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Fossil fuels utilized in the transportation sector affect our climate negatively. Implementation of renewable fuels for transportation becomes a very important step in the reduction of the sector’s carbon footprint. The effective solution for GHGs reduction is the substitution of fossil fuels used in the current fleet by fuels produced from renewable sources such as biofuels. This work investigates how alternative fuel properties affect engine performance and greenhouse gases (GHG) emissions of the current fleet of light-duty vehicles. Based on the experimental results, data-driven black-box modeling has been applied to develop two models, one for spark-ignition (SI) and the second for compression-ignition (CI) unmodified engines. The multiple independent variables of the models (inputs) are represented by fuel properties, whereas single dependent variable (output) stands for fuel consumption (FC) over driving cycles such as Worldwide harmonized Light-duty Test Cycle (WLTC) or New European Driving Cycle (NEDC). Both input and outputs are described by percentage changes relative to the standard fossil-based fuel, gasoline for SI and diesel for CI engines. The chosen modeling methodology is based on multilinear regression. Additionally, quantitative analysis was performed in order to achieve the final state of inputs, significance level below 5%. In both cases, coefficients of determination (R-Square) turned out to be relatively high in SI case 0.99 and in CI case 0.97. The model for SI engines represents how fuel consumption is affected by the research octane number (RON), density, net calorific value volume based (NCV vol) and oxygen content (O2). Whereas model for CI part reveals density, cetane number (CN) and net calorific value mass based (NCV mass) impact on FC. Developed models represent the end-use performance of alternative fuels and are dedicated to support decision makers and accelerate commercialization of transport biofuels.