There is an important body of literature using multi-criteria distance function methods for the aggregation of a battery of sustainability indicators in order to obtain a composite index. This index is considered to be a proxy of the sustainability goodness of a natural system. Although this approach has been profusely used in the literature, it is not exempt from difficulties and potential pitfalls. Thus, in this paper, a significant number of critical issues have been identified showing different procedures capable of avoiding, or at least of mitigating, the inherent potential pitfalls associated with each one. The recommendations made in the paper could increase the theoretical soundness of the multi-criteria distance function methods when this type of approach is applied in the sustainability field, thus increasing the accuracy and realism of the sustainability measurements obtained.

The application of optimization methods to forest management has given rise to a successful line of investigation in recent decades. However, there have been few publications associated with the application of these techniques to the management of industrial forest plantations (those with short or medium rotations, always less than 50 years), which consider the important role played by these forest systems in the supply of diverse goods and services. This study presents an overview of this literature which, by analyzing 67 articles published in journals contained in the ISI Web of Science, highlight, among other aspects, the techniques employed, their evolution, their planning type (strategic, tactical or operational), the functional objectives and constraints considered, or the type of software deployed in these studies. The results show how Model I has been the one most frequently used in these studies, and how the spatial component is increasing in importance. However, classic optimization methods, such as mixed integer programming, have been those most commonly resorted to, although the employment of multi-criteria techniques such as goal programming and analytic hierarchical process have strongly emerged in recent years.

We developed and applied a spatial optimization algorithm to prioritize forest and fuel management treatments within a proposed linear fuel break network on a 0.5 million ha Western US national forest. The large fuel break network, combined with the logistics of conducting forest and fuel management, requires that treatments be partitioned into a sequence of discrete projects, individually implemented over the next 10–20 years. The original plan for the network did not consider how linear segments would be packaged into projects and how projects would be prioritized for treatments over time, as the network is constructed. Using our optimization algorithm, we analyzed 13 implementation scenarios where size-constrained projects were prioritized based on predicted wildfire hazard, treatment costs, and harvest revenues. We found that among the scenarios, the predicted net revenue ranged from USD 3495 to USD 6642 ha⁻¹, and that prioritizing the wildfire encounter rate reduced the net revenue and harvested timber. We demonstrate how the tradeoffs could be minimized using a multi-objective optimization approach. We found that the most efficient implementation scale was a sequence of relatively small projects that treated 300 ha ± 10% versus larger projects with a larger treated area. Our study demonstrates a decision support model for multi-objective optimization to implement large fuel break networks such as those being proposed or implemented in many fire-prone regions around the globe.