The doctoral dissertation Contribution to the Analysis of the Environmental Impact of Smart City Logistics analyses how smart city logistics’ technologies and models can significantly reduce the ecological effect of urban mobility. Nowadays, cities represent centres with the largest number of inhabitants. Namely, 70 % of the EU population lives in urban areas and they account for 80 % of EU’s GDP. It is estimated that the costs arising from traffic congestion amount to EUR 80 billion a year. This amount should be increased by additional costs incurred due to ecology, CO2 emissions and traffic accidents. Logistics needs are often neglected in the planning and management of cities and therefore, there is a need for considerable improvements in performance and services. The author analyses the social impacts of ecological activity on LPG. The fundamental factors are defined as follows: fuel types, improvements in vehicle performance, vehicle technologies, transport efficiency, management of transport infrastructure, integration of transport systems, safety and security, and economic aspects of change, broader impact on the environment, fairness and accessibility, knowledge and awareness, infrastructure, pricing and taxation, laws (regulations), the cost of reducing emissions, possibilities for reducing social /political problems, other environmental influences, social equality, quality of life, creation of new jobs and competitiveness. The effects of ecological activity have been unclassified and their interconnections undefined. As correlation was not sufficient for a hierarchical classification of factors, the author applied a data mining technique – the Bayesian classification, which provided for a hierarchical structure model of ecological effects and factors. At the same time, the dependencies between data have been analysed by mining data collected on the ECO test during technical inspection of vehicles using the CHAD method. The eco-test measurements are taken as dependent variables and data mining has, as expected, shown that the basic tree branching primarily depends on the type of engine. By applying simulation models and statistical software tools on vehicle data distributions are derived for car power, engine volume, car weight, mileage and age. These results represent basis for future research. The dissertation defines different measures that help in the analysis of a city’s transport network. This particularly applies to pivoting measures that have proven to be significant in the diversification of social networks for the purpose of analysing a city’s transport infrastructure. This method makes it possible to identify the critical areas and intersections prior to designing the transport structure and thus by making changes in the flows within the network reduce the structural pressure in certain areas. The author developed a model of ecological effects of the LPG system by dissipating the demand along the entire transport network integrated by social network methodology and the distribution of the ecological effects of vehicles. The model is built on the assumption that the ecological effect is in the node and represents the sum of ecological effects of other nodes minus the factor that is inversely proportional to the distance between the nodes and the factor obtained from the probability distribution of ecological effects. All results are confirmed on the transport model of the city of Rijeka.