ПОРІВНЯННЯ ЕКОНОМЕТРИЧНИХ МЕТОДІВ ОЦІНКИ ЕФЕКТИВНОСТІ В ЕНЕРГЕТИЦІ

Abstract

Evaluation of the efficiency of enterprises in energy sector is a rather difficult problem, since the production of useful energy services (electricity and heat energy) is often accompanied by the emission of harmful substances (carbon dioxide, sulfur dioxide, nitrogen oxides, etc.), which should be taken into account when assessing efficiency of the enterprises’ activity. The purpose of this article is to determine the main features of the application of main econometric methods, which are used in order to assess the efficiency of enterprises in energy sector, as well as to identify the advantages and disadvantages of these methods. Using general scientific research methods, namely analysis, synthesis, theoretical generalization, abstraction and analogy, the author characterizes the following parametric methods: Stochastic Frontier Approach (SFA), Distribution Free Approach (DFA) and Thick Frontier Approach (TFA). Moreover, the following nonparametric methods are considered: Data Envelopment Analysis (DEA) and Free Disposal Hull (FDH). All these methods are based on the calculation of approximation of indicators of the enterprise to the potential or actual efficiency frontier. The concept of “frontier efficiency” is characterized. The main features of the above-mentioned methodological approaches are considered by the author. The advantages and disadvantages of given methodological approaches due to the existence of mistakes, quantity of input and output factors, subjectivity of estimation, accuracy of the results obtained, etc. are indicated in the article. The author shows that the analyzed methodical approaches can be applied for the estimation of the efficiency of enterprises in energy sector, taking into account the multiple inputs and outputs of such enterprises. It is emphasized that further research will be focused on determination of the efficiency of enterprises in energy sector. The conducted analysis could be used as a basis for further managerial decision-making, both at the micro level (enterprise) and at the macro level (regions and the state as a whole).