Safe and just operating spaces for regional social-ecological systems

Abstract

Humanity faces a major global challenge in achieving wellbeing for all, while simultaneously ensuring that the biophysical processes and ecosystem services that underpin wellbeing are exploited within scientifically informed boundaries of sustainability. We propose a framework for defining the safe and just operating space for humanity that integrates social wellbeing into the original planetary boundaries concept (Rockström et al., 2009a,b) for application at regional scales. We argue that such a framework can: (1) increase the policy impact of the boundaries concept as most governance takes place at the regional rather than planetary scale; (2) contribute to the understanding and dissemination of complexity thinking throughout governance and policy-making; (3) act as a powerful metaphor and
communication tool for regional equity and sustainability. We demonstrate the approach in two rural Chinese localities where we define the safe and just operating space that lies between an environmental ceiling and a social foundation from analysis of time series drawn from monitored and palaeoecological data, and from social survey statistics respectively. Agricultural intensification has led to poverty reduction, though not eradicated it, but at the expense of environmental degradation. Currently, the environmental ceiling is exceeded for degraded water quality at both localities even though the least well-met social standards are for available piped water and sanitation. The conjunction of these social needs and environmental constraints around the issue of water access and quality illustrates the broader value of the safe and just operating space approach for sustainable development.

Full text

Safe
and
just
operating
spaces
for
regional
social-ecological
systems
John
A.
Dearing
a,
*,
Rong
Wang
a,b
,
Ke
Zhang
a,c
,
James
G.
Dyke
d
,
Helmut
Haberl
e
,
Md.
Sarwar
Hossain
a
,
Peter
G.
Langdon
a
,
Timothy
M.
Lenton
f
,
Kate
Raworth
g
,
Sally
Brown
h
,
Jacob
Carstensen
i
,
Megan
J.
Cole
j
,
Sarah
E.
Cornell
k
,
Terence
P.
Dawson
l
,
C.
Patrick
Doncaster
m
,
Felix
Eigenbrod
m
,
Martina
Flo
¨rke
n
,
Elizabeth
Jeffers
o
,
Anson
W.
Mackay
p
,
Bjo
¨rn
Nykvist
k,q
,
Guy
M.
Poppy
m
a
Palaeoecology
Laboratory,
Geography
and
Environment,
University
of
Southampton,
Highfield
Campus,
Southampton
SO17
1BJ,
UK
b
Nanjing
Institute
of
Geography
and
Limnology,
73
East
Beijing
Road,
Nanjing
210008,
PR
China
c
ARC
Centre
of
Excellence
for
Coral
Reef
Studies,
James
Cook
University,
PO
Box
6811,
Cairns,
Queensland
4870,
Australia
d
Institute
for
Complex
Systems
Simulation,
Highfield
Campus,
University
of
Southampton,
Southampton
SO17
1BJ,
UK
e
Institute
of
Social
Ecology
Vienna
(SEC),
Faculty
for
Interdisciplinary
Studies
Klagenfurt,
Wien,
Graz
(IFF),
Alpen-Adria-Universitaet
Klagenfurt
(AAU),
Schottenfeldgasse
29,
A-1070
Vienna,
Austria
f
Earth
System
Science,
College
of
Life
and
Environmental
Sciences,
University
of
Exeter,
Laver
Building,
Exeter
EX4
4QE,
UK
g
Environmental
Change
Institute,
Oxford
University
Centre
for
the
Environment,
South
Parks
Road,
Oxford
OX1
3QY,
UK
h
Faculty
of
Engineering
and
the
Environment,
and
Tyndall
Centre
for
Climate
Change
Research,
University
of
Southampton,
Southampton
SO17
1BJ,
UK
i
Department
of
Bioscience,
Aarhus
University,
Frederiksborgvej
399,
DK-4000
Roskilde,
Denmark
j
School
of
Geography
and
the
Environment,
Oxford
University
Centre
of
the
Environment,
University
of
Oxford,
South
Parks
Road,
Oxford
OX1
3QY,
UK
k
Stockholm
Resilience
Centre,
Stockholm
University,
Kra
¨ftriket
2B,
Stockholm
SE-106
91,
Sweden
l
School
of
the
Environment,
University
of
Dundee,
Dundee
DD1
4HN,
UK
m
Centre
for
Biological
Sciences,
Institute
for
Life
Sciences,
University
of
Southampton,
Highfield
Campus,
Southampton
SO17
1BJ,
UK
n
Center
for
Environmental
Systems
Research
(CESR),
University
of
Kassel,
D-34109
Kassel,
Germany
o
Long
Term
Ecology
Laboratory,
Department
of
Zoology,
Tinbergen
Building,
South
Parks
Road,
Oxford
OX1
3PS,
UK
p
Environmental
Change
Research
Centre,
Department
of
Geography,
University
College
London,
London
WC1E
6BT,
UK
q
Stockholm
Environment
Institute,
Linne
´gatan
87
D,
Stockholm
SE-115
23,
Sweden
Global
Environmental
Change
28
(2014)
227–238
A
R
T
I
C
L
E
I
N
F
O
Article
history:
Received
22
November
2013
Received
in
revised
form
16
June
2014
Accepted
19
June
2014
Available
online
Keywords:
Regional
boundaries
Social-ecological
systems
Social
wellbeing
Environmental
thresholds
A
B
S
T
R
A
C
T
Humanity
faces
a
major
global
challenge
in
achieving
wellbeing
for
all,
while
simultaneously
ensuring
that
the
biophysical
processes
and
ecosystem
services
that
underpin
wellbeing
are
exploited
within
scientifically
informed
boundaries
of
sustainability.
We
propose
a
framework
for
defining
the
safe
and
just
operating
space
for
humanity
that
integrates
social
wellbeing
into
the
original
planetary
boundaries
concept
(Rockstro
¨m
et
al.,
2009a,b)
for
application
at
regional
scales.
We
argue
that
such
a
framework
can:
(1)
increase
the
policy
impact
of
the
boundaries
concept
as
most
governance
takes
place
at
the
regional
rather
than
planetary
scale;
(2)
contribute
to
the
understanding
and
dissemination
of
complexity
thinking
throughout
governance
and
policy-making;
(3)
act
as
a
powerful
metaphor
and
communication
tool
for
regional
equity
and
sustainability.
We
demonstrate
the
approach
in
two
rural
Chinese
localities
where
we
define
the
safe
and
just
operating
space
that
lies
between
an
environmental
ceiling
and
a
social
foundation
from
analysis
of
time
series
drawn
from
monitored
and
palaeoecological
data,
and
from
social
survey
statistics
respectively.
Agricultural
intensification
has
led
to
poverty
reduction,
though
not
eradicated
it,
but
at
the
expense
of
environmental
degradation.
Currently,
the
environmental
ceiling
is
exceeded
for
degraded
water
quality
at
both
localities
even
though
the
least
well-met
social
standards
are
for
available
piped
water
and
sanitation.
The
conjunction
of
these
social
needs
and
environmental
constraints
around
the
issue
of
water
access
and
quality
illustrates
the
broader
value
of
the
safe
and
just
operating
space
approach
for
sustainable
development.
ß
2014
The
Authors.
Published
by
Elsevier
Ltd.
This
is
an
open
access
article
under
the
CC
BY
license
(http://creativecommons.org/licenses/by/3.0/).
*Corresponding
author.
Tel.:
+44
02380
594648.
E-mail
address:
j.dearing@soton.ac.uk
(J.A.
Dearing).
Contents
lists
available
at
ScienceDirect
Global
Environmental
Change
jo
ur
n
al
h
o
mep
ag
e:
www
.elsevier
.co
m
/loc
ate/g
lo
envc
h
a
http://dx.doi.org/10.1016/j.gloenvcha.2014.06.012
0959-3780/ß
2014
The
Authors.
Published
by
Elsevier
Ltd.
This
is
an
open
access
article
under
the
CC
BY
license
(http://creativecommons.org/licenses/by/3.0/).

1.
Introduction
1.1.
Rationale
and
motivation
The
planetary
boundaries
framework
(Rockstro
¨m
et
al.,
2009a,b)
has
significantly
influenced
the
international
discourse
on
global
sustainability.
In
short,
it
proposes
nine
interlinked
biophysical
(hereafter
referred
to
as
ecological)
boundaries
at
the
planetary
scale
(Fig.
1a)
that
global
society
should
remain
within,
if
it
is
to
avoid
‘‘disastrous
consequences
for
humanity’’.
The
proposition
of
planetary
boundaries
has
provoked
discussion
in
the
science
and
policy
communities.
Recently
published
commentaries
include
refinement
of
the
boundaries
for
phosphorus
(Carpenter
and
Bennett,
2011),
nitrogen
(de
Vries
et
al.,
2013)
and
freshwater
use
(Rockstro
¨m
and
Karlberg,
2010);
the
proposal
of
a
potential
state
shift
in
the
global
biosphere
(Barnosky
et
al.,
2012);
a
new
approach
to
defining
land-related
boundaries
using
net
primary
plant
production
(Erb
et
al.,
2012;
Running,
2012);
analyses
of
the
governance
implications
(Biermann,
2012;
Galaz,
2012;
Nordhaus
et
al.,
2012);
and
critical
assessment
of
the
nature
of
the
proposed
planetary
boundaries
(Brook
et
al.,
2013).
Raworth’s
(2012)
extension
of
the
planetary
boundary
concept
to
include
social
objectives
in
the
context
of
sustainability
policy
and
practice
has
produced
a
framework
that
has
become
known
as
the
‘Oxfam
doughnut’,
with
an
explicit
focus
on
the
social
justice
requirements
underpinning
sustainability
(Fig.
1b).
This
allows
multi-metric
‘compasses’
to
be
elaborated
for
directing
decision-making.
In
this
paper,
we
develop
the
‘doughnut’
idea
at
the
regional
scale
in
terms
of
the
levels
of
societal
wellbeing
and
conditions
of
ecological
processes
that
co-exist
within
regional
social-ecological
systems,
using
the
terms
‘social
foundation’
and
‘environmental
ceiling’
to
represent
the
social
and
ecological
boundaries.
In
doing
so,
we
define
the
regional
safe
and
just
operating
space
(RSJOS).
Our
main
motivation
is
to
show
how
the
concept
of
ecologically
safe
and
socially
just
planetary
boundaries
can
be
adapted
and
applied
at
regional
levels,
for
example:
watersheds,
national
parks,
sub-national
administrative
divisions,
and
nation
states.
Because
critical
transitions
can
occur
at
any
scale
(Scheffer
et
al.,
2001;
Folke
et
al.,
2004;
Lenton,
2013),
the
original
planetary
boundaries
framework
recognized
that
the
effects
of
crossing
multiple
thresholds
at
regional
scales
can
aggregate
to
become
a
global
concern
(Rockstro
¨m
et
al.,
2009a,b).
But
the
cascading
effects
of
environmental
degradation
(Peters
et
al.,
2011)
can
have
critical
consequences
for
the
sustainability
of
regional
systems
them-
selves,
well
before
the
effects
are
obvious
at
the
global
scale.
This
means
that
global
sustainability
requires
both
regional
and
planetary
dimensions
to
be
addressed.
Hence
our
view
is
that
concepts
sharpened
by
consideration
of
regional
scales
can
feed
back
iteratively
to
help
refine
or
redefine
planetary
boundaries.
The
argument
for
considering
regional-scale
boundaries
is
reinforced
by
an
equally
strong
equity
and
governance
rationale.
In
the
planetary
boundaries
framework,
protecting
human
wellbeing
is
the
rationale
for
the
scientific
assessment
of
how
to
limit
the
use
and
degradation
of
natural
resources
in
order
to
avoid
critical
transitions
in
Earth
system
processes.
At
the
same
time,
human
wellbeing
depends
fundamentally
upon
each
person
having
claim
to
the
natural
resources
required
to
meet
their
physiological
needs
such
as
food,
water,
shelter
and
sanitation
(Folke
et
al.,
2011).
It
follows
from
these
fundamental
equity
considerations
that
social
foundations
(sensu
Raworth,
2012)
should
be
considered
alongside
planetary
and
regional
boundaries.
Traversing
the
scales
to
regional
boundaries
requires
explicit
attention
to
both
the
human
drivers
of
change
and
social
distributional
issues,
bringing
new
transdisciplinary,
concep-
tual
and
ethical
challenges
to
the
planetary
boundaries
concept.
Many
nations
and
regions
face
significant
and
urgent
challenges
in
ensuring
that
available
resources
are
used
to
meet
the
needs
of
all,
emphasizing
the
sustainable
use
of
regional
resources
for
human
wellbeing.
In
particular,
while
agricultural
intensification
in
developing
countries
is
widely
seen
as
promoting
rapid
economic
growth
and
poverty
alleviation,
evidence
exists
to
show
that
the
associated
degradation
of
ecosystem
processes
may
be
unsustainable
(e.g.
Tilman
et
al.,
2002;
Dearing
et
al.,
2012a).
Natural
resource
management
takes
place
predominantly
at
regional
scales
as
part
of
national
and
regional
development
planning.
Therefore,
analytical
tools
that
map
the
condition
of
ecological
processes
at
these
scales
are
more
likely
to
have
relevance
and
traction
for
policy
design
and
resource
governance.
Above
all,
there
is
a
need
to
counter
the
limitations
of
current
political-strategic
timeframes
that
are
too
often
aligned
with
short
term
‘discounting’
perspectives
that
place
emphasis
on
near
future
decisions.
An
ability
to
identify
and
stay
within
ecological
boundaries
over
longer
timescales
would
help
to
ensure
inter-
generational
sustainable
resource
use.
A
longer
timeframe
is
also
in
tune
with
‘‘perfect
storm’’
projections
for
converging
trends
by
mid-
century
(Godfray
et
al.,
2010;
Dearing
et
al.,
2012b).
For
communi-
ties
in
regions
that
already
occupy
dangerous
operating
spaces,
a
new
framework
that
captures
multiple
timescales
could
provide
a
scientifically
informed
prioritization
of
restorative
action.
1.2.
A
regional
framework
A
regional
boundaries
framework
can
be
designed
in
alternative
ways,
depending
on
its
motivation.
One
approach
would
be
to
calculate
the
regional
share
of
global
resource
use
(e.g.
water)
and
Fig.
1.
Merging
(a)
the
planetary
boundary
framework
(Rockstro
¨m
et
al.,
2009a,b)
and
(b)
the
social
‘doughnut’
framework
(Raworth,
2012)
into
a
new
framework
and
tool
for
defining
safe
and
just
operating
spaces
for
sustainable
development
at
regional
scales.
J.A.
Dearing
et
al.
/
Global
Environmental
Change
28
(2014)
227–238
228

impacts
on
planetary
boundaries
(e.g.
CO
2
emissions)
in
the
light
of
social
conditions
(e.g.
in
a
less
developed
country).
Another
approach
could
focus
on
the
links
between
social
wellbeing
(e.g.
food
security)
and
the
sustainable
management
of
resources
(e.g.
sustainable
fish
farming)
within
a
particular
region.
Both
demand
the
integration
of
social
and
ecological
data
with
equity
issues
placed
centrally.
In
this
paper
we
focus
on
the
latter
approach:
developing
a
RSJOS
conceptual
framework
that
allows,
as
a
first
step,
setting
and
assessing
performance
against
boundaries
on
both
environmental
and
social
fronts
in
two
rural
case
studies
from
China.
This
approach
is
complementary
to,
and
equally
important
as,
evaluating
the
impact
of
human
actions
at
the
global
scale
because
staying
within
regional
boundaries
is
a
prerequisite
for
reducing
the
aggregated
effects
on
six
of
the
proposed
planetary
boundaries.
Ongoing
work
is
exploring
alternative
frameworks
for
national
levels
(Nykvist
et
al.,
2013)
where
the
focus
is
on
burden
sharing,
fairness
and
national
responsibility
for
planetary
boundaries.
2.
Theory
and
methods
2.1.
Environmental
ceilings
The
scientific
logic
of
defining
environmental
limits
and
boundaries
for
regional
social-ecological
systems
draws
on
relevant
theoretical
insights
from
other
systems
approaches
(Dearing
et
al.,
2012a,
2010)
and
links
to
current
policy
discourses
(Cornell,
2012).
In
particular,
it
is
generally
agreed
that
critical
transitions
and
early
warning
signals
of
impending
thresholds
need
to
be
better
defined
(Lenton,
2011;
Dakos
et
al.,
2012;
Scheffer
et
al.,
2012)
in
order
to
allow
a
more
robust
assessment
of
environmental
risk.
Similarly,
ecosystem
services
have
become
central
to
the
discourse
in
current
environmental
assessment
and
policy
(Millennium
Ecosystem
Assessment,
2005,
The
Economics
of
Ecosystems
and
Biodiversity,
2010
and
United
Kingdom
National
Ecosystem
Assessment,
2011)
but,
with
the
exception
of
global
services
such
as
climate
regulation,
are
more
appropriate
for
regional
scales
where
natural
resources
and
processes
are
managed,
such
as
within
land
(or
water)
use
planning
sectors
(Cowling
et
al.,
2008;
Reyers
et
al.,
2009).
Balmford
et
al.
(2011)
argue
that
the
human
benefits
from
ecosystems
can
usefully
be
broken
down
into
three
groupings:
(1)
core
ecosystem
processes;
(2)
beneficial
ecosystem
processes;
and
(3)
ecosystem
benefits.
Ecosystem
benefits
have
a
direct
impact
on
human
welfare,
and
they
arise
from
beneficial
ecosystem
processes
(i.e.
water
provisioning)
that
in
turn
are
part
of
core
ecosystem
processes
(i.e.
nutrient
or
water
cycling);
analogous
to
regulating
and
supporting
services
in
the
Millennium
Ecosystem
Assessment
(2005).
In
our
framework,
we
focus
on
environmental
ceilings
for
core
and
beneficial
ecosystem
processes
and
conditions
because
they:
(1)
represent
the
ultimate
constraints
or
boundaries
on
social
activities
within
the
region;
and
(2)
link
directly
to
the
biogeochemical
cycles
that
underpin
global
boundaries.
Rockstro
¨m
et
al.
(2009b)
argued
that
boundaries
should
be
defined
through
an
understanding
of
nonlinear
systemic
change
and
focused
particularly
on
ecological
thresholds
and
dangerous
aggregate
effects.
We
agree
on
the
priority
to
have
criteria
that
recognize
dynamic
change
and
seek
to
widen
the
two
categories
used
for
defining
boundaries
at
the
planetary
scale
by
drawing
upon
theory
for
early
warning
signals.
We
also
include
information
on
environmental
regulatory
limits
that
are
widely
used
in
regional
management.
Taking
a
wider
range
of
metrics
obviates
the
need
to
make
sharp
distinctions
between
‘environmental
limits’
and
‘environmental
thresholds’
(Haines-Young
et
al.,
2006)
because,
in
practice,
these
represent
two
end
members
of
the
scheme.
Our
aim
is
to
develop
a
pragmatic
guide
for
identifying
regional
boundaries
from
observations
of
system
dynamical
behaviour
in
real
world
time
series
drawn
from
monitoring,
survey,
remote
sensing
and
sediment
analysis
covering
the
multi-decadal
timescales
that
capture
most
social-ecological
system
behaviour,
while
bearing
in
mind
the
narrow
lens
of
system
behaviour
afforded
by
human
timescales
(Dearing
et
al.,
2010).
A
practical
classification
of
ecological
boundaries
with
reference
to
environmental
limits
and
the
dynamical
properties
of
ecological
variables
is
shown
in
Fig.
2
and
Table
1.
The
different
types
of
boundary
(Fig.
2)
are
not
necessarily
mutually
exclusive
in
theoretical
terms
but
rather
are
set
according
to
the
analysis
of
the
actual
observed
system
behaviour.
Each
type
of
boundary
may
demand
a
particular
resolution
of
time
series,
involving
specific
statistical
analyses.
The
regional
boundary
may
be
relevant
to
a
whole
system
(e.g.
extent
of
forest
ecosystem),
a
system
condition
(e.g.
water
quality)
or
a
process
(e.g.
soil
erosion).
Setting
boundaries
according
to
the
observed
record
of
system
behaviour
provides
new
evidence,
based
on
current
complex
system
theory,
for
accepting
a
business-as-usual
policy,
applying
constraints
on
the
continuation
of
the
system
or
taking
remedial
action.
Type
I
boundaries
refer
to
linear
trending
data,
where
a
regional
regulatory
limit
on
a
particular
ecosystem
process
is
set
by
scientific,
expert
or
public
opinion,
through
negotiation
and
trade-
offs
between
the
benefits
and
damages
arising
from
particular
activities.
This
type
of
boundary
is
commonly
used
for
manage-
ment
of
degraded
landscapes
and
ecosystems
(e.g.
quality
targets,
critical
loads),
but
it
is
only
weakly
linked
to
system
dynamical
properties
and
generally
assumes
stationarity
in
the
trend.
Type
II
boundaries
can
be
placed
on
nonlinear
trends,
but
unlike
Type
I
boundaries,
they
need
to
be
set
in
ways
that
recognize
the
dynamics
of
the
system
described
by
trend
and
variability.
Type
III
boundaries
describe
threshold-dependent
transitions
in
a
system
or
condition
towards
new
states
that
are
considered
abrupt
within
human
timescales
(decades
and
shorter).
The
causes
of
transitions
may
be
the
gradual
erosion,
or
more
stochastic
forcing,
of
system
resilience.
But
beyond
a
point,
changes
in
the
internal
system
structure
and
the
transition
to
positive
feedback
loops
produce
relatively
rapid
changes
until
the
system
settles
into
a
new
attractor.
Such
transitions
are
observed
in
mathematical
models
for
many
ecological
systems
(Scheffer,
2009),
but
in
real
systems
can
only
be
observed
after
they
are
crossed
(Groffman
et
al.,
2006).
A
key
distinction
between
two
types
of
threshold
change
is
the
existence
of
different
degrees
of
reversibility
or
hysteresis.
Type
IV
boundaries
refer
to
the
rapidly
developing
area
in
dynamical
theory
of
early
warning
signals
where
the
sensitivity
of
a
system
to
impacts
grows
disproportionally
as
a
system
loses
resilience
prior
to
a
threshold.
A
number
of
new
analytical
techniques
defined
by
changes
in
magnitude-frequency,
variability,
skewness
or
auto-
correlation
metrics
(Lenton,
2013;
Dakos
et
al.,
2012;
Carstensen
and
Weydmann,
2012)
offer
the
promise
of
providing
Type
IV
early
warning
signals.
Types
I–IV
represent
guides
to
defining
bound-
aries
and
are
not
necessarily
mutually
exclusive
(see
also
Table
1).
The
challenges
of
communicating
complexity
concepts
in
real
world
situations
are
significant.
In
this
initial
attempt,
we
use
a
colour-coded
scheme
to
identify
‘safe’,
‘cautious’
and
‘dangerous’
categories
of
operating
space
(Fig.
2)
with
the
environmental
ceiling
set
between
the
safe/cautious
and
dangerous
categories.
This
simple
imagery
condenses
powerful
complexity
concepts
and
time-series
analyses
into
an
easily
understood
qualitative
basis
of
assessment.
At
this
stage,
it
is
important
to
set
down
a
generic
and
dynamical
basis
for
the
definition
of
boundaries.
It
is
easier
to
define
ecological
boundaries
retrospectively
when
they
have
already
been
crossed.
Where
they
have
not
been
crossed,
the
process
of
setting
boundaries
needs
to
utilize
all
possible
ways
of
defining
systemic
change:
observation
of
Type
IV
early
warning
signals,
model
simulations,
and
expert
judgement
(Balmford
et
al.,
2011;
Moss
and
Schneider,
2000),
which
together
represent
an
important
research
priority.
J.A.
Dearing
et
al.
/
Global
Environmental
Change
28
(2014)
227–238
229

2.2.
Social
foundations
In
direct
contrast
to
environmental
ceilings,
social
foundations
are
not
defined
by
social
dynamics
but
by
nationally
or
internation-
ally
agreed
minimum
standards
for
human
outcomes
(Raworth,
2012).
While
some
social
standards
have
been
established
at
the
global
scale,
such
as
through
the
Universal
Declaration
of
Human
Rights
and
subsequent
human
rights
law,
their
governance
is
enforced
at
the
regional/national
level
with
supranational
gover-
nance
continuing
to
be
the
exception
for
the
foreseeable
future.
Therefore
to
have
most
policy
impact,
our
framework
must
be
applicable
to
regional
governance
systems,
their
practicalities
and
motivations.
For
example,
the
management
of
water
resource
catchments
to
provide
adequate
water
and
sanitation
for
all
is
typically
a
regional
issue.
Political
and
administrative
boundaries
shape
where
governance
happens
and
consequently
will
determine
how
the
framework
can
be
made
operational,
how
flows
of
materials
in
and
out
of
the
social-ecological
system
may
be
delineated,
and
how
trade-offs
can
be
agreed.
Data
are
available
for
many
social
indicators.
In
this
initial
demonstration,
we
follow
Raworth
(2012)
and
use
national
governments’
stated
social
priorities,
as
set
out
in
their
national
and
regional
submissions
to
the
Rio
+
20
Earth
Summit.
Analysis
of
those
submissions
(Raworth,
2012)
reveals
strong
global
consen-
sus
on
eleven
social
priorities,
including:
food
security,
income,
water
and
sanitation,
health
care,
education,
energy,
gender
equality,
social
equality,
voice,
employment
and
resilience
(Table
A.1).
These
priorities
are
used
as
the
basis
for
selecting
indicators
within
regions
to
define
a
social
foundation.
In
practice,
data
are
often
available
through
the
Millennium
Development
Goals
(a
suite
of
global
human
development
targets
set
by
the
United
Nations)
that
are
adjusted
to
provide
national
and
sub-national
level
data
for
poverty
and
health
(United
Nations,
2012a).
Illustrative
indicators
include
the
percentage
figures
for:
popula-
tion
undernourished;
population
living
below
$1.25
(PPP)
per
day;
population
without
access
to
an
improved
drinking
water
source;
and
population
without
access
to
improved
sanitation.
2.3.
Case-study
methods
Ecological
time
series
were
drawn
from
a
combination
of
monitored
instrument
records
and
lake
sediment
proxy
records.
Sediment
cores
were
sampled
from
the
deepest
zones
of
lakes
with
intact
modern
sediment-water
interfaces
and
analyzed
at
0.5
cm
intervals
to
obtain
proxy
records
of
water
quality,
soil
stability,
air
quality,
sediment
quality,
and
sediment
regulation.
Sediment
dating
was
based
on
210
Pb
and
137
Cs
analyses
that
provide
timescales
covering
roughly
the
last
century.
The
analytical
techniques
used
are
published
in
detail
elsewhere
(Dearing
et
al.,
2012a;
Wang
et
al.,
2012).
Each
time
series
was
examined
for
trends,
nonlinear
system
behaviour
and
proximity
to
environ-
mental
limits
(Fig.
2
and
Table
1).
Social
data,
other
environmental
data
and
relevant
environmental
regulatory
limits
were
collected
from
official
Chinese
statistical
yearbooks
and
government
reports.
The
main
interactions
between
the
different
ecosystem
service/
process
records
and
the
human
welfare
categories
recorded
at
each
Fig.
2.
A
classification
of
possible
system
behaviour
as
an
ecological
boundary
is
reached
showing
Types
I,
II,
III
and
IV,
with
colour
coded
categories
(green,
yellow
and
red)
for
attributed
‘safe’,
‘cautious’
and
‘dangerous’
status
of
key
ecological
services/processes
and
respectively
(see
also
text
and
Table
1
for
detailed
explanation).
J.A.
Dearing
et
al.
/
Global
Environmental
Change
28
(2014)
227–238
230

site
are
(at
least)
qualitatively
known
(Fig.
A.1).
For
example,
at
Erhai
(Fig.
A.1a)
the
first
order
effects
of
upland
soil
instability
are
on
water
quality,
sediment
regulation
and
food
security
(crops
and
fisheries)
through
flooding,
the
natural
fertilization
of
paddy
fields
and
lake
water
quality.
Higher
order
effects
create
more
complex
webs
of
interactions
with
potential
feedback
loops.
However,
our
focus
at
this
initial
stage
is
simply
to
derive
the
current
status
of
the
individual
social
and
ecological
conditions.
Further
work
will
combine
the
information
on
status
with
known
interactions
to
create
dynamic
modelling
tools
for
management.
3.
Results
Our
case
studies
are
represented
by
two
similarly
sized
low-
income
rural
communities
in
China,
each
covering
an
area
of
2000
km
2
with
1
M
population.
In
each
case
we
define
the
ecological
boundaries,
social
standards,
environmental
ceiling
and
social
foundation
from
available
time
series,
historical
records
and
survey
data.
The
two
areas
differ
in
terms
of
regional
governance,
physical
landscape
and
proximity
to
large
economic
centres,
yet
provide
similar
challenges
for
sustainable
development
based
largely
on
agricultural
intensification.
3.1.
Erhai
lake-catchment,
Yunnan
Province,
China
The
mountainous
Erhai
lake-catchment,
Yunnan,
China
(25
0
48
I
02.38
II
N
100
0
11
I
33.86
II
E),
including
the
ancient
city
of
Dali,
has
a
long
history
of
unsustainable
pressures
on
ecosystems
(Dearing
et
al.,
2008).
Today,
the
rural
community
is
involved
in
wet
and
dry
cropping,
dairy
farming,
fishing,
aquaculture,
and
forestry.
The
southern
part
of
the
lake
catchment
is
the
focus
for
targeted
industrial
development.
Documented
environmental
impacts
include
deforestation,
soil
erosion
and
eutrophication
of
the
lake
and
inflowing
rivers
(Wang
et
al.,
2012).
The
ecological
time
series
(Fig.
3
and
Table
A.2)
show
recent
changes
in
dynamical
behaviour
for
water
quality,
air
quality
and
water
regulation.
Two
measures
of
water
quality
describe
a
hysteretic
threshold
change
around
2001
that
have
taken
the
lake
into
the
dangerous
category
represented
by
a
highly
eutrophic
state
(Wang
et
al.,
2012).
Water
regulation
over
the
past
four
decades
has
shifted
the
lake
water
level
beyond
the
long-term
envelope
of
variability
but
has
recently
changed
from
the
dangerous
to
the
cautious
status.
Three
measures
of
air
quality
show
mixed
trends
but
the
change
in
rate
for
Pb
deposition
in
the
sediments
indicates
a
cautious
status
for
emitted
heavy
metals
in
the
current
environment.
Much
longer
Table
1
Types
of
ecological
boundaries
based
on
environmental
limits
and
dynamical
properties
of
time
series.
Type
I
Linear
trends
(Ia)
Environmental
limits.
These
are
quantitative
measures
of
the
state
of
beneficial
ecosystem
processes
that,
once
exceeded,
significantly
constrain
conventional
resource
use
(Haines-Young
et
al.,
2006).
For
example,
exceeding
a
given
level
of
dissolved
sodium
in
soil
water
means
that
the
beneficial
ecosystem
process
of
water
purification
is
lost,
leading
to
losses
of
an
ecosystem
benefit
(e.g.
healthy
grain
production).
Such
values
are
often
empirically
determined
from
local
experiments
and
can
often
be
applied
throughout
a
region.
(Ib)
Distance
from
a
baseline
or
background/low
impact
state.
Setting
these
boundaries
involves
defining
relative
measures
linked
to
beneficial
ecosystem
processes.
For
example,
the
European
Union
Water
Framework
Directive
(http://ec.europa.eu/environment/water/water-framework/)
requires
member
countries
to
restore
or
manage
water
quality
to
a
‘good
ecological
status’,
defined
as
a
slight
deviation
from
a
reference
condition
with
no,
or
only
very
minor,
anthropogenic
disturbance.
Type
II
Nonlinear
trends
(IIa)
Rate
of
change.
A
boundary
can
be
set
where
there
is
an
unacceptable
acceleration
in
a
harmful
effect
or
a
decline
in
a
beneficial
ecosystem
process.
The
change
in
rate
may
be
caused
by
the
cumulative
effect
of
smaller
scale
changes
on
a
process
or
condition,
corresponding
directly
to
the
‘dangerous
aggregated
effect’
category
used
by
Rockstro
¨m
et
al.
(2009).
For
example,
soil
erosion
may
continue
for
centuries
without
major
impact
on
crop
yields.
Yet
a
maximum
soil
erosion
rate
may
be
determined
from
observations
and
modelling
that
acknowledges
unsustainable
losses
of
soil
over
soil
formation,
and
off-site
effects
like
increased
sediment
delivery
to
rivers.
(IIb)
Envelope
of
variability.
A
regional
boundary
can
be
defined
by
the
point
at
which
the
system
moves
outside
of
the
long-term
normal
envelope
of
variability,
or
is
statistically
different
from
the
long-term
quasi-stationary
mean.
By
this
definition,
extreme
events
like
‘1
in
100
years
floods’
are
deemed
part
of
normal
variability.
This
idea
is
the
basis
of
arguments
for
the
existence
of
contemporary
anthropogenic
global
warming
(Mann
et
al.,
1998).
A
regional
scale
example
is
an
analysis
of
river
discharge
data
that
show
recent
divergence
from
stationary
time-series
(Milly
et
al.,
2008).
Such
changes
may
imply
that
system
boundary
conditions
may
be
changing
and
equilibrium-models
for
medium-term
forecasting
are
probably
invalid.
Type
III
Thresholds
(IIIa)
Abrupt
non-hysteretic
changes.
Some
systems
oscillate
easily
between
different
states
(e.g.,
predator/prey
populations,
El
Nin
˜o/La
Nin
˜a
cycles).
These
relatively
fast
and
reversible
(from
historical
evidence)
transitions
can
be
disruptive
to
both
ecosystems
and
society
but
are
non-catastrophic
(Scheffer,
2009).
(IIIb)
Abrupt
hysteretic
change.
Some
systems
exhibit
catastrophic
shifts
that
are
hard
to
reverse
because
they
involve
the
forced
loss
of
stability
of
one
system
state
and
abrupt
switch
to
an
alternative
state.
The
metaphor
employed
to
visualize
these
systems
is
of
neighbouring
valleys
with
a
hill
in-between
them.
The
bottom
of
the
valleys
are
attractors.
Driving
a
ball
from
the
bottom
of
one
valley,
over
the
hill
and
then
releasing
it
would
roll
the
ball
down
to
the
bottom
of
a
different
attractor.
The
transition
from
one
attractor
to
another
may
only
require
a
very
small
input
if
the
ball
is
near
the
top
of
the
hill.
However
reversing
a
transition
could
require
significant
input
into
the
system.
Although
such
fold
bifurcations
with
hysteresis
are
widely
discussed
in
the
literature
the
evidence
in
real
systems
is
quite
limited
(Bascompte
et
al.,
2005;
Wang
et
al.,
2012;
Lenton,
2013).
Type
IV
Early
warning
signals
(IVa)
Shifts
in
magnitude
and
frequency.
The
time-series
of
many
human-affected
biophysical
phenomena
show
pronounced
changes
that
can
be
viewed
as
a
shift
in
the
‘risk
spectrum’
(Dearing
et
al.,
2010).
Bivariate
frequency-magnitude
log-log
plots
may
show
evidence
for
power
law
behaviour
that
defines
naturally
evolved
system
properties,
giving
a
possible
baseline
for
judging
the
impacts
of
human
activities
(Dearing
and
Zolitschka,
1999).
Examples
include
the
increasing
frequency
of
extreme
weather
events
at
regional
scales
associated
with
anthropogenic
climate
change
(e.g.,
Webster
et
al.,
2005),
and
altered
fire
regimes
in
southwestern
USA
to
larger,
less
frequent
fires
as
a
result
of
fire
suppression
measures
(Swetnam
et
al.,
1999).
(IVb)
Variability
metrics.
Changes
in
statistical
properties
of
mathematical
model
output
and
empirical
data
offer
possible
early
warning
signals
of
regime
shifts
in
systems
(Scheffer
et
al.,
2012).
This
has
been
shown
for
changes
in
time-series
of
lake
ecosystems
(Wang
et
al.,
2012)
and
also
in
spatial
patterns
of
vegetation
cover
(e.g.,
Hirota
et
al.,
2011).
Analysis
of
mathematical
systems
suggests
that
these
early
warning
signals
are
not
specific
to
either
catastrophic
or
non-catastrophic
transitions
(Ke
´fi
et
al.,
2012).
We
interpret
such
statistical
signals
as
signs
of
decreasing
system
stability,
loss
of
resilience,
and
the
start
of
a
path
towards
a
relatively
rapid
(though
not
always
catastrophic)
transition,
especially
where
independent
metrics
mutually
corroborate
(Dakos
et
al.,
2012;
Lindegren,
2012).
From
a
human
time
perspective,
a
Type
I
linear
trend
in
the
short
term
may
prove
to
be
part
of
the
forward
limb
of
a
Type
IIIb
fold
bifurcation
in
the
long
term.
A
change
in
the
rate
of
a
system
variable
(Type
II)
over
several
years
may
be
part
of
increasing
system
variability
(that
could
be
detected
through
Types
IVa
and
IVb)
or
the
beginning
of
a
regime
shift
(Type
III)
when
viewed
over
a
multi-decadal
timescale.
Transitions
are
often
referred
to
as
abrupt,
but
many
transitions
unfold
slowly
after
transgressing
a
threshold
(Fischer-Kowalski
and
Haberl,
2007;
Hughes
et
al.,
2012).
Thus,
both
Type
IIa
and
IIb
changes
could
represent
a
relatively
slow
transition
towards
a
new
state
(Type
III).
The
different
boundaries
could
also
be
ranked
according
to
their
severity,
with
well-understood
Type
Ia
boundary
setting
more
stringent
limits
than
Type
III.
J.A.
Dearing
et
al.
/
Global
Environmental
Change
28
(2014)
227–238
231

sediment
records
(Dearing,
2008)
show
that
the
upland
land
use-soil
system
currently
sits
in
the
dangerous
category,
having
settled
into
an
alternate
degraded
steady
state
about
850
years
ago.
The
social
foundation
(Table
A.3)
is
virtually
met
in
terms
of
health
care
but
provision
of
food,
water
and
sanitation
falls
short
by
substantial
margins.
Mapping
the
ecological
boundaries
with
respect
to
the
regional
environmental
ceiling
and
social
foundation
(Fig.
5a)
highlights
the
importance
of
water
quality
management.
Taking
these
preliminary
findings
together
suggests
that
the
process
of
economic
development,
particularly
through
agricultural
intensifi-
cation
largely
based
on
increasing
fertilizer
applications,
has
reduced
but
not
eradicated
poverty.
There
is
a
clear
trade-off
in
terms
of
deterioration
of
key
ecological
processes
and
condi-
tions
that
have
led
to
dangerous
threshold
changes
in
lake
water
quality.
3.2.
Shucheng
County,
Anhui
Province,
China
Shucheng
County
(31
0
27
I
43.29
II
N
116
0
56
I
55.20
II
E)
is
situated
in
eastern
China
in
the
lower
Yangtze
basin
about
200
km
W
of
Nanjing
City.
It
is
a
‘poverty-stricken
county’
as
defined
by
the
central
government,
and
previous
research
has
shown
that
the
region
has
undergone
serious
environmental
degradation
due
to
intensive
agricultural
activities
during
the
last
60
years
(Dearing
et
al.,
2012a).
The
ecological
records
from
Shucheng
(Fig.
4,
and
Table
A.4)
show
that
water
quality,
sediment
quality
and
air
quality
have
moved
into
the
dangerous
status,
with
soil
stability
given
cautious
status
in
response
to
the
increasing
volatility
in
the
recent
records.
Given
the
findings
from
Erhai
about
alternate
soil
stability
states,
it
is
feasible
that
the
farmed
Shucheng
soils
have
also
passed
into
an
alternate
degraded
state,
meaning
that
the
designated
cautious
status
based
on
recent
observations
should
be
regarded
as
a
minimum
status.
Like
Erhai,
the
lake
water
quality
transition
is
probably
hysteretic
(Wang
et
al.,
2012)
implying
a
high
degree
of
irreversibility.
Levels
of
sediment-
associated
P
from
intensive
farming
and
deposited
Pb
from
local
fossil
fuel
powered
industries
are
not
only
very
high
by
international
standards
but
also
80–100%
higher
than
pre-
1960s
levels.
Mapping
these
boundaries
with
respect
to
the
regional
environmental
ceiling
(Fig.
5b)
highlights
the
importance
of
soil,
water
and
air
management.
The
social
foundation
is
virtually
met
in
terms
of
food
security,
health
care
and
minimum
income
(Fig.
5b
and
Table
A.5)
implying
that
the
government
strategy
of
providing
extra
resources
to
‘poverty-stricken’
counties
is
relatively
successful.
But
like
Erhai,
access
to
piped
water
and
sanitation
still
lags
behind
other
poverty
alleviation
measures.
There
are
huge
challenges
in
these
Chinese
regions
for
the
local
governments
to
harness
the
momentum
of
economic
growth
to
reduce
poverty
while
reconciling
growth
with
the
need
to
restore
badly
damaged
ecosystems
and
ecological
processes,
and
to
avoid
further
irreversible
and
costly
environment
damage.
Managing
the
two
regions
as
social-ecological
systems
that
remain
within
a
RSJOS
now
needs,
particularly,
to
prioritize
the
challenge
of
reducing
nutrient
loadings
to
the
rivers
and
lakes
whilst
improving
the
rural
access
to
water
and
sanitation.
Air
quality
and
water
regulation
are
also
in
need
of
continuous
environmental
monitor-
ing
and
evidence-based
management
if
they
are
to
stay
within
the
regional
environmental
ceilings.
Soils
in
both
regions
need
targeted
conservation
measures.
4.
Discussion
These
case
studies
demonstrate
proof-of-concept
and
validity
of
a
new
conceptual
framework
that
may
help
raise
the
standards
of
social
conditions
while
reducing
the
likelihood
of
moving
into
dangerous
operating
spaces
with
respect
to
ecological
boundaries.
Our
framework
offers
a
clear
visual
image
for
making
comparisons
between
different
regions
and,
potentially,
provides
a
basis
for
assessing
a
region’s
impact
on
planetary
boundaries.
The
outputs
could
usefully
inform
the
Post-2015
UN
Development
Agenda
and
new
Sustainable
Development
Goals
(United
Nations,
2012b).
Although
these
are
good
reasons
to
advocate
its
use,
particularly
in
rural
regions
within
developing
nations,
there
are
a
number
of
caveats
and
remaining
challenges.
Fig.
3.
Erhai
lake-catchment,
Yunnan
Province:
ecological
boundaries
(top
to
bottom
panels)
for
sediment
regulation,
two
measures
of
water
quality,
three
measures
of
air
quality
and
water
regulation
based
on
time
series
extending
back
from
2006
over
one
to
eleven
decades.
The
time
series
are
illustrated
with
red
dashed
lines,
where
appropriate,
to
show
the
basis
for
defining
the
different
types
of
dynamical
behaviour
and
boundary
(Fig.
2)
described
in
each
panel
(italicized).
Colour
coded
segments
show
historical
changes
in
the
safe
(green),
cautious
(yellow)
and
dangerous
(red)
status
of
each
ecological
process
(Fig.
2).
The
status
shown
for
2006
is
used
in
the
social
and
environmental
integrated
plot
(Fig.
5a).
Details
of
data
and
the
categorization
of
boundaries
are
given
in
Table
A.2.
(For
interpretation
of
the
references
to
color
in
this
text,
the
reader
is
referred
to
the
web
version
of
the
article.)
J.A.
Dearing
et
al.
/
Global
Environmental
Change
28
(2014)
227–238
232

4.1.
Complex
interactions
While
systems
dynamic
theory
is
used
to
help
define
ecological
boundaries,
as
with
the
original
planetary
boundaries
framework
(Rockstro
¨m
et
al.,
2009a,b),
the
framework
does
not
provide
a
systems
dynamic
analysis
of
the
relationships
between
any
of
the
social
and
environmental
conditions.
Rather,
it
provides
a
basis
for
judging
the
relative
state
of
current
environmental
viability
and
societal
wellbeing
within
a
region.
Clearly,
caution
must
be
exercised
in
considering
causative
links
between
the
different
variables
because
interactions
within
and
between
a
social
foundation
and
environment
ceiling
are
likely
to
be
complex,
nonlinear
and
difficult
to
confirm.
The
analyses
of
biophysical
time-series
alone
underline
the
need
to
consider
the
full
range
of
timescales
(annual-centennial)
embedded
within
the
social-
ecological
dynamics
when
assessing
the
environmental
outcomes
from
a
specific
social
policy
or
land
management
decision.
There
are
likely
to
be
important
lags
in
the
feedback
effects
of
excessive
resource
use
and
resource
stress
(such
as
from
climate
change,
eutrophication,
biodiversity
loss,
and
so
on)
on
human
health,
income,
food
and
water
availability
and
resilience.
These
lagged
environmental
feedbacks
may
be
related
to
hysteretic
processes
with
potentially
irreversible
effects
meaning
that
early
warnings
of
direct
stress
on
those
ecological
systems,
as
provided
by
the
RSJOS
methodology,
is
essential.
Thus
the
framework
is
best
used
to
formulate
hypotheses
about
links
and
interactions
for
further
testing
and
investigation.
For
example,
a
new
family
of
integrative
social-ecological
models
(e.g.
ARIES
http://www.ariesonline.org)
might
help
to
identify
critical
points
in
the
flows
of
ecosystem
processes/services
that
could
be
used
in
highlighting
vulnerable
areas
at
risk
from
development.
The
framework
provides
a
strong
basis
for
designing
systems
dynamics
models
that
capture
feedback
mechanisms
(e.g.
using
Fig.
4.
Shucheng
County,
Anhui
Province:
ecological
boundaries
(top
to
bottom
panels)
for
sediment
regulation,
soil
stability,
water
quality,
sediment
quality
and
air
quality
based
on
time
series
extending
back
from
2006
over
one
to
eleven
decades.
The
time
series
are
illustrated
with
red
dashed
lines,
where
appropriate,
to
show
the
basis
for
defining
the
different
types
of
dynamical
behaviour
and
boundary
(Fig.
2)
described
in
each
panel
(italicized).
Colour
coded
segments
show
historical
changes
in
the
safe
(green),
cautious
(yellow)
and
dangerous
(red)
status
of
each
ecological
process
(Fig.
2).
The
status
shown
for
2006
is
used
in
the
social
and
environmental
integrated
plot
(Fig.
5a).
Details
of
data
and
categorization
of
boundaries
are
given
in
Table
A.4.
(For
interpretation
of
the
references
to
color
in
this
text,
the
reader
is
referred
to
the
web
version
of
the
article.) Fig.
5.
Safe
and
just
operating
spaces
mapped
for
two
Chinese
regions
in
2006.
(a)
Erhai
lake-catchment
system,
Yunnan
Province;
(b)
Shucheng
County,
Anhui
Province.
The
figures
show
the
extent
to
which
each
region
currently
meets
expected
social
standards
(blue
sectors)
for
an
acceptable
social
foundation
(green
circle),
and
the
current
status
of
key
ecological
services/processes
(from
Figs.
3
and
4):
safe
(green
sectors),
cautious
(yellow
sectors)
and
dangerous
(red
sectors).
The
environmental
ceiling
(red
circle)
defines
the
approximate
boundary
between
sustainable
and
unsustainable
use
of
ecological
processes.
The
RSJOS
exists
as
a
‘doughnut’
between
the
environmental
ceiling
and
social
foundation.
Data
for
sediment
quality
(a)
and
water
regulation
(b)
unavailable.
Note
that
the
relative
sizes
of
green,
yellow
and
red
sectors
are
illustrative:
they
are
not
plotted
to
any
scale
and
are
not
plotted
from
the
centre
of
the
circles
(see
text
for
further
explanation).
Blank
sectors
indicate
unavailability
of
data.
Ecological
data
are
shown
in
Figs.
3
and
4,
and
Tables
A.2
and
A.4;
social
data
in
Tables
A.3
and
A.5.
An
additional
sector
for
upland
soil
stability
at
Erhai
(a)
is
based
on
assessment
of
centennial
records
(see
text
and
Dearing,
2008).
(For
interpretation
of
the
references
to
color
in
this
text,
the
reader
is
referred
to
the
web
version
of
the
article.)
J.A.
Dearing
et
al.
/
Global
Environmental
Change
28
(2014)
227–238
233

multi-agent
modelling
tools,
such
as
STELLA,
Matlab-Simulink)
in
order
to
explore
the
ecological
impacts
of
alternative
social
futures.
Multivariate
analyses
within
pressure-state-response
frameworks
(OECD,
1993)
may
be
able
to
estimate
future
probabilities
of
extreme
events
as
a
result
of
co-occurring
drivers
(Denny
et
al.,
2009;
Rounsevell
et
al.,
2010).
Drawing
on
econometric
methods,
it
may
also
be
possible
to
aggregate
individual
time-series
signals
to
give
a
region-wide
assessment
of
growing
connectivity
(Wang
et
al.,
2011)
and
causality
(Sugihara
et
al.,
2012)
between
variables,
and
the
associated
risk
of
systemic
failure
(Billio
et
al.,
2012).
The
growing
numbers
of
long
term
social-ecological
reconstructions
(Singh
et
al.,
2013)
will
provide
new
material
for
extending
the
RSJOS
framework
to
embrace
the
interactions
between
social
foundations
and
environmental
ceilings.
But
until
these
opportunities
are
realized,
the
RSJOS
images
presented
are
essentially
informative,
teleological
devices
providing
a
regional
barometer
of
sustainable
development
for
policy
traction
and
strategic
scientific
studies.
4.2.
Inter-regional
fluxes
To
a
lesser
or
greater
extent,
all
regions
are
connected
to
all
other
regions.
The
use
of
natural
resources
and
ecosystem
services
within
a
region
is
frequently
driven
by
larger
scale
motivations,
resulting
in
flows
of
energy,
people,
money,
and
goods
between
regions.
This
means
that
the
social
and
ecological
variables
within
many
regions
are
not
necessarily
strongly
linked
to
local
resource
availability.
Proximity
to
dangerous
ecological
boundaries
may
drive
imports
(Seto
et
al.,
2012)
but
conversely
regional
environmental
degrada-
tion
may
be
driven
by
production
for
export
(Muradian
and
Martinez-Alier,
2001).
Thus
the
welfare
data
produced
in
the
social
foundation
are
focused
on
deprivations
to
be
avoided,
and
do
not
indicate
the
extent
to
which
other
parts
of
a
society
may
be
engaged
in
excessive
consumption
patterns
that
directly
affect
the
ecological
conditions
of
a
region.
The
rise
in
the
global
urban
population,
in
particular,
is
tied
to
the
sustainability
of
human
and
natural
resources
in
both
proximal
and
distant
regions
(Seitzinger
et
al.,
2012).
Whiteman
et
al.
(2012)
argue
that
corporate
sustainability
should
include
the
impact
of
companies
on
the
planetary
boundaries
but,
as
they
also
state,
there
is
a
need
to
include
collective
targets
at
local/regional
scales
to
avoid
problem-shifting
among
actors
and
geographic
regions.
In
representing
the
fundamental
limits
to
impacts
on
regional
ecosystems,
from
whatever
combination
of
drivers,
the
RSJOS
framework
outlined
here
is
a
first,
and
necessary,
bottom-up
step
in
improving
our
understanding
of
multi-scale
interconnections
(cf.
Nilsson
and
Persson,
2012)
in
setting
local
or
regional
boundaries.
4.3.
Tradeoffs
The
impacts
of
regional
resource
scarcities
(e.g.
limits
to
food
production)
or
overexploitation
of
regional
resources
(e.g.
overuse
of
forests)
may
be
overcome
by
switching
to
alternative
resources,
often
resulting
in
new
sustainability
challenges.
For
example,
Erb
et
al.
(2008)
describe
a
typical
pattern
in
their
analysis
of
historic
tradeoffs
in
land
use
in
Austria.
Fossil
fuel
based
yield
increases
in
agriculture
and
the
substitution
of
fossil
fuels
for
biomass
as
the
main
energy
source
resulted
in
the
recovery
of
previously
degraded
forests.
From
1830
to
2000,
local
food
supply
multiplied
and
forests
increased
substantially
in
both
area
and
stocking
density.
These
regional
gains
were
accomplished
without
substantially
raising
the
net
import
of
food
and
other
land-based
resources,
but
at
the
expense
of
increased
greenhouse
gas
emissions
(Folke
et
al.,
2007)
and,
most
likely,
unsustainable
soil
use
(Winiwarter
and
Gerzabeck,
2012).
While
the
latter
would
be
detected
within
the
proposed
RSJOS
framework,
the
former
would
require
a
global
framework
with
complementary
indicators
capable
of
showing
unsustainably
high
levels
of
regional
contributions
to
global
problems.
Methods
to
construct
such
nested
indicators
are
available
for
problems
such
as
fossil
fuel
related
emissions
‘embodied’
in
traded
products
(Peters
et
al.,
2012)
and
in
regards
to
biodiversity
pressures
(Haberl
et
al.,
2012)
and
land-use
impacts
(Lenzen
et
al.,
2012)
related
to
trade.
These
underline
the
need
and
challenge
to
extend
the
RSJOS
framework
presented
to
include
regional
contributions
to
the
pressures
on
planetary
boundaries.
Acknowledgements
This
paper
is
a
product
of
a
two-day
workshop
held
at
the
University
of
Southampton,
July
10–11
2012,
within
the
Evidence
and
Impacts
Research
Grant
programme
‘Safe
operating
spaces
for
regional
rural
development:
a
new
conceptual
tool
for
evaluating
complex
socio-ecological
system
dynamics’
funded
(grant
refer-
ence
EIRG-2011-166)
by
the
Ecosystem
Services
for
Poverty
Alleviation
Programme
(ESPA).
The
ESPA
programme
is
funded
by
the
Department
for
International
Development
(DFID),
the
Economic
and
Social
Research
Council
(ESRC)
and
the
Natural
Environment
Research
Council
(NERC),
as
part
of
the
UK’s
Living
with
Environmental
Change
Programme
(LWEC).
We
acknowledge
additional
financial
support
for
the
workshop
from
the
Interna-
tional
Geosphere-Biosphere
Programme
(IGBP)
and
the
University
of
Southampton.
The
research
is
an
outcome
of
the
IGBP
Past
Global
Changes
Focus
4
‘Regional
Integration’
initiative.
H.H.
acknowledges
funding
from
the
FP7
project
VOLANTE
(grant
agreement
no.
265104).
We
thank
four
anonymous
reviewers
for
their
thoughtful
comments.
This
is
a
Sustainability
Science
at
Southampton
publication.
Appendix
A
See
Fig.
A.1
and
Tables
A.1–A.5.
Fig.
A.1.
Known
interactions
between
ecosystem
service/processes
(shaded
boxes)
and
social
indicators
(dotted
boxes)
for
(a)
the
Erhai
lake-catchment
system
and
(b)
Shucheng
County.
J.A.
Dearing
et
al.
/
Global
Environmental
Change
28
(2014)
227–238
234

Table
A.1
Eleven
indicators
of
a
social
foundation
based
on
national
governments’
social
priorities
for
Rio+20
(Raworth,
2012).
Social
foundation
Indicators
Food
security
Population
undernourished
13%
2006–2008
Income
Population
living
below
$1.25
(PPP)
per
day
21%
2005
Water
and
sanitation
Population
without
access
to
an
improved
drinking
water
source
Population
without
access
to
improved
sanitation
Health
care
Population
estimated
to
be
without
regular
access
to
essential
medicines
Education
Children
not
enrolled
in
primary
school
Illiteracy
among
15–24-year-olds
Energy
Population
lacking
access
to
electricity
Population
lacking
access
to
clean
cooking
facilities
Gender
equality
Employment
gap
between
women
and
men
in
waged
work
(excluding
agriculture)
Representation
gap
between
women
and
men
in
national
parliaments
Social
equity
Population
living
on
less
than
the
median
income
in
countries
with
a
Gini
coefficient
exceeding
0.35
Voice
E.g.
Population
living
in
countries
perceived
(in
surveys)
not
to
permit
political
participation
or
freedom
of
expression
Jobs
E.g.
Labour
force
not
employed
in
decent
work
Resilience
E.g.
Population
facing
multiple
dimensions
of
poverty
Table
A.2
Erhai
lake-catchment:
ecological
boundaries.
Ecological
process
or
condition
IIndicator
(measurable
variable)
IEcological
boundary
(type)
IEcological
boundary
(definition)
Current
status
Boundary
value
Current
state
IKnown
drivers
Air
quality
1
Acidity
of
precipitation
(monitored
pH)
Type
Ia
Linear
Environmental
limit
Limit
pH
=
5.6
according
to
national
regulatory
standards
Safe
pH
5.6
pH
7.5
Fossil
fuel
power
generation/industrial
emissions
Air
quality
2
Industrial
soot/
particulate
emission
(monitored
>0.1
m
m
particle
emissions)
Type
Ib
Linear
trends
Distance
from
a
(high)
background
state
Emission
regulatory
standards
for
Dali
prefecture
(<2
t/yr)
Safe
2
t/yr
1
t/yr
Fossil
fuel
power
generation
Air
quality
3
Heavy
metals
(lake
sediment
deposited
sediment
Pb)
Type
Ib
Linear
trends
Distance
from
baseline
Baseline
Pb
concentration
before
1980s
Cautious
3%
5%
Industrial
emissions
Water
regulation
Lake
water
volume
(monitored
lake
level)
Type
IIb
Nonlinear
Envelope
of
variability
Earlier
low
lake
levels
<1972
m
triggered
lake
eutrophication
Cautious
1973
1972
Dam
construction/
hydroelectric
power
station
demands/
climate
change
Sediment
regulation
Sediment
delivery
from
catchment
(lake
sediment
mass
accumulation
rate)
No
detectable
change
Long-term
trend
and
variability
indicate
no
major
changes
in
driver-responses
Safe
Land
use
change/dam
constructions
Water
quality
1
Water
transparency
(monitored
secchi
depth)
Type
IVb
Thresholds
Abrupt
hysteretic
change
Relative
steady
state
values
before
2000.
Critical
transition
2001
with
evidence
for
fold
bifurcation.
(Wang
et
al.,
2012)
Dangerous
3
m
2
m
Nutrients
enrichment
from
fertilizers
and
untreated
sewage/fish
farming
Water
quality
2
Algal
growth
(lake
sediment
diatom
community
expressed
through
detrended
correspondence
analysis
(DCA
axis
1)
Type
IVb
Thresholds
Abrupt
hysteretic
change
Relative
steady
state
values
before
2000.
Critical
transition
2001
with
evidence
for
fold
bifurcation
(Wang
et
al.,
2012)
Dangerous
1.0
0.2
Nutrients
enrichment
from
fertilizers
and
untreated
sewage/fish
farming
Upland
soil
stability
Eroded
soil
and
substrate
indicative
of
gullying
on
steep
slopes
(lake
sediment
record
of
topsoil
and
subsoil
fingerprints
frequency
dependent
magnetic
susceptibility
and
low
field
susceptibility)
Type
IVb
Thresholds
Abrupt
hysteretic
change
Steady
non-degraded
state
before
AD
520.
Critical
transition
to
modern
degraded
state
lasted
600
years
until
AD
1150.
Evidence
for
fold
bifurcation
with
strong
hysteresis
and
irreversibility
(Dearing
et
al.,
2008)
Dangerous
n/a
n/a
Upslope
movement
of
farming
and
ineffective
terracing
Sources:
Anhui
Province
Statistical
Yearbooks
(1989–2011),
Dearing
et
al.
(2008),
Dearing
et
al.
(2012a,b),
Li
(2008),
Wang
et
al.
(2012),
Yan
et
al.
(2005),
Yunnan
Digital
Village
(2013).
J.A.
Dearing
et
al.
/
Global
Environmental
Change
28
(2014)
227–238
235

Table
A.3
Erhai
lake-catchment:
social
foundation.
Social
foundation
Indicator
Current
deficit
from
100%
(year)
Food
security
Children
undernourished
(0–5
years
old)
>5%
(2012)
Income
Population
living
below
$1.25(PPP)/day
2%
(2010)
Water
and
sanitation
Households
with
piped
water
(Cibihu)
9%
(not
known)
Health
care
Children
(0–5
years
old)
mortality
rate
>1%
(2012)
Education
Illiteracy
rate
(Eryuan)
2%
(2010)
Energy
Households
with
clean
energy
17%
(not
known)
Gender
equality
To
be
determined
Social
equity
To
be
determined
Voice
To
be
determined
Jobs
Urban
unemployment
2%
(2005)
Resilience
To
be
determined
Sources:
Bai
(2003),
),
Eryuan
County
Bureau
of
Statistics
(2011),
Dali
Environmental
Protection
Bureau
(1999–2010)
and
Dali
Bai
Autonomous
Perfecture
(2012).
Table
A.4
Shucheng
County:
ecological
boundaries.
Ecological
process
or
condition
Indicator
(measurable
variable)
Ecological
boundary
(type)
Ecological
boundary
(definition)
Current
status
Boundary
value
Current
state
Known
drivers
Air
quality
Heavy
metals
(lake
sediment
deposited
sediment
Pb)
Type
Ib
Linear
trends
Distance
from
baseline
Baseline
Pb
concentration
before
1960s.
Modern
values
now
2
baseline
Dangerous
30
mg/g
60
mg/g
Industrial
emissions
Sediment
regulation
Sediment
delivery
from
catchment
(lake
sediment
mass
accumulation
rate)
Type
IIb
Nonlinear
No
detectable
change
Low
absolute
values,
stationary
data
and
reducing
variability
indicate
increasing
stability
or
no
major
changes
in
driver-
responses.
Safe
Land
use
change/dam
constructions
Water
quality
Algal
growth
(lake
sediment
diatom
inferred
transfer
function)
Type
IIIb
Thresholds
Abrupt
hysteretic
change
Critical
transition
1980
with
assumed
fold
bifurcation
as
shown
in
similar
contexts
(Wang
et
al.,
2012).
Dangerous
100
m
g/l
160
m
g/l
Nutrient
enrichment
from
fertilizers/
untreated
sewage/lake
reclamation
Soil
stability
Topsoil
erosion
(lake
sediment
record
of
topsoil
fingerprint
frequency
dependent
magnetic
susceptibility)
Type
IVa
Early
warning
signal
Magnitude-
frequency
Relative
steady
state
before
1985.
Increasing
magnitude
and
frequency
since
the
1960s
and
especially
after
1985
suggests
growing
topsoil
instability
Cautious
2–6

10
6
m
3
/kg
2–8

10
6
m
3
/kg
Land
use
change/
deforestation/
cultivated
slopes
Sediment
quality
Sediment-associated
nutrients
and
contaminants
(lake
sediment
records
of
bound
total
P)
Type
Ib
Linear
Distance
from
baseline
P
concentration
increase
after
1960s
to
a
high
level.
Mean
value
in
1960s
taken
as
a
baseline
condition.
Dangerous
500
mg/g
900
mg/g
Excess
fertilizer/soil
erosion
Source:
Dearing
et
al.
(2012a,b).
Table
A.5
Shucheng
County:
social
foundations.
Social
foundation
Indicator
Current
deficit
from
100%
(year)
Food
security
Children
undernourished
(0–5
years
old)
1%
(2009)
Income
Population
living
below
$1.25(PPP)/day
6%
(2010)
Water
and
sanitation
Households
with
piped
water
55%
(2011)
Households
with
lavatories
(Anhui)
42%
(2010)
Health
care
Children
(0–5
years
old)
mortality
(Anhui)
2%
(2010)
Education
Illiteracy
rate
8%
(2010)
Energy
To
be
determined
Gender
equality
To
be
determined
Social
equality
To
be
determined
Voice
To
be
determined
Jobs
Urban
unemployment
rate
(Anhui)
4%
(2010)
Resilience
To
be
determined
Source:
Anhui
Province
Statistical
Yearbooks
(1989–2011).
J.A.
Dearing
et
al.
/
Global
Environmental
Change
28
(2014)
227–238
236

References
Anhui
Province
Statistical
Yearbooks,
1989–2011.
http://tongji.cnki.net/overseas/
engnavi/navidefault.aspx
(in
Chinese
with
English
summaries)
.
Bai,
J.K.,
2003.
Dali
Erhai
Lake
Scientific
Research,
vol.
675.
The
Ethnic
Publishing
House,
Beijing
(in
Chinese).
Balmford,
A.,
Fisher,
B.,
Green,
R.E.,
Naidoo,
R.,
Strassburg,
B.,
Turner,
R.K.,
Rodrigues,
A.S.L.,
2011.
Bringing
ecosystem
services
into
the
real
world:
an
operational
framework
for
assessing
the
economic
consequences
of
losing
wild
nature.
Environ.
Resour.
Econ.
48,
161–175.
Barnosky,
A.D.,
Hadly,
E.A.,
Bascompte,
J.,
Berlow,
E.L.,
Brown,
J.H.,
Fortelius,
M.,
Getz,
W.M.,
Harte,
J.,
Hastings,
A.,
Marquet,
P.A.,
Martinez,
N.D.,
Mooers,
A.,
Roopnarine,
P.,
Vermeij,
G.,
Williams,
J.W.,
Gillespie,
R.,
Kitzes,
J.,
Marshall,
C.,
Matzke,
N.,
Mindell,
D.P.,
Revilla,
E.,
Smith,
A.B.,
2012.
Approaching
a
state
shift
in
Earth’s
biosphere.
Nature
486,
52–58.
Bascompte,
J.,
Melia,
C.J.,
Sala,
E.,
2005.
Interaction
strength
combinations
and
the
overfishing
of
a
marine
food
web.
Proc.
Natl.
Acad.
Sci.
102,
5443–5447.
Biermann,
F.,
2012.
Planetary
boundaries
and
earth
system
governance:
exploring
the
links.
Ecol.
Econ.
81,
4–9.
Billio,
M.,
Getmansky,
M.,
Lo,
A.W.,
Pelizzon,
L.,
2012.
Econometric
measures
of
connectedness
and
systemic
risk
in
the
finance
and
insurance
sectors.
J.
Finan.
Econ.
14,
535–559.
Brook,
B.W.,
Ellis,
E.C.,
Perring,
M.P.,
Mackay,
A.W.,
Blomqvist,
L.,
2013.
Does
the
terrestrial
biosphere
have
planetary
tipping
points?
Trends
Ecol.
Evol.
28,
396–
401.
Carpenter,
S.R.,
Bennett,
E.M.,
2011.
Reconsideration
of
the
planetary
boundary
for
phosphorus.
Environ.
Res.
Lett.
6,
014009,
http://dx.doi.org/10.1088/1748-
9326/6/1/014009.
Carstensen,
J.,
Weydmann,
A.,
2012.
Tipping
points
in
the
Arctic:
eyeballing
or
statistical
significance?
Ambio
41,
34–43.
China
Civil
Affair’s
Statistical
Yearbook,
2011.
China
Civil
Affair’s
Statistical
Year-
book
–
Appendix.
Ministry
of
Civil
Affairs
of
the
PRC,
Beijing
(in
Chinese).
Cornell,
S.,
2012.
On
the
system
properties
of
the
planetary
boundaries.
Ecol.
Soc.
17,
r2,
http://dx.doi.org/10.5751/ES-04731-1701r02
(online).
Cowling,
R.M.,
Egoh,
B.,
Knight,
A.T.,
O’Farrell,
P.J.,
Reyers,
B.,
Rouget,
M.,
Roux,
D.J.,
Welz,
A.,
Wilhelm-Rechman,
A.,
2008.
An
operational
model
for
mainstreaming
ecosystem
services
for
implementation.
Proc.
Natl.
Acad.
Sci.
105,
9483–9488.
Dakos,
V.,
Carpenter,
S.R.,
Brock,
W.A.,
Ellison,
A.M.,
Guttal,
V.,
Ives,
A.R.,
Ke
´fi,
S.,
Livina,
V.,
Seekell,
D.A.,
van
Nes,
E.H.,
Scheffer,
M.,
2012.
Methods
for
detecting
early
warnings
of
critical
transitions
in
time
series
illustrated
using
simulated
ecological
data.
PLoS
ONE
7,
e41010,
http://dx.doi.org/10.1371/journal.-
pone.0041010.
Dali
Bai
Autonomous
Perfecture,
2012.
Dali
Child
Development
Plan
(2011–2020).
Provincial
Government
of
Dali
Bai
Autonomous
Perfecture,
Dali
(in
Chinese).
Dali
Environmental
Protection
Bureau,
1999–2010.
Environment
Bulletin
of
Dali
Bai
Autonomous
Prefecture.
Provincial
Government
of
Dali
Bai
Autonomous
Per-
fecture,
Dali
(in
Chinese).
Dearing,
J.A.,
2008.
Landscape
change
and
resilience
theory:
a
palaeoenvironmental
assessment
from
Yunnan,
SW
China.
The
Holocene
18,
117–127.
Dearing,
J.A.,
Zolitschka,
B.,
1999.
System
dynamics
and
environmental
change:
an
exploratory
study
of
Holocene
lake
sediments
at
Holzmaar,
Germany.
Holocene
9,
531–540.
Dearing,
J.A.,
Jones,
R.T.,
Shen,
J.,
Yang,
X.,
Boyle,
J.F.,
Foster,
G.C.,
Crook,
D.S.,
Elvin,
M.J.D.,
2008.
Using
multiple
archives
to
understand
past
and
present
climate–
human–environment
interactions:
the
lake
Erhai
catchment,
Yunnan
Province,
China.
J.
Paleolimnol.
40,
3–31.
Dearing,
J.A.,
Braimoh,
A.K.,
Reenberg,
A.,
Turner,
B.L.,
Van
der
Leeuw,
S.,
2010.
Complex
land
systems:
the
need
for
long
time
perspectives
to
assess
their
future.
Ecol.
Soc.
15,
21.
(online)
URL:
http://www.ecologyandsociety.org/
vol15/iss4/art21/
Dearing,
J.A.,
Yang,
X.,
Dong,
X.,
Zhang,
E.,
Chen,
X.,
Langdon,
P.G.,
Zhang,
K.,
Zhang,
W.,
Dawson,
T.P.,
2012a.
Extending
the
timescale
and
range
of
ecosystem
services
through
paleoenvironmental
analyses:
the
example
of
the
lower
Yangtze
basin.
Proc.
Natl.
Acad.
Sci.
109,
E1111–E1112.
Dearing,
J.A.,
Bullock,
S.,
Costanza,
R.,
Dawson,
T.P.,
Edwards,
M.E.,
Poppy,
G.M.,
Smith,
G.,
2012b.
Navigating
the
Perfect
Storm:
research
strategies
for
social-
ecological
systems
in
a
rapidly
evolving
world.
Environ.
Manag.
49,
767–775.
Denny,
M.W.,
Hunt,
L.J.H.,
Miller,
L.P.,
Harley,
C.D.G.,
2009.
On
the
prediction
of
extreme
ecological
events.
Ecol.
Monogr.
79,
397–421.
Erb,
K.-H.,
Gingrich,
S.,
Krausmann,
F.,
Haberl,
H.,
2008.
Industrialization,
fossil
fuels,
and
the
transformation
of
land
use.
J.
Ind.
Ecol.
12,
686–703.
Erb,
K.-H.,
Haberl,
H.,
DeFries,
R.,
Ellis,
E.C.,
Krausmann,
F.,
Verburg,
P.H.,
2012.
Pushing
the
planetary
boundaries.
Science
338,
1419–1420.
Eryuan
County
Bureau
of
Statistics,
2011.
Sixth
Census
in
Eryuan.
Provincial
Government
of
Dali
Bai
Autonomous
Prefecture,
Dali
(in
Chinese).
Fischer-Kowalski,
M.,
Haberl,
H.
(Eds.),
2007.
Socioecological
Transitions
and
Global
Change.
Trajectories
of
Social
Metabolism
and
Land
Use.
Edward
Elgar
Publish-
ers,
Cheltenham
and
Northampton.
Folke,
C.,
Carpenter,
S.R.,
Walker,
B.,
Scheffer,
M.,
Elmqvist,
T.,
Gunderson,
L.H.,
Holling,
C.S.,
2004.
Regime
shifts,
resilience,
and
biodiversity
in
ecosystem
management.
Ann.
Rev.
Ecol.
Evolut.
Systemat.
35,
557–581.
Folke,
C.,
Pritchard,
L.,
Berkes,
F.,
Colding,
J.,
Svedin,
U.,
2007.
The
problem
of
fit
between
ecosystems
and
institutions:
ten
years
later.
Ecol.
Soc.
12,
30.
(online)
URL:
http://www.ecologyandsociety.org/vol12/iss1/art30/
Folke,
C.,
Jansson,
A.,
Rockstro
¨m,
J.,
Olsson,
P.,
Carpenter,
S.R.,
Chapin
3rd,
F.S.,
Cre
´pin,
A.S.,
Daily,
G.,
Danell,
K.,
Ebbesson,
J.,
Elmqvist,
T.,
Galaz,
V.,
Moberg,
F.,
Nilsson,
M.,
Osterblom,
H.,
Ostrom,
E.,
Persson,
A.,
Peterson,
G.,
Polasky,
S.,
Steffen,
W.,
Walker,
B.,
Westley,
F.,
2011.
Reconnecting
to
the
biosphere.
Ambio
40,
719–738.
Galaz,
V.,
2012.
Geo-engineering,
governance,
and
social-ecological
systems:
criti-
cal
issues
and
joint
research
needs.
Ecol.
Soc.
17,
24,
http://dx.doi.org/10.5751/
ES-04677-170124
(online).
Godfray,
H.C.J.,
Beddington,
J.R.,
Crute,
I.R.,
Haddad,
L.,
Lawrence,
D.,
Muir,
J.F.,
Pretty,
J.,
Robinson,
S.,
Thomas,
S.M.,
Toulmin,
C.,
2010.
Food
security:
the
challenge
of
feeding
9
billion
people.
Science
327,
812–818.
Groffman,
P.M.,
Baron,
J.S.,
Blett,
T.,
Gold,
A.J.,
Goodman,
I.,
Gunderson,
L.H.,
Levinson,
B.M.,
Palmer,
M.A.,
Paerl,
H.W.,
Peterson,
G.D.,
Poff,
N.L.,
Rejeski,
D.W.,
Reynolds,
J.F.,
Turner,
M.G.,
Weathers,
K.C.,
Wiens,
J.,
2006.
Ecological
thresholds:
the
key
to
successful
environmental
management
or
an
important
concept
with
no
practical
application.
Ecosystems
9,
1–13.
Haberl,
H.,
Kastner,
T.,
Schaffartzik,
A.,
Ludwiczek,
N.,
Erb,
K.-H.,
2012.
Global
effects
of
national
biomass
production
and
consumption:
Austria’s
embodied
HANPP
related
to
agricultural
biomass
in
the
year
2000.
Ecol.
Econ.
84,
66–73.
Haines-Young,
R.,
Potschin,
M.,
Cheshire,
D.,
2006.
Defining
and
Identifying
Envi-
ronmental
Limits
for
Sustainable
Development.
A
Scoping
Study,
Final
Full
Technical
Report
NR0102
Department
for
Food
and
Rural
Affairs,
London.
Hirota,
M.,
Holmgren,
M.,
van
Nes,
E.H.,
Scheffer,
M.,
2011.
Global
resilience
of
tropical
forest
and
savanna
to
critical
transitions.
Science
334,
232–235.
Hughes,
T.P.,
Linares,
C.,
Dakos,
V.,
van
de
Leemput,
I.A.,
van
Nes,
E.H.,
2012.
Living
dangerously
on
borrowed
time
during
slow,
unrecognized
regime
shifts.
Trends
Ecol.
Evol.
28,
149–155.
Ke
´fi,
S.,
Dakos,
V.,
Scheffer,
M.,
van
Nes,
E.H.,
Rietkerk,
M.,
2012.
Early
warning
signals
also
precede
non-catastrophic
transitions.
Oikos
122,
641–648.
Lenton,
T.M.,
2011.
Early
warning
of
climate
tipping
points.
Nat.
Clim.
Change
1,
201–209.
Lenton,
T.M.,
2013.
Environmental
tipping
points.
Ann.
Rev.
Environ.
Resour.
38,
1–
29.
Lenzen,
M.,
Moran,
D.,
Kanemoto,
K.,
Foran,
B.,
Lobefaro,
L.,
Geschke,
A.,
2012.
International
trade
drives
biodiversity
threats
in
developing
nations.
Nature
486,
109–112.
Li,
P.,
2008.
The
analysis
of
Erhai
Lake’s
feature
water
level.
Pearl
River,
vol.
85,
pp.
46–48
(in
Chinese).
Lindegren,
M.,
2012.
Early
detection
of
ecosystem
regime
shifts:
a
multiple
method
evaluation
for
management
application.
PLoS
ONE
7,
e38410,
http://dx.doi.org/
10.1371/journal.pone.0038410.
Mann,
M.E.,
Bradley,
R.S.,
Hughes,
M.K.,
1998.
Global-scale
temperature
patterns
and
climate
forcing
over
the
past
six
centuries.
Nature
392,
779–787.
Millennium
Ecosystem
Assessment,
2005.
Ecosystems
and
Human
Wellbeing:
Synthesis.
Island
Press,
Washington,
DC.
Milly,
P.C.D.,
Betancourt,
J.,
Falkenmark,
M.,
Hirsch,
R.M.,
Kundzewicz,
Z.W.,
Let-
tenmaier,
D.P.,
Stouffer,
R.J.,
2008.
Stationarity
is
dead!
Whither
water
management
Science
319,
573–574.
Moss,
R.H.,
Schneider,
S.H.,
2000.
In:
Pachauri,
R.,
Taniguchi,
T.,
Tanaka,
K.
(Eds.),
Guidance
Papers
on
the
Cross
Cutting
Issues
of
the
Third
Assessment
Report
of
the
IPCC.
World
Meteorological
Organization,
Geneva.
Muradian,
R.,
Martinez-Alier,
J.,
2001.
Trade
and
the
environment:
from
a
‘Southern’
perspective.
Ecol.
Econ.
36,
281–297.
Nilsson,
M.,
Persson,
A
˚.,
2012.
Can
Earth
system
interactions
be
governed?
Gover-
nance
functions
for
linking
climate
change
mitigation
with
land
use,
freshwater
and
biodiversity
protection.
Ecol.
Econ.
75,
61–71.
Nordhaus,
T.,
Shellenberger,
M.,
Blomqvist,
L.,
2012.
The
Planetary
Boundaries
Hypothesis.
A
Review
of
the
Evidence
Breakthrough
Institute,
Oakland,
CA.
Nykvist,
B.,
Persson,
A
˚.,
Moberg,
F.,
Persson,
L.,
Cornell,
S.,
Rockstro
¨m,
J.,
2013.
National
Environmental
Performance
on
Planetary
Boundaries.
Swedish
Envi-
ronmental
Protection
Agency
Report
6576,
Stockholm.
Organisation
for
Economic
Co-operation
and
Development,
1993.
Core
set
of
indicators
for
environmental
performance
reviews,
OECD
Environment
Mono-
graphs
No.
83
Organisation
for
Economic
Co-operation
and
Development,
Paris.
Peters,
G.P.,
Davis,
S.J.,
Andrew,
R.,
2012.
A
synthesis
of
carbon
in
international
trade.
Biogeosciences
9,
3247–3276.
Peters,
G.P.,
Minx,
J.C.,
Weber,
C.L.,
Edenhofer,
O.,
2011.
Growth
in
emission
transfers
via
international
trade
from
1990
to
2008.
Proc.
Natl.
Acad.
Sci.
108,
8903–8908.
Raworth,
K.,
2012.
A
Safe
and
Just
Space
for
Humanity:
Can
We
Live
Within
the
Doughnut?
Oxfam
Discussion
Paper.
Oxfam,
Oxford,
UK.
Reyers,
B.,
O’Farrell,
P.J.,
Cowling,
R.M.,
Egoh,
B.N.,
Le
Maitre,
D.C.,
Vlok,
J.H.J.,
2009.
Ecosystem
services,
land-cover
change,
and
stakeholders:
finding
a
sustainable
foothold
for
a
semiarid
biodiversity
hotspot.
Ecol.
Soc.
14,
38.
(online)
URL:
http://www.ecologyandsociety.org/vol14/iss1/art38/
Rockstro
¨m,
J.,
Karlberg,
L.,
2010.
The
quadruple
squeeze:
defining
the
safe
operating
space
for
freshwater
use
to
achieve
a
triply
green
revolution
in
the
Anthro-
pocene.
Ambio
39,
257–265.
Rockstro
¨m,
J.,
Steffen,
W.,
Noone,
K.,
Persson,
A
˚.,
Chapin
III,
F.S.,
Lambin,
E.,
Lenton,
T.M.,
Scheffer,
M.,
Folke,
C.,
Schellnhuber,
H.,
Nykvist,
B.,
De
Wit,
C.A.,
Hughes,
T.,
van
der
Leeuw,
S.,
Rodhe,
H.,
So
¨rlin,
S.,
Snyder,
P.K.,
Costanza,
R.,
Svedin,
U.,
Falkenmark,
M.,
Karlberg,
L.,
Corell,
R.W.,
Fabry,
V.J.,
Hansen,
J.,
Walker,
B.,
Liverman,
D.,
Richardson,
K.,
Crutzen,
P.,
Foley,
J.,
2009a.
A
safe
operating
space
for
humanity.
Nature
46,
472–475.
Rockstro
¨m,
J.,
Steffen,
W.,
Noone,
K.,
Persson,
A
˚.,
Chapin
III,
F.S.,
Lambin,
E.,
Lenton,
T.M.,
Scheffer,
M.,
Folke,
C.,
Schellnhuber,
H.,
Nykvist,
B.,
De
Wit,
C.A.,
Hughes,
T.,
van
der
Leeuw,
S.,
Rodhe,
H.,
So
¨rlin,
S.,
Snyder,
P.K.,
Costanza,
R.,
Svedin,
U.,
Falkenmark,
M.,
Karlberg,
L.,
Corell,
R.W.,
Fabry,
V.J.,
Hansen,
J.,
Walker,
B.,
J.A.
Dearing
et
al.
/
Global
Environmental
Change
28
(2014)
227–238
237

Liverman,
D.,
Richardson,
K.,
Crutzen,
P.,
Foley,
J.,
2009b.
Planetary
boundaries:
exploring
the
safe
operating
space
for
humanity.
Ecol.
Soc.
14,
32.
(online)
URL:
http://www.ecologyandsociety.org/vol14/iss2/art32/
Rounsevell,
M.D.A.,
Dawson,
T.P.,
Harrison,
P.A.,
2010.
A
conceptual
framework
to
assess
the
effects
of
environmental
change
on
ecosystem
services.
Biodivers.
Conserv.
19,
2823–2842.
Running,
S.,
2012.
A
measurable
planetary
boundary
for
the
biosphere.
Science
337,
1458.
Scheffer,
M.,
2009.
Critical
Transitions
in
Nature
and
Society.
Princeton
University
Press,
Princeton.
Scheffer,
M.,
Carpenter,
S.R.,
Foley,
J.A.,
Folke,
C.,
Walker,
B.,
2001.
Catastrophic
shifts
in
ecosystems.
Nature
413,
591–596.
Scheffer,
M.,
Carpenter,
S.R.,
Lenton,
T.M.,
Bascompte,
J.,
Brock,
W.,
Dakos,
V.,
van
de
Koppel,
J.,
van
de
Leemput,
I.A.,
Levin,
S.A.,
van
Nes,
E.H.,
Pascual,
M.,
Vanderm-
eer,
J.,
2012.
Anticipating
critical
transitions.
Science
338,
344–348.
Seitzinger,
S.P.,
Svedin,
U.,
Crumley,
C.,
Steffen,
W.,
Abdullah,
S.A.,
Alfsen,
C.,
Biermann,
F.H.B.,
Bondre,
N.R.,
Dearing,
J.A.,
Deutsch,
L.,
Dhakal,
S.,
Elmqvist,
T.,
Farahbakhshazad,
N.,
Gaffney,
O.,
Haberl,
H.,
Lavorel,
S.,
Mbow,
C.,
McMi-
chael,
A.J.,
Morais,
J.,
Olsson,
P.,
Pinho,
P.,
Seto,
K.C.,
Sinclair,
P.,
Stafford-Smith,
M.,
Sugar,
L.,
2012.
Planetary
stewardship
in
an
urbanizing
world:
beyond
city
limits.
Ambio
41,
787–794.
Seto,
K.C.,
Reenberg,
A.,
Boone,
C.G.,
Fragkias,
M.,
Haase,
D.,
Langanke,
T.,
Marco-
tullio,
P.,
Munroe,
D.K.,
Olah,
B.,
Simon,
D.,
2012.
Urban
land
teleconnections
and
sustainability.
Proc.
Natl.
Acad.
Sci.
109,
7687–7692.
Singh,
et
al.
(Eds.),
2013.
Long-Term
Socio-Ecological
Research.
Springer.
Sugihara,
G.,
May,
R.,
Ye,
H.,
Hsieh,
C.,
Deyle,
E.,
Fogarty,
M.,
Munch,
S.,
2012.
Detecting
causality
in
complex
ecosystems.
Science
338,
496–500.
Swetnam,
T.W.,
Allen,
C.D.,
Betancourt,
J.L.,
1999.
Applied
historical
ecology:
using
the
past
to
manage
for
the
future.
Ecol.
Appl.
9,
1189–1206.
The
Economics
of
Ecosystems
and
Biodiversity,
2010.
The
Economics
of
Ecosystems
and
Biodiversity,
Ecological
and
Economics
Foundations.
Earthscan,
London.
Tilman,
D.,
Cassman,
K.G.,
Matson,
P.A.,
Naylor,
R.,
Polasky,
S.,
2002.
Agricultural
sustainability
and
intensive
production
practices.
Nature
418,
671–677.
United
Kingdom
National
Ecosystem
Assessment,
2011.
The
UK
National
Ecosystem
Assessment:
Synthesis
of
the
Key
Findings.
UN
Environment
Program-World
Conservation
Monitoring
Centre,
Cambridge.
United
Nations,
2012a.
Millennium
Development
Goals
Report
2012.
United
Nations,
New
York.
United
Nations,
2012b.
Realizing
the
Future
We
Want.
United
Nations,
New
York.
de
Vries,
W.,
Kros,
J.,
Kroeze,
C.,
Seitzinger,
S.P.,
2013.
Assessing
planetary
and
regional
nitrogen
boundaries
related
to
food
security
and
adverse
environmen-
tal
impacts.
Current
Opinion
in
Environmental
Sustainability
5,
392–402.
Wang,
D.,
Podobnik,
B.,
Horvati,
D.,
Stanley,
H.E.,
2011.
Quantifying
and
modeling
long-range
cross
correlations
in
multiple
time
series
with
applications
to
world
stock
indices.
Phys.
Rev.
E83,
046121.
Wang,
R.,
Dearing,
J.A.,
Langdon,
P.G.,
Zhang,
E.,
Yang,
X.,
Dakos,
V.,
Scheffer,
M.,
2012.
Flickering
gives
early
warning
signals
of
a
critical
transition
to
a
eutrophic
lake
state.
Nature
492,
419–422.
Webster,
P.J.,
Holland,
G.J.,
Curry,
J.A.,
Chang,
H.R.,
2005.
Changes
in
tropical
cyclone
number,
duration,
and
intensity
in
a
warming
environment.
Science
309,
1844–
1846.
Whiteman,
G.,
Walker,
B.,
Perego,
P.,
2012.
Planetary
boundaries:
ecological
foun-
dations
for
corporate
sustainability.
J.
Manag.
Stud.
50,
307–336.
Winiwarter,
V,
Gerzabeck,
M.H.
(Eds.),
2012.
The
Challenge
of
Sustaining
Soils:
Natural
and
Social
Ramifications
of
Biomass
Production
in
a
Changing
World.
Austrian
Academy
of
Sciences
Press,
Vienna.
Yan,
C.Z.,
Jin,
X.C.,
Zhao,
J.Z.,
Shen,
B.,
Li,
N.B.,
Huang,
C.Z.,
Xiong,
Z.H.,
2005.
Ecological
protection
and
sustainable
utilization
of
Erhai
Lake,
Yunnan.
Environ.
Sci.
26,
38–43.
Yunnan
Digital
Village,
2013.
Households
With
Clean
Energy.
Ministry
of
Civil
Affairs
of
the
PRC,
Beijing.
http://www.ynszxc.gov.cn/tj/tj/tj_s.aspx
(in
Chinese).
J.A.
Dearing
et
al.
/
Global
Environmental
Change
28
(2014)
227–238
238