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Let us consider i = 1, 2,...n alternative forest plans to be evaluated according to j = 1, 2,...m indicators of sustainability. An expert or panel of experts suggests a set of targets or desirable levels of achievement for the m indicators of sustainability considered. Within this context, an important problem is to determine the system with a higher level of achievement with respect to the targets attached by the expert to the m indicators. A natural extension of this problem involves determining a ranking of the n systems considered. A general procedure based on discrete goal programming is proposed to address these problems. The methodology is applied in a case study of a Spanish forest.

This work aimed to tackle a timber harvest scheduling problem by simultaneously integrating into the analysis two forestry products derived from the same species: the timber and the pine nut. For this purpose, three management scenarios were proposed: two in which each of the productions is maximised separately, and a third mixed where, in each management unit, the product to which the silvicultural effort should be devoted is decided. After defining a set of objectives, and optimising the rotation length, a multi-criteria model based on goal programming was considered since no feasible solutions have been obtained when employing linear programming. The results in our case study show how the feasible solutions reached can be more attractive for the manager. Specifically, the area to be devoted to timber and cone/pine-nut production was computed in a scenario where the optimal silviculture (oriented towards timber or pine nuts) in each stand was selected, and it was concluded that the area allocated to pine nuts should be notably greater. This situation is the opposite of the current management.

This paper characterizes the sustainability of the European paper industry. To undertake this task the sustainability of each country is defined by using fourteen indicators of a diverse nature (economic, environmental and social). These indicators are aggregated into a composite or synthetic index with the help of a compromise programming model. In order to associate different weights with each indicator, a survey among international experts has been carried out. In this way, a ranking of seventeen European countries analysed in terms of the sustainability of the European paper industry has been established, where Finland is the most sustainable paper industry in Europe except when the most balanced solution is chosen. Also, the results are robust when different preferential weights are attached. Finally, this methodology can be applied at a more disaggregated level and other indicators can be introduced.

Abstract: This paper provides a survey of the literature on multiple criteria decision-making (MCDM) applications to forestry problems undertaken in the last 30 years or so. More than 250 references regarding 9 forestry topics and 9 different MCDM approaches have been categorized and evaluated. This provides a unified source of references that could be useful for forest management students, researchers and practitioners. The paper ends with an assessment of the literature presented, aiming to reach some conclusions, as well as indicate future trends in this line of research. [Copyright &y& Elsevier]

Reducing students' dropout rate is a major challenge for universities. Based on a quantitative approach, this study aims to analyze and explain students' dropout intention at the largest forestry school in Romania by collecting data in their first two months of the academic year prior to failing any courses. The study participants are undergraduate freshmen, male and female students, and the tools are psychological instruments commonly used or specifically developed for this study. Our findings partly confirm previous results, such as the higher intention of dropping out among male students compared to females, and also among students with admission GPA lower or equal to seven, on a GPA scale from 1 to 10, and among students from families with low income. Dropout intention can be explained by the academic performance expectancy, gender, family, socio-economic status, and perceived barriers to completion of studies. There were no differences regarding whether students were from urban or rural areas, proximity of university to home, and high school academic program. Our results may help to implement efficient measures for increasing student retention. [ABSTRACT FROM AUTHOR]