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ORY ZIK: Thank you.

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It's a pleasure to be here.

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So I'm going to talk
about Greenometry.

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Greenometry is a new nonprofit
that enlists the market

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in solving climate change.

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Enlisting the
market means that we

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want to allow everyone
to know the carbon

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footprint of everything.

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And for that, we need
to fix carbon footprint.

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So that's what I
want to talk about.

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And it's not a secret
to you that the country

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took like a different
trajectory than reality.

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16 of the last 17 years
were the hottest on record.

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And then policy is going
on the opposite direction.

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So if we would expect the
emitters, the supply side,

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to be a major part
of the solution,

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they have less incentive to be
a major part of the solution

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right now because
they're not forced to.

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So the power that we want
to engage is the market.

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And if you think
about it, the market

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is maybe the largest
force on the planet.

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Think about nearly 7
billion buyers, trillions

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of dollars of investors.

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About 400 companies control
70% of the commodity trading.

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Think about the decision
power these entities have.

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Universities, every
university, including this one,

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has a climate action plan.

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Cities, about 70% of the
carbon emission on the planet

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is related to cities and so on.

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So we want to engage the market.

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The oxygen of
markets is metrics.

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Markets need to
be run by numbers.

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And the problem is that
carbon footprint is broken.

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The system doesn't add up.

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If I would ask any of you what
the carbon footprint of nearly

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anything, you wouldn't know.

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It's so vague.

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So we need to fix
carbon footprint

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to activate the market to
be part of the solution.

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And I want to talk
more about this point.

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So if you look at
how many searches

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are done for the term
carbon footprint on Google,

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you would see the rise and
fall of carbon footprint.

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It was high and then went low.

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If you look at newspaper
articles, it also went lower.

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On the other hand, academic
publications went up.

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So there's a gap between
knowledge and actual public

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engagement in the actual
information of what

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is a carbon footprint.

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And part of the
mission of Greenometry,

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in order to allow everyone
to know the carbon

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footprint of
everything, is to bridge

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this gap between knowledge
and actual behavior.

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So what makes a good metric?

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So how do we build a
good carbon footprint?

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To answer this question,
a few years ago,

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I went to meet Daniel
Kahneman, the Nobel Prize

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winner in Economics.

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And we had a long conversation
about what makes a good metric.

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And essentially you need
to think of two components.

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One is simplicity.

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It needs to be simple.

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We need to be able
to make estimations,

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to make back-of-the-envelope
quantitative reasoning.

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And you need accuracy.

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In most decisions in
life, we use this ability

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to make estimations.

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We estimate distances.

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We estimate price.

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We estimate the
probability that the jury

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will be more in our favor.

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We do those estimations
all the time.

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Someone on a diet
can estimate calories

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with pretty good accuracy.

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If you like sports,
then you would

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know that an Olympic
athlete will run

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100 meters at about 10 seconds.

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But then you need
the accuracy in order

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to have a race to the top.

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Who is the best?

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It's fractions of seconds.

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And if you think
about carbon footprint

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and you want to compare two
products that are the same,

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two running shoes,
you need the accuracy

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in order to determine
between the two of them.

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So you need simplicity,
and you need accuracy.

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The simplicity is this
quantitative reasoning

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or quantitative intuition,
which is sort of vague or sort

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of elusive.

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But quantitative
intuition, the way

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to build it, according to
this beautiful book, Thinking

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of Fast and Slow, is by thinking
about two things, practice

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and consistency.

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If you have a consistent signal,
like the calories of food,

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and you think about it
daily, it becomes intuitive.

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So how well are we doing
on carbon footprint?

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And the easiest thing to think
about is a gallon of gas.

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This is like the
one major decision

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that we really make
daily, put gas in our car.

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It's very energy dense, and this
is like the largest emission

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that we do.

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And about two hours
a year, we have

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nothing better to do, just fuel
our cars and look at the price.

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That's why the
price is presented

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with fractions of cents.

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So how well can we tell
the carbon content,

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how much the carbon
emission of a gallon of gas?

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So we were curious
about this question.

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And with a friend at
Northeastern University,

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we've asked 300 people, how much
carbon they emit when they put

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one gallon of gas in their car?

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The variation of the answer
was two to three orders

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of magnitude between
grams and tons,

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sort of, or tens
of grams of tons.

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Think about going into
a Starbucks wanting

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to buy a cup of
coffee and don't know

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if it's going to cost
you $300 or 0.3 cents.

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That's how bad we are.

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So what does the market
have to fix this problem?

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If we look at numbers in an
anecdotal way, it's adjectives.

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Grams, tons,
everything is confused.

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It's either that we
don't care, or that we're

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too lazy to actually
do the math,

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or something else is wrong in
the system that we want to fix.

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By the way, we were so
surprised by this result,

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that we asked 1,000 people
and got the same result.

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This is a log scale.

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The x-axis are
questions that are also

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relevant to the daily life, like
what's the weight of the car?

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You don't lift your car.

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And still it's orders of
magnitude better estimation

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than carbon.

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Something is fundamentally
wrong with carbon footprint.

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If you put the same data in
different carbon calculators

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online, the result
varies by 300%.

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So MIT wants to reduce its
carbon footprint by, let's say,

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30%.

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And we use a system with
uncertainty of 300%.

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So where is the math?

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And that obviously gives rise
to all kinds of anecdotes.

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We can see newspaper articles
about saving the planet

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by sending less emails
because an email is 0.3 grams.

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And in the world of anecdotes
of adjectives, a gram and a ton

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is the same.

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So you don't bother
to do the math.

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It's 0.00003 a
gallon of gas, right?

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If you look at the
way that companies

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are handling this problem,
look at Timberland.

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Timberland is probably
one of the companies

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that were the best geared to
have a good sustainability

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program.

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They had a committed CEO,
committed shareholders.

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The customers are
the outdoor people.

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So it's better.

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So they wanted to reduce
their carbon footprint.

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And they built great
sustainability reports.

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They were prize winners in
terms of the sustainability

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performance.

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The way carbon
footprint is built,

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so they looked at
only their own site

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emissions, what they're
responsible for,

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which turns out to be about
4% of the total emission.

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The other about
20% is electricity,

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and then the rest
is supply chain.

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So they did phenomenal work
reducing 20% of the 4%.

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And that's the best company.

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And then they said, let's
engage our customers,

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our outdoor people.

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So they had a label, a
product label on the shoes.

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An average Timberland shoe
is about two kilowatt hour.

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The accuracy was such
that all the shoes

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were two kilowatt hour.

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And who is the
consumer that knows

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what's a kilowatt hour
in the context of a shoe?

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What buying decisions it make?

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So after 18 months of
building this program,

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they had the label,
and they took it out.

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So something is fundamentally
wrong with the way

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we try to fix the
problem of climate change

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through the market because the
market doesn't have metrics,

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because carbon
footprint doesn't work.

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So let's look at how
carbon footprint is built.

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What are we doing?

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So it's divided to a few scopes.

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Scope one is what we do on site.

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Scope two is the electricity
that we purchase.

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And it's divided to scope
to avoid a double counting.

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Then supply chain
is hugely complex.

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It is scope 3.

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It is complex
because how do I know

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what the supply in China or
in Vietnam or in Malaysia

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is doing, and how do I
allocate their emissions

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to the different
other customers?

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And then there are things
that are really important

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and are not included,
like water or land use.

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Next step is to convert this
consumption to a metric, which

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is tons of CO2.

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And then we need to
read the tons of CO2.

00:09:27.680 --> 00:09:30.830
So what needs fixing?

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The on-site emission
is pretty well.

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We can measure it.

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We can measure our natural gas,
that you'll hear about soon.

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Everything that's on site is OK.

00:09:42.380 --> 00:09:45.890
Electricity is a
very complex problem

00:09:45.890 --> 00:09:49.850
because we need to solve
the inverse problem.

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I'm here consuming
electricity in this room.

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Some of it might come from the
co-gen plant down the road.

00:09:54.710 --> 00:09:58.280
Some of it might come from New
England ISO, from solar panels,

00:09:58.280 --> 00:09:59.660
from Hydro-Québec.

00:09:59.660 --> 00:10:01.670
How do I solve this problem?

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It's a problem that
wasn't solved yet.

00:10:03.650 --> 00:10:05.495
Supply chain, as I
said, is hugely complex.

00:10:05.495 --> 00:10:06.930
It needs to be fixed.

00:10:06.930 --> 00:10:09.240
And then water
needs to be fixed.

00:10:09.240 --> 00:10:12.510
And then I need a metric
which is intuitive.

00:10:12.510 --> 00:10:15.440
So we won't talk
with those anecdotes.

00:10:15.440 --> 00:10:17.870
So it's a hugely
complex system, because

00:10:17.870 --> 00:10:21.020
agricultural and
gradual waste and where

00:10:21.020 --> 00:10:24.620
everything comes from is--

00:10:24.620 --> 00:10:28.430
solving for the infrastructure
is very complex.

00:10:28.430 --> 00:10:31.860
And as I mentioned, we need
to solve the inverse problem.

00:10:31.860 --> 00:10:35.480
We're in the end
receiving electricity.

00:10:35.480 --> 00:10:37.190
And we need to work
out all the way

00:10:37.190 --> 00:10:39.530
upstream to see where this
electricity is coming from.

00:10:39.530 --> 00:10:41.750
And electricity is one example.

00:10:41.750 --> 00:10:46.340
And the other problem is that
your generation is busy doing

00:10:46.340 --> 00:10:48.590
something else, clicking ads.

00:10:48.590 --> 00:10:52.820
The best data scientists
in this country

00:10:52.820 --> 00:10:56.630
are busy doing not
solving social problems,

00:10:56.630 --> 00:10:59.340
but solving other problem.

00:10:59.340 --> 00:11:01.670
So part of the things that
we're doing with Greenometry

00:11:01.670 --> 00:11:05.060
is just trying to build a
tech company, nonprofit,

00:11:05.060 --> 00:11:07.400
nonprofit that acts
like a tech company.

00:11:07.400 --> 00:11:08.790
But the success
criteria, instead

00:11:08.790 --> 00:11:10.290
of being building
shareholder value,

00:11:10.290 --> 00:11:17.070
it will be just abated CO2, just
reducing a carbon footprint.

00:11:17.070 --> 00:11:22.255
So we want to fix the
electricity, the supply chain,

00:11:22.255 --> 00:11:24.380
obviously with the others--
we cannot do everything

00:11:24.380 --> 00:11:28.290
ourselves-- water, and
have a very simple unit,

00:11:28.290 --> 00:11:29.990
which we call an energy point.

00:11:29.990 --> 00:11:33.680
An energy point is simply 10
kilograms of CO2, very simple.

00:11:33.680 --> 00:11:36.200
But it's equivalent
to one gallon of gas.

00:11:36.200 --> 00:11:41.865
So if I bought a shoe that
is three energy points.

00:11:41.865 --> 00:11:44.240
I know that it's equivalent
to about three gallons of gas

00:11:44.240 --> 00:11:47.180
in my car, start
building this intuition.

00:11:47.180 --> 00:11:49.880
And it's a huge path, and we're
in the beginning of this road

00:11:49.880 --> 00:11:53.360
because creating a new
language, a new quantitative way

00:11:53.360 --> 00:11:56.310
of thinking about
things is a huge path.

00:11:56.310 --> 00:11:59.750
So let me show you a few
ways that we've handled

00:11:59.750 --> 00:12:03.890
this problem with data science.

00:12:03.890 --> 00:12:07.670
So thinking about
electricity, the carbon

00:12:07.670 --> 00:12:10.280
footprint of electricity
is measured in this country

00:12:10.280 --> 00:12:11.090
right now.

00:12:11.090 --> 00:12:13.400
And the US, by the way,
is leading in the world.

00:12:13.400 --> 00:12:14.990
And God bless the
EPA, and I hope

00:12:14.990 --> 00:12:19.770
that they'll exist and
be safe for a long time.

00:12:19.770 --> 00:12:24.380
So the EPA divides
the US to 24 regions,

00:12:24.380 --> 00:12:28.280
provides an annual
average information,

00:12:28.280 --> 00:12:29.600
with two years delay.

00:12:29.600 --> 00:12:33.020
So just two weeks ago, we
got the 2014 information,

00:12:33.020 --> 00:12:34.740
which is an annual average.

00:12:34.740 --> 00:12:39.580
Now, as we know, electricity is
traded in 10 minutes intervals.

00:12:39.580 --> 00:12:42.350
And there's about 20,000
power plants in the US.

00:12:42.350 --> 00:12:44.450
So it's a hugely complex
problem that needs

00:12:44.450 --> 00:12:48.410
to be solved with more details.

00:12:48.410 --> 00:12:51.380
So we've developed a
data science model, where

00:12:51.380 --> 00:12:53.010
we didn't solve it entirely.

00:12:53.010 --> 00:12:54.150
We just improved it.

00:12:54.150 --> 00:12:56.690
And then published and
made the data available.

00:12:56.690 --> 00:13:01.070
We improved the cadence from
annual to monthly or hourly--

00:13:01.070 --> 00:13:03.260
it depends on the data
available from the power

00:13:03.260 --> 00:13:07.070
plants-- and the spatial
resolution from 24 to 138.

00:13:07.070 --> 00:13:08.480
So it's progress.

00:13:08.480 --> 00:13:12.990
And we're not providing this
information to all developers

00:13:12.990 --> 00:13:15.830
through an API that will
be launched next week.

00:13:15.830 --> 00:13:18.080
So every developer
that would like

00:13:18.080 --> 00:13:21.800
to develop an app
that uses electricity

00:13:21.800 --> 00:13:25.460
can use our information
and develop cool apps.

00:13:25.460 --> 00:13:27.620
Because the developers
market is kind of

00:13:27.620 --> 00:13:31.640
stagnant in carbon footprint
if the data is so boring

00:13:31.640 --> 00:13:36.070
and in such a low cadence.

00:13:36.070 --> 00:13:38.510
Another example of
solving carbon footprint

00:13:38.510 --> 00:13:41.421
is how to introduce water
into carbon footprint.

00:13:41.421 --> 00:13:42.920
So as someone who
grew up in Israel,

00:13:42.920 --> 00:13:45.272
I'm very sensitive
to the water issue.

00:13:45.272 --> 00:13:46.730
Just like you have
college football

00:13:46.730 --> 00:13:50.100
in the newspaper in the US, you
have water issues in Israel,

00:13:50.100 --> 00:13:51.650
along with other issues.

00:13:51.650 --> 00:13:56.120
So the way we developed it, we
looked at the energy intensity

00:13:56.120 --> 00:13:57.030
of water.

00:13:57.030 --> 00:14:00.330
How much energy is invested
in water in each location?

00:14:00.330 --> 00:14:02.330
And then what's the source
of this energy, which

00:14:02.330 --> 00:14:03.680
is the previous problem?

00:14:03.680 --> 00:14:08.330
And together we mapped
water into energy.

00:14:08.330 --> 00:14:10.010
Now, there's a
huge remaining work

00:14:10.010 --> 00:14:12.740
to do because it's very local.

00:14:12.740 --> 00:14:15.680
Last summer farmers
in Massachusetts

00:14:15.680 --> 00:14:17.904
had to truck water
into their fields.

00:14:17.904 --> 00:14:19.070
How do you account for that?

00:14:19.070 --> 00:14:20.990
How do you account for scarcity?

00:14:20.990 --> 00:14:23.390
So what we do is we try
to solve this problem,

00:14:23.390 --> 00:14:27.650
publish papers in academic
journals or other places,

00:14:27.650 --> 00:14:29.600
engage into a dialogue.

00:14:29.600 --> 00:14:32.630
So let's see how the world, if
we have this carbon footprint

00:14:32.630 --> 00:14:34.730
2.0, if we have like
a quantitative way

00:14:34.730 --> 00:14:39.570
to think about climate,
how the world looks like.

00:14:39.570 --> 00:14:43.400
So think about the
possibility that each of you

00:14:43.400 --> 00:14:46.940
will have a carbon budget.

00:14:46.940 --> 00:14:49.610
In very simple terms, EP
equivalent to a gallon of gas,

00:14:49.610 --> 00:14:51.109
as we've discussed.

00:14:51.109 --> 00:14:52.400
So you have a household budget.

00:14:52.400 --> 00:14:54.233
You have a city budget,
a university budget,

00:14:54.233 --> 00:14:56.270
a company budget in your EP.

00:14:56.270 --> 00:14:58.200
And you have a context.

00:14:58.200 --> 00:15:00.930
If you're in Texas
in the summer,

00:15:00.930 --> 00:15:05.390
it will be about 200 EPs,
equivalent to a gallon of gas,

00:15:05.390 --> 00:15:10.640
or 2,000 kilos of CO2 per
month for your household.

00:15:10.640 --> 00:15:14.180
And part of it is water
just because a lot of energy

00:15:14.180 --> 00:15:15.730
goes into water.

00:15:15.730 --> 00:15:17.750
In New England in the
winter will be less.

00:15:17.750 --> 00:15:20.199
But it can be very specific.

00:15:20.199 --> 00:15:21.740
And you start thinking
quantitatively

00:15:21.740 --> 00:15:23.360
about your impact.

00:15:23.360 --> 00:15:25.350
So if you have, just
like we've discussed,

00:15:25.350 --> 00:15:30.500
a home energy device, so a home
energy device, like a sense,

00:15:30.500 --> 00:15:33.240
will give you a reading
in kilowatt hours.

00:15:33.240 --> 00:15:35.300
So we have, let's say,
29 kilowatt hours.

00:15:35.300 --> 00:15:38.510
It doesn't have
any carbon context

00:15:38.510 --> 00:15:42.470
unless you translate it to what
happens on the grid right now.

00:15:42.470 --> 00:15:45.450
So using our API, you can
have this translation.

00:15:45.450 --> 00:15:47.660
So you can see that
if I'm in Brookline

00:15:47.660 --> 00:15:50.510
and I have in my
energy sources solar,

00:15:50.510 --> 00:15:54.470
this 29 kilowatt hours
is less than 1 EP.

00:15:54.470 --> 00:15:57.050
But on the other hand,
if I'm in Wyoming

00:15:57.050 --> 00:16:00.500
and my electricity source
happens to be coal,

00:16:00.500 --> 00:16:05.030
it can be significantly
more, maybe five times more.

00:16:05.030 --> 00:16:06.950
So I can have a
budget that allows

00:16:06.950 --> 00:16:08.300
me to think quantitatively.

00:16:08.300 --> 00:16:10.850
Just like a diet, I
think about calories,

00:16:10.850 --> 00:16:13.700
think about my carbon footprint.

00:16:13.700 --> 00:16:16.610
If I happen to drive
a Tesla, so the Tesla

00:16:16.610 --> 00:16:18.350
doesn't have an MPG
rating because it

00:16:18.350 --> 00:16:20.270
doesn't consume gasoline.

00:16:20.270 --> 00:16:24.170
But if you think about this
conversion of 10 kilograms

00:16:24.170 --> 00:16:28.250
of CO2 is a gallon of gas,
so I can convert the same 10

00:16:28.250 --> 00:16:31.670
kilograms of CO2 to
the energy sources that

00:16:31.670 --> 00:16:33.830
feed the Tesla right now.

00:16:33.830 --> 00:16:36.020
Instead of having
just watt hours

00:16:36.020 --> 00:16:38.690
per mile, which is the reading
on the Tesla dashboard,

00:16:38.690 --> 00:16:40.610
I can have the Tesla MPG.

00:16:40.610 --> 00:16:43.430
So I can actually compare
the Tesla to other cars.

00:16:43.430 --> 00:16:46.760
Tesla versus Lexus,
if I'm in Wyoming

00:16:46.760 --> 00:16:49.120
and I have an
energy-intensive grid,

00:16:49.120 --> 00:16:51.230
actually the Lexus is better.

00:16:51.230 --> 00:16:54.140
If I'm in California,
most of California

00:16:54.140 --> 00:16:57.010
or most of Massachusetts,
actually Tesla is better.

00:16:57.010 --> 00:16:59.510
And if I have a cleaner power
source, obviously it's better.

00:16:59.510 --> 00:17:00.801
But everything is quantitative.

00:17:00.801 --> 00:17:02.480
It's not anecdotal.

00:17:02.480 --> 00:17:04.920
It's the actions
actually add up.

00:17:04.920 --> 00:17:07.339
So I can see how
much money I need

00:17:07.339 --> 00:17:11.339
to invest per unit of carbon.

00:17:11.339 --> 00:17:13.700
One of my favorite examples
is the running shoe.

00:17:13.700 --> 00:17:16.579
If I buy and Nike, Nike is
very proud of the Flyknit

00:17:16.579 --> 00:17:18.290
because they have
this great innovation

00:17:18.290 --> 00:17:21.349
of having one thread that
ties the entire shoe.

00:17:21.349 --> 00:17:25.160
But the Flyknit
reduces about 20%

00:17:25.160 --> 00:17:29.120
of the material in the shoe,
which is a huge accomplishment.

00:17:29.120 --> 00:17:31.550
But if you think about
the climate impact

00:17:31.550 --> 00:17:33.770
and you add the energy
and water, which

00:17:33.770 --> 00:17:37.790
are pretty similar to the
competing issue, the Pegasus,

00:17:37.790 --> 00:17:39.290
the accomplishment
is not that huge.

00:17:39.290 --> 00:17:42.270
It's like about, let's say, 10%.

00:17:42.270 --> 00:17:45.320
What's interesting, especially
with those conversations

00:17:45.320 --> 00:17:48.770
on domestic manufacturing,
is that if I

00:17:48.770 --> 00:17:50.760
will know the entire
infrastructure,

00:17:50.760 --> 00:17:52.950
I'll solve this reverse
problem and understand

00:17:52.950 --> 00:17:57.207
the infrastructure, and
look at what does it

00:17:57.207 --> 00:17:58.790
mean for the climate,
not financially?

00:17:58.790 --> 00:18:05.120
To move manufracturing to
Portland, Oregon, Nike's

00:18:05.120 --> 00:18:08.810
headquarter, I will reduce
the impact of these shoes

00:18:08.810 --> 00:18:11.990
significantly, by about 30%,
because they have cleaner

00:18:11.990 --> 00:18:14.150
water and cleaner power.

00:18:14.150 --> 00:18:15.750
And I have this
quantitative notion.

00:18:15.750 --> 00:18:18.526
I step into a shop, want
to buy a running shoe.

00:18:18.526 --> 00:18:20.150
And I know that it's
about three energy

00:18:20.150 --> 00:18:23.660
points, which is equivalent
to about three gallons of gas.

00:18:23.660 --> 00:18:28.730
So I can know how much it
relates to my other activities.

00:18:28.730 --> 00:18:30.590
I start having a budget.

00:18:30.590 --> 00:18:32.240
Just like people
that are athletes

00:18:32.240 --> 00:18:34.190
that run their
lives with a Fitbit,

00:18:34.190 --> 00:18:35.460
I can have a "carbon bit."

00:18:35.460 --> 00:18:37.760
There are endless possibilities.

00:18:37.760 --> 00:18:41.530
And I'll end with the
last example, investors.

00:18:41.530 --> 00:18:43.090
There are trillions
of dollars that

00:18:43.090 --> 00:18:45.440
claim to be impact investors.

00:18:45.440 --> 00:18:48.110
And they're thirsty for data.

00:18:48.110 --> 00:18:49.820
For one example,
imagine that you want

00:18:49.820 --> 00:18:52.280
to invest in a solar project.

00:18:52.280 --> 00:19:02.000
Now, a solar project, if you
take a mono-silicon panels

00:19:02.000 --> 00:19:05.270
produced in China, installed
in a relatively clean grid

00:19:05.270 --> 00:19:10.250
in California, it can
take up to nine years just

00:19:10.250 --> 00:19:13.220
to pay back the carbon.

00:19:13.220 --> 00:19:18.975
If the lifetime of these panels
is 20 years, it's nearly half.

00:19:18.975 --> 00:19:22.250
It's a serious time just
paying back the carbon.

00:19:22.250 --> 00:19:26.540
So the MPG of solar using
Carbon Footprint 2.0

00:19:26.540 --> 00:19:27.860
can be calculated.

00:19:27.860 --> 00:19:30.200
On the other hand, if I take
cadmium telluride produced

00:19:30.200 --> 00:19:32.990
in Malaysia
installed in Wyoming,

00:19:32.990 --> 00:19:35.340
it returns its carbon
within two years.

00:19:35.340 --> 00:19:38.010
So it won't drive the
decisions, but it needs

00:19:38.010 --> 00:19:40.100
to be an additional factor.

00:19:40.100 --> 00:19:41.850
The economics will
drive the decisions.

00:19:41.850 --> 00:19:44.310
But if I have a
carbon budget, I start

00:19:44.310 --> 00:19:46.960
thinking in numbers
and not in adjectives.

00:19:46.960 --> 00:19:48.900
So these things start
to be meaningful.

00:19:48.900 --> 00:19:53.640
Because today, investors can
calculate their financial ROI.

00:19:53.640 --> 00:19:56.160
They're totally lost
on their social ROI.

00:19:56.160 --> 00:19:56.970
What's the climate?

00:19:56.970 --> 00:19:57.810
Everything is renewable.

00:19:57.810 --> 00:19:58.530
Everything is great.

00:19:58.530 --> 00:20:00.446
And we're having a great
party, and everything

00:20:00.446 --> 00:20:03.520
is green except the planet.

00:20:03.520 --> 00:20:08.320
So only three points that
I want you to remember.

00:20:08.320 --> 00:20:12.810
We must engage the market
because the polluters

00:20:12.810 --> 00:20:15.600
will be less engaged.

00:20:15.600 --> 00:20:18.540
The market lives on metrics.

00:20:18.540 --> 00:20:22.330
And to have a good metric, we
need to fix carbon footprint.

00:20:22.330 --> 00:20:26.940
So the privilege of knowledge
brings a duty to act.

00:20:26.940 --> 00:20:28.740
So let's all act about it.

00:20:28.740 --> 00:20:30.747
Thank you very much.

00:20:30.747 --> 00:20:32.703
[APPLAUSE]

00:20:42.972 --> 00:20:44.928
AUDIENCE: So I
have solar panels,

00:20:44.928 --> 00:20:48.740
and I pay for wind power.

00:20:48.740 --> 00:20:53.160
But by my address, I think I
know Google, at some point,

00:20:53.160 --> 00:20:55.510
it would tell you where your
coal is coming from, right,

00:20:55.510 --> 00:20:58.130
based on your zip code
or something like that?

00:21:01.900 --> 00:21:04.040
So there's a market
in my neighborhood.

00:21:04.040 --> 00:21:05.044
I would have Pepco.

00:21:05.044 --> 00:21:06.650
It would probably be coal.

00:21:06.650 --> 00:21:08.856
But I've opted for wind power.

00:21:08.856 --> 00:21:11.600
I've had some people
tell me that's not really

00:21:11.600 --> 00:21:13.742
doing anything.

00:21:13.742 --> 00:21:17.810
ORY ZIK: So obviously it does.

00:21:17.810 --> 00:21:20.510
Having wind is better
than not having wind.

00:21:20.510 --> 00:21:22.340
The way it works
today, since once

00:21:22.340 --> 00:21:23.990
you have an electron
on the grid,

00:21:23.990 --> 00:21:27.080
it doesn't run with an
ID card, you don't know.

00:21:27.080 --> 00:21:30.440
So people are loading
wind electricity on their,

00:21:30.440 --> 00:21:34.040
let's say, New England IOS, and
then it's assumed to be shared.

00:21:34.040 --> 00:21:37.460
I think we can do a
better job with science

00:21:37.460 --> 00:21:41.310
for you to allow you to know
your actual carbon footprint.

00:21:41.310 --> 00:21:43.910
So you can have a
budget that actually

00:21:43.910 --> 00:21:46.700
relates to your activity.

00:21:46.700 --> 00:21:50.480
And I think that those numbers
will create extra motivation.

00:21:50.480 --> 00:21:52.580
But obviously, your
solar panels and the wind

00:21:52.580 --> 00:21:55.460
is a contribution, especially
if the benchmark is coal.

00:21:55.460 --> 00:21:59.240
I mean, there's no
argument about that.

00:21:59.240 --> 00:22:02.220
It's almost like the lemon
problems in economics,

00:22:02.220 --> 00:22:08.290
that more transparency
provides better value.