Data Envelopment Analysis in Energy Efficiency and Environmental Sustainability
Summary
Data Envelopment Analysis (DEA) has emerged as a versatile non-parametric technique for benchmarking the relative performance of decision-making units—ranging from power plants and manufacturing sectors to national and regional energy systems—by comparing multiple inputs (such as labour, capital and fuel) against desirable outputs (electricity, heat, goods) and undesirable outputs (carbon dioxide, pollutants). Its capacity to incorporate extensions such as slacks-based measures, directional distance functions and meta-frontiers allows researchers to capture technological heterogeneity, dynamic productivity changes and environmental constraints. In energy studies, DEA underpins assessments of total-factor energy efficiency, quantifies energy-saving potential and evaluates policy impacts under varying regulatory regimes. By integrating Malmquist–Luenberger productivity indices, analysts can track shifts in the efficiency frontier over time, distinguishing technical progress from improvements in managerial or scale efficiency. Globally, DEA applications inform policy on decarbonisation pathways, guide investment in renewable technologies and support sustainable planning in transport, agriculture and heavy industry. Its transparent mathematical formulation and capacity to handle undesirable by-products make DEA an indispensable tool for aligning economic goals with environmental sustainability.
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Data Envelopment Analysis in Energy Efficiency and Environmental Sustainability publication trend
The graph below shows the total number of articles in data envelopment analysis in energy efficiency and environmental sustainability across all publications each year (not limited to Nature Index journals).
Technical terms
Data Envelopment Analysis (DEA): A non-parametric linear programming technique for assessing the relative efficiency of entities by comparing multiple inputs and outputs.
Undesirable output: A by-product of production processes (for example, carbon dioxide or pollutants) that DEA models can treat explicitly in efficiency assessments.
Slacks-based measure (SBM): An extension of DEA that directly accounts for input excesses and output shortfalls without proportional adjustments.
Directional distance function: A DEA extension that measures efficiency improvement by simultaneously expanding desirable outputs and contracting undesirable outputs along a chosen direction.
Malmquist–Luenberger productivity index: A dynamic DEA-based indicator that captures productivity change over time, including environmental factors, by integrating undesirable outputs into the Malmquist framework.
Meta-frontier: A unified efficiency frontier that envelops group-specific frontiers, allowing comparison across entities operating under different technologies or regulatory contexts.
References
- Energy Efficiency Evaluation Based on Data Envelopment Analysis: A Literature Review. Energies (2020).
- Data Envelopment Analysis in Energy and Environmental Economics: An Overview of the State-of-the-Art and Recent Development Trends. Energies (2018).
- A framework for measuring global Malmquist–Luenberger productivity index with CO2 emissions on Chinese manufacturing industries. Energy (2016).
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