Data Envelopment Analysis (DEA) is widely applied to evaluate technological efficiency in the energy sector, yet its results are often difficult to interpret, particularly when extended model specifications are used. This study investigates how alternative treatments of environmental conditions and undesirable outputs influence efficiency measurement in energy technologies. Four DEA specifications are compared: the classical CCR model, a model incorporating non-discretionary inputs (CCR-ND), a model including undesirable outputs (CCR-B), and a combined specification (CCR-ND-B). The empirical analysis is based on data describing coal gasification technologies and is supplemented with controlled hypothetical cases designed to isolate the effects of environmental parameters. The results show that incorporating non-discretionary inputs and undesirable outputs does not necessarily reduce efficiency scores but reshapes the geometry of the production possibility set and modifies the structure of benchmark technologies. The findings highlight the importance of careful classification of inputs and outputs and emphasize that DEA results should be interpreted in relation to the underlying modeling assumptions. Comparing alternative model specifications improves transparency and helps avoid treating DEA as a “black box”, supporting more informed efficiency assessment in energy technology analysis.
Radosław Kapłan (Mon,) studied this question.