References
- [1] Rosato, A., Sibilio, S., Ciampi, G., Entchev, E. & Ribberink, H. (2017). Energy, Environmental and Economic Effects of Electric Vehicle Charging on the Performance of a Residential Building-integrated Micro-trigeneration System. Energy Procedia 111, 699–709. DOI: 10.1016/j.egypro.2017.03.232.
- [2] Stojanová, H. & Blašková, V. (2018). Cost benefit study of a safety campaign’s impact on road safety. Accident Analysis & Prevention 117, 205–215. DOI: 10.1016/j.aap.2018.04.012.
- [3] Hasan, S. & Simsekoglu, Ö. (2020). The role of psychological factors on vehicle kilometer travelled (VKT) for battery electric vehicle (BEV) users. Research in Transportation Economics 82, 1000880. DOI: 10.1016/j.retrec.2020.100880.
- [4] Bienias, K., Kowalska-Pyzalska A. & Ramsey D. (2020). What do people think about electric vehicles? An initial study of the opinions of car purchasers in Poland. Energy Reports 6(1), 267–273. DOI: 10.1016/j.egyr.2019.08.055.
- [5] Rota, M., Carcedo, J. & García, J. (2016). Dual approach for modelling demand saturation levels in the automobile market. The Gompertz curve: Macro versus micro data. Investigación Económica 75(296), 43–72. DOI: 10.1016/j.inveco.2016.07.003.
- [6] Nykvist, B. & Nilsson, M. (2015). Rapidly falling costs of battery packs for electric vehicles. Nature climate change 5, 329–332. DOI: 10.1038/nclimate2564.
- [7] Lerch, C., Kley, F. & Dallinger, D. (2011). New business models for electric cars—A holistic approach. Energy policy 39(6), 3392–3403. DOI: 10.1016/j.enpol.2011.03.036.
- [8] Long, Z., Axsen, J., Miller, J. & Kormos, Ch. (2019). What does Tesla mean to car buyers? Exploring the role of automotive brand in perceptions of battery electric vehicles. Transportation Research Part A: Policy and Practice 129, 185–204. DOI: 10.1016/j.tra.2019.08.006.
- [9] Kabát, L., Hampel, D., Issever Grochová, L, Janová, J. & Střelec, L. (2014). Alternative Approaches for Assessing the European Countries Economic and Social Results. Procedia Economics and Finance 12, 273–282. DOI: 10.1016/S2212-5671(14)00345-1.
- [10] Staňková, M. & Hampel, D. (2017). Identification of bankruptcy factors for engineering companies in the EU. In Mathematical Methods in Economics 2017: Conference Proceedings (pp.714–719). Hradec Králové: Univerzita Hradec Králové.
- [11] Everitt, B.S., Landau, S., Leese, M. & Stahl, D. (2011). Cluster Analysis (5th ed.). Hoboken, NJ: John Wiley & Sons, Inc.
- [12] Zámková, M. & Blašková, V. (2013). Identification of factors affecting birth rate in Czech Republic. In SIMOS, T E. 11th International Conference of Numerical Analysis and Applied Mathematics 2013: ICNAAM 2013, AIP Conference Proceedings (pp. 1871–1874). Melville, New York: American Institute of Physics.
- [13] Staňková, M. & Hampel, D. (2018). Bankruptcy Prediction of Engineering Companies in the EU Using Classification Methods. Acta univ. agric. et silvic. Mendel. Brun. 66(5), 1347–1356. DOI: 10.11118/actaun201866051347.
- [14] Staňková, M. & Hampel, D. (2021). On the influence of model setting on stochastic frontier analysis. Mathematical Methods in the Applied Sciences 44(1), 274–283. DOI: 10.1002/mma.6730.
- [15] Střelec, L. & Adamec, V. (2013). Exploration into power of homogeneity and serial correlation tests. Acta univ. agric. et silvic. Mendel. Brun. 61(4), 1129–1136. DOI: 10.11118/actaun201361041129.
- [16] Dougherty, Ch. (2016). Introduction to Econometrics (5th ed.). Oxford: Oxford University Press.
- [17] Urbanovský, T. (2017). Granger Causalities Between Interest Rate, Price Level, Money Supply and Real GDP in the Czech Republic. Acta univ. agric. et silvic. Mendel. Brun. 65(2), 745–757. DOI: 10.11118/actaun201765020745.
- [18] Gujarati, D.N. & Porter, D.C. (2017). Basic Econometrics (5th ed.). Boston: McGraw-Hill Education.
- [19] Meckling, J. & Nahm, J. (2017). When do states disrupt industries? Electric cars in Germany and the United States. MIT Center for Energy and Environmental Policy Research, Working Paper, 6.
- [20] Stojanová, H., Lietavcova, B. & Raguž, I. V. (2019). The dependence of unemployment of the senior workforce upon explanatory variables in the European Union in the context of Industry 4.0. Social Sciences, 8(1): 29. DOI: 10.3390/socsci8010029.