Analysis of scaling indicators when assessing the resource potential of objects of the oil-contaminated waste recycling system based on the DEA method

Abstract

The paper proposes a new approach to assessing the resource potential of the objects of the oil-contaminated waste (OCW) recycling system based on the Data Envelopment Analysis (DEA) method, taking into account the constant and variable economies of scale, and a comparative analysis of the scaling indicators used in selecting the best facilities of the OCW recycling system in making informed managerial decisions. A four-stage calculation algorithm has been developed, in which the first stage determines estimates of the resource value of OCW in storage facilities. At the second and third stages, estimates of the resource potential of combinations of "OCW storage - recycling technology" are calculated taking into account constant and variable economies of scale, respectively, as well as the effect of scale (constant, increasing or decreasing), which characterizes the qualitative assessment of the scale properties of objects of the OCW recycling system. At the fourth stage, an assessment of the effectiveness of scaling, which characterizes the magnitude of the impact of scale of technological operations on the efficiency of OCW recycling in the analyzed system, and the optimal sequence of processing of OCW in storages by the criterion of increasing the resource potential of the system objects, taking into account indicators of scaling, is determined. By the example of the system of OCW recycling in the Samara region estimates of resource value of OCW in storages, assessment of resource potential of combinations "OCW storage – recycling technology" and corresponding indicators of scaling (effect of scale and evaluation of scaling efficiency) have been calculated. The comparison of two optimal sequences by the criterion of increasing the resource potential of the system objects, taking into account constant and variable economies of scale was made, and it is shown how the indicators of scaling can influence the adoption of sound management decisions.