Re: do you have any concerns that your users might shortcut the work to produce high quality estimates of their Scope 3 emissions when you have made it so easy to get lower quality estimates?
> I think the high order bit here is that 99%+ of companies don't measure their emissions at all. This is for a good reason — measuring your emissions historically has been quite labor-intensive. Even for large companies, there is always a 'long tail' of 'Scope 3 goods and services' transactions that are hard to measure. Our goal is to create a scalable solution so that a much larger share of companies are able to participate.
Re: your grocery store example —
> This is a fair point. Our main belief is that realtime, actionable data trumps perfectly attributed data, if perfectly attributed data requires a bottoms-up manual model. The advantage of the spend-based approach is that (1) it's realtime, and (2) it aligns incentives at the company level. The holy grail, however, would be itemized spend data (level 3 data), where you could factor in the emissions of your specific line-items. Unfortunately, that data is nearly impossible to get (yet). Maybe that's Bend 2.0 :)
Re: re-accounting historical emissions —
> Yes! We use the emissions 'factor' that most closely matches the transaction date. So for example, if you bought a Starbucks coffee in 2020, we would use the 2020 Starbucks factor, and if you bought a Starbucks in 2021, we would use the 2021 Starbucks factor. If Starbucks is late to publish their 2022 report, we would recalculate the emissions when the info is updated. For our category fallbacks, we also take currency / region into consideration.
Happy to chat more, either with you or the WRI folks! Thanks for the questions.
On your belief that actionable data _trumps_ perfectly attributed data, I'm not entirely convinced. I think actionable data _complements_ attributable data. But you need the attributable data to accurately measure the impact and learn what specific actions caused that impact.
I think all companies want to make actions that are well-informed, 'the right choice', and have the potential to demonstrate it was 'the right choice'. My concern is that when someone takes a 'good action' such as replacing high intensity animal protein with low intensity plant protein there is no evidence from your side that it made any difference. You are divorcing the actual choice that was made from what is perceived as the outcome.
The dollars-to-emissions relationship is just not as simple as is being represented, and for those who are not specialists there might be a false sense of progress.
"We cut our daily emissions from transport by having employees purchase Uber rides in off-peak times."
"We cut our emissions for business travel by setting up a policy that flights must be purchased at least 2 months in advance."
"We cut our emissions from our regular food purchases by looking at the local newspaper for coupons and signing up for a customer loyalty account."
"Maybe we all should fly to Las Vegas (tickets are cheap) rather than have Linda fly to NYC (an expensive ticket)."
Each of these might be smart business choices, but they have absolutely no real world effect on emissions that should be attributable to a company, but that isn't what the company is being told.
As a first pass to estimate sense of scale and where to look into unsustainable practices and prioritize better data collection, Bend seems to be valuable.
It would help if they include some information about the upfront costs of electrification. For example, How much does the charging infrastructure cost?
[0] https://www.wri.org/research/electric-school-bus-us-market-s...