DEA (Data Envelopment Analysis) is a widely used method to evaluate efficiency in various fields, including environmental performance assessment. Regarding the latest approaches specifically tailored for evaluating environmental efficiency, several advancements have emerged in recent years.
One notable advancement involves incorporating undesirable outputs or environmental impacts into the traditional DEA models. This modification enables the assessment of not only productive efficiency but also environmental performance. Techniques like Slack-Based Measure (SBM) or Directional Distance Function (DDF) extensions of DEA are increasingly used to address these environmental considerations.
Additionally, some studies have focused on integrating environmental variables or constraints within the DEA framework, emphasizing the importance of considering environmental factors as part of the efficiency evaluation process. This integration ensures a more comprehensive assessment that aligns with sustainable development goals.
Furthermore, there's ongoing research into dynamic DEA models that consider temporal aspects, allowing for the assessment of changes in environmental efficiency over time. These models enable a more nuanced understanding of how environmental efficiency evolves and responds to various factors.
Overall, the latest approaches in DEA for evaluating environmental efficiency encompass incorporating environmental considerations into traditional models, integrating environmental variables, and developing dynamic models to capture changes in efficiency over time. These advancements aim to provide a more holistic evaluation framework that accounts for both productivity and environmental impacts.