## 4 Discussion and Conclusions  
GHG concentrations on Earth have fluctuated cyclically over geological timescales, but they have been growing dramatically since the Industrial Revolution due to human activities. This study presents a new model for GHG estimation for EU member states (MSs), considering three independent variables: GDP, population and renewable energy share (RES). None of the studies in the literature uses past data to forecast different GHG emissions scenarios in the coming decades at the country or regional levels. The corrected weighting factor (\(FP_{c}\)) is determined for each MS once the values of the three independent variables are known, and the amount of GHGs released in the atmosphere is estimated by means of an equation presented in the Section 3. The multifactor algorithm developed using the data from the EU MSs shows a strong correlation between the amounts of GHG emissions and the independent variables. The determination coefficient (R\({}^{2}\)) reaches a value of 0.96. In general, the proposed estimation model differs by 22% from the actual data reported by Eurostat. However, it could reach a higher accuracy (15%) if some threshold values (GHG/population ratio below 6.19; GDP/RES ratio below 601.69 and GHG/population ratio above 920.68) are taken into account and countries that do not meet these values are excluded. Therefore, the model developed in this article can be used by EU member states to know in advance the amount of GHGs generated and thus optimise and plan GHG management strategies. Germany is removed because it reaches \(FP_{c}\) values above 0.20 in scenario 3 starting from 2029. In the model, the country is characterised by a consistent deviation compared to the standard deviation of the other European countries, because the Integrated Energy and  
Figure 6: Evolution of GHG emissions for the UE-23 until 2050. Climate Programme (Integrierte Energie-und Klimaprogram, 2007), which set as a target the reduction of emissions by 40% in 2020 [33], was in opposition with the simultaneous shutdown of all nuclear power plants and the resulting higher dependence on coal for electricity production, which caused a constant increase in GHG emissions. The importance of sound policy was recognised, and in 2021 the German Bundestag passed the new Climate Protection Act, which established the national goal of climate neutrality by 2045. As a milestone towards the final goal, GHG emissions are to be reduced by at least 65% from 1990 levels by 2030. The model's future projection shows that GHG emissions in 2050 could vary significantly compared to 2022 across the EU-23 studied (Germany, France, Italy and Ireland are not included since they have corrected weighting values above the threshold value of 0.156); an increase of up to 104% in a high growth rate (scenario 3); an increase of up to 24% in an average growth rate (scenario 2); and a decrease of up to 14% in a low growth rate (scenario 1). The results of scenario 1 (low growth rate) show that GHG emissions decrease in most countries, except in Sweden, Luxemburg and Denmark where a small upward trend is observed. In fact, these countries have already reached high levels of renewable electricity generation thanks to the implementation of several initiatives. For instance, Sweden has introduced various incentives such as subsidy schemes, a quota system, special tax regulation mechanisms and tax exemptions in case RES is adopted [37]. To continue on the decarbonisation path, these three countries should explore additional levers to offset the impact of growing population and human activities, for example, by focusing on reducing overall energy demand or developing renewable power generation plans. Scenario 1 also shows countries with a consistent decrease in GHG emissions, such as Croatia, Latvia and Cyprus, where population and related polluting activities are expected to decline.