Forecasting EU27 CO2 emissions until 2030 through Artificial Neural Network and scenario analysis

Details

Supervisors

Iania, Leonardo

Faculty

Louvain School of Management

Degree label

Master [120] : ingénieur de gestion, à finalité spécialisée

Abstract

Do we actually find ourselves in a climate urgency? Why is it that we hear about greenhouse gas (GHG) emissions anytime we put our ear to the ground? What are GHGs and why does CO2 seem to be the main focus of our century? The central theme of this thesis revolves around forecasting CO2 emissions within the EU27 until the year 2030, using macroeconomic data. By making use of a comprehensive forecasting method and macroeconomic indicators, this quantitative research seeks to provide insights into the potential trajectories of CO2 emissions in the European Union. In addition, this thesis aims to assess the impact of an increased share of energy from renewable sources on CO2 emissions using scenario analysis. We are asking ourselves: how likely is the EU27 to meet its 2030 CO2 emission target, and are increased shares of renewable energy sources sufficient to achieve this goal during this period? Under the first scenario, known as the "business as usual" (BAU) scenario, only a few countries reach their 2030 CO2 emissions reduction target, namely Greece, Lithuania, Hungary, Romania, and Slovakia. Under the second scenario, which builds on the BAU scenario but incorporates an increased share of renewable energies in the energy mix of EU27 countries, no additional country achieves its 2030 target. In fact, some countries are emitting even more CO2 emissions: Denmark, Greece, Italy, Cyprus, Luxembourg and Finland under this scenario. Considering these findings, European countries can't rely solely on the increasing share of renewable energies to hope achieving their goal. More research needs to be made about renewable energies and their effect on net GHG emissions in European countries.