The paper describes the energy consumption and GHG production comparison of three transport modes-road, rail and waterborne. The calculations are done according to the legislation in force-standard EN 16 258:2012 Methodology for calculation and declaration of energy consumption and GHG emissions of transport services (freight and passengers). The results have high informative value because they take into account energy consumption and emissions from primary and secondary consideration. The calculation is done by real fuel consumption values (road and waterborne) and by simulation of energy consumption (railway). The energy and emission coefficients from the standard EN were used for estimating the results according to the well-to-wheels and tank-to-wheels principles.
Abstract. Main challenge of logistics is delivering right assortment of products in exact amount, to exact place, in exact time, ecologically and for exact price. Logistics deals with freight transport but when the word 'products' is changed to 'passengers', then many principles can be applied to passenger transport. Railway passenger transport is the key part of passenger transport system, so it is necessary to optimize it on logistics philosophy at first.
Credit to the private sector has risen rapidly in European emerging markets but its risk evaluation has been largely neglected. Using retail-loan banking data from the Czech Republic we construct two credit risk models based on logistic regression and Classification and Regression Trees. Both methods are comparably efficient and detect similar financial and socioeconomic variables as the key determinants of default behavior. We also construct a model without the most important financial variable (amount of resources) that performs very well. This way we confirm significance of socio-demographic variables and link our results with specific issues characteristic to new EU members.
Credit to the private sector has risen rapidly in European emerging markets but its risk evaluation has been largely neglected. Using retail-loan banking data from the Czech Republic we construct two credit risk models based on logistic regression and Classification and Regression Trees. Both methods are comparably efficient and detect similar financial and socio-economic variables as the key determinants of default behavior. We also construct a model without the most important financial variable (amount of resources) that performs very well. This way we confirm significance of socio-demographic variables and link our results with specific issues characteristic to new EU members.
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