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Electric mobility is promoted as a future transport option that has environmental and economic benefits and encourages sustainable urban transportation. The aim of this study is to reveal the changes in environmental and economic performance if we switched from internal combustion engine vehicles (ICEVs) to battery electric (BEV) or hybrid electric (HEV) vehicles. Therefore, this research presents a comparative environmental life cycle assessment (LCA) from the Cradle-to-Grave perspective of the vehicles and a Well-to-Wheel analysis of their fuel supply. Moreover, an LCA of a BEV was performed under diverse electricity mix scenarios, which are forecasted for 2015–2050 in Lithuania. From an economic point of view, a life cycle costing was conducted for the same vehicles to estimate the economic impacts over the vehicle life cycles under Lithuanian conditions. The results show that ICEV-petrol contributes the major environmental damage in all damage categories. BEVs with the electricity mix of 2020–2050 scenarios, which are composed mainly of renewable energy sources, provide the least environmental impact. The economic results reveal that BEV and ICEV-diesel are the most cost-efficient vehicles, with the total consumer life cycle costs of approximately 5% and 15% less than ICEV-petrol and HEV, respectively.

The paper provides a comparative analysis of economic growth in Estonia, Latvia and Lithuania and discusses differences in development of the main sectors during the period 2000–2016. Based on detailed analysis of energy sector development, the driving factors influencing changes in primary energy consumption in each country and in the Baltic region are discovered. Increase of renewable energy sources (RES) consumption in the Baltic region over this period by 73.6% is emphasized. The paper presents valuable insights from analysis of trends in final energy consumption by sectors of the national economies, branches of the manufacturing sector, and by energy carriers. Long-term relationships between economic growth and final energy consumption are established. An econometric model was applied to predict final energy demand in the Baltic States for the 2020 horizon. It is emphasized that growing activities in the manufacturing and transport sectors will cause increase of final energy demand in all three countries. Based on detailed analysis of greenhouse gas (GHG) emissions trends some positive shifts are shown and the necessity of new policies in the transport sector and agriculture is identified. Changes of emission intensity indicators are examined and a potential for decoupling of carbon dioxide (CO2) emissions from economic growth in Estonia is indicated.

The representation of wind power plants electricity generation in economic models for energy planning is problematic, since generation in wind power plants is variable and it is not possible to predict accurately enough wind fluctuations for more than a few days. Often, wind power plants generation patterns from a single historical year are repeated throughout the modelled time period. Typically, this method is used when analysing power system operation in hourly time intervals for all days in a year for each year. However, hourly time resolution is not feasible in large-scale models, which tend to require considerable amounts of computing power. Thus, some kind of time aggregation is needed. On the other hand, currently used methods for models with less than hourly temporal resolution are becoming inadequate because of increasing share of fluctuating electricity production from wind in total power generation.In this article, a methodology for evaluation of wind power plants stochastic electricity generation in power system development models is described. The methodology is based on evaluation of how much time single or multiple wind power plants generate a certain output range during a season or some time period within a year, modelling of output distribution for a typical day of the selected time period, and preparation of electricity output curves. These electricity output curves when modelling wind power plants in models with aggregated time allow to assess fluctuations in generation, observable regularities and enhance the objectivity of balancing power demand assessment, also ensure that electricity generated during a typical day corresponds to electricity generated during the selected time period. This methodology will help to determine the rational perspective power generation mix more accurately and make a better assessment of cost-effectiveness of wind power plants in economic models for energy planning, without significantly increasing the size of already large-scale models.

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