Abstract
Large forest fires are notorious for their environmental and socio-economic impacts and are assigned a disproportionately high percentage of the fire management budget. This study addresses extremely large fires (ELF, C2500 ha) in Portugal (2003-2013). We analysed the effect of fire-suppression force variation on ELF duration, size and growth rate, versus the effect of the concomitant fire environment (namely fuel and weather) conditions. ELF occurred in highly flammable landscapes and typically were impelled by extreme fire weather conditions. Allocation of suppression resources (normalized per unit of burned area or perimeter length) was disparate among fires, suggesting inadequate incident management. Fire-suppression effort did not affect time to containment modelled by survival analysis. Regression tree analysis indicated ELF spread to be negatively affected by higher fire-suppression resourcing, less severe fire weather, lower time to containment and higher presence of <9-year-old fuels, by decreasing order of importance; regional variability was relevant. Fire environment-to-fire suppression ratios of influence were 3: 1 for fire size and 1: 1 for fire growth rate, respectively, explaining 76 and 60 % of the existing variability. Results highlight the opportunistic nature of large-fire containment. To minimize the area burned by ELF, management and operational improvements leading to faster containment are recommended, rather than higher fire-suppression resourcing; more effective identification and exploration of containment opportunities are preferable to the accumulation of suppression resources.

Abstract
We adopt in this paper a perspective of portfolios of real options, to propose a mixed integer linear programming model for multistage Transmission Network Expansion Planning. The model is then used to analyze three fundamental network building blocks - an independent design, a radial design, and a meshed design - seeking to develop network design insights, in particular regarding the joint value of postponement and other sources of operational flexibility. The results clearly point to the importance of explicitly incorporating uncertainty, adopting a multistage perspective, and addressing complex interactions between different sources of flexibility, in the design of transmission networks.

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
Airports have traditionally been considered as public utility providers, with little potential to develop significant market opportunities. Liberalisation of the air transport market around the globe has introduced new dynamics into the airport industry. The emergence of competition and the transition in ownership (towards privatisation or commercialisation) demand a different perspective in airport management. This paper aims at explaining the complexity of the airport business, in particular in what concerns the role of the airport as a firm that operates in a network of stakeholders, to produce a set of service packages targeted at several types of customers. (C) 2013 The Authors. Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Scientific Committee

Abstract
The liberalisation of the European air transport market has introduced new dynamics in the airport industry. In recent decades, airports evolved from infrastructure providers in a monopolistic context, to commercially orientated enterprises in a competitive environment. Current studies of airport strategic management lack a comprehensive perspective that enables airport operators to best identify the opportunities created by such dynamics. This paper analyses an airport as a multi-service firm that interacts with a network of stakeholders - the airport business network - to deliver several service packages to different groups of customers. An integrated conceptual framework was developed to aid academics and practitioners in the appraisal and design of competitive strategies for airports. Such framework covers a clear gap in existing literature, partly due to the fact that current perspectives on airport management fail to address the complexity of the industry in the present competitive environment.

Abstract
The explicit incorporation of uncertainty in transmission network design can help to improve the balance between different and important concerns such as network utilization, demand satisfaction, or dynamic sourcing from lower-cost generation options. The explicit study of mean-risk trade-offs in network design can also better support planners in risk-related decisions. With these motivations, we present in this paper a mean-risk mixed integer linear programming model for transmission network expansion planning. The model has the potential to be used in practical applications, but in the scope of the paper is used to search for network design insights, with a study of loss-averse design of three fundamental network building blocks-an independent design, a radial design, and a meshed design. The study illustrates how different network designs feature different trade-offs between mean cost minimization and risk mitigation, focusing on the impact of network structure, loss aversion, variability, and demand correlation.