Purpose Input parameters required to quantify environmental impact in life cycle assessment (LCA) can be uncertain due to e.g. temporal variability or unknowns about the true value of emission factors. Uncertainty of environmental impact can be analysed by means of a global sensitivity analysis to gain more insight into output variance. This study aimed to (1) give insight into and (2) compare methods for global sensitivity analysis in life cycle assessment, with a focus on the inventory stage. Methods Five methods that quantify the contribution to output variance were evaluated: squared standardized regression coefficient, squared Spearman correlation coefficient, key issue analysis, Sobol' indices and random balance design. To be able to compare the performance of global sensitivity methods, two case studies were constructed: one small hypothetical case study describing electricity production that is sensitive to a small change in the input parameters and a large case study describing a production system of a northeast Atlantic fishery. Input parameters with relative small and large input uncertainties were constructed. The comparison of the sensitivity methods was based on four aspects: (I) sampling design, (II) output variance, (III) explained variance and (IV) contribution to output variance of individual input parameters. Results and discussion The evaluation of the sampling design (I) relates to the computational effort of a sensitivity method. Key issue analysis does not make use of sampling and was fastest, whereas the Sobol' method had to generate two sampling matrices and, therefore, was slowest. The total output variance (II) resulted in approximately the same output variance for each method, except for key issue analysis, which underestimated the variance especially for high input uncertainties. The explained variance (III) and contribution to variance (IV) for small input uncertainties were optimally quantified by the squared standardized regression coefficients and the main Sobol' index. For large input uncertainties, Spearman correlation coefficients and the Sobol' indices performed best. The comparison, however, was based on two case studies only. Conclusions Most methods for global sensitivity analysis performed equally well, especially for relatively small input uncertainties. When restricted to the assumptions that quantification of environmental impact in LCAs behaves linearly, squared standardized regression coefficients, squared Spearman correlation coefficients, Sobol' indices or key issue analysis can be used for global sensitivity analysis. The choice for one of the methods depends on the available data, the magnitude of the uncertainties of data and the aim of the study.

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Purpose Capture fisheries are the only industrial-scale harvesting of a wild resource for food. Temporal variability in environmental performance of fisheries has only recently begun to be explored, but only between years, not within a year. Our aim was to better understand the causes of temporal variability within and between years and to identify improvement options through management at a company level and in fisheries management. Methods We analyzed the variability in broad environmental impacts of a demersal freeze trawler targeting cod, haddock, saithe, and shrimp, mainly in the Norwegian Sea and in the Barents Sea. The analysis was based on daily data for fishing activities between 2011 and 2014 and the functional unit was a kilo of landing from one fishing trip. We used biological indicators in a novel hierarchic approach, depending on data availability, to quantify biotic impacts. Landings were categorized as target (having defined target reference points) or bycatch species (classified as threatened or as data-limited). Indicators for target and bycatch impacts were quantified for each fishing trip, as was the seafloor area swept. Results and discussion No significant difference in fuel use was found between years, but variability was considerable within a year, i.e., between fishing trips. Trips targeting shrimp were more fuel intensive than those targeting fish, due to a lower catch rate. Steaming to and from port was less important for fuel efficiency than steaming between fishing locations. A tradeoff was identified between biotic and abiotic impacts. Landings classified as main target species generally followed the maximum sustainable yield (MSY) framework, and proportions of threatened species were low, while proportions of data-limited bycatch were larger. This improved considerably when reference points were defined for saithe in 2014. ConclusionsThe variability between fishing trips shows that there is room for improvement through management. Fuel use per landing was strongly influenced by target species, fishing pattern, and fisheries management. Increased awareness about the importance of onboard decision-making can lead to improved performance. This approach could serve to document performance over time helping fishing companies to better understand the effect of their daily and more long-term decision-making on the environmental performance of their products.Recommendations Fishing companies should document their resource use and production on a detailed level. Fuel use should be monitored as part of the management system. Managing authorities should ensure that sufficient data is available to evaluate the sustainability of exploitation levels of all harvested species.