Frontiers in Environmental Science,
Год журнала:
2024,
Номер
12
Опубликована: Ноя. 5, 2024
The
global
climate
crisis
is
escalating,
and
how
to
reduce
land
use
carbon
emission
(LUCE)
while
promoting
social
economic
development
a
issue.
purpose
of
this
study
was
investigate
the
spatio-temporal
evolution
characteristics
influencing
factors
LUCE
at
county
scale.
To
accomplish
goal,
based
on
Zibo
County
data
societal
energy
consumption
statistics,
for
predicting
net
in
2010,
2015,
2020.
GIS
spatial
analysis
autocorrelation
model
were
utilized
LUCE.
geographical
temporal
weighted
regression
(GTWR)
used
differences.
findings
demonstrate
that:
(1)
rate
change
City
decreased
between
2010
2020,
with
overall
motivation
falling
from
0.14%
0.09%.
area
arable
land,
forest
grassland
decreased,
amount
water,
developed
unutilized
increased.
Between
emissions
increased
significantly,
3.011
×
10
7
tC
3.911
tC.
distribution
followed
clear
pattern
“elevated
east
diminished
west,
elevated
south
north.”
agglomeration
are
obvious,
trend
Moran
I
value
falling,
0.219
0.212.
elements
that
determine
vary
greatly
by
location,
most
major
influences
being,
descending
order,
per
unit
GDP,
urbanization
rate,
land-use
efficiency,
population
size.
GDP
has
greatest
impact
Linzi
District,
coefficients
ranging
55.4
211.5.
clearly
depicts
resulting
contribute
them.
Simultaneously,
it
provides
scientific
framework
improving
structure
implementing
low-carbon
programs
throughout
region.
Land,
Год журнала:
2023,
Номер
12(12), С. 2160 - 2160
Опубликована: Дек. 13, 2023
With
regard
to
the
aims
of
achieving
“Dual
Carbon”
goal
and
addressing
significant
greenhouse
gas
emissions
caused
by
urban
expansion,
there
has
been
a
growing
emphasis
on
spatial
research
prediction
carbon
emissions.
This
article
examines
land
use
data
from
2000
2020
combines
Grid
PLUS
model
predict
in
2030
through
multi-scenario
simulation.
The
findings
indicate
following:
(1)
Between
2020,
construction
increased
95.83%,
with
also
increasing.
(2)
By
2030,
for
NDS
(natural
development
scenario),
are
expected
peak
at
6012.87
×
104
t.
Regarding
ratio
obtained
EDS
(economic
is
projected
grow
3990.72
km2,
6863.29
For
LCS
(low-carbon
“carbon
peak”
be
reached
before
2030.
(3)
intensity
decreases
as
city
size
increases.
(4)
shift
center
emission
all
movement
towards
southeast.
Studying
trends
regional
change
patterns
beneficial
optimizing
structure,
thereby
enabling
us
achieve
low-carbon
reductions
sustainable
development.
Sustainable Development,
Год журнала:
2024,
Номер
unknown
Опубликована: Сен. 8, 2024
Abstract
In
the
context
of
climate
change
and
sustainable
development,
there
has
been
numerous
researches
studying
influence
land
use
policies
on
carbon
sequestration.
However,
most
them
focus
specific
experimental
area
to
explore
impact
fixation,
or
their
impacts
a
certain
aspect,
lacking
comprehensive
explanation
both
internal
mechanism
external
effects.
Therefore,
this
article
presents
review
results
show
that
scientific
have
capacity
increasing
net
sinks
soil
organic
realize
environmental
sustainability.
addition,
these
low‐carbon
can
not
only
bring
out
mitigation
influence,
but
also
effects
cities
food
security,
ecological
protection,
economic
disparities
promote
development.
if
fail
adapt
local
natural
socio‐economic
conditions,
overlook
potential
negative
they
could
pose,
cannot
facilitate
achievement
development
even
impede
progress.
Applied Sciences,
Год журнала:
2025,
Номер
15(4), С. 1886 - 1886
Опубликована: Фев. 12, 2025
With
rapid
economic
development
and
the
change
in
land
use
patterns,
region
faces
environmental
challenge
of
increasing
carbon
emission
risk.
The
research
on
analyzing
identifying
risk
is
helpful
to
realize
regional
sustainable
development.
This
study
takes
Yanshan-Taihang
Mountain
area
Hebei
Province
as
a
case
study.
Based
remote
sensing
monitoring
data
2010,
2015,
2020,
emissions
are
calculated
by
direct
indirect
calculation
methods.
Then,
footprint
pressure
index
introduced
analyze
temporal
spatial
differentiation
characteristics
area.
findings
indicate
that
associated
with
initially
exhibited
an
trend,
followed
subsequent
decline
over
time.
In
space,
high-value
areas
mainly
distributed
south
area,
low-value
concentrated
northeast
experiences
significant
carbon-cycling
risk,
proportion
counties
balance
decreasing
from
27.27%
2010
18.18%
imbalance
72.72%
81.82%
2020.
divided
into
micro-risk
low-risk
medium-risk
high-risk
severe-risk
From
there
was
increase
percentage
classified
areas.
Additionally,
regions
identified
hotspots
for
trend
expansion.
phenomenon
indicates
these
have
not
successfully
managed
mitigate
pollution
or
ensure
resources
context
their
efforts.
series
dynamic
changes
shows
facing
use.
Governments
at
all
levels
should
strengthen
governance
areas,
implement
stricter
policies,
promote
green
cleaner
production
attain
mutually
beneficial
outcome
both
ecological
protection.
Sustainability,
Год журнала:
2025,
Номер
17(8), С. 3371 - 3371
Опубликована: Апрель 10, 2025
Megacities
in
developing
countries
are
still
undergoing
rapid
urbanization,
with
different
cities
exhibiting
ecosystem
services
(ESs)
heterogeneity.
Evaluating
ESs
among
various
and
analyzing
the
influencing
factors
from
a
resilience
perspective
can
effectively
enhance
ability
of
to
deal
react
quickly
risks
uncertainty.
This
approach
is
also
crucial
for
optimizing
ecological
security
patterns.
study
focuses
on
Xi’an
Jinan,
two
important
megacities
along
Yellow
River
China.
First,
we
quantified
four
both
cities:
carbon
storage
(CS),
habitat
quality
(HQ),
food
production
(FP),
soil
conservation
(SC).
Second,
analyzed
synergies
trade-offs
between
these
using
bivariate
local
spatial
autocorrelation
Spearman’s
rank
correlation
coefficient.
Finally,
conducted
driver
analysis
Geographic
Detector.
Results:
(1)
The
temporal
distribution
Jinan
quite
different,
but
show
lower
ES
levels
urban
core
area.
(2)
showed
strong
synergistic
effect.
Among
them,
CS-HQ
had
strongest
synergy
0.93.
In
terms
space,
north
dominated
by
low–low
clustering,
while
south
high–high
clustering.
FP-SC
trade-off
effect
−0.35
2000,
which
gradually
weakened
over
time
was
mainly
distributed
northern
area
city
where
cropland
construction
were
concentrated.
(3)
Edge
density,
patch
NDVI
have
greatest
influence
CS
Jinan.
DEM,
slope,
density
HQ.
Temperature,
edge
impact
temperature
FP
cities.
SC.
Landscape
fragmentation
has
great
CS,
HQ,
SC
Due
insufficient
research
data,
this
focused
only
middle
reaches
River.
However,
results
provide
new
solving
problem
regional
sustainable
development
directions
ideas
follow-up
field.
