A study on estimation of CO2 emission using computational techniques

Air pollution is considered as a severe environmental problem in the world. Air gets polluted due to different Greenhouse Gases (GHGs) present in our atmosphere, which is produced by anthropogenic and natural activities. GHG emissions from various source affects the quality of air in huge areas, which will effect on living organisms and ecosystem. Among all the GHGs carbon-di-oxide (CO
₂
) is reflected as a most hazardous gas in the world. GHG emission study reveals the need for emission modelling of carbon-di-oxide (CO
₂
), nitrous oxide (N
₂
O) and other gases. Thus, the estimation and modelling of GHGs emission become necessary for policy makers and decision making. The estimation and forecasting of GHGs can be done using mathematical programming and computational intelligence techniques such as evolutionary algorithm, genetic algorithm, artificial neural network and bio-inspired algorithm. This paper aims to survey about the different techniques which are used for the CO
₂
estimation and the sources of CO
₂
emission.