Business environment drivers and technical efficiency in the Chinese energy industry: A robust Bayesian stochastic frontier analysis

Abstract

Improving the technical efficiency of the energy industry is a fundamental way to ensure energy security and sustainable development and is also a requirement of the supply-side structural reform of China's energy. Although the business environment plays an important role in the energy industry, this relationship between efficiency and business environment has been scarcely studied. In addition, there has been no attempt made to link the financial sector and energy industry together. To this end, a novel Robust Bayesian Stochastic Frontier Analysis (RBSFA) is developed here, where DIC (Deviance Information Criteria) is minimized by the variance/covariance optimization of three classical distributional assumptions for the inefficiency term (u): Gamma, Exponential, and Half-Normal. The goal is to develop a RBSFA model to relate the technical efficiency of the energy industry in China with major business environment performance variables (financial sector variables, energy industry variables and macroeconomic variables). Results indicate that the efficiency level of the Chinese energy industry is quite high over the examined period, although there is a large variance between different companies. Our findings further show that increases in the efficiency of the Chinese energy industry can be achieved by increasing the level of inventories and fixed assets, as well as research and development expenses. Finally, we find that the efficiency level in the Chinese energy industry is affected by the business environment. In particular, we notice that financial sector development and competition are helpful to improve the efficiency of Chinese energy companies.

Evaluation of Innovation Efficiency on Chinese Commercial Banks under the Internet Finance: A DEA-Malmquist Index-Based Approach

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“…e DEA method has been widely used in efficiency estimation in various industries, including manufacturing industry (Walheer & He, 2020) [6]; agricultural sector (Wang & Feng, 2020) [7]; iron and steel industry (Lee et al, 2019) [8]; energy industry (Wanke et al, 2020) [2]; hospital sector (Rouyendegh et al, 2019) [9]; education sector (Aparicio et al, 2019) [10]; high-technology industry [11]; petroleum industry [12]; telecommunications industry (Sahoo & Sahoo, 2020) [13]; tourism industry (Song & Li, 2019) [14]; transportation industry (Stefaniec et al, 2020) [15]; insurance industry (Eling & Jia, 2019) [16]; and hotel sector (Yin et al, 2020; Yu & Chen, 2020) [17,18]. In addition, the DEA method has been applied to analyze the efficiency level including innovation system (Kalapouti et al, 2020) [19]; sustainability and environmental performance (Allevi et al, 2019) [20]; and supply chain management [21], and so on.…”

Do high tariffs provide high efficiency: a case of Ukrainian electricity distribution companies

“…For example, Hattori (Hattori 2002) applied the stochastic frontier analysis (SFA) to evaluate the efficiency of Japanese and US electricity distribution. Recently, the SFA was also used by Wu (Wu 2020), Khetrapal (Khetrapal 2020), and Wanke et al (Wanke et al 2020) to analyze efficiency of electricity distribution in Asian countries. However, the SFA has an important lack -it only allows one output to be used.…”

Bank efficiency estimation in China: DEA-RENNA approach

“…The performance assessment in the empirical literature can be generally divided into two groups, with the first one focusing on the use of accounting ratios and the second stream concentrating on the use of parametric or non-parametric operational research methods. In particular, the use of non-parametric data envelopment analysis has been gaining popularity in estimating efficiency in various economic sectors, including the high technology industry (An et al, 2020); the hotel sector (Assaf et al, 2010;Yin et al, 2020;Yu & Chen, 2020); insurance industry (Barros et al, 2010;Cummins et al, 2010;Eling & Jia, 2019;Eling & Luhnen, 2010): telecommunications industry (Bayraktar et al, 2012;Sahoo & Sahoo, 2020); transportation industry (Chang et al, 2013;Merkert & Hensher, 2011;Stefaniec et al, 2020;Wu & Goh, 2010;Yu, 2010); iron and steel industry (He et al, 2013;Lee et al, 2019); the tourism industry (Pestana et al, 2011;Song & Li, 2019); the agricultural sector (Picazo-Tadeo et al, 2011;Wang & Feng, 2020); the hospital sector (Rouyendegh et al, 2019); petroleum industry (Sueyoshi & Goto, 2012;Wang et al, 2019); the education sector (Aparicio et al, 2019;Thanassoulis et al, 2011); and the energy industry (Wang et al, 2013;Wanke et al, 2020;Zhou et al, 2012Zhou et al, , 2013. Also, the DEA models have been applied to analyse the efficiency level in various business-related operational issues, including supply chain management (Azadi et al, 2015;Malesios et al, 2020;Mirheda...…”

“…The empirical research has been consistently making great efforts in achieving this goal through analyzing the efficiency level of various economic sectors. The widely used methods to estimate efficiency can be classified into two streams: one is the parametric stochastic frontier analysis, which has been applied to the non-banking sector (Charoenrat & Harvie, 2014;Lin, & Long, 2015;Wanke et al, 2020) as well as the banking sector (Fang et al, 2019;Tan & Floros, 2019;. However, the parametric stochastic frontier approach suffers from several limitations: (1) it does not work very well with small samples; (2) it needs to specify a specific function form and different specifications will lead to variations of efficiency scores (Charnes et al, 1995).…”