Abstract
Increasing wildfire threats and costs escalate the complexity of forest fire management challenges, which is grounded in complex interactions between ecological, social, economic, and policy factors. It is immersed in this difficult context that decision-makers must settle on an investment mix within a portfolio of available options, subject to limited funds and under great uncertainty. We model intra-annual fire management as a problem of multistage capacity investment in a portfolio of management resources, enabling fuel treatments and fire preparedness. We consider wildfires as the demand, with uncertainty in the severity of the fire season and in the occurrence, time, place, and severity of specific fires. We focus our analysis on the influence of changes in the volatility of wildfires and in the costs of escaped wildfires, on the postponement of capacity investment along the year, on the optimal budget, and on the investment mix. Using a hypothetical test landscape, we verify that the value of postponement increases significantly for scenarios of increased uncertainty (higher volatility) and higher escape costs, as also does the optimal budget (although not proportionally to the changes in the escape costs). Additionally, the suppression/prevention budget ratio is highly sensitive to changes in escape costs, while it remains mostly insensitive to changes in volatility. Furthermore, we show the policy implications of these findings at operational (e.g., spatial solutions) and strategic levels (e.g., climate change). Exploring the impact of increasing escape costs in the optimal investment mix, we identified in our instances four qualitative system stages, which can be related to specific socioecological contexts and used as the basis for policy (re)design. In addition to questioning some popular myths, our results highlight the value of fuel treatments and the contextual nature of the optimal portfolio mix.

Abstract
In this paper we bring together a stochastic mixed integer programming model for transmission network expansion planning, incorporating portfolios of real options to address the evolution in time of uncertain parameters, with the adjusted generalized log-transformed model, to expand the number of correlated parameters that can be modeled. We apply these methods to evaluate the potential contribution of underwater transmission investments to increase renewables penetration in the Azores archipelago. The approach also includes expansion lead times, due to the large timespans involved in the construction of new transmission lines. Our analysis focuses on a set of the four closest islands in the archipelago, Pico, Faial, S. Jorge and Terceira, and shows that even though investments are delayed and the future network configuration varies according to the evolution of renewable generation scenarios, an investment in underwater transmission, if technically feasible, within the assumptions of our model, could in fact contribute to increase renewables penetration, by enabling islands with an excess in generation from renewable sources to supply other islands in a deficit situation.

Abstract
It is estimated that Europe alone will need to add over 250,000 km of transmission capacity by 2050, if it is to meet renewable energy production goals while maintaining security of supply. Estimating the cost of new transmission infrastructure is difficult, but it is crucial to predict these costs as accurately as possible, given their importance to the energy transition. Transmission capacity expansion plans are often founded on optimistic projections of expansion costs. We present probabilistic predictive models of the cost of submarine power cables, which can be used by policymakers, industry, and academia to better approximate the true cost of transmission expansion plans. The models are both generalizable and well specified for a variety of submarine applications, across a variety of regions. The best performing statistical learning model has slightly more predictive power than a simpler, linear econometric model. The specific decision context will determine whether the extra data gathering effort for the statistical learning model is worth the additional precision. A case study illustrates that incorporating the uncertainty associated with the cost prediction to calculate risk metrics - value-at-risk and conditional-value-at-risk provides useful information to the decision-maker about cost variability and extremes.

Abstract
The turnarounds from decrease to expansion in forest areas that took place during the last century have been examined through the lens of forest transition theory (FTT). Among temperate and Mediterranean European countries that have seen an expansion of forest cover, Portugal stands out as the only case in which this trend has recently been reverted. In this study, we explicitly map and document the forest transition (FT) in the country over the period 1907-2006, and investigate when and where forest transition happened de facto, and which were the land use transition pathways that resulted from the shrublands, agriculture, and forest interplay dynamics. After thematic and geometric harmonization of land cover maps from 1907, 1955, 1970, 1990, and 2006, a cluster analysis established four typologies, and a transition matrix was constructed to assess land cover dynamics. We found that up to 1955, FT occurred simultaneously with agricultural expansion, as shrubland areas diminished. Afterwards, with the retraction of agricultural area and the consequential decoupling of forest management from local actors, FT gained momentum and expanded up to the 1990s. While during the first half of the 20th century, forest expansion followed the "Scarcity" and "State Policy" pathways fostered by local socio-ecological feedback loops, throughout the second half of the century forest transition was driven by exogenous socio-economic forces, following "Economic Development" and "Globalization" pathways. We show how, despite these forces, FT can be derailed by endogenous factors such as wildfires, which limited and in some areas even reverted the afforestation process, initiating a deforestation phase. Since the necessary conditions for FT (technology shift, urbanization, agriculture retraction and public afforestation programs) were available in mainland Portugal, we advance the hypothesis that critical wildfire risk governance deficits may have been responsible for arresting FT. Considering the critical role of forests and other wooded areas in supporting climate change mitigation and sustainable development, our work provides useful evidence and insights for public decision makers on previously unaddressed dimensions of FTT.

Abstract
Airport systems adapted to the influx of Low-Cost Carriers (LCC) as the segment grew in prominence in the European market during the last decades. The generalised perspective that LCCs are attached to remote secondary airports is being increasingly challenged by recent moves of the largest European LCC. The reality is that the impact of LCCs has spread to most commercial airports in Europe, primary and secondary alike. Yet, despite valuable insights on the evolution of airline networks, the existing literature lacks a clear understanding of why this has occurred. This paper explains the dynamics in the evolution of airports systems that resulted in significant growth for the low-cost segment in Europe. A multiple case study involving 42 European airports was used to identify the mechanisms that triggered the traffic patterns leading to the ascendency of LCCs in their respective airport systems. Understanding these mechanisms may prove valuable for supporting airport strategic planning.