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It is necessary to choose proper methodology and indicators for assessing sustainable economic development as the information becomes a tool for decision-making support of sustainable development policies and implementation of programs. In Ukraine, evaluating the results of implementation of different programs for development is essential as an analytical basis for making a strategy for the next period and a prerequisite for further progress.Certain shortcomings of linear models for evaluating the results appeared during the design and implementation of the strategy to manage sustainable economic development. The potential for establishing erroneous targets increases in the formation of strategic objectives for the next forecast period. There is a special need to choose adequate indicators to comprehensively approximate the factors of economic development and evaluation methods that allow more sensitively measuring the results of management decisions in the implementation of the strategy.The article evaluates the results of the Sustainable Development Strategy “Ukraine – 2020”, employing the potential of the neural network method for a flexible combination of a large number of factors in constructing nonlinear models of impact on the resulting indicator. As a result of applying the neural network model with one hidden layer for evaluation, based on 16 indicators identifying economic, social, and institutional aspects of sustainable development of Ukraine, it was found that institutional transformations contribute most to achieving sustainable development. Reforms in terms of deregulation and support of entrepreneurship, property rights protection, and competitive environment have the most significant positive impact. On the other hand, low efficiency of capital market reforms, implementation of the energy efficiency program, and reform in the field of public procurement determine the need to revise the program of their fulfilment.

Assessment of the reforms and programs results of Ukraine’s economy sustainable development by means of neural networks