Disclosure of Product System Models in Life Cycle Assessment: Achieving Transparency and Privacy

Kuczenski, Brandon

doi:10.1111/jiec.12810

Cited by 12 publications

(13 citation statements)

References 38 publications

(60 reference statements)

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“…In the absence of a single unambiguous system for referencing LCI datasets (as advocated by Kuczenski et al (2019), Futura uses the filtering method implemented in wurst. Under this approach, single datasets or groups of datasets are identified by a filtering term based on combination of their attributes.…”

Section: Futuramentioning

confidence: 99%

“…They have an obvious and clearly visible part, the foreground model, which garners most of the interest and attention, but they are underpinned by a far more extensive and mostly invisible part, the background system. The foreground model describes the specific system under investigation (Kuczenski, 2019). This is where the focus of an LCA practitioners effort in conducting a study will lie; the compilation of system descriptions and diagrams, the setting of the appropriate functional unit and reference flows, the collection of primary data, etc.…”

Section: Introductionmentioning

confidence: 99%

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Futura: A new tool for transparent and shareable scenario analysis in prospective life cycle assessment

Joyce

Björklund

2021

J of Industrial Ecology

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While it may be impossible to accurately predict what the world will look like in the future, we can be certain that it will be different from the world of today. By extension, we know that using today's data in life cycle assessment (LCA) studies claiming to represent future scenarios is problematic. For the future impact of products to be estimated in a consistent and meaningful manner in LCA, the background system, most commonly the ecoinvent database, needs to be projected into the future alongside the foreground system modeled in a given study. Futura is a new piece of open‐source software which allows LCA practitioners to create and share novel background databases representing arbitrary scenarios. It allows users to import a base database and then start making targeted changes. These changes take three main forms—adding new technologies, regionalizing new or existing technologies, and altering market compositions. All changes made are automatically added to a "recipe." This recipe file can be shared publicly. This recipe can be imported by other users and used to exactly recreate the modified database. The additive and transparent nature of this system means that initially simple scenarios can be built upon by others to progress toward more comprehensive scenarios in a stepwise manner. The inability to build on the work of others is a serious barrier to the progress of the LCA field. Futura goes some way to reduce this barrier in the field of prospective LCA.

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Section: Futuramentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Futura: A new tool for transparent and shareable scenario analysis in prospective life cycle assessment

Joyce

Björklund

2021

J of Industrial Ecology

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“…As the software develops beyond proof-of-concept, interoperability with other LCA modelling frameworks will be desirable. One potentially important recent development in this regard is the approach set out by Kuczenski (2018) for the transparent and platform-independent disclosure of product system models.…”

Section: Discussionmentioning

confidence: 99%

Computer vision for LCA foreground modelling—an initial pipeline and proof of concept software, lcopt-cv

Joyce

2019

Int J Life Cycle Assess

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Purpose The majority of LCA studies begin with the drawing of a process flow diagram, which then needs to be translated manually into an LCA model. This study presents an initial image processing pipeline, implemented in an open-source software package, called lcopt-cv, which can be used to identify the boxes and links in a photograph of a hand-drawn process flow diagram and automatically create an LCA foreground model. Methods The computer vision pipeline consists of a total of 15 steps, beginning with loading the image file and conversion to greyscale. The background is equalised, then the foreground of the image is extracted from the background using thresholding. The lines are then dilated and closed to account for drawing errors. Contours in the image are detected and simplified, and rectangles (contours with four corners) are identified from the simplified contours as 'boxes'. Links between these boxes are identified using a flood-filling technique. Heuristic processing, based on knowledge of common practice in drawing of process flow diagrams, is then performed to more accurately identify the typology of the identified boxes and the direction of the links between them. Results and discussion The performance of the image processing pipeline was tested on four flow diagrams of increasing difficulty: one simple computer drawn diagram and three photographs of hand-drawn diagrams (a simple diagram, a complex diagram and a diagram with merged lines). A set of default values for the variables which define the pipeline was developed through trial and error. For the two simple flow charts, all boxes and links were identified using the default settings. The complex diagram required minor tweaks to the default values to detect all boxes and links. An 'unstacking' heuristic allowed the diagram with merged lines to be correctly processed. After some manual reclassification of link directions and process types, the diagrams were turned into LCA models and exported to open-source LCA software packages (lcopt and Brightway) to be verified and analysed. Conclusions This study demonstrates that it is possible to generate a fully functional LCA model from a picture of a flow chart. This has potentially important implications not only for LCA practitioners as a whole, but in particular for the teaching of LCA. Skipping the steep learning curve required by most LCA software packages allows teachers to focus on important LCA concepts, while participants maintain the benefits of experiential learning by doing a 'real' LCA.

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“…Full product inventories are documented in the supporting information file: "customProcessesLCI.xlsx" for review and reuse. 48 Since the feed commodities market fluctuates widely, and therefore commercial feed compositions as well, the standard feed used in this study was formulated based on the relative amounts of feed ingredients imported by the Norwegian fish feed industry in 2016 49 and balanced to meet the nutritional needs of salmon. This feed includes fishmeal and soy protein concentrate as the main protein ingredients.…”

Section: Methodsmentioning

confidence: 99%

Environmental Benefits of Novel Nonhuman Food Inputs to Salmon Feeds

Couture

Geyer

Hansen

et al. 2019

Environ. Sci. Technol.

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Global population growth and changing diets increase the importance, and challenges, of reducing the environmental impacts of food production. Farmed seafood is a relatively efficient way to produce protein and has already overtaken wild fisheries. The use of protein-rich food crops, such as soy, instead of fishmeal in aquaculture feed diverts these important protein sources away from direct human consumption and creates new environmental challenges. Single cell proteins (SCPs), including bacteria and yeast, have recently emerged as replacements for plantbased proteins in salmon feeds. Attributional life cycle assessment (ALCA) is used to compare salmon feeds based on protein from soy, methanotrophic bacteria, and yeast ingredients. All ingredients are modeled at the industrial production scale and compared based on seven resource use and emissions indicators. Yeast protein concentrate showed drastically lower impacts in all categories compared to soy protein concentrate. Bacteria meal also had lower impacts than soy protein concentrate for five of the seven indicators. When these target meals were incorporated into complete feeds the relative trends remain fairly constant, but benefits of the novel ingredients are dampened by high impacts from the non-target ingredients, Particularly, primary production requirements (PPR) are about equal and constant across all feeds for both analyses since PPR was driven by fishmeal and oil. The bacteria-based feed has the highest climate change impacts due to the use of methane to feed the bacteria who then release carbon dioxide. Overall, the results of this study suggest that incorporating SCP ingredients into salmon feeds can help reduce the environmental impacts of salmon production. Continued improvements in SCP production would further increase the sustainability of salmon farming.

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Disclosure of Product System Models in Life Cycle Assessment: Achieving Transparency and Privacy

Cited by 12 publications

References 38 publications

Futura: A new tool for transparent and shareable scenario analysis in prospective life cycle assessment

Futura: A new tool for transparent and shareable scenario analysis in prospective life cycle assessment

Computer vision for LCA foreground modelling—an initial pipeline and proof of concept software, lcopt-cv

Environmental Benefits of Novel Nonhuman Food Inputs to Salmon Feeds

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