Luis Diaz-Balteiro

Linear programming formulations of forest ecosystem management (FEM) problems proposed in the 1960s have been adapted and improved upon over the years. Generating management alternatives for forest planning is a key step in building these models. Global forests are diverse, and a variety of models have been developed to simulate management alternatives. This paper describes iGen, a forest prescription generator that employs a rule-based system (AI-RBS), an AI technique that is often used for expert systems. iGen was designed with the goal of being able to generate management alternatives for virtually any FEM problem. The prescription generator is not designed for, adapted to, focused on—and ideally not limited to—any specific region, landscape, forest condition, projection method, or yield function. Instead, it aims to maximize generality, enabling it to address a broad range of FEM problems. The goal is that practitioners and researchers who do not have and do not want to develop their own alternative generator can use iGen as a prescription generator for their problem instances. For those who choose to develop their own alternative generators, we hope that the concepts and algorithms we propose in this paper will be useful in designing their own systems. iGen’s flexibility can be attributed to three key features. First, users can define the state variable vector for management units according to the available data, models (production functions), and objectives of their problem instance. Second, users also define the types of interventions that can be applied to each type of management unit and create a rule base describing the conditions under which each intervention can be applied. Finally, users specify the equations of motion that determine how the state vector for each management unit will be updated over time, depending on which, if any, interventions are applied. Other than this basic structure, virtually everything in an iGen problem instance is user-defined. iGen uses these key elements to simulate all possible management prescriptions for each management unit and stores the resulting information in a database that is structured to efficiently store the output data from these simulations and to facilitate the generation of optimization models for ultimately determining the Pareto frontier for a given FEM problem. This article introduces iGen, illustrating its concepts, structure, and algorithms through two FEM example problems with contrasting forest management practices: natural regeneration with shelterwood harvests and plantation/coppice. For data and iGen source programs, visit github.com/SilvanaNobre/iGenPaper.

By developing products derived from materials included in what is nowadays called bioeconomy, the wood products industry is today receiving considerable attention for its potential to provide more environment‐friendly materials. At the same time, this and other industries are transforming to obtain more circular production systems. This study aims to analyze circularity in this type of industry at a European Union level, by establishing a ranking of countries based on their performance in an aggregate circularity index. This task has been set up by taking an initial joint set of indicators for all countries and covering a time horizon of up to 19 years. After proceeding to normalize those indicators and applying an analysis of correlation, a biannual ranking (from 2008 to 2018) was made, using for this purpose a multicriteria decision‐making model that includes three different types of solutions. Next, a statistical model that helped to explain the rankings previously obtained was proposed, in which a set of 15 explanatory variables were defined. The results showed that Sweden, and to a lesser extent Portugal, are the countries presenting a more circular wood products industry, whereas the Benelux countries give the worst result. Moreover, the statistical models showed that the variables related to trade had a negative relationship with circularity, but some of them exerted a strong effect, such as the ratio “production/exports.” Global competitiveness also tended to diminish wood circularity. Other variables, like those related to research and development, had a strong effect positively linked to circularity.