The purpose of this paper is to measure the relative performance of forest management units, and to analyze the impact of the external non-discretionary (ND) factors on these units’ technical efficiency. Toward this end, data envelopment analysis (DEA) technique in variable returns to scale environment with both discretionary and ND factors has been used. The required data are collected from 24 Iranian forest management units (as decision-making units (DMUs)) and included four inputs and two outputs. The ND factors are first ignored and it is shown that most of the forest management units are operating at high efficiency levels. Then the variable “area” is considered as the exogenously fixed (or the external ND) input in a modified ND model because it is outside of the forest manager’s control. The results indicate that the number of efficient units and the average technical efficiency score are reduced to 12 (approximately 50%) and 0.85, respectively. The forest management units should therefore increase their average efficiency score by 0.15 to move onto the new efficient frontier which is made by applying this strict criterion. As a consequence, it is recommended to apply the ND models for controlling the exogenously fixed factors and carrying out a correct and accurate evaluation, because the traditional DEA approaches may overestimate the efficiency of DMUs.

Industrial economic activities produce pollutants and environmentally sustainable production systems in forestry aim to minimize these undesirable outputs while maintaining high production and economic growth. In this contribution, we assume that in addition to plot-specific inputs and outputs, there are some contextual variables that may be exogenously fixed or may be under the control of the decision-makers. In this sense, we first propose a novel and practical approach to calculate environmental efficiency by reducing undesirable products. Then, we utilize an inverse data envelopment analysis (IDEA) model to effectively manage and reduce CO₂ emissions. In doing so, the applied models have been utilized to evaluate the efficiencies of 89 forest plots in the USA. Given our estimations in a real application to the forest plots, the study revealed that the average environmental efficiency score is nearly 0.75 (out of 1). However, there is potential for improvement by adjusting the impacts of contextual factors, which could raise the score to approximately 0.8. Furthermore, the analysis indicates a positive correlation between ownership and environmental efficiency, suggesting that increased ownership leads to higher environmental efficiency. Conversely, temperature exhibits a negative correlation with environmental efficiency. Finally, the results obtained from the IDEA indicate that in order to reduce undesirable outputs by a specific level of 5–10%, it is necessary to decrease other inputs and outputs. This is because, under the assumption of weak disposability, reducing the level of undesirable outputs requires a reduction in certain factors that influence production capacity. In other words, achieving the desired reduction in undesirable outputs inevitably involves diminishing certain aspects of the production process. As the major conclusion, the emergence of IDEA as a powerful tool for sensitivity analysis, along with its flexible nature, offers exciting opportunities for research and practical applications in various fields, including forestry activities. It has the potential to enhance overall environmental efficiency and enable better control over GHG emissions levels.