Fossil-fueled development (SSP5): An energy and resource intensive scenario for the 21st century

TL;DR: In this paper, a set of energy and resource intensive scenarios based on the concept of Shared Socio-economic Pathways (SSPs) is presented, characterized by rapid and fossil-fueled development with high socio-economic challenges to mitigation and low socioeconomic challenge to adaptation.

Abstract: This paper presents a set of energy and resource intensive scenarios based on the concept of Shared Socio-Economic Pathways (SSPs). The scenario family is characterized by rapid and fossil-fueled development with high socio-economic challenges to mitigation and low socio-economic challenges to adaptation (SSP5). A special focus is placed on the SSP5 marker scenario developed by the REMIND-MAgPIE integrated assessment modeling framework. The SSP5 baseline scenarios exhibit very high levels of fossil fuel use, up to a doubling of global food demand, and up to a tripling of energy demand and greenhouse gas emissions over the course of the century, marking the upper end of the scenario literature in several dimensions. These scenarios are currently the only SSP scenarios that result in a radiative forcing pathway as high as the highest Representative Concentration Pathway (RCP8.5). This paper further investigates the direct impact of mitigation policies on the SSP5 energy, land and emissions dynamics confirming high socio-economic challenges to mitigation in SSP5. Nonetheless, mitigation policies reaching climate forcing levels as low as in the lowest Representative Concentration Pathway (RCP2.6) are accessible in SSP5. The SSP5 scenarios presented in this paper aim to provide useful reference points for future climate change, climate impact, adaption and mitigation analysis, and broader questions of sustainable development.

Full text

Fossil-fueled
development
(SSP5):
An
energy
and
resource
intensive
scenario
for
the
21st
century
Elmar
Kriegler
a,
*
,
Nico
Bauer
a
,
Alexander
Popp
a
,
Florian
Humpenöder
a
,
Marian
Leimbach
a
,
Jessica
Strefler
a
,
Lavinia
Baumstark
a
,
Benjamin
Leon
Bodirsky
a,h
,
Jérôme
Hilaire
a,g
,
David
Klein
a
,
Ioanna
Mouratiadou
a
,
Isabelle
Weindl
a
,
Christoph
Bertram
a
,
Jan-Philipp
Dietrich
a
,
Gunnar
Luderer
a
,
Michaja
Pehl
a
,
Robert
Pietzcker
a
,
Franziska
Piontek
a
,
Hermann
Lotze-Campen
a,b
,
Anne
Biewald
a
,
Markus
Bonsch
a
,
Anastasis
Giannousakis
a
,
Ulrich
Kreidenweis
a
,
Christoph
Müller
a
,
Susanne
Rolinski
a
,
Anselm
Schultes
a
,
Jana
Schwanitz
a,1
,
Miodrag
Stevanovic
a
,
Katherine
Calvin
c
,
Johannes
Emmerling
d
,
Shinichiro
Fujimori
e
,
Ottmar
Edenhofer
a,f,g
a
Potsdam
Institute
for
Climate
Impact
Research,
Telegraphenberg
A
31,
14473
Potsdam,
Germany
b
Humboldt-Universität
Berlin,
Department
of
Agricultural
Economics,
Berlin,
Germany
c
Pacific
Northwest
National
Laboratory's
Joint
Global
Change
Research
Institute,
College
Park,
MD,
United
States
d
Fondazione
Eni
Enrico
Mattei
and
Euro-Mediterranen
Center
on
Climate
Change,
Milan,
Italy
e
National
Institute
for
Environmental
Studies,
Tsukuba,
Japan
f
Technische
Universität
Berlin,
Berlin,
Germany
g
Mercator
Research
Institute
on
Global
Commons
and
Climate
Change,
Berlin,
Germany
h
Commonwealth
Scientific
and
Industrial
Research
Organization,
St.
Lucia,
QLD,
Australia
A
R
T
I
C
L
E
I
N
F
O
Article
history:
Received
15
December
2015
Received
in
revised
form
2
May
2016
Accepted
30
May
2016
Available
online
18
August
2016
Keywords:
Shared
Socio-economic
Pathway
SSP5
Emission
scenario
Energy
transformation
Land-use
change
Integrated
assessment
modeling
A
B
S
T
R
A
C
T
This
paper
presents
a
set
of
energy
and
resource
intensive
scenarios
based
on
the
concept
of
Shared
Socio-Economic
Pathways
(SSPs).
The
scenario
family
is
characterized
by
rapid
and
fossil-fueled
development
with
high
socio-economic
challenges
to
mitigation
and
low
socio-economic
challenges
to
adaptation
(SSP5).
A
special
focus
is
placed
on
the
SSP5
marker
scenario
developed
by
the
REMIND-
MAgPIE
integrated
assessment
modeling
framework.
The
SSP5
baseline
scenarios
exhibit
very
high
levels
of
fossil
fuel
use,
up
to
a
doubling
of
global
food
demand,
and
up
to
a
tripling
of
energy
demand
and
greenhouse
gas
emissions
over
the
course
of
the
century,
marking
the
upper
end
of
the
scenario
literature
in
several
dimensions.
These
scenarios
are
currently
the
only
SSP
scenarios
that
result
in
a
radiative
forcing
pathway
as
high
as
the
highest
Representative
Concentration
Pathway
(RCP8.5).
This
paper
further
investigates
the
direct
impact
of
mitigation
policies
on
the
SSP5
energy,
land
and
emissions
dynamics
confirming
high
socio-economic
challenges
to
mitigation
in
SSP5.
Nonetheless,
mitigation
policies
reaching
climate
forcing
levels
as
low
as
in
the
lowest
Representative
Concentration
Pathway
(RCP2.6)
are
accessible
in
SSP5.
The
SSP5
scenarios
presented
in
this
paper
aim
to
provide
useful
reference
points
for
future
climate
change,
climate
impact,
adaption
and
mitigation
analysis,
and
broader
questions
of
sustainable
development.
ã
2016
The
Authors.
Published
by
Elsevier
Ltd.
This
is
an
open
access
article
under
the
CC
BY
license
(
http://creativecommons.org/licenses/by/4.0/).
1.
Introduction
Climate
change
and
sustainable
development
are
central
global
and
long-term
challenges
facing
humankind
today.
Scenarios
of
societal
developments
over
the
21st
century
are
a
primary
tool
for
investigating
the
scope
and
evolution
of
these
challenges,
and
therefore
have
been
used
in
climate
change
research
for
a
long
time
(
Leggett
et
al.,
1992).
In
the
past
years,
a
new
scenario
framework
for
climate
change
research
has
been
presented
that
further
systematizes
the
exploration
of
relevant
socio-economic
futures
for
climate
policy
analysis
(Ebi
et
al.,
2014;
van
Vuuren
et
al.,
2014;
O’Neill
et
al.,
2014;
Kriegler
et
al.,
2014a).
To
this
end,
a
set
of
five
*
Corresponding
author.
E-mail
address:
kriegler@pik-potsdam.de
(E.
Kriegler).
1
Present
address:
Sogn
og
Fjordane
University
College,
Norway.
http://dx.doi.org/10.1016/j.gloenvcha.2016.05.015
0959-3780/ã
2016
The
Authors.
Published
by
Elsevier
Ltd.
This
is
an
open
access
article
under
the
CC
BY
license
(http://creativecommons.org/licenses/by/4.0/).
Global
Environmental
Change
42
(2017)
297–31 5
Contents
lists
available
at
ScienceDirect
Global
Environmental
Change
journa
l
home
page
:
www.e
lsevier.com/loca
te/gloenv
cha

Shared
Socio-Economic
Pathways
(SSPs)
has
been
developed
with
different
levels
of
socio-economic
challenges
to
the
two
generic
policy
responses
to
climate
change,
mitigation
and
adaption
(SSP1
to
SSP5;
Kriegler
et
al.,
2012;
O’Neill
et
al.,
2014;
O’Neill
et
al.,
2017).
The
associated
scenarios
aim
to
facilitate
and
integrate
future
research
on
mitigation,
adaptation
and
residual
climate
impacts
and
are
thus
targeting
climate
change
researchers
and
climate
policy
analysts.
Even
though
the
SSP
scenarios
were
developed
for
climate
change
research
as
primary
recipient,
they
are
also
highly
relevant
for
investigating
broader
questions
of
sustainable
development
(O’Neill
et
al.,
2017).
This
paper
describes
the
energy,
land-use,
and
emissions
outcomes
in
a
future
unfolding
according
to
SSP5,
called
“Fossil-
Fueled
Development”.
SSP5
is
characterized
by
high
socio-
economic
challenges
to
mitigation
and
low
socio-economic
challenges
to
adaptation
(O’Neill
et
al.,
2017).
It
describes
a
world
of
resource
intensive
development,
where
high
economic
growth
is
combined
with
material
intensive
production
and
consumption
patterns
and
a
strong
reliance
on
abundant
fossil
fuel
resources.
This
leads
to
high
levels
of
greenhouse
gas
emissions,
and
to
large
challenges
to
reduce
them
in
response
to
climate
change.
At
the
same
time,
the
SSP5
narrative
foresees
a
peak
and
decline
in
global
population,
rapid
human
development,
fast
income
convergence
between
regions
and
an
increasingly
inclusive
and
globalized
economy,
giving
rise
to
high
and
growing
adaptive
capacity
to
climate
change
(see
Section
5
of
the
supplementary
online
material
(SOM)
for
a
full
description
of
the
SSP5
narrative
reproduced
from
O’Neill
et
al.
(2017).
There
have
been
a
number
of
narratives
in
the
global
scenarios
literature
(Raskin
et
al.,
2005)
with
some
resemblance
to
the
SSP5
narrative
including
the
Market
Forces
and
Markets
First
Narratives
of
the
Global
Scenario
Group
(
Raskin
et
al.,
2010)
and
the
Global
Enviromental
Outlook
(UNEP,
2003
),
respectively,
the
global
orchestration
narrative
of
the
Millennium
Ecosystem
Assessment
(Carpenter
et
al.,
2005),
and
the
A1FI
scenario
family
of
the
IPCC
Special
Report
on
emissions
scenarios
(Naki

cenovi

c
and
Swart,
2000).
The
analysis
is
part
of
a
multi-model
exercise
to
generate
a
range
of
energy-land-economy-climate
scenarios
for
the
full
set
of
SSPs
with
a
coll ection
of
integrated
assessment
models
(IAMs)
(Riahi
et
al.,
2017;
Bauer
et
al.,
2017;
Popp
et
al.,
2017).
To
streamline
the
use
of
the
SSP5
scenario
in
future
appl ications,
a
single
IAM
marker
scenario
was
selected
among
the
SSP5
scenarios—for
recommended
use
in
appl ications
which
cannot
consider
the
full
set
of
IAM
scenarios.
The
SSP5
marke r
scenario
was
developed
with
the
REMIND-MAgPIE
integrated
assessment
modeling
framework
(Popp
et
al.,
2011;
Bauer
et
al.,
2014).
Four
companion
papers
in
this
special
issue
describe
the
marke r
scenarios
for
the
other
SSPs
(SSP 1
IMAGE,
van
Vuuren
et
al.,
2017;
SSP2
MESSAGE-GLOBIOM,
Fricko
et
al.,
2017
;
SSP3
AIM/CGE,
Fujimo ri
et
al.,
2017;
SSP4
GCAM,
Calvin
et
al.,
2017).
The
SSP5
emissions
outcomes
can
be
compared
with
earlier
“high
emissions”
scenarios
following
storylines
with
some
resemblance
to
SSP5.
This
includes
in
particular
the
emissions
scenario
underlying
the
highest
Representative
Concentration
Pathway
(RCP)
reaching
a
radiative
forcing
of
8.5
W/m
2
by
the
end
of
the
century
(RCP8.5;
Riahi
et
al.,
2011)
and
the
A1FI
scenario
family
in
the
IPCC
Special
Report
on
Emissions
Scenarios
(SRES;
Naki

cenovi

c
and
Swart,
2000).
We
will
provide
a
quantitative
comparison
of
the
SSP5
scenarios
with
those
scenarios
as
well
as
with
the
range
of
baseline
and
mitigation
scenarios
in
the
emissions
scenario
database
of
the
Fifth
Assessment
Report
(AR5)
of
Working
Group
III
of
the
IPCC
(IPCC,
2014).
The
SSP5
scenario
family
presented
in
this
study
is
built
around
a
SSP5
baseline
scenario
without
dedicated
climate
policy
and
without
impacts
of
climate
change
and
other
dimensions
of
global
environmental
change
on
society.
This
scenario
aims
to
provide
a
baseline
case
for
future
investigations
of
mitigation,
adaptation
and
residual
climate
impacts.
Of
course,
accounting
for
climate
impacts
and
climate
policies
can
significantly
alter
the
energy,
land-use,
and
emissions
outcomes
as
well
as
other
socio-
economic
outcomes.
In
line
with
the
conceptual
approach
of
the
new
scenario
framework
(van
Vuuren
et
al.,
2014),
the
impact
of
policy
interventions
and
climate
change
can
be
analyzed
with
respect
to
this
baseline
to
explore
the
contingency
of
future
developments
on
prese nt
and
future
actions.
While
much
of
this
analysis
is
subject
to
concurrent
(e.g.,
Wiebe
et
al.,
2015)
or
future
research,
this
study
already
presents
a
set
of
SSP5-based
climate
change
mitigation
scenarios.
The
mitigation
scenarios
can
be
used
to
assess
the
challenges
to
mitigation
in
SSP5
by
exploring
the
socio-economic
consequences
of
reaching
increasingly
stringent
forcing
targets.
While
the
paper
focuses
on
the
SSP5
marker
scenario
developed
by
REMIND-MAgPIE,
it
will
also
explore
the
impact
of
model
choi ce
and
inherently
uncertain
assumptions
about
future
socio-economic
and
technological
developments
on
the
scenario
outcomes.
Concerning
the
unce rtain ty
about
socio-
economic
developments
and
future
technologi es,
the
SSP5
energy,
land-use,
emissions,
and
economic
outcomes
will
be
compared
with
SSP1,
a
sustainability
oriented
world
with
low
challenges
to
mitigation
and
adaptation
(O’Neill
et
al.,
2017;
van
Vuuren
et
al.,
2017)
and
a
midd le-of-the
road
development
in
SSP2,
a
world
with
intermediate
challenges
to
mitigation
and
adaptation
(Fricko
et
al.,
2017).
Concerning
the
impact
of
model
choi ce
and
differences
in
the
implementation
of
the
SSP5
narrative,
the
paper
will
compare
the
SSP5
marke r
scenario
with
alternative
interpretations
of
SSP5
by
the
GCAM
(Calvin
et
al.,
2017
),
WITCH-GLOBIOM
(Emmerling
et
al.,
2016),
and
AIM/CGE
(
Fujimo ri
et
al.,
2017)
integrated
assessment
models.
Still,
the
deep
uncer tainty
about
long-term
developments
gives
rise
to
a
myriad
of
choices
in
projecting
the
energy,
land
use,
and
emissions
outcomes
even
within
the
bounds
of
the
SSP5
narrative.
Therefore
the
range
of
SSP5
projections
may
still
increase
as
more
SSP5
interpretations
from
other
models
or
SSP5
model
sensitivity
studies
become
available.
Further
information
about
the
SSP
scenarios
can
be
found
at
https://secure.iiasa.ac.at/web-apps/ene/SspDb.
2.
Methods
2.1.
The
REMIND-MAgPIE
integrated
assessment
modeling
framework
The
REMIND-MAgPIE
integrated
assessment
modeling
frame-
work
consists
of
an
energy-economy-climate
model
(REMIND)
(
Bauer
et
al.,
2008,
2012;
Leimbach
et
al.,
2010a,b;
Luderer
et
al.,
2013,
2015)
coupled
to
a
land-use
model
(MAgPIE)
(Lotze-Campen
et
al.,
2008;
Popp
et
al.,
2010,
2014b ).
REMIND
(Regional
Model
of
Investment
and
Development)
is
an
energy-economy
general
equilibrium
model
linking
a
macro-economic
growth
model
with
a
bottom-up
engineering
based
energy
system
model.
It
covers
eleven
world
regions,
differentiates
various
energy
carriers
and
technologies
and
represents
the
dynamics
of
economic
growth
and
international
trade
(Leimbach
et
al.,
2010a,b;
Mouratiadou
et
al.,
2016
).
A
Ramsey-type
growth
model
with
perfect
foresight
serves
as
a
macro-economic
core
projecting
growth,
savings
and
invest-
ments,
factor
incomes,
energy
and
material
demand.
The
energy
system
representation
differentiates
between
a
variety
of
fossil,
biogenic,
nuclear
and
renewable
energy
resources
(Bauer
et
al.,
2012,
2016a,b;
Klein
et
al.,
2014a;
Pietzcker
et
al.,
2014a,b).
The
model
accounts
for
crucial
drivers
of
energy
system
inertia
and
path
dependencies
by
representing
full
capacity
vintage
structure,
technological
learning
of
emergent
new
technologies,
as
well
as
298
E.
Kriegler
et
al.
/
Global
Environmental
Change
42
(2017)
297–31 5

adjustment
costs
for
rapidly
expanding
technologies.
The
emis-
sions
of
greenhouse
gases
(GHGs)
and
air
pollutants
are
largely
represented
by
source
and
linked
to
activities
in
the
energy-
economic
system
(Strefler
et
al.,
2014a,b).
Several
energy
sector
policies
are
represented
explicitly
(Bertram
et
al.,
2015),
including
energy-sector
fuel
taxes
and
consumer
subsidies
(Schwanitz
et
al.,
2014
).
The
model
also
represents
trade
in
energy
resources
(Bauer
et
al.,
2015).
MAgPIE
(Model
of
Agricultural
Production
and
its
Impacts
on
the
Environment)
is
a
global
multi-regional
economic
land-use
optimization
model
designed
for
scenario
analysis
up
to
the
year
2100.
It
is
a
partial
equilibrium
model
of
the
agricultural
sector
that
is
solved
in
recursive
dynamic
mode.
The
objective
function
of
MAgPIE
is
the
fulfilment
of
agricultural
demand
for
ten
world
regions
at
minimum
global
costs
under
consideration
of
biophysi-
cal
and
socio-economic
constraints.
Major
cost
types
in
MAgPIE
are
factor
requirement
costs
(capital,
labor,
fertilizer),
land
conversion
costs,
transportation
costs
to
the
closest
market,
investment
costs
for
yield-increasing
technological
change
(TC)
and
costs
for
GHG
emissions
in
mitigation
scenarios.
Biophysical
inputs
(0.5

resolu-
tion)
for
MAgPIE,
such
as
agricultural
yields,
carbon
densities
and
water
availability,
are
derived
from
a
dynamic
global
vegetation,
hydrology
and
crop
growth
model,
the
Lund-Potsdam-Jena
model
for
managed
Land
(LPJmL)
(Bondeau
et
al.,
2007;
Müller
and
Robertson,
2014).
Agricultural
demand
includes
demand
for
food
(
Bodirsky
et
al.,
2015),
feed
(Weindl
et
al.,
2015),
bioenergy
(Popp
et
al.,
2011),
material
and
seed.
For
meeting
the
demand,
MAgPIE
endogenously
decides,
based
on
cost-effectiveness,
about
intensi-
fication
of
agricultural
production
(TC),
cropland
expansion
and
production
relocation
(intra-regionally
and
inter-regionally
through
international
trade)
(Dietrich
et
al.,
2014;
Lotze-Campen
et
al.,
2010;
Schmitz
et
al.,
2012).
MAgPIE
derives
cell
specific
land-
use
patterns,
rates
of
future
agricultural
yield
increases
(Dietrich
et
al.,
2014),
food
commodity
and
bioenergy
prices
as
well
as
GHG
emissions
from
agricultural
production
(Bodirsky
et
al.,
2012;
Popp
et
al.,
2010)
and
land-use
change
(Humpenöder
et
al.,
2014;
Popp
et
al.,
2014b).
Emiss ions
in
the
land-use
and
energy
sectors
are
interlinked
by
overarching
climate
policy
objectives
and
the
deployment
of
bioenergy
(Klein
et
al.,
2014b;
Popp
et
al.,
2014a;
Rose
et
al.,
2014).
REMIND
and
MAgPIE
models
are
coupled
to
establish
an
equilibrium
of
bioenergy
and
emissions
markets
in
an
iterative
procedure
(Bauer
et
al.,
2014).
The
atmospheric
chemistry-
climate
model
MAGICC
(Meinshausen
et
al.,
2011)
is
used
to
evaluate
the
climate
outcomes
of
the
REMIND-MAgPIE
emission
path ways.
More
details
about
the
REMIND-MAgPIE
modeling
framework
and
the
coupling
approach
can
be
found
in
Section
S2
of
the
SOM.
2.2.
Implementation
of
SSPs
REMIND-MAgPIE
so
far
developed
integrated
energy-land-
economy-climate
scenarios
for
SSP5
(Fossil
Fueled
Development;
this
article),
SSP1
(Sustainability;
van
Vuuren
et
al.,
2017)
and
SSP2
(Middle
of
the
Road;
Fricko
et
al.,
2017).
REMIND-MAgPIE
scenarios
for
SSP3
(Regional
Rivalry;
Fujimori
et
al.,
2017)
and
SSP4
(Inequality;
Calvin
et
al.,
2017)
that
are
characterized
by
stronger
inter-
and
intraregional
disparities
than
SSP1,
2,
and
5
are
a
subject
of
future
work.
The
interpretation
of
SSP1,
2
and
5
by
REMIND-MAgPIE
is
based
on
the
SSP
narratives
(O’Neill
et
al.,
2017)
and
more
detailed
energy
and
land-use
specifications
developed
for
the
SSP
interpretations
by
IAMs
(Riahi
et
al.,
2017).
Model
assumptions
and
parameters
directly
relating
to
these
features
were
identified,
and
varied
across
the
three
SSPs
(Table
1).
Further
details
on
the
parameter
variations
are
provided
in
Section
S3
of
the
SOM.
Population
projections
are
an
exogenous
input
to
REMIND-
MAgPIE
and
are
directly
taken
from
the
country-level
population
projections
for
SSP1,
2,
and
5
(KC
and
Lutz,
2017).
Regional
economic
output
is
deduced
from
the
SSP
country-level
projec-
tions
of
gross
domestic
product
(GDP)
by
the
OECD
team
(Dellink
et
al.,
2017).
GDP
is
an
endogenous
variable
in
REMIND,
largely
driven
by
exogenous
assumptions
about
labor
productivity
increases.
Those
were
adjusted
to
reproduce
the
GDP
projections
in
the
SSP
baseline
cases.
The
mitigation
scenarios
show
an
endogenous
GDP
response
to
mitigation
policies
which
can
serve
as
a
measure
for
the
challenges
to
mitigation
in
the
individual
SSPs
(see
Section
5).
SSP5
scenarios
have
also
been
produced
by
the
AIM/CGE,
GCAM,
and
WITCH-GLOBIOM
integrated
assessment
models.
Their
implementation
of
SSP5
is
briefly
summarized
in
Section
S3.3
of
the
SOM.
2.3.
Implementation
of
mitigation
scenarios
The
SSP
mitigation
scenarios
were
derived
by
implementing
mitigation
policies
in
the
SSP
baselines
aiming
at
a
climate
forcing
target
in
2100.
The
target
levels
of
anthropogenic
climate
forcing
were
chosen
to
be
consistent
with
the
2100
forcing
levels
obtained
by
the
Representative
Concentration
Pathways
(RCPs;
van
Vuuren
et
al.,
2011),
i.e.,
RCP6.0
(Scenario
SSP5-6.0;
reaching
about
5.4
W/
m
2
as
estimated
by
the
reduced-form
atmospheric
chemistry-
climate
model
MAGICC;
Riahi
et
al.,
2017),
RCP4.5
(SSP5-4.5;
about
4.2
W/m
2
)
and
RCP2.6
(SSP5-2.6;
2.6
W/m
2
).
In
addition,
an
intermediate
forcing
level
of
3.4
W/m
2
was
investigated.
Since
such
policies
are
not
only
characterized
by
the
long
term
forcing
target,
but
also
by
other
factors
such
as
their
regional,
sectoral
and
temporal
profile,
their
qualitative
features
were
harmonized
across
IAMs
by
use
of
shared
climate
policy
assumptions
(SPAs,
Kriegler
et
al.,
2014a).
A
detailed
discussion
of
the
SPAs
can
be
found
in
Riahi
et
al.
(2017).
In
the
energy
sector,
regionally
fragmented
carbon
pricing
as
implied
by
existing
climate
policy
pledges
was
assumed
until
2020
(Kriegler
et
al.,
2015
),
followed
by
a
transition
period
to
globally
uniform
carbon
pricing
at
the
level
mandated
by
the
long
term
forcing
target
in
2100.
The
assumptions
about
the
transition
period
reflected
different
abilities
to
establish
effective
international
cooperation
to
solve
environmental
problems
in
the
SSPs
(see
Table
1):
full
global
cooperation
after
2020
in
SSP1,
and
transition
to
a
global
carbon
price
from
2020
to
2040
in
SSP2
and
SSP5.
Both
SSP1
and
SSP5
assume
effective
institutions
to
manage
land-use,
and
therefore
associated
SPAs
assume
effective
pricing
of
land-use
emissions
at
the
level
of
the
energy
sector.
In
SPA2,
the
control
of
emissions
from
land
conversion
is
weaker
in
the
near
term
so
that
deforestation
is
not
fully
eliminated
before
2030.
A
detailed
description
of
the
SPA
implementation
in
REMIND-MAgPIE
is
provided
in
Section
S4
of
the
SOM.
The
SPAs
try
to
incorporate
short
term
climate
policy
develop-
ments
in
the
long
term
mitigation
scenarios.
Although
they
were
formulated
before
the
adoption
of
the
Paris
Agreement
in
December
2015,
they
are
to
some
extent
compatible
with
the
intended
nationally
determined
contributions
(NDCs)
to
the
agreement,
particularly
for
SSP2
(SOM
Figs.
S4.2
and
S4.3).
Remaining
differences
are
within
the
range
of
the
uncertainty
about
the
final
scope
of
NDCs
and
in
particular
their
actual
implementation,
which
will
be
influenced
by
the
underlying
socio-
economic
pathway
the
world
will
follow
in
the
coming
decades.
2.4.
Regional
reporting
Scenario
outcomes
are
provided
on
the
global
level
and
the
level
of
five
macro-regions:
Latin
America
(LAM),
Middle
East
and
E.
Kriegler
et
al.
/
Global
Environmental
Change
42
(2017)
297–315
299

Table
1
Overview
of
the
SSP
implementation
in
REMIND-MAgPIE.
The
table
links
the
implementation
settings
(right
columns)
to
the
associated
high
level
characterization
of
SSPs
in
O’Neill
et
al.
(2017)
(left
columns).
HICs
stands
for
High
Income
Countries.
The
concrete
implementation
was
based
on
more
detailed
specifications
of
energy
and
land-use
characteristics
developed
for
the
IAM
interpretations
of
SSPs
(Riahi
et
al.,
2017);
SOM
Tables
S3.1
and
S3.5).
A
detailed
quantitative
description
of
the
SSP
implementation
in
REMIND-MAgPIE
is
provided
in
SOM
Section
S3.
Narrative
(O’Neill
et
al.,
2017)
REMIND-MAgPIE
implementation
Indicator
SSP1
–
Sustainability
SSP2
–
Middle
of
the
Road
SSP5
–
Fossil
Fueled
Development
Parameter
SSP1
SSP2
SSP5
Demographics
Population
growth
Low
(medium
fertility
in
HICs)
Medium
Low
(high
fertility
in
HICs)
Population
KC
and
Lutz
(2017)
Migration
Medium
Medium
High
Economy
&
lifestyle
GDP
growth
(per
capita)
High
(medium
in
HICs)
Medium,
uneven
High
GDP/cap
growth
Dellink
et
al.
(2017)
Inequality
Reduced
Uneven,
reduced
moderately
Strongly
reduced
GDP/cap
convergence
Dellink
et
al.
(2017)
Traditional
biomass
use
Rapid
phase-
out
Intermediate
phase-out
Rapid
phase-
out
Globalization
Connected
markets
Semi-open
global
economy
Strong
Regional
capital
intensities
Converging
Non-
converging
Converging
International
trade
Moderate
Moderate
High
Capital
markets
Global
Global
Global
Energy
markets
Global
Global
Global
Agricultural
trade
Global
Regional
Global
Consumption
Low
material
consumption
Material
intensive
Materialism,
Status
consumption,
High
mobility
Energy
demand
Low
Medium
High
Transport
liquids
Low
Medium
High
Diet
Low
meat
diets
Medium
meat
consumption
Meat-rich
diets
Calories
per
capita
Low
Medium
High
Livestock
share
Low
Medium
High
Technology
Development
Rapid
Medium,
uneven
Rapid
GDP/cap
growth
Dellink
et
al.
(2017)
Energy
technology
change
Directed
away
from
fossil
fuels,
toward
efficiency,
renewables
Some
investment
in
renewables,
continued
reliance
on
fossil
fuels
Directed
toward
fossil
fuels;
alternative
sources
not
actively
pursued
Renewable
energy
Favorable
outlook
Intermediate
outlook
Pessimistic
outlook
Nuclear
energy
Pessimistic
Intermediate
Intermediate
CCS
Intermediate
Intermediate
Favorable
Environment
&
resources
Fossil
constraints
Preferences
shift
away
from
fossil
fuels
No
reluctance
to
use
unconv.
Resources
None
Oil,
coal
and
gas
resources
Low
Medium
High
Land-use
Strong
regulations
to
avoid
environmental
tradeoffs
Medium
regulations
lead
to
slow
decline
in
the
rate
of
deforestation
Medium
regulations
lead
to
slow
decline
in
the
rate
of
deforestation
Forest
protection
rate
High
Medium
Medium
Agriculture
Improvements
in
ag
productivity;
rapid
diffusion
of
best
practices
Medium
pace
of
tech
change
in
ag
sector;
entry
barriers
to
ag
markets
reduced
slowly
Highly
managed,
resource-intensive,
rapid
increase
in
productivity
Crop
productivity
Endogenous
Endogenous
Endogenous
Livestock
productivity
Medium/
high
Medium
High
Nutrient
efficiency
High
Medium
Low
Biomass
supply
(2nd
generation)
Low
Medium
High
Policies
&
institutions
International
cooperation
Effective
Relatively
weak
Effective
for
development,
limited
for
environment
See
international
trade
settings
above,
and
discussion
of
SPAs
in
Section
2.3
Environmental
(and
energy)
policy
Improved
management
of
local
and
global
issues;
tighter
regulation
of
pollutants
Concern
for
local
pollutants
but
only
moderate
success
in
implementation
Focus
on
local
environment,
little
concern
with
global
problems
Air
pollutant
control
High
Medium
High
Bioenergy
tax
High
Medium
to
high
High
Fossil
fuel
policies
(Subsidies/
taxes)
Restrictive
Intermediate
Supportive
300
E.
Kriegler
et
al.
/
Global
Environmental
Change
42
(2017)
297–31 5

Africa
(MAF),
Asia
not
including
the
Middle
East
(ASIA),
the
reforming
economies
of
the
former
Soviet
Union
(REF),
and
the
original
OECD
countries
(in
1990)
plus
European
Union
and
candidate
countries
(OECD)
(Riahi
et
al.,
2017;
https://secure.iiasa.
ac.at/web-apps/ene/SspDb
).
Since
the
native
model
regions
of
REMIND-MAgPIE
are
not
perfect
subsets
of
these
macro-regions,
small
deviations
between
the
definition
of
these
regions
and
the
country
groups
mapped
to
these
regions
by
REMIND-MAgPIE
exist
(SOM
Section
S2.4).
3.
Energy
and
food
demand
and
their
drivers
in
SSP5
Energy
and
food
demand
are
strongly
influenced
by
population
and
economic
developments.
Food
demand
was
constructed
exogenously
based
on
SSP5
population
and
economic
output
trajectories
and
additional
assumptions
in
the
SSP5
narrative
(SOM
Section
S3.2),
and
remained
unchanged
between
baseline
and
mitigation
cases.
Energy
demand
is
an
endogenous
output
of
the
REMIND
model,
and
differs
between
baseline
and
mitigation
cases
due
to
changes
in
energy
mix
and
energy
prices.
3.1.
Population
SSP5
is
a
world
with
a
fast
demographic
transition
in
developing
countries
driven
by
improving
education,
health,
and
economic
conditions,
and
a
stabilization
of
fertility
rates
above
replacement
levels
in
high
income
countries
due
to
optimistic
economic
outlooks
(KC
and
Lutz,
2017).
Migration
from
poorer
to
wealthier
countries
further
bolsters
the
dynamic
population
development
in
industrialized
countries.
The
starkly
different
trends
in
population
before
and
after
2050
are
an
important
feature
of
SSP5
affecting
associated
energy,
emissions
and
land
use
projections.
Specifically,
in
the
first
half
of
the
century
population
is
increasing
in
all
regions
except
the
reforming
economies
(REF),
and
after
2050
it
is
decreasing
in
all
regions
except
in
the
Middle
East
and
Africa
(MAF)
and
high
income
OECD
regions.
Globally,
population
peaks
at
around
8.6
billion
between
2050
and
2060
followed
by
a
decline
to
7.4
billion
in
2100
(Fig.
1,
top
row).
Overall,
global
population
growth
is
projected
to
be
similar
to
SSP1
and
slower
than
in
SSP2
and
the
UN
medium
projection
(United
Nations,
2015)
in
all
regions
except
OECD.
It
is
also
similar
to
the
SRES
A1FI
scenario
family
(Naki

cenovi

c
and
Swart,
2000),
but
significantly
lower
than
in
the
high
population
RCP8.5
scenario
(
Riahi
et
al.,
2011).
3.2.
Economic
output
Economic
growth
is
rapid
in
developing
countries
and
high
in
industrialized
countries,
with
a
strong
convergence
of
income
levels
between
countries.
GDP
per
capita
levels
by
the
end
of
the
century
are
projected
to
increase
by
factors
of
5
(OECD;
annual
average
growth
of
1.8%/yr)
to
28
(MAF;
3.8%/yr)
relative
to
2010,
reaching
120
thousand
(MAF)
to
160
thousand
(OECD)
US
Dollars
per
year
in
2100
(in
purchasing
power
parity
(PPP)
units;
Dellink
et
al.,
2017).
This
translates
into
a
rapid
increase
of
global
economic
output
from
67
trillion
USD
in
2010
to
360
trillion
USD
in
2050
and
1000
trillion
USD
(PPP)
in
2100
(Fig.
1,
upper
middle
row).
End
of
century
economic
output
in
SSP5
is
almost
twice
as
high
as
in
SSP2
and
SSP1,
with
the
strongest
differences
in
OECD
due
to
the
compounding
effects
of
significantly
higher
population
and
GDP
per
capita
growth.
Income
convergence
between
developing
and
industrialized
countries
is
equally
rapid
in
SSP1
and
SSP5,
but
at
lower
overall
income
levels
in
SSP1
due
to
less
emphasis
on
economic
growth
in
high
income
countries.
Since
the
SSP
economic
output
assumptions
are
specified
in
PPP
units,
the
GDP
values
cannot
directly
be
compared
to
GDP
projections
based
on
market
exchange
rates
as
reported
for
emissions
scenarios
in
the
literature.
However,
GDP
information
in
PPP
is
available
for
A1FI
(Naki

cenovi

c
and
Swart,
2000)
and
a
subset
of
scenarios
in
the
AR5
scenario
database
(IPCC,
2014).
They
all
assume
slower
global
economic
growth
over
the
21st
century
than
SSP5.
3.3.
Energy
demand
Historically,
energy
intensity
of
economic
output
decreased
and
per
capita
energy
use
increased
with
increasing
GDP
per
capita
levels
(Grübler
et
al.,
2012;
Fouquet,
2014).
In
SSP2
and
SSP5,
the
developing
regions
MAF,
ASIA,
and
LAM
exhibit
a
roughly
constant
growth
of
per
capita
final
energy
demand
with
income,
while
in
the
OECD
and
REF
regions,
it
saturates
starting
from
considerably
higher
levels
of
per
capita
energy
use
(SOM
Fig.
S1.1).
The
resulting
final
energy
intensity
improvement
rates
over
the
century
range
from
1.2%/yr
in
OECD
to
2.3%/yr
in
MAF
in
line
with
historic
trends
in
developing
and
industrialized
countries
(Grübler
et
al.,
2012;
Stern,
2012;
IEA,
2015).
In
the
sustainability
oriented
world
described
by
SSP1,
per
capita
energy
demand
grows
significantly
slower
with
income
in
the
developing
regions
and
even
decreases
in
OECD
and
REF.
As
a
result,
global
final
energy
demand
in
SSP5
(1170
EJ/yr)
is
more
than
twice
as
high
as
in
SSP1
(470
EJ/yr)
by
the
end
of
the
century,
with
SSP2
positioned
in
between
these
two
cases
(Fig.
1,
lower
middle
row).
This
trend
in
energy
demand
is
confirmed
by
other
interpretations
of
SSP5
by
AIM/CGE,
GCAM
and
WITCH-GLOBIOM,
which
find
global
energy
demand
levels
in
2100
between
980
and
1190
EJ/yr
(Figs.
3,
SOM
S1.3).
SSP5
final
energy
demand
levels
are
similar
to
RCP8.5
(Riahi
et
al.,
2011)
and
at
the
upper
end
of
energy
demand
projections
in
the
AR5
database
(
IPCC,
2014),
but
significantly
lower
than
in
A1FI
(Naki

cenovi

c
and
Swart,
2000).
3.4.
Food
demand
Food
demand
reflects
human
metabolic
requirements,
but
food
consumption
is
also
a
function
of
economic
and
social
develop-
ment
as
consumption
patterns,
especially
the
share
of
livestock
products
within
diets
and
food
waste,
change
with
income
(
Bodirsky
et
al.,
2015).
This
is
particularly
true
for
SSP5,
where
diets
with
high
animal
and
waste
shares
prevail
(Figs.
2
,
SOM
S3.7).
Under
this
assumption,
the
income
dynamics
in
SSP5
result
in
increasing
per-capita
food
demand
at
household
level
(including
household
waste)
until
late
in
the
century,
reaching
a
global
average
crop
demand
of
3250
kcal/cap/day
(45%
higher
than
in
2010)
and
livestock
demand
of
860
kcal/cap/day
(85%
higher
than
in
2010)
in
2100
(Fig.
2).
By
2100,
SSP5
shows
substantially
higher
per-capita
food
demand
(crops
and
livestock)
across
all
regions
compared
to
SSP2
(3320
kcal/cap/day)
and
in
particular
compared
to
SSP1
(2830
kcal/cap/day)
with
its
emphasis
on
limiting
meat
consumption
and
food
waste
(SOM
Fig.
S1.2).
Total
global
food
demand
by
2100,
however,
is
similar
in
SSP2
and
SSP5
(46
EJ/yr)
because
population
in
SSP2
is
substantially
higher
than
in
SSP5
(
Fig.
1,
bottom
row).
In
contrast,
total
food
demand
in
SSP1
(30
EJ/
yr)
is
considerably
lower
compared
to
SSP5
and
SSP2
because
of
the
coincidence
of
lower
population
and
lower
per-capita
food
demand
in
SSP1.
Food
demand
projections
in
other
SSP5
IAM
interpretations
are
lower
than
in
the
REMIND-MAgPIE
marker
scenario
(AIM/CGE:
3600
kcal/cap/day,
40
EJ/yr;
GCAM:
3420
kcal/
cap/day,
39
EJ/yr).
Regional
food
demand
in
REMIND-MAgPIE
is
identical
in
the
baseline
and
climate
policy
scenarios,
i.e.,
food
demand
is
insensitive
to
climate
policy
intervention.
Regional
food
produc-
tion,
however,
differs
between
baseline
and
climate
policy
scenarios
because
agricultural
productivity
and
trade
patterns
react
to
mitigation
policies
(SOM
Figs.
S1.9,
S1.12).
E.
Kriegler
et
al.
/
Global
Environmental
Change
42
(2017)
297–315
301