I would Imagine the carbon footprint of iNaturalist is quite large considering the amount of photos/data being stored and the huge calculations being done on the photos to suggest identifications (I read in the NewScientist that training AI can amount to about 284 tonnes of carbon dioxide footprint equivalent https://www.newscientist.com/article/2205779-creating-an-ai-can-be-five-times-worse-for-the-planet-than-a-car/). I think it would be interesting to know, considering this is an environmental project, what the carbon footprint of iNaturalist and where the energy sources for the servers come from.
Not familiar with any calculations that have been done or published, but you can find some information here:
- https://www.inaturalist.org/pages/privacy (lists some of the third party services they use, like Amazon Web Services)
- https://www.calacademy.org/efficient-daily-operations (the iNat office, which to my understanding is where they do computer vision training. Many staff work remotely. “Nearly 100 percent of the Academy’s electricity comes from clean energy sources”)
- https://aws.amazon.com/about-aws/sustainability/ (AWS sustainability info)
If you include contributors impact from traveling, taking, posting, and identification of the observations, I imagine it might be quite high. Otherwise I imagine it pales in comparison to AI creation.
That’s an interesting question, I guess- how much, if at all, does iNat increase travel for the sake of nature observation?
Personally, I do and have traveled more (on a local scale) with iNat observations as an explicit motivator- suggesting that participation in iNat has amplified my preexisting motivation to do such things anyways.
That said, a lot of longer distance, more impactful travel for nature observation probably would be undertaken with or without the existence of iNaturalist- it requires a significant preexisting motivation anyways.
It increases the amount I travel on foot for sure. I’d be really interested in a poll on the ways people have changed their patterns of travel for different modes/distances since joining iNat!
For naturalists seeking novel species, iNaturalist can encourage people to stay local as iNaturalist makes it easier to seek out and identify less popular taxa.
For me personally, it has injected much interest in going out. I have mined out birds, butterflies and dragonflies in my local area (over 70% in each), and getting new species in these groups is tough. Some people in my situation may feel compelled to travel further and further in order to make gain in their favourite groups. By taking on everything, with a focus on a new taxonomy group each year or so, I can keep going out in nature interesting, and avoid long distance travel. In my case, the long distance travel is to stay within Ontario involved drives of four to eight hours about twice a year for about four days each. The longest trip was to Pelee Island from my home town, North Bay.
@Allan2700, it’s an interesting question, but a complicated one, and honestly not one we have the resources to answer in a quantitative fashion (assuming that’s even possible). @alex estimates that we spent ~1,400 kilowatt hours training our last computer vision model, and if you believe the 2.21 pounds / kilowatt hour stat for US coal-fueled power plants (and you might not, given the current state of the US government), that would be 3,094 pounds of carbon emitted, ~1.4 metric tons. However, that electricity was all consumed at the California Academy of Sciences, which, as Cassi pointed out in her links above, derives all of its electricity from hydroelectric and solar power, not coal (with the usual caveats about the impossibility of sourcing electricity on an electrical grid), so you might say the carbon footprint of the energy consumed to create that model was zero.
You might also say that electricity in a grid comes form a diverse array of sources, and that most electricity production in California is from burning natural gas. You might further say that the components in the solar arrays on the roof of the California Academy of Sciences might have their own pollution problems, to say nothing of the electrical infrastructure required to keep the building provisioned with electricity. You might even say that hydroelectric power from Hetch Hetchy Reservoir comes at the cost of destroying one of the most beautiful natural areas in California, the controversy over which galvanized (pun intended!) the environmental movement in the United States. So, it’s complicated.
Overall, I think the electricity consumption of the iNat team in the CAS building is relatively small, and that CAS does a good job trying to use renewable sources of electricity, so take that for what it’s worth.
We probably consume much more electricity on our virtual servers at Microsoft Azure, but they claim to be carbon neutral in their electricity consumption and on their way to being carbon negative, though a significant portion of that is due to purchasing carbon offsets according to their whitepaper. We use machines in Microsoft’s “West US 2” data center, which is in Washington state, where hydroelectric plants are the biggest electricity providers.
The carbon footprint of iNaturalist as a whole is an even more ponderous and hard/impossible-to-answer question. Does iNat encourage people to consume more carbon through travel? To consume more electricity through more use of digital devices? To contribute to more pollution by supporting the market for electronics? My guess is that we’re a part of all these problems, but probably a very small part.
Our current computer vision training workstation, at full train, seems to pull 450-500 watts (per
nvidia-smi and the rated TDP of our CPU).
At 120 training days, and 24 hours in a day, I believe that works out to ~1,400 kilowatt hours. However, I’m stupid with electrical stuff (and most stuff, honestly), so I’m happy to be corrected.
Our current computer vision system uses an EVGA SuperNOVA 1200 P2 power supply, which was the most efficient power supply I could get my hands on in 2017, back when cryptocurrency mining was taking over the world and efficient power supplies were very hard to source. It’s Platinum certified, which means it’s ~94% efficient at half-load (meaning we would need to draw 480 watts from the wall to fulfill 450 watts of need). The new computer vision system we’re building will have a titanium certified power supply, which means it’s at least ~96% efficient at half-load. So we’d only need to draw 470 watts from the wall to fulfill the same 450 watt need. Titanium is the highest certification rating for computer power supplies.
Quantifiable amounts are probably unrealistic, but perhaps relative differences could be considered!
With the CV having put preliminary if not final (as one of two needed) IDs, there is a reduced need for identifiers to make high level identifications. There is probably a nett reduction in whatever the carbon footprint is from those identifiers not spending that additional time (cpu and screen display, also perhaps lighting and heating/cooling in the room) to achieve the same level of CID.
“Relative” assessment and “quantitative” assessment are not mutually exclusive. A thorough relative assessment would require quantification in my mind, e.g. “Amazon virtual servers would result in X tons carbon emitted, while Azure virtual servers would result in Y tons carbon emitted.”
We have always used both single-ID observations and “confirmed” observations for different parts of our vision training process, so it is not true that there is a reduced need for identifications.
Uploaded as “Unknown” - ID’d as Class or Order - ID’d to species - confirmed and now RG [4 steps]
Upload as AI suggestion - confirmed and now RG [2 steps]
or Upload as AI suggestion - ID to different species - uploader changes ID and now RG [3 steps]
or Upload as AI suggestion - ID to different species - 2 confirmations to get to RG [4 steps]
I can calculate the difference in lengths of two pieces of timber by measuring them both and deducting one from the other. Or I can hold them together and measure from the end of the shortest to the end of the longest. That’s kinda what I mean by “relative”.
I’ve been trying to be a good citizen scientist and recently started loading observations from my garden/natural surroundings around the rural village where I live, in the hope it could somehow be useful to research/conservation somewhere, but also to practice my photographic skills. The question you raise often came to mind as I’ve started following botanists/scientists around the world on this platform, but also on social media. What I’ve noticed is that there appear to be some kind of “competition” going on between people in this fraternity. Like in so many other spheres of society, the one is always trying to outshine the rest – ie. who’s got the most observations and who spotted that endangered species first. Many seem to travel all over the world to achieve this goal. There are even travel agencies who specialise in Botanical adventures – brilliant, but at what cost/expense to the wellbeing of the planet?! Don’t get me wrong, I have the highest respect for these professionals and believe the work they do is extremely important, but it should NEVER be at the expense of nature/the future of this beautiful planet. We all need to be more mindful of our “footprint” on this planet, yip, even those who are supposedly trying to save it.
Baby and bathwater stuff though…
That ticket around the world for eco tourism leads to a high value being developed for the natural world, which equates to votes in elections that stop governments allowing conglomerates to slash down the forests…
You are far more likely to fight to look after something you flew around the world for than you are a picture in a book.
But it’s a conversation that I think will change considerably as the covid situation unfolds… I am pretty sure life will be different afterward, in the same way that 9/11 changed airtravel… Life will go on, it will just be different!
Covid-19 has reduced emissions a lot!
Just trying to polish a . . .
roadtoll here in NZ will be the lowest for a month for a very long time…
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