Applying Artificial Neural Networks to Forecast European Union Allowance Prices: The Effect of Information from Pollutant-Related Sectors

Agustín García-García,Miguel A. Jaramillo-Morán

doi:10.3390/en12234439

Agustín García-García, Miguel A. Jaramillo-Morán

Open Access

https://doi.org/10.3390/en12234439

Copy DOI

Abstract

In this paper, we forecast the price of CO2 emission allowances using an artificial intelligence tool: neural networks. We were able to provide confident predictions of several future prices by processing a set of past data. Different model structures were tested. The influence of subjective economic and political decisions on price evolution leads to complex behavior that is hard to forecast. We analyzed correlations with different economic variables related to the price of CO2 emission allowances and found the behavior of two to be similar: electricity prices and iron and steel prices. They, along with CO2 emission allowance prices, were included in the forecasting model in order to verify whether or not this improved forecasting accuracy. Only slight improvements were observed, which proved to be more significant when their respective time series trends or fluctuations were used instead of the original time series. These results show that there is some sort of link between the three variables, suggesting that the price of CO2 emission allowances is closely related to the time evolution of the price of electricity and that of iron and steel, which are very pollutant industrial sectors. This can be regarded as evidence that the CO2 market is working properly.

Highlights

The European Union Directorate-General for Climate Action (EU DG CLIMA) refers to the European Union Emissions Trading System (EU ETS) as a cornerstone of the EU’s policy to combat climate change and its key tool for cost-effectively reducing greenhouse gas emissions [1]
The neural model selected to forecast future prices of CO2 emission allowances was the multilayer perceptron (MLP), because it has been widely used to forecast different kinds of time series and has been proven to provide very accurate predictions and to regularly outperform predictions provided by other forecasting tools [19,21,27,33,34,35]
We can conclude that there is no point in including those exogenous variables in the model as it does not lead to any improvement in the forecasting accuracy and makes the network structure more complex

Summary

Introduction

The European Union Directorate-General for Climate Action (EU DG CLIMA) refers to the European Union Emissions Trading System (EU ETS) as a cornerstone of the EU’s policy to combat climate change and its key tool for cost-effectively reducing greenhouse gas emissions [1]. Companies producing CO2 emissions are supposed to effectively manage associated costs by buying or selling CO2 emission allowances. They are allowed to trade allowances freely with one another within the EU. The aim of the system is to ensure that overall emissions are reduced, as well as to encourage firms that can achieve the most efficient abatement costs to make cuts (European Commission, 2003)

Objectives

Methods

Results

Conclusion