Abstract

In the light of recent developments regarding electric vehicle market share, we assess the carbon footprint and water footprint of electric vehicles and provide a comparative analysis of energy use from the grid to charge electric vehicle batteries in the Czech Republic. The analysis builds on the electricity generation forecast for the Czech Republic for 2015&ndash 2050. The impact of different sources of electricity supply on carbon and water footprints were analyzed based on electricity generation by source for the period. Within the Life Cycle Assessment (LCA), the carbon footprint was calculated using the Intergovernmental Panel on Climate Change (IPCC) method, while the water footprint was determined by the Water Scarcity method. The computational LCA model was provided by the SimaPro v. 8.5 package with the Ecoinvent v. 3 database. The functional unit of study was running an electric vehicle over 100 km. The system boundary covered an electric vehicle life cycle from cradle to grave. For the analysis, we chose a vehicle powered by a lithium-ion battery with assumed consumption 19.9 kWh/100 km. The results show that electricity generated to charge electric vehicle batteries is the main determinant of carbon and water footprints related to electric vehicles in the Czech Republic. Another important factor is passenger car production. Nuclear power is the main determinant of the water footprint for the current and future electric vehicle charging, while, currently, lignite and hard coal are the main determinants of carbon footprint. Document type: Article

Abstract

Purpose The aim of this paper is to identify the factors influencing efficiency of urban public transport (UPT) systems and to benchmark Czech UPT systems according to their efficiency. Methods The analysis was carried out on a sample of 19 UPT systems in the Czech Republic during 2010–2015. Efficiency was evaluated through a two-stage analysis. Data envelopment analysis (DEA) was used in the first stage. It was based on three inputs (employees, rolling stock and energy) and one output (passengers). DEA efficiency scores were computed for all 19 systems for each year and under two different assumptions regarding returns to scale. In the second stage of the analysis, DEA scores were used in Tobit regression with a set of operational, socio-economic, and demographic explanatory variables in order to find determinants of efficiency. Results Several variables were identified as factors increasing efficiency - proportion of drivers, average vehicle age, the presence of tramlines in the city, total vehicle kilometres, and population density. Some variables were identified as decreasing efficiency – ticket price, proportion of subsidies in revenues, and presence of a two-city system. Czech cities with most efficient transport systems were Prague, Brno, Mariánské Lázně, Olomouc, and Pilsen. The least efficient cities were Chomutov–Jirkov, Ostrava, and Děčín. Conclusions The principal lesson from this study is that bigger cities with greater population densities are more efficient than smaller cities, and the key efficiency factors that local authorities have under their control are the ticket price, rate of subsidies, and structure of the city transport system. The paper contributes to current debate about the efficiency of the urban transport systems and their determinants. There was not much difference between the constant and variable returns to scale results. The results from the second stage could help policy makers make the public transport systems more efficient. Future research could be devoted to gaining data on additional operators which would also enable using additional inputs and outputs for DEA analysis. Document type: Article