Stochastic Energy Performance Evaluation Using a Bayesian Approach

• Published in: Journal of mathematics (Hidawi) Vol. 2023; pp. 1 - 7
• Main Authors: Terzi, Erol, Gumustekin Aydin, Serpil, Cengiz, Mehmet Ali
• Format: Journal Article
• Language: English
• Published: Cairo Hindawi 2023; Hindawi Limited; Wiley
• Subjects: Bayesian analysis; Decision making; Energy efficiency; Energy industry; Markov analysis; Markov chains; Mathematics; Maximum likelihood method; Parameter estimation; Performance evaluation; Probability distribution functions; Production functions; Productivity
• ISSN: 2314-4629; 2314-4785
• DOI: 10.1155/2023/5522746

In the past two decades, stochastic frontier analysis (SFA) has been extensively employed to assess energy efficiency. However, the use of the Bayesian approach in SFA for energy performance evaluation has not received significant attention. This study aims to address this gap by measuring the energy-based development performance of 29 OECD countries using stochastic frontier analysis with a Bayesian approach. In the existing literature, there is no apparent method for selecting the distribution of the inefficiency term, which represents the unexplained deviation from the production frontier. To address this issue, we propose different models with various inefficiency components, namely, the half normal, truncated normal, exponential distribution, and gamma distribution. Our analysis utilizes a panel dataset covering the period from 2004 to 2010. The Bayesian implementation of the proposed models is conducted using the WinBUGS package, employing the Markov chain Monte Carlo (MCMC) method. The primary objective of our study is to compare these models, each assuming a different distribution for the inefficiency term, using the deviance information criterion (DIC). The DIC serves as a reliable measure for model comparison and enables us to identify the most suitable model that accurately captures the energy efficiency scores of the countries. Based on the comparison of models with different distributional assumptions using the DIC, we find that the model with a half-normal inefficiency distribution yields the lowest DIC score. Consequently, this model is employed to rank the energy efficiency scores of the countries. In summary, our study fills a research gap by applying the Bayesian approach to SFA in the context of energy efficiency analysis. By proposing and comparing models with different inefficiency components, we contribute to the literature and offer insights into the relative energy efficiency performance of 29 OECD countries. The findings of our study not only inform the selection of an appropriate model but also facilitate the ranking of countries based on their energy efficiency using the identified best model.