베이지안확률변경분석을 이용한 국내 철강기업의 에너지효율성 분석

Abstract

Steel companies, due to their production processes, emit a large amount of greenhouse gases. Therefore, improving the energy efficiency of them is a very important issue in responding to climate change. The starting point for effective management of energy efficiency improvements in steel companies is the objective measurement of their energy efficiency. Previous studies primarily utilized traditional Stochastic Frontier Analysis to estimate the energy efficiency of steel companies. Meanwhile, Bayesian Stochastic Frontier Analysis, which incorporates Bayesian Inference into Stochastic Frontier Analysis, offers the advantage of more accurate and flexible estimates by considering the uncertainty of parameters. Despite the advantages, there has been no research applying Bayesian Stochastic Frontier Analysis to analyze the energy efficiency of Korean steel companies. This study is the first to apply Bayesian Stochastic Frontier Analysis to Korean steel companies, using the Gibbs Sampling technique to estimate the energy efficiency of 22 major Korean steel companies from 2011 to 2019. The key findings of this study are as follows: First, the overall average energy efficiency of steel companies is 0.5450. Second, from 2011 to 2013, the yearly average energy efficiency of the steel companies analyzed shows an improving trend, peaking in 2013, followed by a decreasing trend. Third, a comparison of average energy efficiency across individual companies reveals the firms with relatively high energy efficiency, exceeding 0.8