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The paper contributes to the literature by developing a linear programming (LP) model for optimization of the development of the regional energy system according to environmental and economic criteria by involving various types of renewable energy sources in the energy balance. The environmental criteria are taken into account throughout the entire life cycle of each energy product. Validation of the model was carried out on the example of a large southern region of Russia - Krasnodar Territory. This region was chosen for testing for several reasons: firstly, the region has significant potential for the development of various types of renewable energy sources, including solar and wind energy, hydro- and geothermal energy, rich biological resources, as well as a large number of bio-waste that can be considered as resources for bioenergy. Secondly, the Krasnodar Territory is currently one of the most densely populated and dynamically developing regions of Russia, experiencing a serious energy shortage and problems with air quality in large cities. The solution of the LP problem shows that when optimizing the development of the regional energy system of the Krasnodar Territory in terms of economic parameters, it is advisable to include bio-waste and municipal solid waste as priority energy sources. Solar power generation is involved in development on a leftover basis in order to make up the difference between the already used renewable energy sources and the required heat and electricity demand. When optimizing the energy balance according to environmental criteria, the involvement of biogas in the energy balance becomes impractical, therefore, after the complete use of the potential for processing solid waste and wind energy, the gap between the used potential of renewable energy sources and the required volume of generation can be replenished through the development of photovoltaics. Keywords: regional energy system, energy balance, environmental footprint, product life cycle analysis, linear programming, simplex method, shadow pricesJEL Classifications: O44, Q01DOI: https://doi.org/10.32479/ijeep.11377