i am more than willing to engage on any positive claim you want to make (i probably agree with a lot of them). what i’m not willing to do is tolerate personal attacks and dogpiling.

cartoonishly evil

I keep an org-mode file of all the useful commands, configs, and settings. and bash automatically keeps a history of all your commands anyway.

it’s not the exact same comment, it’s another study that poore-nemecek cites.

shun and exile. let him roam the earth seeking shelter and food.

I wasn’t looking for a conversation either. I’m just stating relevant facts

most calves are brought to full weight before slaughter, and artificial insemination is safer than being mounted by a bull

but they never actually mitigate the differences in methodology between the studies they selected.

it’s absolutely falsifiable: show how the problems of analyzing diverse LCA models have been rectified. they don’t do this, though, they just charge ahead compiling the data.

they temper their own conclusions by pointing out the problems with their methodology. poore-nemecek doesn’t even have the honesty to do that.

quoting their own source material is not an appeal to authority. it’s pointing out flawed methodology.

this doesn’t address what I said. it’s a pure red herring attacking my style instead of the facts.

I strongly prefer to keep each comment to one idea. it helps break up Gish gallops. if you don’t like my style, you’re free to block me and remove me from you Lemmy experience.

your personal attacks are inappropriate.

your accusation of bad faith is, itself, bad faith

that doesn’t make their methodology any good.

they were honest enough to acknowledge that these studies varied so widely in methodology that combining them would be bad science, but went on to do it anyway. poore-nemecek doesn’t even acknowledge their faux pas.

your characterization of my expertise is bare ad hominem. what I’m saying is true or false regardless of your opinion of me and my expertise.

First, it is often cited that LCA results should not be compared (Desjardins et al., 2012; Foster et al., 2006; McAuliffe et al., 2016; Röös et al., 2013) due to variation in methodology choices, functional units, as well as temporal and regional differences2. Second, no single comprehensive review was identified that adequately covers the breadth of fresh foods available to consumers and caterers. As Helle et al. (2013, p.12643) state ‘data availability and quality remain primary obstacles in diet-level environmental impact assessment’, while Pulkkinen et al. (2015) calls for the creation of a database that communicates data quality, uncertainty and variability to reliably differentiate between the GWP of food types. Previous studies have compiled LCA data to compare different foods (e.g. Audsley et al., 2009; Berners-Lee et al., 2012; Bradbear and Friel, 2011; de Vries and de Boer, 2010; Foster et al., 2006; Nijdam et al., 2012; Sonesson et al., 2010; Roy et al., 2009). While these are useful attempts, the identified studies are inadequate in the coverage of fresh foods available. Environmental Product Declarations (EPDs) attempt to inform consumers of the environmental impacts (carbon, water and ecological footprint) of specific foods, however they also fall short in breadth of items covered at present. The most comprehensive attempt at carbon footprint labelling was performed by Tesco (2012), however failed to label key categories such as fresh fish, pork, lamb or beef before finishing in 2012 due to the scale of the labelling scheme and a lack of participation from other retailers (Head et al., 2013). Third, studies that do compare results may often present singular figures. Peters et al. (2010) and Röös et al. (2011) argue that a range of impacts should be reported from LCA’s to better represent the variety of environmental impacts, as opposed to a singular figure. Finally, there is a lack of synthesised open access LCA data in the public domain available to consumers to inform decision-making.

LCA results can have high uncertainties because of the large amounts of measured and simulated data and the simplified modeling of complex en- vironmental cause-effect chains. Recent studies have highlighted the contribution that system as- sumptions and value choices can also make to overall uncertainty (36, 37). A number of quantita- tive uncertainty assessments are available (38) butare rarely used in practice. One of the key questions is, how much uncertainty is acceptable, depending on the application? In some cases, rough estimates of input values can be enough to identify supply- chain hotspots (39), but for other applications, such as product comparisons (37), the demands for more accurate values are higher. For some im- pact categories such as toxicity, very large differ- ences in inventory results are needed to statistically differentiate product systems, whereas for other categories, differences of a factor of two or less may be enough (40). LCA practitioners should al- ways attempt to manage the decision-maker’s expectations and clarify that LCA is not always a tool to provide a single answer, but rather one that permits comprehensive understanding of a problem and its possible solutions.

I’ve asked you before to point to even a single paper responding to this extremely high-profile meta-analysis with something even resembling this vague concern;

the references themselves say this explicitly.