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<p>Evaluating energy efficiency (EE) in the manufacturing sector at the national level is analytically challenging due to the sector's heterogeneity and the limitations of commonly used indicators. Despite manufacturing’s central role in industrial decarbonization, there is no comprehensive overview of the quantitative methods used to assess its EE. This study addresses this gap by systematically reviewing 110 peer-reviewed studies published between 2005 and 2024, focusing on the evolution, application, and reliability of ratio-based indicators, decomposition techniques (Index and Structural Decomposition Analysis), frontier methods (Data Envelopment and Stochastic Frontier Analysis), and econometric approaches. The review reveals a strong preference for econometric analysis, followed by ratio indicators and index decomposition, while frontier and structural decomposition techniques remain underused. We identify four key challenges that can affect the robustness of EE assessments: definition inconsistencies in conceptualizing EE, data limitations affecting disaggregation and comparability, potential misalignment between methods and data, and interpretation challenges when translating findings into policy insights. Our systematic assessment indicates that most studies fall short of good methodological practices, with ratio-based methods performing strongest overall, suggesting that robust EE assessment requires (a) methods aligned with specific contexts, (b) sufficiently disaggregated data, and (c) awareness of methodological limitations. This review offers a framework for addressing methodological challenges in manufacturing EE analysis, improving the reliability of information available to policymakers for effective EE interventions.</p>

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Industrial energy use, efficiency, and savings: methods for quantitative analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月3日

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  • Janita Andrijevskaja,
  • Anna Volkova

摘要

Evaluating energy efficiency (EE) in the manufacturing sector at the national level is analytically challenging due to the sector's heterogeneity and the limitations of commonly used indicators. Despite manufacturing’s central role in industrial decarbonization, there is no comprehensive overview of the quantitative methods used to assess its EE. This study addresses this gap by systematically reviewing 110 peer-reviewed studies published between 2005 and 2024, focusing on the evolution, application, and reliability of ratio-based indicators, decomposition techniques (Index and Structural Decomposition Analysis), frontier methods (Data Envelopment and Stochastic Frontier Analysis), and econometric approaches. The review reveals a strong preference for econometric analysis, followed by ratio indicators and index decomposition, while frontier and structural decomposition techniques remain underused. We identify four key challenges that can affect the robustness of EE assessments: definition inconsistencies in conceptualizing EE, data limitations affecting disaggregation and comparability, potential misalignment between methods and data, and interpretation challenges when translating findings into policy insights. Our systematic assessment indicates that most studies fall short of good methodological practices, with ratio-based methods performing strongest overall, suggesting that robust EE assessment requires (a) methods aligned with specific contexts, (b) sufficiently disaggregated data, and (c) awareness of methodological limitations. This review offers a framework for addressing methodological challenges in manufacturing EE analysis, improving the reliability of information available to policymakers for effective EE interventions.