In the last decades, Data Envelopment Analysis (DEA), which was proposed as a measurement tool for performance by Charnes, Cooper and Rhodes, has evolved into a conventional decision tool. This study synthesizes an overview of the development of DEA, the applicability of using the method in the field of forest management, where more complex data-driven techniques have gradually replaced conventional approaches of measuring input–output analysis. DEA finds its main applications in forestry in the areas of improving operational (driving productivity) and sustainable (economic and environmental) performance. Following the analysis of DEA-related publications until 2024 (with a post-search update through December 2025), it finds that, although there have been considerable theoretical developments, in practice, most DEA applications associated with forestry and related research areas are still based on more conventional models, leading to relatively simple technical performance evaluations. Forest management, in particular, has evolved into a leading domain of application, with recent publication trends reaching their height in 2022. In addition, the highest number of citations for this trend was published in 2023, showing a continuous interest and increase in this field of research. The bibliometric results reveal unexplored avenues for implementing advanced DEA models, creating sector-specific suites of applications tailored to forest interventions, and constructing large-scale benchmarking databases with production data specific to forested landscapes. Pursued future research toward more inclusive combined DEA methodologies (i.e., multi-objective optimization, non-oriented approach, artificial intelligence algorithms, and etc.) to further appropriately integrated high variability by which forestry management is elicited, facilitating more robust data-informed decision-making regarding forest policy, technology use and sustainability benchmarks.
Zadmirzaei et al. (Wed,) studied this question.