Abstract
In this paper, we propose a multiobjective local search metaheuristic for a mean-risk multistage capacity investment problem with process flexibility, irreversibility, lumpiness and economies of scale in capacity costs. In each period, discrete decisions concerning the investment in capacity expansion, and continuous decisions concerning the utilization of the available capacity to satisfy demand are considered. We solve the capacity utilization problems with linear programming, in order to find the minimum capacity for each resource with the other resources remaining unchanged, this way providing information on the feasibility of the discrete investment decisions. Conditional value-at-risk is considered as a risk measure. Results of a computational study are presented, that show the approach is capable of obtaining high-quality approximations to the efficient sets, with a modest computational effort.

Abstract
We propose a multiobjective local search metaheuristic for a mean-risk multistage capacity investment problem with irreversibility, lumpiness and economies of scale in capacity costs. Conditional value-at-risk is considered as a risk measure. Results of a computational study are presented and indicate that the approach is capable of producing high-quality approximations to the efficient sets with a modest computational effort. The best results are achieved with a new hybrid approach, combining Tabu Search and Variable Neighbourhood Search.

Abstract
In this paper we address two major challenges presented by stochastic discrete optimisation problems: the multiobjective nature of the problems, once risk aversion is incorporated, and the frequent difficulties in computing exactly, or even approximately, the objective function. The latter has often been handled with methods involving sample average approximation, where a random sample is generated so that population parameters may be estimated from sample statistics-usually the expected value is estimated from the sample average. We propose the use of multiobjective metaheuristics to deal with these difficulties, and apply a multiobjective local search metaheuristic to both exact and sample approximation versions of a mean-risk static stochastic knapsack problem. Variance and conditional value-at-risk are considered as risk measures. Results of a computational study are presented, that indicate the approach is capable of producing high-quality approximations to the efficient sets, with a modest computational effort.

Abstract
Fire has major impacts on forest ecosystems, with heightened relevance in a Mediterranean country such as Portugal, which within Europe features the highest number of wildfires and the second larger burnt area. After each significant wildfire, the Portuguese Institute for Nature Conservation and Forests (ICNF) assesses the main environmental impacts and proposes emergency stabilisation measures following specific regulations. This study seeks to improve such assessments by using a data triangulation approach to characterise the impacts of wildfires on ecosystem services in the country. First, a systematic literature review is performed to identify the scientific studies that address the issue. Next, a document analysis of all the emergency stabilisation reports and technical reports available on ICNF's website is performed. Finally, a survey of experts' perceptions on the topic completes the analysis. The Economics of Ecosystems and Biodiversity definitions of ecosystem services were employed to compare the different findings. The results indicate that the experts perceive wildfires to significantly impact all ecosystem services, even though the literature has so far only focused on 12 of them, and ICNF has so far only focused on 7 in its reports. The potential underlying motives are discussed. In particular, some important impacts identified in the literature, as is the case of Climate regulation, a topic of the highest priority in the European environmental agenda, have not so far been a topic of focus in ICNF's reports, which suggests relevant opportunities for enhancing its reporting process in the future.