Economies of Scale in City Gas Sector in Seoul, South Korea: Evidence from an Empirical Investigation

Abstract

The city gas sector in Seoul, the capital of South Korea, consists of five locally monopolized companies. As the city gas penetration reaches 98% and city gas as cooking fuel and heating fuel is being converted to electricity and district heating system, respectively, the need to redefine the role of the city gas sector is being raised. In this respect, this study aims to analyze the economies of scale in the city gas sector using the translog variable cost function model over the period 2008–2020 and to compute the minimum efficient scale. The scale economy index ranged from 0.1 to 0.2, which was larger than 1.0. The results show that the city gas sector still enjoys economies of scale, although the economies of scale are gradually disappearing. The minimum efficiency scale was estimated to be 1.06 times the size of the total market, which is the total output of the five companies. This finding vividly suggests that reducing the number of city gas companies through mergers and acquisitions among five city gas companies is more desirable in terms of cost reduction. This study suggests that the business structure favorable to the city gas business, such as high population density and urbanization, can rapidly lose economies of scale under rapid electrification and a rigid wholesale market. The central and local governments, which have the authority to regulate the city gas business, need to promote mergers and acquisitions between city gas operators, and to normalize distorted energy rates in order to prevent excessive electrification.

A TLBO-Tuned Neural Processor for Predicting Heating Load in Residential Buildings

“…Facilitating the simulations of real-world events has been a primary objective in this way [8][9][10]. Diversity of solutions has given the chance to experts to choose the most appropriate approach (e.g., numerical [11,12], experimental [13,14], empirical [15,16], etc.) with respect to the to-be-solved problem.…”

A TLBO-Tuned Neural Processor for Predicting Heating Load in Residential Buildings

“…Facilitating the simulations of real-world events has been a primary objective for this purpose [8][9][10]. A diversity of solutions have given experts the chance to choose the most appropriate approach (e.g., numerical [11,12], experimental [13,14], empirical [15,16], etc.) with respect to the unsolved problem.…”