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A
Thank
you,
everyone
for
coming,
I'm
going
to
talk
about
d
climate.
What
are
premises,
why
it's
needed
and
what
are
some
of
the
things
we
are
building
to
make
a
difference
in
the
refi
ecosystem.
A
So
d
climate
was
actually
birthed
out
of
the
first
venture
that
we
started,
which
was
parametric
insurance,
and
the
idea
was
that
when
you
do
something
like
parametric
insurance,
you
need
a
lot
of
climate
data,
and
so
the
climate
came
out
of
that
effort
and
as
a
separate
entity,
it
is
an
a
transparent,
decentralized
marketplace
and
ecosystem
to
shine
a
light
on
the
climate
information
space.
We
found
that
just
like
with
insurance.
A
The
climate
space
was
full
of
opaque
structures,
opaque
entities
and
it
was
difficult
to
make
sense
of
the
huge
amounts
of
data
and
converted
to
actionable
intelligence
in
an
affordable
way
in
a
transparent
way,
and
so
that's
the
problem
that
the
climate
was
built
to
solve
these
problems
in
climate
data
span,
many
issues
there's
large
gaps.
A
lot
of
the
world
actually
doesn't
even
have
basic
weather
data.
Let
loan
data
on
soil,
carbon
and
all
the
different
things
we
need
to
understand.
A
Our
biosphere,
there
is,
you
know,
lack
of
technical
standards
that
cross
different
data
sets.
If
you
look
at
these
data
sets,
they
all
have
very
different
ways.
They
are
stored
and
downloaded,
and
it
takes
a
lot
of
work
and
generally
a
specialized
team
to
even
work
with
the
standard.
Big
public
data
sets,
let
alone
some
of
the
more
computationally
heavy
niche
data
sets.
A
A
A
A
Our
baseline
product
is
the
marketplace,
and
this
is
to
allow
somebody
to
have
a
clean
usable
interface
and
a
back-end
system
that
gives
them
clean
data
that
can
be
used
for
everything
from
building
their
own
apps
to
running
financial
applications
like
insurance-
and
you
know,
the
idea
is
for
many
of
these
data
sets.
If
you
go
to
the
source,
you
have
to
download
gigabytes,
sometimes
terabytes
of
data
and
then
parse
it
yourself.
A
All
of
that
will
be
easily
done
on
the
climate,
and
you
don't
need
to
do
any
of
that
work,
so
it
really
starts
to
free
up.
If
you
talk
to
almost
any
climate
startup,
they
almost
you
know
often
spend
like
six
months
to
a
year,
just
cleaning
data.
So
this
is
the
baseline
and
it
allows
a
lot
of
stuff
is
free,
and
you
know
it
allows
other
companies
that
are
looking
for
clients
to
have
a
place.
They
can
get
clients
which
is
a
very
difficult
and
expensive
process.
In
this
space,
client
acquisition.
A
A
So
one
example
is
cyclops,
which
is
monitoring
forestry
assets
on
chain,
so
our
community
built
this
and
it
is
hitting
the
relative
error
rates
that
the
european
space
agency
targets,
as
you
know,
their
holy
grail,
so
to
speak.
The
idea
is
that
it's
using
a
huge
amount
of
satellite
data,
as
well
as
machine
learning
models,
different
bands
from
different
satellites
to
assess
the
health
of
a
forest.
A
The
the
need
for
this
is
urgent
because
most
of
the
force
monitoring
space
still
relies
on
on
the
ground
measurements
and
most
places
in
the
world.
Where
you
have
forests,
you
want
to
really
protect
urgently,
aren't
places
you
can
send
people
easily
to
verify.
This
would
be
the
congo
basin.
This
would
be
remote
parts
of
the
amazon
and
so
you're
not
going
to
be
able
to
solve
the
issues
around
carbon,
offsets
or
you're
not
going
to
be
able
to
solve
the
issues
around
deforestation.
A
The
infrastructure
on
this
is
relatively
involved,
but
you
can
basically
see
the
flow
going
from
chain
link,
polygon
and
file
coin
at
the
bottom.
You
know
for
taking
the
satellite
image
and
and
running
cyclops
the
model
through
chain
link
adapters
and
putting
it
on
polygon.
A
We
will
talk
more
about
this
at
future
events,
but
cyclops
underpins
a
much
more
sort
of
involved.
Carbon
offset
rethink
that
we
are
doing
in
conjunction
with
countries
to
pay
them
to
avoid
deforestation,
and
you
cannot
have
a
system
like
that
until
you
can
monitor
in
a
transparent
and
accurate
way.
A
When
you
compare
it
to
vera,
which
is
the
industry
standard,
they
can
take
five
to
ten
years
to
verify.
There
is
no
scale
in
that
process.
It
still
relies
on
boots
on
the
ground.
It
is
not
relying
on
cutting
edge.
You
know
advances
that
we
have
had
in
both
satellites
and
machine
learning,
and
you
know
it's
closed
source
and
that's
a
problem.
It's
difficult
to
verify
what
vera
itself
is
doing.
A
A
The
other
app
I
want
to
show
is
espresso,
which
is
for
our
coffee
clients,
and
why
did
we
pick
these
two?
The
idea
is
to
show
the
variety.
These
are
just
two
of
many
apps
that
are
and
will
be
coming
on
d
climate,
but
the
idea
is
that
different
sources
of
data,
when
overlaid
with
analysis
and
understanding
lead
to
very
different
applications,
so
espresso
is
for
the
coffee
market.
A
Coffee
is
a
complex
tree.
It
is
one
of
the
it's
I
think,
the
second
largest
commodity
by
value
after
oil.
It
touches
on
a
large
portion
of
the
world.
It
is
the
chief
income
source
for
millions
of
people,
and
the
problem
with
coffee
is
that
it
is
a
very,
very
you
know,
finicky
plant
when
it
comes
to
weather.
I
traded
it
for
a
long
time
and
it
was
an
extremely
stressful
market
to
trade,
because
every
single
weather
event
has
a
massive
impact
on
prices.
A
The
same
weather,
that's
good
in
one
part
of
the
season,
is
not
good
in
another
part
of
the
season.
So
with
all
these
different
stages,
it
is
very
difficult
to
make
sense
of
how
the
crop
is
doing
and
add
to
that,
the
you
can
see
the
dots,
the
extreme
geographical
spread
as
well.
As
you
know,
it's
grown
in
areas
that
have
poor
quality
data.
A
To
begin
with,
there
is
a
very
strong
need
by
both
producers,
importers,
exporters,
traders
to
understand
how
the
crop
is
doing
in
each
part
of
the
world,
and
so
the
espresso's
job,
if
you
will,
is
to
convert
the
large
amounts
of
weather
and
climate
data,
as
well
as
yield
data
that
we
have
on
the
climate
into
actionable
intelligence
for
the
coffee
market
to
see
okay,
based
on
these
weather
patterns,
what
is
the
yield?
Looking
like,
based
on
historical
relationships?
A
You
know
you
can
highlight
different
areas.
You
can
see
different
regions
and
sub-regions,
and
you
know
this
helps
you
make
sense
of
rainfall,
heat
data
and
all
these
other
data
sets
into
what
the
crop
is
looking
like,
and
this
is
very
important,
whether
you're
a
producer
at
a
local
level
or
you're,
an
importer
in
europe
and
you
import
from
six
different
countries.
A
So
you
know
that
was
the
idea
behind
showing
the
showcasing
these
two
very
disparate
apps.
If
you
look
at
you
know
what
d
climate
is
you
know
supporting
the
first
was
always
insurance
arbol,
you
know,
arbol,
you
know
in
the
last
two
years
has
gone
from
doing
15
million
in
risk
to
probably
in
the
500
to
a
billion
dollars
of
risk
this
year.
This
is
climate
insurance
across
agriculture,
energy
and
many
other
asset
classes.
Nd
climate
helps
to
process
and
settle
all
that
data.
A
A
You
know
projects
in
carbon,
including
green
hydrogen,
and
with
every
single
sub
item.
The
goal
is
to
bring
transparency
into
this
market,
to
remove
the
you
know,
sort
of
the
entities
that
have
been
there
just
due
to
legacy
it's
time
for
a
change
in
many
aspects
of
climate
and
carbon
is
no
exception,
and
you
know
a
third
group
of
apps
is
catastrophe
models,
so
I
showed
you
guys
espresso.
These
are
for
specific
industries,
so
espressos
for
coffee
worksite
is
for
construction.
A
A
That
is
difficult
for
a
construction
company
to
obviously
build
internally,
but
also
not
particularly
easily
available
on
the
internet,
and
so,
if
you
are
building
anything
around
climate,
please
reach
out
to
us
we'd
love
to
support
you
and
we,
you
know
we
give
out
grants
as
well
and
we
want
a
very
healthy
ecosystem
of
apps
that
help
consumers
from
retail
to
industry
make
sense
of
the
climate.