Aquaculture intensity has been used for years as a means to gauge how much production a site makes using three terms: extensive, semi-intensive and intensive aquaculture production systems. The industry has a relatively coordinated understanding of these terms, but an explicit general definition does not seem to exist. This paper aims to use three kinds of production function groups; the input, treatment and output functions to describe and define the terms extensive, semi-intensive and intensive explicitly. This is done with extensive literature review to find the meaning of the terms. The terms are then mapped onto the three production function groups. The resulting framework accomplishes two things. Firstly, it defines extensive, semi-intensive and intensive aquaculture in terms of production functions. Secondly, it creates an eight level scale, the aquaculture production intensity scale (APIS), that provides three levels of extensive systems, two level of semi-intensive systems and three level of intensive systems. APIS allows mapping of all uses of the terms in current literature to an APIS score, though some results might differ from current usage.

Wind energy harnessing is a new energy production alternative in Iceland. Current installed wind power in Iceland sums to 1.8 MW, which in contrast is 0.1% of the country’s total electricity production. This article is dedicated to the exploration of the potential cost of wind energy production at Búrfell in the south of Iceland. A levelized cost of energy (LCOE) approach was applied to the estimation of the potential cost. Weibull simulation is used to simulate wind data for calculations. A confirmation of the power law is done by comparing real data to calculated values. A modified Weibull simulation is verified by comparing results with actual on-site test wind turbines. A wind farm of 99MWis suggested for the site. Key results were the capacity factor (CF) at Búrfell being 38.15% on average and that the LCOE for wind energy was estimated as 0.087–0.088 USD/kWh (assuming 10% weighted average cost of capital (WACC)), which classifies Búrfell among the lowest LCOE sites for wind energy in Europe.

A novel Monte Carlo (MC) approach is proposed for the simulation of wind speed samples to assess the wind energy production potential of a site. The Monte Carlo approach is based on historical wind speed data and reserves the effect of autocorrelation and seasonality in wind speed observations. No distributional assumptions are made, and this approach is relatively simple in comparison to simulation methods that aim at including the autocorrelation and seasonal effects. Annual energy production (AEP) is simulated by transforming the simulated wind speed values via the power curve of the wind turbine at the site. The proposed Monte Carlo approach is generic and is applicable for all sites provided that a sufficient amount of wind speed data and information on the power curve are available. The simulated AEP values based on the Monte Carlo approach are compared to both actual AEP and to simulated AEP values based on a modified Weibull approach for wind speed simulation using data from the Burfell site in Iceland. The comparison reveals that the simulated AEP values based on the proposed Monte Carlo approach have a distribution that is in close agreement with actual AEP from two test wind turbines at the Burfell site, while the simulated AEP of the Weibull approach is such that the P50 and the scale are substantially lower and the P90 is higher. Thus, the Weibull approach yields AEP that is not in line with the actual variability in AEP, while the Monte Carlo approach gives a realistic estimate of the distribution of AEP.

Fairness issues within food systems are of increasing concern for policy makers and other stakeholders. Given the topicality and policy relevance of fairness within food systems, there is value in exploring the subject further. Simulation modelling has been successfully used to develop and test policy interventions. However, the subjectivity and intangibleness of fairness perceptions make them difficult to operationalize in a quantitative model. The objective of this study is to facilitate research on fairness in food systems using simulation modelling by defining the social construct of fairness in model operational terms. The operationalization is conducted in two steps. First, the construct of fairness is conceptually defined in terms of its dimensions, antecedents, and consequences using the literature on interorganizational fairness. Then, by focusing specifically on fairness issues within food systems, the conceptual definition is used as a basis for the identification of proxy indicators of fairness. Seven groups of factors related to fairness perceptions were identified during the conceptualization phase: financial outcomes, operational outcomes, power, environmental stability, information sharing, relationship quality, and controls. From these factor groups, five indicators of fairness that are operational in a quantitative model were identified: profit margin as an indicator of distributive fairness and four indicators of procedural fairness related to market power and bargaining power.

Salmon is the most consumed farmed seafood in the EU and there is no indication that demand will abate. Yet salmon aquaculture’s environmental impacts are significant, and its future is likely to be shaped by demands of increased but at the same time more sustainable production. This study developed an integrated theoretical framework based on the multi-level perspective (MLP) and a global value chain (GVC) governance framework and applied it to the global farmed salmon value chain. The objective was to provide insights on the most likely transition pathway towards sustainability based on industry and expert perspectives. The perceptions on challenges and drivers of change, were gathered through focus groups and in-depth interviews, and fitted to the integrated framework to facilitate the transition pathway analysis. Viewing the qualitative findings in the context of the MLP framework provided information about the current workings of the system, the drivers of change in the socio-technical landscape and niche-innovations and their potential to challenge or enhance the current system and thus indicated possible system transitions. To emphasize the role of industry actors in shaping the future of the salmon value chain, the analysis was strengthened using the GVC model which added information about power relations, signaling the ability of system actors to motivate or resist change. The findings indicate that, due to resistance in the regime and the fact that niche-innovations are not yet sufficiently developed, the farmed salmon value chain will continue to be predominated by traditional sea-based aquaculture but that there will be a gradual shift towards more diversity in terms of production methods in response to landscape pressures. The discussion addresses sustainability challenges and policy implications for the farmed salmon value chain and highlights the need for a food system perspective.