(upbeat instrumental music)
-: This is our fourth year
of, um, running a sustainability report
or a carbon emissions report, um,
that looks at corporates, about 2,000 of them.
I'll go through the details in a second.
We do it in partnership with CO2 AI, um, and I'll let Dmitry perhaps introduce CO2 AI a little bit
before we get into the details of the report.
-: So, ah, CO2 AI is a carbon management emission platform
that helps you to compute your emissions.
Actually, you did a great job explaining what we do.
We focus, not on the specific problem like you,
but we're rather trying to help big corporations
to measure their emissions across their - all scopes,
across all their, uh, data, and to then drive the carbonization,
try to find, uh, best levers that can help you
to reduce your emissions.
Ah, we're a very young startup that was originated inside BCG
and was created by BCG originally.
Now, we're an independent startup,
but we already have quite cool logos,
that's companies that's working with us.
Ah, you know some of them - some of them are less maybe public known,
but they're huge corporations, like Cisco, Reckitt,
which has challenges especially in the scope 3.1
with a lot of a lot of supplies that you need to help
to decarbonize.
-: Um, the carbon emission survey, ah, this year
roughly covered, um, 2000 organizations.
Um, I think the interesting, um, factor here,
it's about 40% of global emissions.
So the data we're seeing is representative, ah,
of what's out there.
And then you can see the spread across geographies
and industries.
Um, if you have an interest in geography
there's some really interesting insights about countries
that are actually leading on, um, measurement and reduction.
and it is not the countries you expect, um, which I think is an interesting, um, set of, ah, results anyway, um...
Ah, it's a bit of a sad picture... so, um,
and I think this is where the power
of the year-on-year comes.
Um, only 9% of organizations
are comprehensively measuring scope one, two,
and three, right?
That is scope three of stream and scope three downstream.
But that's eroding.
Um, that's not the one that scares me the most.
Ah, the middle and the last one are perhaps the problem.
Um, targets... so about 3% of organizations walk back their targets.
Um, we see this, right?
Many of our clients have come to us and said,
the target's too difficult, I can't reach it.
Um, if I keep growing the way I do,
it's, I'm really gonna struggle.
And so they're walking back their targets.
Um, so I think it's an unfortunate, ah, state of play
and then, um, only 11% are reducing in line
with their ambitions, right?
So that's... I think where, ah, hopefully technology,
as we'll talk a little later, green technology can help us.
Now, the bright spot... there are companies
that actually are decarbonizing. Great.
And the leaders of them, so 25% of the companies
that are decarbonizing are getting 7% or more
of their revenue and financial benefits -
and I'll say net financial benefits,
so after their investments.
So we're talking about 200 million bucks to the bottom line.
Okay, finally, a sigh of relief.
Like you can actually make money
and decarbonize at the same time.
So it's not just, you know, moral high grounds.
Um, the main way they do it is they save cost at the same time as they decarbonize.
And and many of us will hear this with our clients,
whether you swap, um, old boilers for new ones,
whether you reduce your footprint.
Um, a lot of it, and we see this in the MACC curves.
I don't know if all of you know what a MACC curve is,
but we generally see the first 20 to 30%
of initiatives you have be cost - cost negative.
Um, so they are really, really impactful.
Um, so this data just essentially prove
that, that it's happening on mass scale, um,
and we are hoping more folks hear this message
and more leaders hear this message, um, so they understand
that there's a really positive ROI on the money
being put forward to decarbonize.
Um, I'll hand over to Dmitry in a second,
but what are companies that lead do?
Um, and this is where you'll see Sylvain's famous number
that he, he suggested earlier.
They do a bunch of basic things, right?
Like, okay, you need to measure, um,
you should report and have a target.
But where things get really interesting
is if you're gonna lead in this space, you need a plan.
Um, and, and this comes up more and more now
and more of the statistically significant data indicates
the climate transition plans and their importance.
You need product-level emissions
'cause actually sentiment of what you buy in any,
whether it's grocery store or vehicle
or whatever, really matters.
So you knowing what the carbon footprint of something is
and then making an informed consumer decision
makes all the difference.
So that's the four times.
So those that lead are four times more likely
to measure product carbon footprints.
It is far more difficult... I think Dmitry tried to code it... He'll tell you all about it if you wanted the break.
Um, and then they leverage AI.
Um, we gotta get some shortcuts in, right?
People are trying and waiting for data to be perfect
and they want the perfect measurement
and, you know, it just doesn't exist.
And AI helps them get there a bit faster
and it helps them get going, um, and and that's a really big unlock.
Um, and with that, I'll hand over to Dmitry
to take us through why.
-: So essentially, you saw on previous slide,
there are two levers that helped a lot companies
to decarbonize faster.
It's AI and product carbon footprint.
Essentially, the reason why AI is so helpful here,
companies have millions of activities,
you called it what you will multiply
and impacts before... abservations... we call it activities, but it's essentially the same thing.
And usually big corporation have millions of them.
Oh, I bought this amount of collaborative electricity
in this day and this month, or I purchased these items
or I resolved these like thousands of items.
And you can imagine, especially for retailers,
it's very, very hard.
They sell hundreds of thousands of items every day.
Ah, they're different.
And you need to understand where the hotspots are,
where the problem is.
You don't need ... you need to know where to focus.
And AI gives you an ability
to quickly match those activities, observations
to emission factors, impact factors
to understand your CO2 emissions.
After that you can go into more deeper, you can try
to maybe estimate a bit more accurate your hotspot,
but you need to know where you need to estimate.
That's probably the biggest unlock.
Without it, you really cannot say what to do next.
So you got your total emissions, but what to do with it?
You really need to understand,
oh, this item is my biggest hotspot.
I want to work with supply of this item to decarbonize it.
(Dmitry faintly speaking)
And second thing is product carbon footprint.
Essentially, you... there are different levers why it's important.
For like producers of product, of course, it's important
to understand what's coming inside of it.
What can I reduce?
What can I swap maybe potentially
for less carbon-emissive item.
And for example, you're, I don't know, a beer producer,
you want to do it at scale.
You have 20, 30 types of different base, maybe hundreds.
So you want to have some methodology
that helps you to do it at scale.
So you blend AI and matching with different emission factors
together with some structure on how to aggregate this data
and to show transparency as you were presenting before, ah,
to your customers in order to convince them
that you actually have really great products
in terms of the carbon.
Um, can I go next, please?
And one of the examples we worked with repeat-recently
on, first, estimating their emissions
and, ah, helping them... um... reduce them... ah... over time.
So the problem is that they have 25,000 products
and there is way more ingredients coming into creation
as of those products.
You need to estimate them to see where your hotspots are
and create a plan that can be executed across the company.
The last bit is actually also very, very complex
because it's a big corporation.
You have hundreds of users internally,
which can affect, like, uh, uh, this equation.
So you need a way to people - for people to communicate,
to set targets, to involve your supply chain into it -
to potentially, ah, talk with your echo design teams
how to change this in the future.
So all of this is a very, very complex part
with a lot of data to play with.
And it's data that, it's more granular than financial
because you need to actual splits
of what is inside your items.
That is very hard to make, very easy surfaceable
for also non-very experienced users.