Ecological Indicators,
Journal Year:
2024,
Volume and Issue:
163, P. 112122 - 112122
Published: May 16, 2024
Identifying
the
spatial
patterns
and
driving
factors
of
ecosystem
service
supply–demand
help
to
regulate
optimize
river
basin's
services'
supply–demand,
which
is
quite
important
for
protection
natural
areas
green
development
in
China.
The
water
yield
services
(WY),
food
production
(FP),
carbon
sequestration
(CS),
soil
retention
(SR)
Yiluo
River
Basin
years
2000,
2010,
2020
were
quantified
using
a
variety
models,
including
Integrated
Valuation
Ecosystem
Services
Trade-offs
(InVEST),
revised
universal
loss
equation
(RUSLE),
ArcGIS,
Canoco5.
Additionally,
features
their
spatial–temporal
distributions
identified.
results
combined
with
redundancy
analysis
(RDA)
identify
drivers
supply/demand
ratios
three
different
years.
There
four
main
findings.
(1)
From
2000
2020,
Basin's
supply
WY
SR
showed
declining
trend,
while
FP
CS
an
increasing
trend.
In
Basin,
there
was
rising
tendency
demand
decreasing
trend
WY,
FP,
SR.
(2)
Whereas
ratio
high-high
aggregation
regions
(high
supply-high
demand)
dominated
relationship
services.
(3)
population
density
forest
land.
Population
had
both
positive
negative
effects
on
SR;
however,
only
CS.
Forest
land
effect
ratios.
(4)
According
principles
clustering
factors,
divided
into
two
first-level
subzones
upper
middle
portions
Ecological
Stability
Zone
lower
reaches
Reserve
Zone,
second-level
Precautionary
Reserve,
Construction
Management
Control
Zone.
characterizedWe
put
forward
practicable
measures
based
characteristics
balancing
Land,
Journal Year:
2025,
Volume and Issue:
14(2), P. 411 - 411
Published: Feb. 16, 2025
Ecosystem
services
(ESs)
are
increasingly
recognized
as
critical
to
sustainable
development
and
human
well-being
frequently
used
indicators
in
environmental
governance
policies.
However,
existing
studies
mostly
assess
the
performance
of
isolated
single
ESs,
ignoring
management
data
needs
local
governments
for
comprehensive
gate-keeping
easy
monitoring
regional
ecosystems,
lacking
holistic
ESs.
To
address
these
shortcomings,
this
study
assessed
spatial
changes
five
main
ESs
Yangtze
River
basin
(YTRB)
China
by
creating
a
indicator
(CESI)
using
multi-source
data,
introduced
hotspot
analyses
econometric
models
explore
driving
forces
CESI.
Results
showed
that
during
period,
CESI
YTRB
increased
from
0.44
2000
0.47
2020.
High-value
areas
were
mainly
concentrated
hilly
mountainous
regions,
whereas
low-value
predominantly
situated
plain
areas.
From
2020,
hot
spots
primarily
located
middle
lower
reaches
YTRB.
Conversely,
cold
upper
The
regression
analysis
revealed
significant
negative
association
between
socioeconomic
factors
CESI,
while
positive
natural
background
Of
factors,
average
precipitation
has
largest
effect
on
with
each
1%
increase
resulting
up
0.369%
In
contrast,
GDP
density
had
greatest
impact
triggering
reduction
6.210%.
findings
suggest
which
integrates
multiple
can
effectively
simplify
difficulty
ecological
regulation.
mechanism
indicates
protection
policies,
when
combined
conditions
intensity
activities
region,
would
be
more
coherent
varying
regulatory
intensities.
Sustainability,
Journal Year:
2024,
Volume and Issue:
16(12), P. 5063 - 5063
Published: June 14, 2024
To
promote
sustainable
development
and
improve
human
well-being,
understanding
how
ecological
quality
influences
ecosystem
service
value
is
essential.
In
this
study,
we
quantified
the
in
Fujian
Province
from
2000
to
2021
using
Remote-Sensing
Ecological
Index
(RSEI)
Equivalent
Factor
Approach,
respectively.
This
analysis
aimed
examine
their
spatial
temporal
dynamic
evolution
explore
correlation
between
two.
The
results
indicate
that
patterns
of
were
similar,
with
both
showing
fluctuating
upward
trends
over
time.
distributions
high
central
northern
low
southeastern
coastal
areas.
Pearson
revealed
reveals
a
significant
positive
RSEI
(r
=
0.673,
p
<
0.01),
suggesting
synergistic
relationship.
highest
was
observed
supporting
indicators
(|r|
0.449–0.815,
NDVI
0.815,
0.01).
autocorrelation
also
showed
positively
correlated
spatially,
high–high
agglomerations
concentrated
northwest
Fujian,
low–low
area
south
Fujian.
