A Novel Bio-Inspired Framework for CO2 Emission Forecast in India

Abstract

Greenhouse gases (GHG) emitted from the combustion of fossil fuels lead to erratic climatic change, and creates severe environmental problem worldwide. GHG emissions from diverse sources have harmful effects on the quality of air, water, soil and living organisms. Carbon-di-oxide (CO2) is one among the GHG which plays a major role in polluting the air, hence the estimation and forecasting of CO2 emission has become essential for energy planning and ecological strategy decisions. The objective of this research work is to estimate and forecast CO2 emission in India from various sources of energy consumption. Multiple linear Regression model and PSO algorithm based on nonlinear model were used for CO2 emission estimation. The obtained results have shown that India’s CO2 emission has alarmingly increased over the past decade. The results reveal that PSO model could obtain a highly accurate estimation compared to MLR model. From the outcome of PSO estimation, the future projection of CO2 emission in India was carried out for the years from 2017 to 2030 using artificial neural network. The prediction results also emphasize that necessary steps must be taken straight away to reduce CO2 emission across the country, as its impulsive increase in India poses extreme threat to nature and environment.