Energy-related carbon dioxide emission forecasting of four European countries by employing data-driven methods

Abstract

Carbon dioxide emission of countries is deeply dependent on the energy system. Share of different energy resources in primary energy consumption of the countries has principal role in the emission of energy-related carbon dioxide. As well as energy consumption, the level of economic activities performs substantial role in the emission of greenhouse gases. By using data-driven methods such as artificial neural networks (ANNs), the emission of greenhouse gases can be precisely modeled. In this work, two types of ANNs, group method of data handling (GMDH) and multi-layer perceptron (MLP), are employed for estimating carbon dioxide, as one of the most important greenhouse gases, emission of four European countries including UK, Germany, Italy and France; in this regard, consumptions of various energy resources in addition to GDP, as an indicator for economic activities, of the mentioned countries are used as the inputs for modeling and forecasting. Comparison of the actual and predicted data reveals great performance of the employed approaches in modeling. R-squared values of the regression by using both GMDH and MLP are 0.9999. In addition, according to the values of average relative error of the models, it is found that using MLP is preferred due to its lower value compared with GMDH. Obtained values of absolute relative deviations of GMDH and MLP models are 0.39% and 0.33%, respectively.