Applying GMDH artificial neural network in modeling CO2 emissions in four nordic countries

Abstract

CO2 emission depends on several parameters. Due to environmental issues, it is necessary to find influential factors on CO2 emission as one of the most critical greenhouse gases. Type of utilized fuels and their share in total primary energy consumption, Gross Domestic Product (GDP) as an indicator for economic activities and the share of renewable energies play key role in the amount of CO2 emission. In the present study, Group method of data handling (GMDH) is applied in order to model CO2 emission as a function of consumption of various fuels, renewable energies and GDP. Obtained data showed that GMDH is an appropriate approach to predict CO2 emission. Comparing between actual data and GMDH output indicates that the R-squared value for the proposed model is equal to 0.998 which shows its high accuracy. In addition, it is observed that the highest absolute error by using GMDH artificial neural network is lower than 4%. The absolute relative error for more than 66% of data is lower than 1% which is another criterion demonstrating acceptable accuracy of the proposed model.