In a recent article and podcast I published I used an oft quoted stat without checking it's accuracy. The stat is that fashion is responsible for more carbon emissions each year than international flights and maritime shipping combined.
It has been pointed out to me that is too simplistic and perhaps even an inaccurate comparison.
There is an excellent discussion about this on the "Big Closets Small Planet" podcast .
The first issue is that, in this industry comparison, Aviation & Maritime data includes direct emissions only. The "fashion" or "textiles" data includes the 80% emissions from their supply chains. The original report does acknowledge this, but in the small print. The argument seems to be that it helps public awareness to use the well known direct emissions from aviation as a useful benchmark to explain the impact.
Others point out (and I agree) that this might prompt a short term short term stirring of action (as is definitely the case here) but in the long term we risk losing public confidence.
There are, of course, other issues the complicate things. Textiles are used in the aviation industry, for example. Or, what do we mean by "fashion"? For example: textiles overlaps with clothing but may not include leather (ie agriculture products).
No one here is downplaying the importance of the climate crises. the argument boils down to awareness raising vs the importance of and challenge of communicating complex data.
IMHO, the accuracy of data and quotes like the above is more important than communicating a simple message to provoke change. (Though I fully acknowledge that we need to provoke change and fast, so even this is complex!)
One reason that accuracy and not hiding complexity matters: is that we also need to measure reductions as the happen (to avoid double counting etc).
I draw two conclusions:
- When publishing state the source. (When reading check the source.) Transparency matters if we want real long term change.
- Comparisons between industries may not be helpful, reality does not separate into neat boxes that we can compare.
And most important: this is a conversation about how to tackle a huge global issue. We must not let debates about data accuracy distract us.