Charlotte Degot A more accurate way to calculate emissions TED

Transcriber: Leslie Gauthier
Reviewer:

For decades now,

we’ve been saying we should
reduce our emissions,

but they’ve kept increasing.

One of the key reasons
is we don’t measure accurately

the climate impact of our actions.

Imagine trying to save money,

but when you go shopping,

there is no price tag on any item …

or trying to lose weight,

but you cannot measure
the portion sizes and the calories.

You would be bound to fail.

This level of blindness
is close to the one we have

when it comes to our climate impact.

Measuring greenhouse gas
emissions is hard.

It has no color,

it has no smell;

it’s invisible.

We cannot put sensors everywhere,

on every building,

every track,

every field,

every cow –

so most of the time,

we give up and we don’t measure.

And when we do measure,

we are reduced to relying on estimations

and conversion factors.

The consequence is we end up working
with highly incomplete

and inaccurate estimations
of our emissions.

Often we have a margin of error
of 30 to 60 percent.

This means targets
and action plans are set

based on inaccurate data.

If we look at the corporations

that report their progress
on climate to the CDP,

which is a nonprofit organization
that runs a global disclosure system

for environmental impacts,

what we see is striking:

more than two-thirds of the companies

are not accurately measuring
their emissions,

and only seven percent of those companies

are ultimately reducing
their impact in some way.

You cannot reduce
what you cannot measure.

It is key for corporations to be able
to measure across all activities,

all sources that drive carbon up or down.

In a way,

that’s just putting the same rigor
to carbon measurements

that we have for financial accounting.

It took more than 100 years to put modern,
automated financial accounting in place.

We don’t have 100 years
when it comes to climate.

But this is crucial for corporations
to set meaningful targets

and successful action plans.

One of the most powerful tools we have

to help us accelerate on this journey
is artificial intelligence.

Artificial intelligence can process
data automatically

from diverse, unstructured sources

like invoices, consumer behavior data.

It can work by modeling to better estimate
the missing information.

It can simulate and ultimately
optimize emissions.

Let me share an example
of how this could work.

A wine and spirits international company:

billions of sales,

hundreds of brands,

consumers across the globe.

When they want to measure their impact,

they need to measure
across the entire set of their emissions.

This means direct emissions
from facilities,

purchased electricity,

raw materials,

leased assets,

IT emissions

business travel,

transportation,

waste,

product end of life,

etcetera, etcetera.

That’s a huge amount
of information to collect.

And most of it is actually inaccessible
to the company itself

because it comes from outside
its direct scope of activity.

For example,

from suppliers that are not yet able
to calculate their emissions either.

So when the sustainability team
calculates their impact,

they have no choice
but to do rough estimates.

Let’s examine the glass for bottles.

The way they calculate
glass emissions is the following.

They take the total amount
of glass bought last year –

let’s say 1,000 tons.

They multiply it by a conversion factor,

which represents the average kilos
of CO2 equivalent for one ton of glass –

let’s say 950.

950 x 1000 makes 950,000.

Of course this is hugely inaccurate

because it does not take into account

all the numerous factors
that impact actual emissions,

so it’s hard to set targets
and action plans.

This is where the sustainability team
calls data scientists

to come in and process detailed data
about the type of glass,

the color of the glass,

the recycling share,

the supplier country of origin,

the transportation mode,

by brand,

by product.

They can simulate the design
and the supply chain

and integrate in the calculation

the importance of the glass color –

1.5 times more emissions
for a clear bottle

versus a green bottle;

the importance of the country of origin –

twice the amount of emissions
for one country versus another one,

depending on the energy mix;

the importance of the design itself –

for the same total weight,

1.5 times more emissions
for one design versus another one.

Instead of having one big, average number,

you now have a model
which correlates and calculates emissions

at a granular level.

With this type of methodology,

the emissions figure is typically
corrected by 30 to 50 percent.

And more importantly,

the company can now move to action

as they can, one, set meaningful targets,

two, identify very concrete initiatives,

and three,

recalculate emissions over time
and measure their progress.

Let me share another example:

cement.

Cement is a massive CO2 emitter.

If cement were a country,

it would rank as
the third-largest emitter,

right after China and the US,

in front of the European Union and India.

Most of the emissions come from
the process of producing clinker,

the key ingredient in cement.

To produce clinker,

you need to maintain a temperature
of over 1,400 degrees Celsius.

It requires a lot of fuel,

and it’s really just carbon
containing the whole materials.

So the secret sauce is to produce
cleaner and higher quality clinker,

because the higher
the quality of the clinker,

the less of it you will need
to produce cement ultimately,

and therefore the less emissions
you will generate.

But producing high-quality clinker
is a complex science.

It depends on multiple factors
that influence each other.

For example, the process parameters,

like the rotation speed of the machine,

how quickly you fill it,

the type of fuel you use,

the raw materials
and their exact chemical composition.

This is where artificial intelligence
can again have an enormous impact.

On-site operational teams
are trying to manually maintain

the best set of parameters possible.

AI can help by measuring better
through different sources,

like direct measurements,

material and mass balance,

etcetera …

simulate all the potential decisions

and recommend the optimal ones
to the operators.

These techniques implemented
in a cement production process

enable a substantial emissions reduction

in a matter of months.

There is an infinity
of applications possible.

There is no company,

no industry that cannot derive
significant climate impact

from the use of artificial intelligence.

I’m not saying artificial intelligence
alone will save us.

But artificial intelligence,

by helping us measure accurately,

simulate

and optimize,

enables significant emissions reduction

in a quite fast, cheap and easy way.

We cannot miss this opportunity.

Thank you.