Evaluating and analyzing renewable energy performance in OECD countries under uncertainty: A robust DEA approach with common weights

Jiang Li, Hecheng Wu, Chen Zhu and Mark Goh

Applied Energy, 2024, vol. 375, issue C, No S0306261924014983

Abstract: Data Envelopment Analysis (DEA) is a mathematical programming model widely used for evaluating renewable energy performance. However, the presence of data uncertainty may make the constraints of traditional DEA infeasible, resulting in unreliable performance assessments. This paper proposes a robust DEA model with common weights to evaluate the renewable energy performance under data uncertainty. Additionally, a novel performance indicator, efficiency vulnerability, is designed to evaluate the ability of Decision-making units to withstand data uncertainty. The impact of data uncertainty on the renewable energy performance ranking is explored through a Monte Carlo simulation. Applying the proposed robust model, we empirically analyze the renewable energy performance of 38 OECD countries. The empirical results demonstrate the effectiveness of our approach in differentiating renewable energy performance.

Keywords: Renewable energy performance; OECD countries; Data uncertainty; Data envelopment analysis; Common weights (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1016/j.apenergy.2024.124115

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