Patrik J. G. Henriksson

China's aquaculture and the world's wild fisheries

Curbing demand for wild fish in aquafeeds is critical China is the worlds largest producer, consumer, processor, and exporter of finfish and shellfish (defined here as “fish”), and its fish imports are steadily rising (1–3). China produces more than one-third of the global fish supply, largely from its ever-expanding aquaculture sector, as most of its domestic fisheries are overexploited (3–6). Aquaculture accounts for ∼72% of its reported domestic fish production, and China alone contributes >60% of global aquaculture volume and roughly half of global aquaculture value (1, 3).

Life cycle assessment of aquaculture systems—a review of methodologies

PurposeAs capture fishery production has reached its limits and global demand for aquatic products is still increasing, aquaculture has become the world’s fastest growing animal production sector. In attempts to evaluate the environmental consequences of this rapid expansion, life cycle assessment (LCA) has become a frequently used method. The present review of current peer-reviewed literature focusing on LCA of aquaculture systems is intended to clarify the methodological choices made, identify possible data gaps, and provide recommendations for future development within this field of research. The results of this review will also serve as a start-up activity of the EU FP7 SEAT (Sustaining Ethical Aquaculture Trade) project, which aims to perform several LCA studies on aquaculture systems in Asia over the next few years.MethodsFrom a full analysis of methodology in LCA, six phases were identified to differ the most amongst ten peer-reviewed articles and two PhD theses (functional unit, system boundaries, data and data quality, allocation, impact assessment methods, interpretation methods). Each phase is discussed with regards to differences amongst the studies, current LCA literature followed by recommendations where appropriate. The conclusions and recommendations section reflects on aquaculture-specific scenarios as well as on some more general issues in LCA.ResultsAquaculture LCAs often require large system boundaries, including fisheries, agriculture, and livestock production systems from around the globe. The reviewed studies offered limited coverage of production in developing countries, low-intensity farming practices, and non-finfish species, although most farmed aquatic products originate from a wide range of farming practices in Asia. Apart from different choices of functional unit, system boundaries and impact assessment methods, the studies also differed in their choice of allocation factors and data sourcing. Interpretation of results also differed amongst the studies, and a number of methodological choices were identified influencing the outcomes.Conclusions and recommendationsEfforts should be made to increase transparency to allow the results to be reproduced, and to construct aquaculture related database(s). More extensive data reporting, including environmental flows, within the greater field of LCA could be achieved, without compromising the focus of studies, by providing supporting information to articles and/or reporting only ID numbers from background databases. More research is needed into aquaculture in Asia based on the latest progress made by the LCA community.

A protocol for horizontal averaging of unit process data—including estimates for uncertainty

PurposeQuantitative uncertainties are a direct consequence of averaging, a common procedure when building life cycle inventories (LCIs). This averaging can be amongst locations, times, products, scales or production technologies. To date, however, quantified uncertainties at the unit process level have largely been generated using a Numerical Unit Spread Assessment Pedigree (NUSAP) approach and often disregard inherent uncertainties (inaccurate measurements) and spread (variability around means).MethodsA decision tree for primary and secondary data at the unit process level was initially created. Around this decision tree, a protocol was developed with the recognition that dispersions can be either results of inherent uncertainty, spread amongst data points or products of unrepresentative data. In order to estimate the characteristics of uncertainties for secondary data, a method for weighting means amongst studies is proposed. As for unrepresentativeness, the origin and adaptation of NUSAP to the field of life cycle assessment are discussed, and recommendations are given.Results and discussionBy using the proposed protocol, cross-referencing of outdated data is avoided, and user influence on results is reduced. In the meantime, more accurate estimates can be made for horizontally averaged data with accompanying spread and inherent uncertainties, as these deviations often contribute substantially towards the overall dispersion.ConclusionsIn this article, we highlight the importance of including inherent uncertainties and spread alongside the NUSAP pedigree. As uncertainty data often are missing in LCI literature, we here describe a method for evaluating these by taking several reported values into account. While this protocol presents a practical way towards estimating overall dispersion, better reporting in literature is promoted in order to determine real uncertainty parameters.

Product Carbon Footprints and Their Uncertainties in Comparative Decision Contexts

In response to growing awareness of climate change, requests to establish product carbon footprints have been increasing. Product carbon footprints are life cycle assessments restricted to just one impact category, global warming. Product carbon footprint studies generate life cycle inventory results, listing the environmental emissions of greenhouse gases from a product’s lifecycle, and characterize these by their global warming potentials, producing product carbon footprints that are commonly communicated as point values. In the present research we show that the uncertainties surrounding these point values necessitate more sophisticated ways of communicating product carbon footprints, using different sizes of catfish (Pangasius spp.) farms in Vietnam as a case study. As most product carbon footprint studies only have a comparative meaning, we used dependent sampling to produce relative results in order to increase the power for identifying environmentally superior products. We therefore argue that product carbon footprints, supported by quantitative uncertainty estimates, should be used to test hypotheses, rather than to provide point value estimates or plain confidence intervals of products’ environmental performance.

Measures of Difference and Significance in the Era of Computer Simulations, Meta-Analysis, and Big Data

In traditional research, repeated measurements lead to a sample of results, and inferential statistics can be used to not only estimate parameters, but also to test statistical hypotheses concerning these parameters. In many cases, the standard error of the estimates decreases (asymptotically) with the square root of the sample size, which provides a stimulus to probe large samples. In simulation models, the situation is entirely different. When probability distribution functions for model features are specified, the probability distribution function of the model output can be approached using numerical techniques, such as bootstrapping or Monte Carlo sampling. Given the computational power of most PCs today, the sample size can be increased almost without bounds. The result is that standard errors of parameters are vanishingly small, and that almost all significance tests will lead to a rejected null hypothesis. Clearly, another approach to statistical significance is needed. This paper analyzes the situation and connects the discussion to other domains in which the null hypothesis significance test (NHST) paradigm is challenged. In particular, the notions of effect size and Cohens d provide promising alternatives for the establishment of a new indicator of statistical significance. This indicator attempts to cover significance (precision) and effect size (relevance) in one measure. Although in the end more fundamental changes are called for, our approach has the attractiveness of requiring only a minimal change to the practice of statistics. The analysis is not only relevant for artificial samples, but also for present-day huge samples, associated with the availability of big data.

Quantified Uncertainties in Comparative Life Cycle Assessment: What Can Be Concluded?

Interpretation of comparative Life Cycle Assessment (LCA) results can be challenging in the presence of uncertainty. To aid in interpreting such results under the goal of any comparative LCA, we aim to provide guidance to practitioners by gaining insights into uncertainty-statistics methods (USMs). We review five USMs—discernibility analysis, impact category relevance, overlap area of probability distributions, null hypothesis significance testing (NHST), and modified NHST–and provide a common notation, terminology, and calculation platform. We further cross-compare all USMs by applying them to a case study on electric cars. USMs belong to a confirmatory or an exploratory statistics’ branch, each serving different purposes to practitioners. Results highlight that common uncertainties and the magnitude of differences per impact are key in offering reliable insights. Common uncertainties are particularly important as disregarding them can lead to incorrect recommendations. On the basis of these considerations, we recommend the modified NHST as a confirmatory USM. We also recommend discernibility analysis as an exploratory USM along with recommendations for its improvement, as it disregards the magnitude of the differences. While further research is necessary to support our conclusions, the results and supporting material provided can help LCA practitioners in delivering a more robust basis for decision-making.

Measuring the potential for sustainable intensification of aquaculture in Bangladesh using life cycle assessment

Significance Aquaculture has only recently begun to make significant contributions to the global food system but is undergoing rapid growth and intensification. Identifying the most sustainable intensification options for aquaculture provides an opportunity to avoid some of the environmental pitfalls of agriculture and livestock production. Life cycle assessment is operationalized here as a tool to evaluate a range of environmental impacts resulting from the intensification of aquaculture production in Bangladesh and a subset of trade-offs among them. Intensifying aquaculture production results in multidirectional outcomes across different environmental impact categories. These findings are used to identify simple improvements in farm management practices that can make the intensification of aquaculture more sustainable. Food production is a major driver of global environmental change and the overshoot of planetary sustainability boundaries. Greater affluence in developing nations and human population growth are also increasing demand for all foods, and for animal proteins in particular. Consequently, a growing body of literature calls for the sustainable intensification of food production, broadly defined as “producing more using less”. Most assessments of the potential for sustainable intensification rely on only one or two indicators, meaning that ecological trade-offs among impact categories that occur as production intensifies may remain unaccounted for. The present study addresses this limitation using life cycle assessment (LCA) to quantify six local and global environmental consequences of intensifying aquaculture production in Bangladesh. Production data are from a unique survey of 2,678 farms, and results show multidirectional associations between the intensification of aquaculture production and its environmental impacts. Intensification (measured in material and economic output per unit primary area farmed) is positively correlated with acidification, eutrophication, and ecotoxicological impacts in aquatic ecosystems; negatively correlated with freshwater consumption; and indifferent with regard to global warming and land occupation. As production intensifies, the geographical locations of greenhouse gas (GHG) emissions, acidifying emissions, freshwater consumption, and land occupation shift from the immediate vicinity of the farm to more geographically dispersed telecoupled locations across the globe. Simple changes in fish farming technology and management practices that could help make the global transition to more intensive forms of aquaculture be more sustainable are identified.