy consumption. The estimation of CO2 emission and analysis are done through Multiple Linear Regression (MLR) model and Particle Swarm Optimization (PSO) algorithm. In our research, PSO could obtain a more accurate estimation compared to MLR and hence PSO estimation was used for future forecasting of CO2 emission in India. The performance of MLR and PSO were also measured using statistical quality parameters and the results have proven the effectiveness of PSO algorithm.
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