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

Peter Wanke, Aaron Tan, Jorge Antunes, Abdollah Hadi-Vencheh

Research output: Contribution to journalArticle

1 Citation (Scopus)

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.
Original languageEnglish
Article number106487
Number of pages18
JournalComputers and Industrial Engineering
Volume144
Early online date20 Apr 2020
DOIs
Publication statusPublished - 1 Jun 2020

Fingerprint Dive into the research topics of 'Business environment drivers and technical efficiency in the Chinese energy industry: A robust Bayesian stochastic frontier analysis'. Together they form a unique fingerprint.

  • Cite this