►
Description
A presentation at the Hyperledger Climate Action and Accounting Special Interest Group on November 2, 2021 featuring Deval Pandya. This presentation covers the background to the development of the FLINT software and the moja global organization and provides participants with a clear understanding of how and why the FLINT was developed and why the modular open-source structure was chosen and is supported by Moja Global.
For more about the open source FLINT project, see: https://github.com/moja-global/FLINT
If you'd like to get involved with the Hyperledger Climate Action and Accounting Special Interest Group, sign up to the group's mailing list at: https://lists.hyperledger.org/g/climate-sig/
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A
Okay,
hello,
everybody
to
this
climate
action,
accounting
meeting.
It's
our
general
meeting
happening
every
other
week.
Today
we
have
a
special
guest
in
our
meeting,
develop
from
moja
global
he's.
The
director
of
the
technical
student
committee
there
mucho
global,
is
a
collaborative
project
under
the
linux
foundation
and
they
support
ambitious
climate
action
by
bringing
together
a
community
of
experts
to
develop
open
source
software
devil
is
predicted,
presenting
their
flagship
project
called
flint.
It's
full
lens
integration
tool
and
it's
a
solution
for
mrb
source
monitoring,
reporting
verification.
A
B
B
Right
thanks
thanks
thanks
everyone
for
joining
this
call,
and
thanks
for
inviting
me
and
as
robin
mentioned,
we'll
spend
next
few
minutes
talking
about
the
flagship
project
from
mojo
global.
It's
called
flint.
It
stands
for
full
line,
integration
tool
and
maybe
I'll
start
with
like
a
bit
of
a
background
and
then
dive
as
deep
as
we
can
without.
B
If
you
are
anticipating
code,
pulling
up
github
and
code
you'll
be
disappointed
a
little
bit,
but
I
think
it's
it's
important
to
discuss
why
and
how
to
design
this
mrv
system
and
what
was
the
motivation
behind
flint
right
in
general
and
how
it
is
probably
different
than
what
other
things
out
there.
So
a
little
bit
about
myself.
I
know
this
is
a
hyperledger
sig
and
I
would
start
by
a
disclaimer.
I
know
nothing
about
dlt
right
and
then
I
I
probably
the
better
ways.
B
I
know
enough
to
be
super
dangerous,
and
so
I
I
claim
to
know
nothing
about
dlp
right.
I
do
know
a
little
bit
about
lando's
land
use,
change
right
and
how
it
affects
into
carbon
accounting,
and
but
I'm
not
a
soil
expert
or
I'm
not
an
agronomist
or
I'm
not
like
so
not
an
expert,
but
I
know
a
little
bit.
B
I
know
a
lot
about
artificial
intelligence
and
machine
learning
and
its
application
in
climate
action
and
fortunately,
or
unfortunately,
we
are
not
going
to
talk
about
it
in
this
call
right
so,
but
if
you
would
want
to
talk
about
that,
I'm
happy
to
have
a
conversation
later
right.
So
let's,
let's
dive
into
this
right.
B
So
what
are
the
expected
outcomes
for
this
session
is,
I
have
to
say,
I
shamefully
stole
a
bunch
of
slides
from
a
much
longer
three-day
course
which
mojo
global
was
delivering
at
unf
triple
c
earlier
last
month,
and
so
we
can
skip
on.
So
anyone
wants
to
raise
them
and
say
this
is
not
useful.
Let's
go
to
the
next
section,
I'm
happy
to
do
that.
We
can.
I'm
also
happy
to
take
questions.
Stop
me
in
the
middle
and
we
can
discuss
more
things.
B
B
There
was.
There
was
a
whole
section
around.
How
do
you
engage
with
open
source
tools,
but
I'm
pretty
sure
this
community
doesn't
need
to
know
that
and
so
we'll
skip
that
one,
and
then
there
are
options
of
what
what
is
flint.
How
are
maybe
give
you
I
do
have
couple
of
videos
which
will
show
some
of
the
examples
on
how
people
are
using
flint
and
and
then
what
are
different
data
and
models
and
options
available
in
designing
the
system.
B
B
We
are
cousins
in
a
sense
because
we
are
also
part
of
the
next
foundation
and
let's
be
sit
together
in
that
foundation,
and
it
has
been
a
work
in
progress.
So
mojo
was
officially
launched
six
years
back,
but
even
before
that,
what
makes
flint
right
now
has
been
a
work
in
progress
for
decades
and
and
some
of
our
key
advisors
and
authors
of
the
flint
system
are
also
say,
lead
authors
on
on
the
policy
recommendations
and
for
the
unf
triple
c
and
and
ipcc
reporting
and
all
of
that
stuff.
B
B
So
what
are
different?
I
think
the
first
place
to
start
discussing
anything
around
like
mrv
are:
is
around
policy
drivers
right.
So
what?
What
and
this
is
this
is
what
we
feel
right.
So
I
think
policies
are
very
regional.
They
vary
from
country
to
country,
they
also
vary
from
region
to
region
and
and
not
just
what
policy
means
for
the
system.
But
what?
What
does
this
output
from
this
systems
can
help
in
designing
policy
right?
So
it's
a
two-way
street
and
so
to
understand
what
are
the
policy
drivers?
B
It
means
that
it
has
to
cover
everything
and
it
has
to
be
very
open
about
what
it
can
and
cannot
do
right
and
then-
and
some
of
that
could
be
very,
very
complex.
So
one
is
transparency.
Other
one
is
accuracy,
and
this
is
a
tough
one,
because
how
you
define
accuracy
will
depend
on
your
methods
and
we'll
quickly
talk
about
that
in
the
next
slides,
but
it
has
to
be
accurate.
B
B
B
There
are
two
major
things
based
on
guidelines,
especially
ipcc
guidelines
and
around
mrv
system,
and
which
is
good
to
understand
because
I'll
be
referring
to
those
going
forward.
One
is
peers,
and
the
other
one
is
approaches
and
in
tier
one,
for
example,
is,
is
a
default.
It's
at
like
the
global
levels.
These
are
emission
factors
which
are
there's
obviously
science
behind
it,
but
it's
it's
a
very,
very
macro
level
and-
and
you
apply
those
right.
Tier
2
is
more
country
specific
right
and
country,
specific
geography,
specific.
B
Comparatively
more
accurate
tier
3
is
more
advanced,
even
more
accurate,
based
on
models
based
on
ground
measurements.
It's
it's
more
flexible,
even
in
some
cases
it
can
be
based
on
the
land
parcel
by
the
end,
parcel
that
you
are
looking
at
more
more
more
information
there
right
same
thing,
with
approaches
and
and
not
to
confuse
tier
with
approaches
right
so
like
approach,
one
is:
what's
the
total
land
area
for
that
period,
so
there's
no
change
information.
B
It's
a
point
in
time
and
says:
okay
and
and
and
most
of
it
is
estimates
right
and
which
is
fine
approach.
Two
is
around
net
change
over
two
periods
and
which
is
to
say
that
what
happened
between,
let's
say,
2020,
to
2022
or
2010
to
2020,
and
it's
between
those
two
periods
and
what's
the
net
change,
but
it
does
not
talk
about
what
happened
in
between
those
things
right
and
so
that's
that's.
The
second
approach
approach
three
is
most
especially
explicit.
B
B
Now
there
are
two
things:
the
spheres
and
approaches
both
are
actually
not
based
on
inputs.
They
are
based
on
outputs
right.
So
just
because
you
have
national
forest
inventories
does
not
mean
that
you
are
using
tier
3.
It
depends
on
what
what
outputs
you
are
using
that
to
create
same
thing
like
if
you're
using
maps,
it
doesn't
necessarily
mean
that
you're
using
approach
two
or
three
it
could
be
different
right.
B
And
so
tears
and
approaches
this
is,
I
think,
a
more
disclaimer,
but
tears
and
approaches
are
not
mutually
exclusive
and
a
country
or
a
project
can
use
a
mix
of
these
different
regions
within
the
country
within
the
project
right,
so
you
could
have
a
tier
one
approach,
two
or
tier
two
approach,
three
and
all
number
of
combinations
of
those
when
you
are
reporting,
as
long
as
again
going
back
to
tech,
which
is
as
long
as
you
are
transparent
about
like
how
you
are
doing
that
right.
So
you
have
that's
the
part
of
transparency.
B
The
big
guiding
principle
need
for
flint
was:
can
we
run
this
different
approaches
and
different
combinations
of
approaches
and
tiers,
using
different
spatial
and
a
special
activity
and
management
all
different
types
of
data,
and
that
that
is
why
flint
was
built
right?
So
that's
that's.
Why
there's
a
huge
emphasis
on
that
integration
piece
and
we'll
keep
on
going
going
back
to
that
integration
piece
as
the
backbone
of
what
flint
can
do
with
this
data.
B
So
yeah,
I
think
this
is
I
I
like
this
table
again
shamelessly
stolen
from
ipcc
2019
guidelines.
It
talks
about
different
methods
and
approaches
right,
and
I
don't
want
to
go
into
like
each
and
every
kind
of
box
in
this
table.
But
so
there
are
wall-to-wall
methods,
the
survey-based
methods
and,
and
you
can
track
when
you're
looking
at
approach.
Three
people
can
with
wall-to-wall
methods.
B
Right
again,
this
is
probably
from
a
previous
report,
but
it
just
talks
about
like
there
are
basically
seven
recognized
carbon
pools
right
and
so
there's
above
brown,
biomass,
that's
below
ground,
biomass
and,
and
so
the
mole
pool-
and
this
is
just
say,
top
level
right,
so
you
can
go
even
deeper
into
it
right
and
say
within
deadpool
biomass.
What
are
different
types
of
deadpool
biomass
within
soil,
organic
matter?
How
do
you
characterize
different
soil,
organic
matter?
B
B
So,
to
keep
in
mind
basically,
like
ipcc
guidelines,
are
like
foundations
right,
so
they
it's
not.
They
don't
tell
you
how
to
build
this
system.
They
tell
you
what
those
systems
should
be
built
on
top
of
right.
So
if
you
have
a
strong
foundation,
you
can
build
different
types
of
systems
and
and
and
so
on,
but
they
are
their
foundations.
They
are
not
guidelines
for
for
building
the
guidelines
for
the
foundations
and,
obviously,
if
your
foundation
is
strong,
it
would
mean
that
you
will
build
a
strong
building
on
top
of
it.
B
B
How
you
use
your
data
and
how
you
model
things
in
your
system
determines
tiers
and
approaches
and
not-
and
this
is
again
going
back
to
the
comment
that
it's
dependent
on
the
outputs
and
not
inputs
right,
so
how
tiers
and
approaches
are
defined
as
like
outputs
right
you
have
to
I
mean
we
went
through
carbon
carbon
fools
and
and
different.
I
I
want
to
emphasize
an
example
here
right,
so
this
is.
B
A
B
B
A
B
Yeah,
so
for
carbon
credit
analysis,
there
are
use
cases
where,
where
it
has
been
used
for
carbon
I'll
show
that
it's
a
very
interesting
hold.
That
thought
there
is
a
use
case,
but
it
doesn't
so
so
this
is
non-profit.
This
is
how
people
use
it
right,
so
people
have
used
it
for
carbon
credit
analysis.
B
To
my
knowledge,
they
haven't
used
it
for
tokenization,
and
but
it
is
mostly
for
analysis
right
and
say:
okay,
how
much
carbon
credit
could
be
within
within
the
given
politic
policy
realms
how
much
carbon
trade
or
what
is
the
potential
for
generating
carbon
phase
within
a
given
land
parcel,
and
those
kind
of
analysis
have
been
done
using
this
system,
there's
actually
a
really
good
example
in
the
video
which
will
follow.
So
you
probably
like
that.
A
lot
around
that.
B
All
right,
so,
let's
keep
going
around
systems,
design
and
policy,
and
so
like
this,
the
systems
design
are
evolving
right
so
that
that's
that's
that's.
That
is
why
you
have
to
start
with
policy
right.
So
now
there
are
obviously
there's
like
ndcs
now,
there's
enhanced
transparency
framework
and
which
is
something
I
guess
a
lot
of
us
are
following
right
now.
What
comes
out
of
cop26?
B
There
is,
I
think,
if
I'm
not
mistaken,
article
six,
which
is
around
trading
based
on
and
verification,
on,
based
on
enhanced
transparency
framework,
and
then
there
are
also
some
new,
like
the
new
york
declaration
around
like
tracking
restorative
activities,
right
against
the
targets
and
stuff
like
that.
So
this
there's
for
every
national
system.
There
are
new
kind
of
regimes
which
are
being
brought
up.
B
This
is
just
to
highlight
that
you
know
understanding
the
policy
is
and
reporting
needs
is,
is
big
right
and
that's
what
drives
this
this
system
right
per
se,
and
so
I
I
don't
want
to
go
into
like
every
single
bullet
points
here,
but
what
I
want
to
highlight
is
there
are
policy
and
reporting
needs
on
one
hand,
but
there
are,
there
are
operational
needs
right
so
say:
how
often
are
you
going
to
run
this
analysis
right?
What
is
the
version
control
for
it.
A
B
B
And
this
is
probably
quite
simplistic,
but
at
the
end
of
it,
what
you
want
is
you
have
different
types
of
data
and
say
forest
data
and
all
the
land
use
data,
remote
sensing
data
you
bring
all
of
that
data
and
you're
creating
products,
which
is
to
say
you
are
building
doing
some
analysis,
building
some
models
which
are
giving
you
some
results.
Now,
all
of
that
products
have
to
be
integrated
and
then
relevant.
B
Information
which
is
very
is
is
produced
right,
and
this
is
how
a
system
operates
and
if
I
put
for
a
second,
I
swear.
This
is
the
only
machine
learning
reference
I'll
do
here.
This
is
like
the
forward
pass
in
your
machine
learning,
which
is
that
once
you
have
a
model,
you
there's
one
way,
information
that
say
you
pass
the
the
information
data
into
the
model.
It
gives
you
an
output,
but
when
you
train
that
model,
when
you
want
to
build
a
system
like
this,
you
have
to
start
from
from
backwards
right.
B
So
you
start
in
machine
learning.
You
start
with
say
if
it's
a
supervised
learning,
you
start
with
like
the
labels
and
what
is
the
output
that
I
want
right,
so,
even
in
this
mrv
system,
a
good
way
of
thinking
about
it
is
what
is
the
information
that
is
needed,
and
how
can
I
enable
this
integration
between
all
the
different
products
which
are
produced
from
different
types
of
data,
which
will
then
guide
my
data
providers
or
my
product
providers
on
on
what
what
needs
to
be
done?
B
B
So
what
makes
a
system
operational
again
going
back
to,
and
this
is
the
definition
which
I
like
and
happy
to
be
challenged
on
this
definition
of
what,
what
what
operational
means,
but
it
has
to
provide
necessary
information
to
those
who
need
it
when
they
need
it
in
a
standard
that
is
needed
right.
So
this
is.
This
is
very
simple.
So
in
this
case,
who
could
be
land
owners
could
be
private
sector
could
be
governments
when
depends
on
the
analysis?
Are
you
doing
say
every
year?
Do
you
need
ad
hoc
on
demand?
B
Do
you
do
it
every
every
month
and
what
are
the
different
standards
here
right?
The
biggest
challenge
here,
there's
a
lot
of
good
science
and-
and
that
is
that
has
been
progressed
there
is
in.
If
you
are
in
many
parts
of
the
world,
there's
a
lot
of
like
data
data
quality
is
an
issue,
but
the
data
availability
is
not.
B
But
how
do
you
move
from
science
to
operations
is?
Is
the
big
challenge
like
how
do
you
move
from
taking
say
this
equations,
these
models
and
operationalize
them
is
the
challenge
which
and
that's
what
was
a
goal
for
flint
that
how
we
can
operationalize
all
of
this
different
different
publication
based
science
and
everything
like
together
again.
This
is
this
sounds
very
much
say
understood
like
I
think
people
recognize
this,
but
but
it's
it's
it's
good
to
emphasize
that
so
the
mojo,
global
and
flint
is
made
up
of
it's
all
about
people
right.
B
But
what
we
see
is
that
three
major
pieces
right.
So
one
is
policy,
so
you
that
policy
sets
the
need
and
timing
and
responsibilities
and
so
on.
The
second
is
a
very
strong
governance
right
and
this
this
is
this
is
what
manages
your
processes,
your
systems
defines
your
rules,
provides
adequate,
sustained
resources
and
the
third
one
is
technical,
which
is
actually
like.
How
do
you
bring
this
together
and
produce
the
outputs
that
are
required,
run
the
models
and
so
on?
B
And
so
we
will
represent
this
three
like
policy
with
green
governance,
with
purple
and
technical
with
red
and
and
for
those
if
to
be
inclusive,
like
the
policy
is
the
first
one
here.
If
there's
someone
who
is
colorblind
line
and
cannot
recognize,
colors
policy
is
a
top
one,
then
the
middle
one
here
is
governance,
and
then
technical
and
usually
what
people.
When
you
start,
you
are
starting
with
a
lot
of
the
mistakes
which
we
have
seen.
A
B
Yeah,
absolutely
so
I
I'll
send
some
links.
Sure
would
also
I'll
briefly
talk
about
governance
in
next
couple
of
slides,
but
in
more
details.
I'll
definitely
share
more
resources
on
that
right.
Thank
you,
yeah.
So,
going
back
like
the
big,
I
wouldn't
call
it
like
mistakes,
but
but
in
for
the
need
of
say,
speed
most
of
time.
What
happens
is
people
think
of
these
systems
in
a
linear
fashion?
Right
so
say
this
is
the
policy
requirements.
B
We
send
this
governance.
My
end
goal
is
producing
a
report
which
will
be
then
submitted
to
x,
y
and
z,
and
they
think
of
it
as
in
linear
terms
and
most
of
them
what
happens
there
is.
It
is
prone
to
errors
a
lot
of
it.
I
have
seen
a
lot
of
it
is
done
on
excel
sheets
and
and
difficult
to
track,
and
then
it's
one
off
and
difficult
to
reuse
right.
B
So
you
have
to
look
beyond
near-term
outputs
but
focus
on
longer-term
process
and
requirements
and
how
those
how
the
system
can
evolve
with
evolving
processes
and
evolving
requirements
right
and
which
will
which
we
are
experiencing
right
now
and
an
example
could
be
let's
say
now:
you
want
to
build
design
a
process
which
has
some
key
milestones
which,
which
could
be
annual
and
depending
on
when
you
want
to
do
that,
and
so
then
system
needs
to
do
certain
say,
hit
certain
milestones
at
certain
point
right.
So
you
have
to
complete
your
annual
plan.
B
Then
there
will
be
new
data
coming
in
and
then,
when
you
have
new
data,
then
you
have
to
run
the
system
again.
You
submit
your
inventory,
there's
assessment
and
then
again
you're
back
at
let's
say
next
year's
plans
right
and
stuff
like
that.
In
this,
the
way
we
see
and
again
sherwood,
going
back
to
the
governance.
Why
governance
is
this
foundational
and
very
important
to
us,
is,
as
you
see
in
this
slide,
what
I
want
to
highlight
is
the
action
points
and
the
involvement
of
policy
and
technical
is
not
around
throughout
right.
B
That
cycle
right
and
they
there
are
certain
tasks
and
certain
deliverables
which
which
needs
to
be
there.
But
governance
is
present
throughout
the
cycle.
Governance
is
what
holds
this
whole
thing
together
and,
and
so
governance
is
fundamental
to
this
process.
Right
and,
and
so
a
good
governance
includes
things
like
clear
objectives,
roles
and
responsibilities,
pathways
of
engaging
decision
making
frameworks
and
so
on.
Right.
B
B
You
do
rigorous
testing
and
review
of
your
data
and
model.
Then
there's
qaqc
process.
The
system
has
now
evolved,
so
you
have
to
kind
of
finalize
the
system
design
for
that
time
frame.
Then
you
run
the
system
and
so
on
right.
So
there's
I
don't
want
to
go
into
like
each
and
every
one
of
them,
but
there
are
multiple
things
which
actually
go
in
to
multiple
tasks
and
deliverables
which
happen
throughout
the
process.
Right.
B
And-
and
so
I
there
will
be
times
where
you
will
be
re-running
these
systems
multiple
times
within
that
key
kind
of
decision
making
or
deliverable
points
right,
and
so
an
operational
mrv
system
should
be
able
to
complete
this
big
one
is
complete
a
qa
kc,
and
you
might
be
running
this
models
again
and
again
right.
It
has
to
run
the
systems,
it
should
be
able
to
assess
uncertainty
and
and
and
and
as
I
said,
it
should
be
able
to
evolve
so
continuous
improvement.
B
It
should
also
be
able
to.
Let's
say
there
was
a
change
in
policy,
and
if
you,
if
you
input
that
or
now
you
have
better
data
and
and
you're
going
from,
say
tier
one
to
tier
two,
all
of
that
it
should
be
able
to
quickly
assess
those
changes
and
and
should
be
well.
It
should
be
able
to
run
right,
so
you
should
be
able
to
do
what
it
is
supposed
to
do.
B
A
B
Hold
that
thought
so
there
is
a
database
which
is
which
is
accessible
to
everyone
and
and
it's
not
by
any
means
complete
right.
We
do
if
anyone
wants
to
contribute
their
data.
We
obviously
highly
encourage
that,
but
even
when
you
get
started
and
that
thanks
to
unf
triple
c,
we
do
have
tier
one
kind
of
database
and
rules
and
everything
right,
and
so
we
we
are
able
to
make
that
available
and
that's
evolving
right.
So
that's
continuous!
B
A
The
link
to
the
database
yeah
or
just
send
afterwards.
Thank
you
very
much,
yep
yep,
yes,
you,
you
might
be.
You
might
already
be
good
speaking
to
this,
but
I'm
curious
about
the
governance
around
the
difference
between
measuring
and
verifying.
Is
it
do
you
use
the
same
tools
to
measure
and
verify?
Does
it
have
to
be
a
different
like
a
third
party?
Well,
what's
your
approach
to
that
for
the
verification
process.
B
Yeah,
so
so,
ideally
in
ideal
case,
so
tools
are
very
much
similar
right.
So
like
what
what
you
measure
and
what
to
verify
the
tools
could
be
similar.
They
don't
have
to
be
either
case
scenario.
Is
you
want
different
bodies,
doing
measurement
and
verification
right?
So,
let's
say,
even
if
you
are
using
same
tools
to
get
that
data,
different
people
should
be
able
to
verify
the
outputs
right
in
many
cases
I
have
seen-
that's
probably
not
the
case
right,
so
I
think
it's
but
yeah.
B
C
Flint
is
a
modular,
flexible,
open
source
software
that
adds
a
new
level
of
sophistication
for
an
incredibly
diverse
group
of
users,
from
governments
to
private
land
managers
and
other
businesses
and
stakeholders.
It
enables
you
to
measure
report,
verify
even
forecast
greenhouse
gas
emissions
and
removals
from
forestry,
agriculture
and
other
land
uses.
C
We
can't
reach
the
goals
set
at
the
paris
agreement
without
nature-based
solutions.
Halting
deforestation
globally,
restoring
vast
areas
of
degraded
lands
all
by
2030.
That's
where
we
want
to
get
to
just
like
gps,
has
come
a
long
way
to
not
just
show
you
the
roads
but
help
you
avoid
traffic
and
speed
bumps
along
the
way.
Flint
is
the
ultimate
open
source
software
that
can
help
you
reach
your
climate
goals
in
the
forest
and
land
sector
working
together.
B
C
B
B
A
Yeah
all
right,
can
you
hear
me
yes,
hi
good
morning,
yeah,
I've
said
a
question
so
clearly
the
the
flint
software
open,
so
software
is
focused
on
land
use
right,
but
it
seems
that
there
are
elements
that
could
potentially
expand
beyond
other
industries
or
sectors,
I'm
guessing
that
it
doesn't
quite
cover
aqueous
environments.
So
I
may
face
some
other
challenges,
but
I
would
imagine
as
far
as
the
governance
there
must
be
elements.
A
B
So
yeah,
so
I
think
great
question
right,
so
it
is
100
focus
currently
on
air
flow
right,
so
agriculture,
forestry,
other
land,
use
right
and
you're
right
right.
So
that's
what
flint
can
do
right
now,
but
at
the
heart
of
it
what
flint
provides
is
actually
a
framework
for
building
your
own
mrb
system
right.
I
I
don't
want
to
give
this
answer.
Yes,
it
can
do
everything
right,
it
can
adapt.
You
can
you're
happy
to
take
that
framework.
B
Take
that
foundations
of
how
you
can
design
this,
but
it
would
be
a
lot
of
work
right
and
I'll
go
into
a
little
bit
more
details
on
what
happens
behind
the
hood
in
flint
and
and
maybe
that
will
help
you
kind
of
determine
whether
it
can
really
work
for
other
industries
or
not
right.
So
it
won't
be
trivial
for
sure
right.
A
I'm
really
curious
about
this.
Why
open
governance
slide,
and
I
know
we're
kind
of
we're
very
much
focused
on,
I
think,
a
lot
of
the
same
nature-based
solution
methodology,
but
we're
really
books
on
the
end
customer
and
kind
of
solve
solving
the
last
mile
logistics
problem,
but
in
a
way
that
actually
incorporates
data
with
our
farmers
that
can
really
drive
towards
nature-based
solutions.
So
I'm
curious,
if
you
have
any
use
studies
or
you
can
talk
a
little
bit
more
about
this
open
governance
idea,
because
I
really
like
it.
B
Yeah,
so
I
think
that
the
big
thing
for
open
governance
was
when-
and
I
am
quite
new
to
mojo
right,
but
but
the
history
there
is
mojo
started
and
flint
development
started
as
collaboration
between
various
different.
So
if
you
remember
the
very
first
slide
team
right,
there
were
individuals,
there
were
governments,
there
were
actually
four
profit
corporations
right.
Who
want
to
do
this,
and,
and
so
the
only
way
of
truly
keeping
it
democratic
was
through
open
governance
right.
B
B
So
yeah,
big,
big
motivation
behind
that
was,
was
having
a
bit
more
democratic
process
on
how
this
will
evolve
right
and
also
enable,
like
all
different
players,
to
come
to
the
to
the
same
table
right.
So
there
will
be
representatives
for
from
from
the
government
side
there
are.
There
are
scientists,
right
and
and
so
on,
right
and
there
is
a
slide
which
talks
about
like
exactly
what
the
government
says.
Right,
buddy.
B
B
B
One
entity
could
solve
it
and
it
is,
it
is
definitely
a
multi-stakeholder.
It
has
to
be
a
multi-stakeholder
challenge
right.
What
flint
can
enable
is,
through
these
scenarios
right,
you
could
run
these
scenarios
and
find
say
a
win-win,
a
plausible
path
to
a
win-win
solutions
which
could
which
could
then
nudge
policy
discussions
and
which
would
then
motivate
different
stakeholders
to
act
in
the
direction
of
avoiding
and
ending
deforestation
right,
but
yeah,
I
think
short
answer
is
in
this
solve
and
deforestation
without
formal
government
assistance.
B
B
Yeah,
so
I
think
we
already
discussed
this
like
so
it
can
facilitate
collaboration.
It's
a
non-profit,
there's,
no
commercial
interest,
just
very
much
like
hyperledger.
We
are
part
of
linux
foundation,
so
the
the
reason
there
was
simple.
We
wanted
to
focus
on
what
we
do
best
and
focus
less
on
things
like
legal
and
accounting
and
and
other
stuff
right,
and
also
we
benefit
a
lot
from
engaging
with
the
wider
linux
foundation,
community
and
learning
about
like
best
practices
and
and
so
on.
B
B
Our
advisory
board
consists
of
our
supporters,
which
could
may
or
may
not
have
representatives
from
government
entities,
and
this
is
for
mojo
global,
but
every
mrv
project
would
have
different
kind
of
governing
structure.
Anybody
can
be
on
technical
studies,
that's
where
I
sit
right,
so
anybody
can
be
on
technical
steering
committee.
We
have
three
steering
committee
directors,
but
everyone
is
welcome
to
be
part
of
that
and
so
on
right.
So
that's
the
governance
and
and
people
can
engage
in
in
different
ways
right.
B
So
if,
if
an
organization
wants
to
just
take
this
use
it
self-run,
it
they're
welcome
to
do
that.
If
they
are
eager
to
contribute
back,
that's
brilliant,
then
we
also
have
some
collaborations
with
different
for-profit
entities,
who
can
provide,
say
trainings
or
software
as
a
service
which
sits
on
top
of
flint
intent.
There's
a
product
called
flint
pro,
which
is
by
million
group
and
and
so
things
like
that.
B
Right
and
I
mean
we
all
know
there
are
different
ways
of
commercializing
and
and
open
source
right
and
so,
but
mojo
global
doesn't
do
that.
Obviously
there
are.
There
are
other
others
who
would
so
now
going
a
bit
into
like
what
is
the
background,
what
happens
a
bit
more
behind
the
scene
or
in
mojo
and
and
then
there's
a
video
about?
B
I
think,
which
a
lot
of
you
would
probably
like
a
case
study
around
how
it
is
being
used
right
now
in
one
like
a
different
case
right
and
and
the
big
motivation
here
was
that
there's
ground
measurements
and
the
remote
sensing
products
and
and
all
of
that
right.
But
then
the
gap
which
was
identified
was
integrating
all
of
it
together,
so
that
it
becomes
a
system
and
not
like
one-off
analysis
pipeline,
and
so
that
is
what
the
big
focus
was.
B
I
know
we
have.
Oh
wow
time
flies.
We
have
10
minutes.
I
quickly
go
through
this,
so
at
the
heart
of
it.
What
it
does
is
it
integrates
everything
right
and-
and
this
is
what
is,
I
think
I
would
definitely
want
to
spend
some
time
here
and
the
concept
is
time
in
space,
integration,
right
and
and
so,
which
is
what
is
different
than
your
traditional
gis
models
right,
and
so,
if
you
are
familiar
with
gis
models,
you
have
maps
and
you
can
overlay
those
maps
and
you
can
do
analysis
on
the
different
layers.
B
B
Even
if
it's
like
a
simple
equation,
which
is
say,
you
multiply
emission
factors
with
and
get
to
like
a
gh
right,
and
so
it
can
become
very
complex
again
going
back
to
like
say
when
it
is
integrated.
That's
where
you
create
the
most
value
versus
individual
methods
and
measurements-
and
this
is
very,
very
subjective
assessment,
and
so
I'm
happy
to
be
challenged
on
this.
But.
B
This
is
what
well
flint
what
flint
does
right.
So
you
have,
you
can
have
all
different
types
of
data
right
and
you
have
the
ground
data
remote
sensing
data.
You
do
data
acquisition,
there's
like
a
bunch
of
data
processing
data
products,
but
as
soon
as
you
hit
reporting,
you
need
to
bring
all
of
this
together.
You
need
to
bring
all
of
this
together
in
a
way
which
which
aligns
with
your
policy
objectives,
which
aligns
with
your
decision-making
objectives
and
that's
what
flynn
provides
is.
B
B
The
core
principles.
Are
we
wanted
to
make
it
flexible,
scalable,
it's
data
agnostic,
and
this
is
a
power
and
a
challenge
in
itself,
and
so
like
every
time
you
go
to
a
new
country
which
wants
to
build
a
new
mrb
system.
The
data
is
definitely
I
can
guarantee
99
probability
that
it's
not
going
to
be
in
the
same
format,
and
so
you
have
to
kind
of
tackle
that.
But
it's
it's
by
design,
because
that's
what
helps
it
make
helps
make
flint
scalable
in
some
sense.
B
And-
and
this
is
like
very
high
level,
what
it
does
so
you
have,
you
can
have
all
these
different,
like
data
inputs
and
this
data
manager,
which
collects
this
data
different
formats,
based
on
how
you
design
those
systems.
Some
of
those
formats
are
are
kind
of
already
available,
but
they
are
extensible.
So
if
you
have
newer
formats
and
newer
transformations,
you
can
add
those
transformations
in
it
each
model.
We
are
calling
these
modules,
but
these
are
at
the
heart
of
it.
B
There
are
different,
say,
models
right,
so
soil
health
could
contain
bio,
geochemical
process
models
in
it
and
then
there's
ghg
model.
Some
of
them
are
just
say
just
getting
started
or
they're
just
concepts.
For
example,
finance
module
is
something
which
you
are
thinking
of.
It's
not
there
yet,
but
you
can
attach
different
modules
to
this
right
and
and
then
you
can
take
that
you
can
integrate
it.
You
can
run
those
those
special
timing,
space,
modular
integration
and
and
then
create
outputs
in
the
way
that's
useful
and
yeah.
C
C
It
was
designed
to
see
if
you
could
get
some
land
holders
interested
in
planting
trees.
Only
small
scale,
plantings
sort
of
five
to
two
hundred
hectare
size.
But
could
you
actually
give
them
numbers
that
would
estimate
the
amount
of
carbon
they
could
get
on
their
land
if
they
planted
trees
so
that
they
could
make
a
decision
on?
Was
this
worthwhile
entering
the
carbon
market
and
trying
to
mitigate
so
it's
only
a
test
at
the
moment.
C
There's
these
six
regions
done
around
australia,
but
if
you
understand
australia
in
size,
then
you
see
that
this
little
box
in
the
burnet
mary
in
queensland,
for
example,
is
probably
half
the
size
of
france.
So
it's
actually
quite
big
areas
being
simulated
here.
It's
just
that
australia
is
obviously
very
very
large,
but
the
tool
itself
was
designed
to
be
very
simple,
so
I
could
come
into
anywhere
I'll
choose
this
region
in
victoria
and
remembering
that
all
of
the
back
end
of
this
close
this
the
application
program
is
closed,
but
that's
fine.
C
C
B
A
A
Tr
go
ahead.
I've
already
asked
a
couple:
okay,
okay,
so
you
just
represented
some
of
the
modules
that
your
system
could
potentially
handle
like
biodiversity.
Have
you
worked
with
like
the
natural
capital
project
from
stanford
who
has
their
open
source
models
for
any
of
the
ecosystem
service
analysis.
B
So
I
one
I'm
not
aware
about
it,
but
we
do
as,
for
example,
we
have
looked
at
ecosystem
services
which
go
into
like
different,
so
one
biodive.
Let
me
start
with
this.
Biodiversity
model
like
module,
is
very
early
right,
and
so
it's
not
fully
integrated
into
the
system.
It's
not
fully
developed
and
that's
why
it's
not
integrated
into
the
system
for
ecosystems
modeling
like
the
for
soil,
for
example.
I
can
tell
you
so
there
is
a
university.
B
B
Yeah
here
right,
so
this
is
where
you
you
can
define
and
bring
in
all
different
models
and
parameters
and
hyper
parameters
right
and
and
I'll
share
the
slides
right.
But
if
there
is
a
model
which
which
is
out
there,
you
could
bring
that
in
in
this
system
and
and
it
can
become
part
of
the
system
right.
A
B
B
Sometimes
you
jump
into
this
analysis
mode
and
start
using
the
system,
and
then
it's
not
producing
the
results
or
or
the
insights
that
you
would
really
like
it
right,
and
so
then,
then
you
have
to
go
back
to
the
drawing
board
and
because
you're,
because
this
is
extensible
system,
it's
it
takes
time
right
to
kind
of
build
those
things
right
and
again
right.
If
you
were
to
build
it
again
right.
The
other
one
is
there
is.
B
There
is
a
lot
of
data
right
like
when
you
when
you
start
thinking
of
like
say,
let's
say
that
I
think
there
was
an
example
here
how
much
yeah
here
right.
So
if
you
just
say
look
at
say
your
stock,
your
stocks
and
flows
every
time
stamp,
100
years,
monthly
steps,
30
pools
and
there
could
be
subpools
of
carbon.
B
You
are
having
a
lot
of
data,
and
so
flint
makes
it
easy
to
kind
of
manage
the
data
versus
like
we
also
like
clear
a
lot
of
the
data
which
is
needed
for
models
to
run,
but
not
really
needed
for
reporting,
right
and
so
in
designing
process.
If
we,
if
we
are
not
really
very
clear
about
about
how
we
distinguish
those
right,
you
will
easily
end
up
slowing
the
system
quite
a
bit
right
and
there
will
be
a
lot
of
computational
and
data
storage,
overheads
there
and
and
there's
always
there's
always
like
when
you.
B
What
we
would
recommend
is
starting
small
and
growing
from
that
right.
There's
always,
as
you
add,
more
complexity
to
it,
there
will
be
more
more
and
more
I'm
trying
to
find
a
slide
here
now.
This
is
this
was
like
a
good
example
right,
so
this
here
tier
one
modules
and
tier
one
database.
This
is
what
I
think
someone
asked
what's
available
and
this
both
are
available,
and
that's
thanks
to
ipcc
and
unf
triple
c.
We
have
this
data
globally
right
for
for
both
this
right,
but
then
you
can
start
adding
complexity.
A
B
B
In
principle,
it's
open
source
right
and
anyone
who
wants
to
use
it
can
use
it
right.
So
great
questions
to
my
knowledge,
we,
although
to
build
this
system,
is
so
complex
right,
I
mean.
Usually
I
obviously
we
don't
know
like
who
is
everyone
using
it,
especially
once
it's?
It
can
be
like
copied
and
redistributed
right
after
that.
So
we
don't
know
that,
but
from
our
knowledge,
I
think
most
of
the
users
currently
are
along
the
lines
of
ndc's
for
governments
right,
so
canada
is
using
it.
B
B
B
I
I
yeah,
I
I
I
think
it's
the
answer
is
we'll
find
out
soon
it's
working
well,
where
we
have
so
countries
like
canada
again
have
used,
they
have
been
using
flint
for
their
ndc
submissions
right,
and
so
it
follows.
It
rigorously
follows
the
ipcc
and
unf
people
see
like
guidelines
right
and
so
in
that
sense
it
it's
easy
to
verify,
and
it's
validated
based
on
that.
B
Yeah,
so
I
think
brilliant
question,
so
there
is
a
board,
and
I
am
that
person
on
the
board,
which
is
looking
at
how
we
can
start
incorporating
ai
and
machine
learning,
especially
like
some
advanced
computer
vision
technologies
into
the
system.
We
are
on
early
stages.
We
didn't
really
get
into
that
project.
B
I
can
totally
use
some
help
in
it.
So
if,
if
you
know,
anyone
who
would
like
to
volunteer
their
time
on
that
aspect
would
be
brilliant
and
happy
to
connect
on
that.
The
project
is
currently
supported
by
different
grants.
Currently
we
are
using
ai
for
good
brand
from
google
for
that,
but
yeah
we
are
definitely
keeping
an
eye
out
on
that
and
that
is
on
the
roadmap.