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

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Literature Review

## 3. Current State of the Retail City Gas Market

^{3}, and the total demand for city gas was 20.1 million tonnes for residential, commercial, industrial and power generation use.

^{3}). However, from 2019, sales volume declined again, reducing sales to 235.6 billion m

^{3}in 2020. The fact that the total number of consumers continuously increases while the sales volume rather decreases means that sales per consumer rather decreases.

^{3}, and out of the total demand for 4.6 million units, households (4.3 million) accounted for most. As presented in Figure 2, Seoul City Gas has the highest market share with 35.4%, followed by Yesco (23.8%) and Cowon Energy Service (19.8%), showing a typical oligopoly market.

^{3}of city gas in 2001, is declining to KRW 60.9 per m

^{3}of city gas in 2020. The situation in which city gas sales are stagnant and retail prices are declining shows the management difficulties city gas companies are facing. In addition, it is difficult to expect a rapid increase in city gas demand in the future when the demand for heating and cooking is switched to electricity and district heating. Due to the expansion of electric induction, the number of households using electricity instead of city gas for cooking continues to increase, and as district heating is basically supplied to new large-scale apartment complexes, individual heating systems where city gas is used are also being replaced with district heating system.

## 4. Materials and Methods

#### 4.1. Variable Cost Function and Economies of Scale

#### 4.2. Data and Variables

## 5. Results

#### 5.1. Estimation Results of the Translog Cost Function

#### 5.2. Forecasting Economies of Scale

^{9}MJ, which means economies of scale disappear when city gas production reaches this scale.

^{9}MJ) of the five city gas companies in 2020, and when converted to volume, MES is 4.442 billion m

^{3}.

## 6. Discussion

## 7. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

- Lee, J.D.; Oh, K.J.; Kim, T.Y. Productivity growth, capacity utilization, and technological progress in the natural gas industry. Util. Policy
**1999**, 8, 109–119. [Google Scholar] [CrossRef] - Worthington, A.C.; Higgs, H. Economies of scale and scope in Australian urban water utilities. Util. Policy
**2014**, 31, 52–62. [Google Scholar] [CrossRef] [Green Version] - Pollitt, M.G.; Steer, S.J. Economies of scale and scope in network industries: Lessons for the UK water and sewerage sectors. Util. Policy
**2012**, 21, 17–31. [Google Scholar] [CrossRef] [Green Version] - Klien, M.; Michaud, D. Water utility consolidation: Are economies of scale realized? Util. Policy
**2019**, 61, 100972. [Google Scholar] [CrossRef] - Dismukes, D.E.; Cope, R.F.; Mesyanzhinov, D. Capacity and economies of scale in electric power transmission. Util. Policy
**1998**, 7, 155–162. [Google Scholar] [CrossRef] - Christensen, L.R.; Greene, W.H. Economies of scale in U.S. electric power generation. J. Polit. Econ.
**1976**, 84, 655–676. [Google Scholar] [CrossRef] - Guldmann, J.M. Modeling the structure of gas distribution costs in urban areas. Reg. Sci. Urban Econ.
**1983**, 13, 299–316. [Google Scholar] [CrossRef] - Gim, B.; Yun, H.C.; Lee, J.D.; Kim, T.Y. A goal programming/constrained regression: Economies of scale for the Korean natural gas industry. Korean Manag. Sci. Rev.
**1997**, 14, 1–10. [Google Scholar] - Shin, S.Y.; Lee, J.D.; Kim, T.Y.; Heo, E. A study on the economies of scale in the Korean LNG industry. Environ. Resour. Econ. Rev.
**1998**, 35, 137–142. [Google Scholar] - Fabbri, P.; Fraquelli, G.; Giandrone, R. Costs, technology and ownership of gas distribution in Italy. Manag. Decis. Econ.
**2000**, 21, 71–81. [Google Scholar] [CrossRef] - Farsi, M.; Filippini, M.; Kuenzle, M. Cost efficiency in the Swiss gas distribution sector. Energy Econ.
**2007**, 29, 64–78. [Google Scholar] [CrossRef] [Green Version] - Alaeifar, M.; Farsi, M.; Filippini, M. Scale economies and optimal size in the Swiss gas distribution sector. Energy Policy
**2014**, 65, 86–93. [Google Scholar] [CrossRef] - Yu, J.J.; Yoo, S.H.; Baek, C. Economies of scale in the South Korean natural gas industry. Energies
**2019**, 12, 1557. [Google Scholar] [CrossRef] [Green Version] - Kim, T.Y.; Lee, J.D. Cost analysis of gas distribution industry with spatial variables. J. Energy Dev.
**1995**, 20, 247–267. [Google Scholar] - Kim, Y. Economies of scale in city gas industry. Korean Econ. Rev.
**2000**, 48, 35–56. [Google Scholar] - Krishnapillai, S.; Thompson, H. Cross section translog production and elasticity of substitution in US manufacturing industry. Int. J. Energy Econ. Policy
**2012**, 2, 50–54. [Google Scholar] - Helali, K.; Kalai, M. Estimate of the elasticities of substitution of the CES and translog production functions in Tunisia. Int. J. Econ. Bus. Res.
**2015**, 9, 245–253. [Google Scholar] [CrossRef] - Banda, H.S.; Verdugo, L.E.B. Translog Cost Functions: An Application for Mexican Manufacturing. Banco Mexico Doc. Investig. Work. Pap. 2007. No. 2007-08. Available online: https://www.banxico.org.mx/publications-and-press/banco-de-mexico-working-papers/%7B77E6E560-E214-465A-9FD5-359A39571AC7%7D.pdf (accessed on 24 April 2022).
- Triebs, T.P.; Saal, D.S.; Arocena, P.; Kumbhakar, S.C. Estimating economies of scale and scope with flexible technology. J. Prod Anal.
**2016**, 45, 173–186. [Google Scholar] [CrossRef] [Green Version] - Filippini, M. Economies of scale and utilization in the Swiss electric power distribution industry. Appl. Econ.
**1996**, 28, 543–550. [Google Scholar] [CrossRef] - Filippini, M.; Luchsinger, C. Economies of scale in the Swiss hydropower sector. Appl. Econ. Lett.
**2007**, 14, 1109–1113. [Google Scholar] [CrossRef] - Rhine, R. Economies of scale and optimal capital in nuclear and fossil fuel electricity production. Atl. Econ. J.
**2001**, 29, 203–214. [Google Scholar] [CrossRef] - Renzetti, S. Municipal water supply and sewage treatment: Costs, prices and distortions. Can. J. Econ.
**1999**, 32, 688–704. [Google Scholar] [CrossRef] [Green Version] - McGeehan, H. Railway costs and productivity growth. J. Transp. Econ. Policy
**1993**, 27, 19–32. [Google Scholar] - Caves, D.W.; Christensen, L.R.; Swanson, J.A. Productivity growth, scale economies, and capacity utilization in U.S. railroads, 1955–1974. Am. Econ. Rev.
**1981**, 71, 994–1002. [Google Scholar] - Evans, D.S.; Heckman, J.J. Natural monopoly and the bell system: Response to Charnes, cooper and Suetoshi. Manag. Sci.
**1988**, 34, 27–38. [Google Scholar] [CrossRef] [Green Version] - Bloch, H.; Madden, G.; Savage, S.J. Economies of scale and scope in Australian telecommunications. Rev. Ind. Organ.
**2001**, 18, 219–227. [Google Scholar] - Clark, J.A.; Speaker, P.J. Economies of scale and scope in banking: Evidence from a generalized translog cost function. Q. J. Bus. Econ.
**1994**, 33, 3–25. [Google Scholar] - Altunbas, Y.; Molyneux, P. Economies of scale and scope in European banking. Appl. Financ. Econ.
**1996**, 6, 367–375. [Google Scholar] [CrossRef] - Mitchell, K.; Onvural, N.M. Economies of scale and scope at large commercial banks: Evidence from the Fourier flexible functional form. J. Money Credit Bank
**1996**, 28, 178–199. [Google Scholar] [CrossRef] - Lin, B.; Tian, P. The energy rebound effect in China’s light industry: A translog cost fucntion approach. J. Clean. Prod.
**2016**, 112, 2793–2801. [Google Scholar] [CrossRef] - Bello, M.O.; Solarin, S.A. Interfuel substitution, hydroelectricity consumption and CO
_{2}emissions mitigation in Malaysia: Evidence from a transcendental logarithm (trans-log) cost function framework. Environ. Sci. Pollut. Res.**2020**, 27, 17162–17174. [Google Scholar] [CrossRef] - Onghena, E.; Meersman, H.; de Voorde, E.V. A translog cost function of the integrated air freight business: The case of FedEx and UPS. Practice
**2014**, 62, 81–97. [Google Scholar] [CrossRef] - Oum, T.H.; Zhang, Y. Utilisation of quasi-fixed inputs and estimation of cost function: An application to airline costs. J. Transp. Econ. Policy
**1991**, 25, 121–134. [Google Scholar] - Nadiri, M.I.; Schankerman, M. Variable Cost Functions and the Rate of Return to Quasi-Fixed Factors: An Application to R&D in the Bell System. NBER Work. Paper. 1980. No. 597. Available online: https://www.researchgate.net/profile/M-Nadiri/publication/5184186_Variable_Cost_Functions_and_the_Rate_of_Return_to_Quasi-Fixed_Factors_An_Application_to_R_and_D_in_the_Bell_System/links/57d85aef08ae0c0081edff3a/Variable-Cost-Functions-and-the-Rate-of-Return-to-Quasi-Fixed-Factors-An-Application-to-R-and-D-in-the-Bell-System.pdf (accessed on 24 April 2022).
- Hunter, W.C.; Timme, S.G. Some evidence on the impact of quasi-fixed inputs on bank scale economy estimates. Econ. Rev.
**1991**, 76, 12–20. [Google Scholar] - Burney, N.A. Economies of scale and utilization in electricity generation in Kuwait. Appl. Econ.
**1998**, 30, 815–819. [Google Scholar] [CrossRef] - Bottasso, A.; Conti, M. Scale economies, technology and technical change in the water industry: Evidence from the English water only sector. Reg. Sci. Urban Econ.
**2008**, 39, 138–147. [Google Scholar] [CrossRef] - Park, S.Y.; Lee, K.S.; Yoo, S.H. Economies of scale in the Korean district heating system: A variable cost function approach. Energy Policy
**2016**, 88, 197–203. [Google Scholar] [CrossRef] - Nelson, R.A. On the measurement of capacity utilization. J. Ind. Econ.
**1989**, 37, 273–286. [Google Scholar] [CrossRef] - Farsi, M.; Fetz, A.; Filippini, M. Economies of scale and scope in multi-utilities. Energy J.
**2008**, 29, 123–143. [Google Scholar] [CrossRef] [Green Version] - Fetz, A.; Filippini, M. Economies of vertical integration in the Swiss electricity sector. Energy Econ.
**2010**, 32, 1325–1330. [Google Scholar] [CrossRef] [Green Version] - Oh, D.H.; Lee, Y.G. Productivity decomposition and economies of scale of Korean fossil-fuel power generation companies: 2001–2012. Energy
**2016**, 100, 1–9. [Google Scholar] [CrossRef] - Yu, C.; Yao, W. Robust linear regression: A review and comparison. Commun. Stat. Simul. Comput.
**2017**, 46, 6261–6282. [Google Scholar] [CrossRef] - Muhlbauer, A.; Spichtinger, P.; Lohmann, U. Application and comparison of robust linear regression methods for trend estimation. J. Appl. Meteorol. Clim.
**2009**, 48, 1961–1970. [Google Scholar] [CrossRef] - Mbah, R.E.; Wasum, D.F. Russian-Ukraine 2022 War: A review of the economic impact of Russian-Ukraine crisis on the USA, UK, Canada, and Europe. Adv. Soc. Sci. Res. J.
**2022**, 9, 144–153. [Google Scholar] [CrossRef] - Liadze, I.; Macchiarelli, C.; Mortimer-Lee, P.; Juanino, P.S. The Economic Costs of the Russia-Ukraine Conflict. NIESR Policy Paper. 2022. No. 32. Available online: https://www.niesr.ac.uk/wp-content/uploads/2022/03/PP32-Economic-Costs-Russia-Ukraine.pdf (accessed on 24 April 2022).
- Bellocchi, S.; Manno, M.; Noussan, M.; Prina, M.G.; Vellini, M. Electrification of transport and residential heating sectors in support of renewable penetration: Scenarios for the Italian energy system. Energy
**2020**, 196, 117062. [Google Scholar] [CrossRef] - Veldman, E.; Gibescu, M.; Slootweg, H.; Kling, W.L. Impact of electrification of residential heating on loading of distribution networks. In Proceedings of the 2011 IEEE Trondheim PowerTech, Trondheim, Norway, 1–7 June 2011. [Google Scholar]
- White, P.R.; Rhodes, J.D.; Wilson, E.J.; Webber, M.E. Quantifying the impact of residential space heating electrification on the Texas electric grid. Appl. Energy
**2021**, 298, 117113. [Google Scholar] [CrossRef] - Chung, H.Y.; Kang, H.J. A study on the cross subsidization of energy industries in Korea. J. Korean Inst. Gas
**2006**, 10, 17–22. [Google Scholar] - Ohrn, E.; Seegert, N. The impact of investor-level taxation on mergers and acquisitions. J. Public Econ.
**2019**, 177, 104038. [Google Scholar] [CrossRef] - Kasipillai, J. Tax implications of mergers and acquisitions involving financial institutions. Manag. Financ.
**2004**, 30, 48–62. [Google Scholar] [CrossRef] - Peterson, C.R.; McDermott, K.A. Mergers and acquisitions in the U.S. electric industry: State regulatory policies for reviewing today’s deals. Electr. J.
**2007**, 20, 8–25. [Google Scholar] [CrossRef] - Monden, Y. M&A and its incentive system for the inter-firm organization. In M&A for Value Creation in Japan; World Scientific Publishing Co. Pte. Ltd.: Singapore, 2010; pp. 67–89. [Google Scholar]

Market | Source | Countries (Covered Period) | Models | Estimation Methods |
---|---|---|---|---|

Wholesale | [8] | South Korea(1987–1993) | Translog cost function | Goal programming |

Wholesale | [9] | South Korea, France, Japan, Italy, Germany, Belgium (1991–1995) | Translog cost function | ITSUR ^{1} |

Wholesale | [13] | South Korea (2000–2018) | Translog cost function | ITSUR |

Wholesale | [10] | Italy (1991–1992) | Translog cost function | SUR ^{2} |

Wholesale | [12] | Switzerland (1996–2000) | Translog cost function | SUR |

Wholesale | [7] | United States (1979) | Log-linear cost function | OLS ^{3} |

Wholesale | [11] | Switzerland (1996–2000) | Cobb-Douglas cost function | GLS ^{4} |

Retail | [14] | South Korea (1987–1992) | Translog cost function | SUR |

Retail | [15] | South Korea (1990, 1991, 1996, 1997) | Non-parametric | LOWESS ^{5} |

^{1}ITSUR stands for iterated seemingly unrelated regression.

^{2}SUR stands for seemingly unrelated regression.

^{3}OLS stands for ordinary least square.

^{4}GLS stands for generalized least square regression.

^{5}LOWESS stands for locally weighted regression.

Variables | Definition | Mean | Standard Deviation |
---|---|---|---|

$VC$ | Variable cost = labor cost + material cost (unit: thousand Korean won) | 6.1 × 10^{8} | 3.1 × 10^{8} |

${P}_{L}$ | Labor price = labor cost divided by the number of employees (unit: thousand Korean won per person) | 8.4 × 10^{4} | 1.5 × 10^{4} |

${P}_{M}$ | Material price = material cost/material input (unit: Korean won per MJ) | 11.5 | 2.7 |

$K$ | Capital cost = total cost − labor cost − material cost (unit: thousand Korean won) | 3.5 × 10^{7} | 2.0 × 10^{7} |

$Q$ | Production = city gas supply (unit: thousand MJ) | 4.0 × 10^{7} | 1.9 × 10^{7} |

$T$ | Technical change = time trend variable from 1 to 13 (1 in 2008 and 13 in 2020) | 7 | 3.8 |

Variables | Coefficients | Standard Errors | t-Values | p-Values |
---|---|---|---|---|

$\mathrm{ln}{P}_{L}$ | −0.162 | 0.046 | −3.50 | 0.000 |

$\mathrm{ln}{P}_{M}$ | 1.162 | 0.046 | 25.11 | 0.000 |

$\mathrm{ln}Q$ | 6.287 | 4.88 | 1.29 | 0.198 |

$\mathrm{ln}{P}_{L}\ast \mathrm{ln}{P}_{L}$ | 0.020 | 0.001 | 21.33 | 0.000 |

$\mathrm{ln}{P}_{M}\ast \mathrm{ln}{P}_{M}$ | 0.020 | 0.001 | 21.33 | 0.000 |

$\mathrm{ln}Q\ast \mathrm{ln}Q$ | −0.325 | 0.281 | −1.16 | 0.247 |

$\mathrm{ln}{P}_{L}\ast \mathrm{ln}{P}_{M}$ | −0.02 | 0.001 | −21.33 | 0.000 |

$\mathrm{ln}{P}_{L}\ast \mathrm{ln}Q$ | −0.012 | 0.01 | −1.22 | 0.223 |

$\mathrm{ln}{P}_{M}\ast \mathrm{ln}Q$ | 0.012 | 0.01 | 1.22 | 0.223 |

$\mathrm{ln}K$ | −6.725 | 2.271 | −2.96 | 0.003 |

$\mathrm{ln}K\ast \mathrm{ln}K$ | 0.396 | 0.135 | 2.94 | 0.003 |

$\mathrm{ln}{P}_{L}\ast \mathrm{ln}K$ | 0.013 | 0.008 | 1.60 | 0.109 |

$\mathrm{ln}{P}_{M}\ast \mathrm{ln}K$ | −0.013 | 0.008 | −1.60 | 0.109 |

$\mathrm{ln}Q\ast \mathrm{ln}K$ | 0.000 | 0.000 | 0.36 | 0.72 |

$T$ | −0.119 | 0.118 | −1.01 | 0.311 |

$T\ast T$ | 0.006 | 0.002 | 3.58 | 0.000 |

$\mathrm{ln}{P}_{L}\ast T$ | 0.001 | 0.000 | 2.79 | 0.005 |

$\mathrm{ln}{P}_{M}\ast T$ | −0.001 | 0.000 | −2.79 | 0.005 |

$\mathrm{ln}K\ast T$ | −0.034 | 0.022 | −1.59 | 0.111 |

$\mathrm{ln}Q\ast T$ | 0.036 | 0.025 | 1.42 | 0.154 |

Constant | 15.225 | 39.484 | 0.39 | 0.700 |

^{6}.

Variables | Coefficients | Standard Errors | t-Values | p-Values |
---|---|---|---|---|

${Q}_{t}$ | −0.0010 | 0.0002 | −4.89 | 0.000 |

${T}_{t}$ | −0.0206 | 0.0010 | −21.19 | 0.000 |

Constant | 0.4499 | 0.116 | 38.87 | 0.000 |

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**MDPI and ACS Style**

Ju, B.-K.; Yoo, S.-H.; Baek, C.
Economies of Scale in City Gas Sector in Seoul, South Korea: Evidence from an Empirical Investigation. *Sustainability* **2022**, *14*, 5371.
https://doi.org/10.3390/su14095371

**AMA Style**

Ju B-K, Yoo S-H, Baek C.
Economies of Scale in City Gas Sector in Seoul, South Korea: Evidence from an Empirical Investigation. *Sustainability*. 2022; 14(9):5371.
https://doi.org/10.3390/su14095371

**Chicago/Turabian Style**

Ju, Byoung-Kuk, Seung-Hoon Yoo, and Chulwoo Baek.
2022. "Economies of Scale in City Gas Sector in Seoul, South Korea: Evidence from an Empirical Investigation" *Sustainability* 14, no. 9: 5371.
https://doi.org/10.3390/su14095371