In the European Green Deal, EU Commission has set a goal to reduce greenhouse gas emissions in the transport sector by 90% by 2050 compared to the 1990 level. Most likely, transport decarbonization will rely on a rapid expansion of electric and hydrogen vehicle fleet, which would seriously affect not just overall electricity demand, but also the shape of the electricity consumption curve. Consequently, our research focuses on integrated energy and transport modelling when analyzing its development pathways up to 2050 and beyond. This paper describes how already established transport modeling practices can be further improved by differentiating vehicles by age groups and setting vehicle age distributions to improve the representation of vehicle stock, fuel efficiencies and emissions, especially for countries that have non-declining vehicle age distributions. Modeling results using proposed and traditional approaches were compared for the Lithuanian case. It shows that the transport fuel shift using the proposed approach is more gradual than the traditional one. Diesel cars are phased out by 2050 versus 2040. Furthermore, the proposed approach provided more realistic CO₂ emissions, 7% lower emissions for 2018 than estimated based on statistical data, while traditional approach was 27% lower.

The objective of this research was to construct energy intensity forecasting models for key manufacturing industries, with a particular focus on “catching up” European economies. Future energy intensity values serve as the foundation for energy demand forecasts, which are essential inputs for the analysis of countries’ decarbonisation scenarios. The Lithuanian case is analysed in the context of its efforts to reach the economic development level of the most advanced European Union (EU) countries. The scientific literature and energy policy analysis, interdependence (correlation and regression), tendency and case analysis, logical economic reasoning, and graphical representation methods have been applied. The energy intensity forecasts until 2050 were based on historical statistical data of value added and final energy consumption of EU countries from 2000 to 2021. The analysis of historical trends revealed a remarkable decrease in industrial energy intensity in most EU countries, including Lithuania. Given the rapid pace of decline in historical energy intensity, the values observed in individual Lithuanian industries have already reached levels comparable to the most economically advanced EU countries. Four econometric trendlines were employed to construct forecasting models for energy intensity. The results for Lithuania demonstrated that the selected trendlines exhibited a high degree of fit with historical energy intensity data from the EU, as evidenced by their R² values. Furthermore, the forecasts were shown to be highly accurate, with their MAPEs remaining below 10% in most cases. Nevertheless, the logarithmic trendline was found to be the most accurate for forecasting energy intensity in total manufacturing (MAPE = 4.0%), non-metallic minerals (MAPE = 3.5%), and food, beverages, and tobacco (MAPE = 4.1%) industries, with the exponential trendline in the chemical industry (MAPE = 8.7%) and the moving average in the total manufacturing industry (MAPE = 4.0%), food industries (MAPE = 4.0%), and remaining aggregate industries (MAPE = 14.5%). It is forecasted that energy intensity could decline by 8 to 16% to 1.10–1.20 kWh/EUR in Lithuania’s manufacturing industries by 2050.

The improvement of energy efficiency (EE) and growing consumption of renewable energy sources (RES) in the commercial and public services sector are playing important roles in seeking to pursue sustainable development in the Baltic States and contributing to the transition to a low-carbon economy. This paper provides findings from a detailed analysis of energy intensity trends in economic sectors from 2005 to 2022 in three countries, considering the role of transformations in the energy and climate framework of the European Union (EU). Based on the Fisher Ideal Index application, the different contributions from improving EE and structural changes are revealed. The dominant role of EE improvements in energy savings is identified in Estonia and Lithuania, and structural changes are dominant in Latvia. Changes in energy-related greenhouse gas (GHG) emissions in the commercial and public services sector and the main determinants of their reduction are examined. Based on applying the Kaya identity and the logarithmic mean Divisia index (LMDI) method, decreasing energy intensity is the most important determinant in all three countries. Due to the different extents of RES deployment, their role was very important in Estonia and Latia but was less effective in Lithuania. Reduction in emission intensity has the largest impact in Latvia. The GHG emissions decreased by 34.1% in Estonia, 17.5% in Latvia, and 16.7% in Lithuania. The results confirm the need for new policies, implementation of relevant EE measures, and the growing contribution from RES in Latvia and Lithuania.