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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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00:00:27,105 --> 00:00:31,500
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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00:00:42,280 --> 00:00:44,650
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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00:01:54,390 --> 00:01:57,600
Markets need to
be run by numbers.

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00:01:57,600 --> 00:02:00,480
And the problem is that
carbon footprint is broken.

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The system doesn't add up.

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00:02:03,120 --> 00:02:06,360
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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00:02:09,780 --> 00:02:11,700
So we need to fix
carbon footprint

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00:02:11,700 --> 00:02:14,870
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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00:09:07,850 --> 00:09:11,420
is doing, and how do I
allocate their emissions

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00:09:11,420 --> 00:09:13,255
to the different
other customers?

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And then there are things
that are really important

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00:09:15,380 --> 00:09:19,190
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.

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So what needs fixing?

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00:09:30,830 --> 00:09:33,380
The on-site emission
is pretty well.

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00:09:33,380 --> 00:09:34,440
We can measure it.

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00:09:34,440 --> 00:09:38,360
We can measure our natural gas,
that you'll hear about soon.

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Everything that's on site is OK.

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Electricity is a
very complex problem

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because we need to solve
the inverse problem.

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00:09:49,850 --> 00:09:52,270
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.

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00:09:54,710 --> 00:09:58,280
Some of it might come from New
England ISO, from solar panels,

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00:09:58,280 --> 00:09:59,660
from Hydro-Québec.

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00:09:59,660 --> 00:10:01,670
How do I solve this problem?

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00:10:01,670 --> 00:10:03,650
It's a problem that
wasn't solved yet.

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00:10:03,650 --> 00:10:05,495
Supply chain, as I
said, is hugely complex.

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00:10:05,495 --> 00:10:06,930
It needs to be fixed.

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00:10:06,930 --> 00:10:09,240
And then water
needs to be fixed.

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00:10:09,240 --> 00:10:12,510
And then I need a metric
which is intuitive.

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So we won't talk
with those anecdotes.

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So it's a hugely
complex system, because

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agricultural and
gradual waste and where

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00:10:21,020 --> 00:10:24,620
everything comes from is--

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solving for the infrastructure
is very complex.

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00:10:28,430 --> 00:10:31,860
And as I mentioned, we need
to solve the inverse problem.

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We're in the end
receiving electricity.

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00:10:35,480 --> 00:10:37,190
And we need to work
out all the way

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00:10:37,190 --> 00:10:39,530
upstream to see where this
electricity is coming from.

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And electricity is one example.

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00:10:41,750 --> 00:10:46,340
And the other problem is that
your generation is busy doing

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something else, clicking ads.

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00:10:48,590 --> 00:10:52,820
The best data scientists
in this country

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00:10:52,820 --> 00:10:56,630
are busy doing not
solving social problems,

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but solving other problem.

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So part of the things that
we're doing with Greenometry

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00:11:01,670 --> 00:11:05,060
is just trying to build a
tech company, nonprofit,

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00:11:05,060 --> 00:11:07,400
nonprofit that acts
like a tech company.

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00:11:07,400 --> 00:11:08,790
But the success
criteria, instead

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00:11:08,790 --> 00:11:10,290
of being building
shareholder value,

243
00:11:10,290 --> 00:11:17,070
it will be just abated CO2, just
reducing a carbon footprint.

244
00:11:17,070 --> 00:11:22,255
So we want to fix the
electricity, the supply chain,

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00:11:22,255 --> 00:11:24,380
obviously with the others--
we cannot do everything

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00:11:24,380 --> 00:11:28,290
ourselves-- water, and
have a very simple unit,

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00:11:28,290 --> 00:11:29,990
which we call an energy point.

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00:11:29,990 --> 00:11:33,680
An energy point is simply 10
kilograms of CO2, very simple.

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00:11:33,680 --> 00:11:36,200
But it's equivalent
to one gallon of gas.

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00:11:36,200 --> 00:11:41,865
So if I bought a shoe that
is three energy points.

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00:11:41,865 --> 00:11:44,240
I know that it's equivalent
to about three gallons of gas

252
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in my car, start
building this intuition.

253
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And it's a huge path, and we're
in the beginning of this road

254
00:11:49,880 --> 00:11:53,360
because creating a new
language, a new quantitative way

255
00:11:53,360 --> 00:11:56,310
of thinking about
things is a huge path.

256
00:11:56,310 --> 00:11:59,750
So let me show you a few
ways that we've handled

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00:11:59,750 --> 00:12:03,890
this problem with data science.

258
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So thinking about
electricity, the carbon

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00:12:07,670 --> 00:12:10,280
footprint of electricity
is measured in this country

260
00:12:10,280 --> 00:12:11,090
right now.

261
00:12:11,090 --> 00:12:13,400
And the US, by the way,
is leading in the world.

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And God bless the
EPA, and I hope

263
00:12:14,990 --> 00:12:19,770
that they'll exist and
be safe for a long time.

264
00:12:19,770 --> 00:12:24,380
So the EPA divides
the US to 24 regions,

265
00:12:24,380 --> 00:12:28,280
provides an annual
average information,

266
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with two years delay.

267
00:12:29,600 --> 00:12:33,020
So just two weeks ago, we
got the 2014 information,

268
00:12:33,020 --> 00:12:34,740
which is an annual average.

269
00:12:34,740 --> 00:12:39,580
Now, as we know, electricity is
traded in 10 minutes intervals.

270
00:12:39,580 --> 00:12:42,350
And there's about 20,000
power plants in the US.

271
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So it's a hugely complex
problem that needs

272
00:12:44,450 --> 00:12:48,410
to be solved with more details.

273
00:12:48,410 --> 00:12:51,380
So we've developed a
data science model, where

274
00:12:51,380 --> 00:12:53,010
we didn't solve it entirely.

275
00:12:53,010 --> 00:12:54,150
We just improved it.

276
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And then published and
made the data available.

277
00:12:56,690 --> 00:13:01,070
We improved the cadence from
annual to monthly or hourly--

278
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it depends on the data
available from the power

279
00:13:03,260 --> 00:13:07,070
plants-- and the spatial
resolution from 24 to 138.

280
00:13:07,070 --> 00:13:08,480
So it's progress.

281
00:13:08,480 --> 00:13:12,990
And we're not providing this
information to all developers

282
00:13:12,990 --> 00:13:15,830
through an API that will
be launched next week.

283
00:13:15,830 --> 00:13:18,080
So every developer
that would like

284
00:13:18,080 --> 00:13:21,800
to develop an app
that uses electricity

285
00:13:21,800 --> 00:13:25,460
can use our information
and develop cool apps.

286
00:13:25,460 --> 00:13:27,620
Because the developers
market is kind of

287
00:13:27,620 --> 00:13:31,640
stagnant in carbon footprint
if the data is so boring

288
00:13:31,640 --> 00:13:36,070
and in such a low cadence.

289
00:13:36,070 --> 00:13:38,510
Another example of
solving carbon footprint

290
00:13:38,510 --> 00:13:41,421
is how to introduce water
into carbon footprint.

291
00:13:41,421 --> 00:13:42,920
So as someone who
grew up in Israel,

292
00:13:42,920 --> 00:13:45,272
I'm very sensitive
to the water issue.

293
00:13:45,272 --> 00:13:46,730
Just like you have
college football

294
00:13:46,730 --> 00:13:50,100
in the newspaper in the US, you
have water issues in Israel,

295
00:13:50,100 --> 00:13:51,650
along with other issues.

296
00:13:51,650 --> 00:13:56,120
So the way we developed it, we
looked at the energy intensity

297
00:13:56,120 --> 00:13:57,030
of water.

298
00:13:57,030 --> 00:14:00,330
How much energy is invested
in water in each location?

299
00:14:00,330 --> 00:14:02,330
And then what's the source
of this energy, which

300
00:14:02,330 --> 00:14:03,680
is the previous problem?

301
00:14:03,680 --> 00:14:08,330
And together we mapped
water into energy.

302
00:14:08,330 --> 00:14:10,010
Now, there's a
huge remaining work

303
00:14:10,010 --> 00:14:12,740
to do because it's very local.

304
00:14:12,740 --> 00:14:15,680
Last summer farmers
in Massachusetts

305
00:14:15,680 --> 00:14:17,904
had to truck water
into their fields.

306
00:14:17,904 --> 00:14:19,070
How do you account for that?

307
00:14:19,070 --> 00:14:20,990
How do you account for scarcity?

308
00:14:20,990 --> 00:14:23,390
So what we do is we try
to solve this problem,

309
00:14:23,390 --> 00:14:27,650
publish papers in academic
journals or other places,

310
00:14:27,650 --> 00:14:29,600
engage into a dialogue.

311
00:14:29,600 --> 00:14:32,630
So let's see how the world, if
we have this carbon footprint

312
00:14:32,630 --> 00:14:34,730
2.0, if we have like
a quantitative way

313
00:14:34,730 --> 00:14:39,570
to think about climate,
how the world looks like.

314
00:14:39,570 --> 00:14:43,400
So think about the
possibility that each of you

315
00:14:43,400 --> 00:14:46,940
will have a carbon budget.

316
00:14:46,940 --> 00:14:49,610
In very simple terms, EP
equivalent to a gallon of gas,

317
00:14:49,610 --> 00:14:51,109
as we've discussed.

318
00:14:51,109 --> 00:14:52,400
So you have a household budget.

319
00:14:52,400 --> 00:14:54,233
You have a city budget,
a university budget,

320
00:14:54,233 --> 00:14:56,270
a company budget in your EP.

321
00:14:56,270 --> 00:14:58,200
And you have a context.

322
00:14:58,200 --> 00:15:00,930
If you're in Texas
in the summer,

323
00:15:00,930 --> 00:15:05,390
it will be about 200 EPs,
equivalent to a gallon of gas,

324
00:15:05,390 --> 00:15:10,640
or 2,000 kilos of CO2 per
month for your household.

325
00:15:10,640 --> 00:15:14,180
And part of it is water
just because a lot of energy

326
00:15:14,180 --> 00:15:15,730
goes into water.

327
00:15:15,730 --> 00:15:17,750
In New England in the
winter will be less.

328
00:15:17,750 --> 00:15:20,199
But it can be very specific.

329
00:15:20,199 --> 00:15:21,740
And you start thinking
quantitatively

330
00:15:21,740 --> 00:15:23,360
about your impact.

331
00:15:23,360 --> 00:15:25,350
So if you have, just
like we've discussed,

332
00:15:25,350 --> 00:15:30,500
a home energy device, so a home
energy device, like a sense,

333
00:15:30,500 --> 00:15:33,240
will give you a reading
in kilowatt hours.

334
00:15:33,240 --> 00:15:35,300
So we have, let's say,
29 kilowatt hours.

335
00:15:35,300 --> 00:15:38,510
It doesn't have
any carbon context

336
00:15:38,510 --> 00:15:42,470
unless you translate it to what
happens on the grid right now.

337
00:15:42,470 --> 00:15:45,450
So using our API, you can
have this translation.

338
00:15:45,450 --> 00:15:47,660
So you can see that
if I'm in Brookline

339
00:15:47,660 --> 00:15:50,510
and I have in my
energy sources solar,

340
00:15:50,510 --> 00:15:54,470
this 29 kilowatt hours
is less than 1 EP.

341
00:15:54,470 --> 00:15:57,050
But on the other hand,
if I'm in Wyoming

342
00:15:57,050 --> 00:16:00,500
and my electricity source
happens to be coal,

343
00:16:00,500 --> 00:16:05,030
it can be significantly
more, maybe five times more.

344
00:16:05,030 --> 00:16:06,950
So I can have a
budget that allows

345
00:16:06,950 --> 00:16:08,300
me to think quantitatively.

346
00:16:08,300 --> 00:16:10,850
Just like a diet, I
think about calories,

347
00:16:10,850 --> 00:16:13,700
think about my carbon footprint.

348
00:16:13,700 --> 00:16:16,610
If I happen to drive
a Tesla, so the Tesla

349
00:16:16,610 --> 00:16:18,350
doesn't have an MPG
rating because it

350
00:16:18,350 --> 00:16:20,270
doesn't consume gasoline.

351
00:16:20,270 --> 00:16:24,170
But if you think about this
conversion of 10 kilograms

352
00:16:24,170 --> 00:16:28,250
of CO2 is a gallon of gas,
so I can convert the same 10

353
00:16:28,250 --> 00:16:31,670
kilograms of CO2 to
the energy sources that

354
00:16:31,670 --> 00:16:33,830
feed the Tesla right now.

355
00:16:33,830 --> 00:16:36,020
Instead of having
just watt hours

356
00:16:36,020 --> 00:16:38,690
per mile, which is the reading
on the Tesla dashboard,

357
00:16:38,690 --> 00:16:40,610
I can have the Tesla MPG.

358
00:16:40,610 --> 00:16:43,430
So I can actually compare
the Tesla to other cars.

359
00:16:43,430 --> 00:16:46,760
Tesla versus Lexus,
if I'm in Wyoming

360
00:16:46,760 --> 00:16:49,120
and I have an
energy-intensive grid,

361
00:16:49,120 --> 00:16:51,230
actually the Lexus is better.

362
00:16:51,230 --> 00:16:54,140
If I'm in California,
most of California

363
00:16:54,140 --> 00:16:57,010
or most of Massachusetts,
actually Tesla is better.

364
00:16:57,010 --> 00:16:59,510
And if I have a cleaner power
source, obviously it's better.

365
00:16:59,510 --> 00:17:00,801
But everything is quantitative.

366
00:17:00,801 --> 00:17:02,480
It's not anecdotal.

367
00:17:02,480 --> 00:17:04,920
It's the actions
actually add up.

368
00:17:04,920 --> 00:17:07,339
So I can see how
much money I need

369
00:17:07,339 --> 00:17:11,339
to invest per unit of carbon.

370
00:17:11,339 --> 00:17:13,700
One of my favorite examples
is the running shoe.

371
00:17:13,700 --> 00:17:16,579
If I buy and Nike, Nike is
very proud of the Flyknit

372
00:17:16,579 --> 00:17:18,290
because they have
this great innovation

373
00:17:18,290 --> 00:17:21,349
of having one thread that
ties the entire shoe.

374
00:17:21,349 --> 00:17:25,160
But the Flyknit
reduces about 20%

375
00:17:25,160 --> 00:17:29,120
of the material in the shoe,
which is a huge accomplishment.

376
00:17:29,120 --> 00:17:31,550
But if you think about
the climate impact

377
00:17:31,550 --> 00:17:33,770
and you add the energy
and water, which

378
00:17:33,770 --> 00:17:37,790
are pretty similar to the
competing issue, the Pegasus,

379
00:17:37,790 --> 00:17:39,290
the accomplishment
is not that huge.

380
00:17:39,290 --> 00:17:42,270
It's like about, let's say, 10%.

381
00:17:42,270 --> 00:17:45,320
What's interesting, especially
with those conversations

382
00:17:45,320 --> 00:17:48,770
on domestic manufacturing,
is that if I

383
00:17:48,770 --> 00:17:50,760
will know the entire
infrastructure,

384
00:17:50,760 --> 00:17:52,950
I'll solve this reverse
problem and understand

385
00:17:52,950 --> 00:17:57,207
the infrastructure, and
look at what does it

386
00:17:57,207 --> 00:17:58,790
mean for the climate,
not financially?

387
00:17:58,790 --> 00:18:05,120
To move manufracturing to
Portland, Oregon, Nike's

388
00:18:05,120 --> 00:18:08,810
headquarter, I will reduce
the impact of these shoes

389
00:18:08,810 --> 00:18:11,990
significantly, by about 30%,
because they have cleaner

390
00:18:11,990 --> 00:18:14,150
water and cleaner power.

391
00:18:14,150 --> 00:18:15,750
And I have this
quantitative notion.

392
00:18:15,750 --> 00:18:18,526
I step into a shop, want
to buy a running shoe.

393
00:18:18,526 --> 00:18:20,150
And I know that it's
about three energy

394
00:18:20,150 --> 00:18:23,660
points, which is equivalent
to about three gallons of gas.

395
00:18:23,660 --> 00:18:28,730
So I can know how much it
relates to my other activities.

396
00:18:28,730 --> 00:18:30,590
I start having a budget.

397
00:18:30,590 --> 00:18:32,240
Just like people
that are athletes

398
00:18:32,240 --> 00:18:34,190
that run their
lives with a Fitbit,

399
00:18:34,190 --> 00:18:35,460
I can have a "carbon bit."

400
00:18:35,460 --> 00:18:37,760
There are endless possibilities.

401
00:18:37,760 --> 00:18:41,530
And I'll end with the
last example, investors.

402
00:18:41,530 --> 00:18:43,090
There are trillions
of dollars that

403
00:18:43,090 --> 00:18:45,440
claim to be impact investors.

404
00:18:45,440 --> 00:18:48,110
And they're thirsty for data.

405
00:18:48,110 --> 00:18:49,820
For one example,
imagine that you want

406
00:18:49,820 --> 00:18:52,280
to invest in a solar project.

407
00:18:52,280 --> 00:19:02,000
Now, a solar project, if you
take a mono-silicon panels

408
00:19:02,000 --> 00:19:05,270
produced in China, installed
in a relatively clean grid

409
00:19:05,270 --> 00:19:10,250
in California, it can
take up to nine years just

410
00:19:10,250 --> 00:19:13,220
to pay back the carbon.

411
00:19:13,220 --> 00:19:18,975
If the lifetime of these panels
is 20 years, it's nearly half.

412
00:19:18,975 --> 00:19:22,250
It's a serious time just
paying back the carbon.

413
00:19:22,250 --> 00:19:26,540
So the MPG of solar using
Carbon Footprint 2.0

414
00:19:26,540 --> 00:19:27,860
can be calculated.

415
00:19:27,860 --> 00:19:30,200
On the other hand, if I take
cadmium telluride produced

416
00:19:30,200 --> 00:19:32,990
in Malaysia
installed in Wyoming,

417
00:19:32,990 --> 00:19:35,340
it returns its carbon
within two years.

418
00:19:35,340 --> 00:19:38,010
So it won't drive the
decisions, but it needs

419
00:19:38,010 --> 00:19:40,100
to be an additional factor.

420
00:19:40,100 --> 00:19:41,850
The economics will
drive the decisions.

421
00:19:41,850 --> 00:19:44,310
But if I have a
carbon budget, I start

422
00:19:44,310 --> 00:19:46,960
thinking in numbers
and not in adjectives.

423
00:19:46,960 --> 00:19:48,900
So these things start
to be meaningful.

424
00:19:48,900 --> 00:19:53,640
Because today, investors can
calculate their financial ROI.

425
00:19:53,640 --> 00:19:56,160
They're totally lost
on their social ROI.

426
00:19:56,160 --> 00:19:56,970
What's the climate?

427
00:19:56,970 --> 00:19:57,810
Everything is renewable.

428
00:19:57,810 --> 00:19:58,530
Everything is great.

429
00:19:58,530 --> 00:20:00,446
And we're having a great
party, and everything

430
00:20:00,446 --> 00:20:03,520
is green except the planet.

431
00:20:03,520 --> 00:20:08,320
So only three points that
I want you to remember.

432
00:20:08,320 --> 00:20:12,810
We must engage the market
because the polluters

433
00:20:12,810 --> 00:20:15,600
will be less engaged.

434
00:20:15,600 --> 00:20:18,540
The market lives on metrics.

435
00:20:18,540 --> 00:20:22,330
And to have a good metric, we
need to fix carbon footprint.

436
00:20:22,330 --> 00:20:26,940
So the privilege of knowledge
brings a duty to act.

437
00:20:26,940 --> 00:20:28,740
So let's all act about it.

438
00:20:28,740 --> 00:20:30,747
Thank you very much.

439
00:20:30,747 --> 00:20:32,703
[APPLAUSE]

440
00:20:42,972 --> 00:20:44,928
AUDIENCE: So I
have solar panels,

441
00:20:44,928 --> 00:20:48,740
and I pay for wind power.

442
00:20:48,740 --> 00:20:53,160
But by my address, I think I
know Google, at some point,

443
00:20:53,160 --> 00:20:55,510
it would tell you where your
coal is coming from, right,

444
00:20:55,510 --> 00:20:58,130
based on your zip code
or something like that?

445
00:21:01,900 --> 00:21:04,040
So there's a market
in my neighborhood.

446
00:21:04,040 --> 00:21:05,044
I would have Pepco.

447
00:21:05,044 --> 00:21:06,650
It would probably be coal.

448
00:21:06,650 --> 00:21:08,856
But I've opted for wind power.

449
00:21:08,856 --> 00:21:11,600
I've had some people
tell me that's not really

450
00:21:11,600 --> 00:21:13,742
doing anything.

451
00:21:13,742 --> 00:21:17,810
ORY ZIK: So obviously it does.

452
00:21:17,810 --> 00:21:20,510
Having wind is better
than not having wind.

453
00:21:20,510 --> 00:21:22,340
The way it works
today, since once

454
00:21:22,340 --> 00:21:23,990
you have an electron
on the grid,

455
00:21:23,990 --> 00:21:27,080
it doesn't run with an
ID card, you don't know.

456
00:21:27,080 --> 00:21:30,440
So people are loading
wind electricity on their,

457
00:21:30,440 --> 00:21:34,040
let's say, New England IOS, and
then it's assumed to be shared.

458
00:21:34,040 --> 00:21:37,460
I think we can do a
better job with science

459
00:21:37,460 --> 00:21:41,310
for you to allow you to know
your actual carbon footprint.

460
00:21:41,310 --> 00:21:43,910
So you can have a
budget that actually

461
00:21:43,910 --> 00:21:46,700
relates to your activity.

462
00:21:46,700 --> 00:21:50,480
And I think that those numbers
will create extra motivation.

463
00:21:50,480 --> 00:21:52,580
But obviously, your
solar panels and the wind

464
00:21:52,580 --> 00:21:55,460
is a contribution, especially
if the benchmark is coal.

465
00:21:55,460 --> 00:21:59,240
I mean, there's no
argument about that.

466
00:21:59,240 --> 00:22:02,220
It's almost like the lemon
problems in economics,

467
00:22:02,220 --> 00:22:08,290
that more transparency
provides better value.