Land,
Journal Year:
2024,
Volume and Issue:
13(12), P. 2160 - 2160
Published: Dec. 11, 2024
Land-use
change
is
an
important
cause
of
carbon
emissions
(CEs).
In
the
context
achieving
peaking
and
neutrality
goals,
understanding
coupling
mechanisms
between
land-use
CEs
great
significance
for
fostering
regional
low-carbon
sustainable
development.
this
study,
net
(LCN)
calculation
evaluation
model
was
built
based
on
perspective
change.
The
variation
matrix,
standard
deviation
ellipse,
spatial
autocorrelation
analysis
were
used
to
analyze
spatio-temporal
evolution
LCN
in
Chengyu
urban
agglomeration
(CUA)
from
2000
2020.
Meanwhile,
economic
contribution
coefficient
ecological
support
applied
evaluate
alignment
among
CEs,
socio-economic
development,
environment.
addition,
modified
Kaya
Logarithmic
Mean
Divisia
Index
(LMDI)
models
quantitatively
drivers
underlying
influence
LCN.
results
showed
following:
(1)
area
built-up
land
forest
expanded
rapidly,
mainly
transforming
grassland
farmland
CUA
during
study
period.
main
source
CEs.
changes
led
migration
center
variations
clustering.
(2)
growth
rate
decreased
after
2010,
disparities
productivity
compensation
cities
gradually
narrowed
environmental
governance
effectively
improved.
(3)
development
level
energy
consumption
intensity
primary
facilitator
inhibitor
LCN,
respectively.
could
offer
valuable
references
insights
formulating
reduction
strategies
policies.
Forests,
Journal Year:
2024,
Volume and Issue:
15(10), P. 1825 - 1825
Published: Oct. 19, 2024
Analyzing
the
spatiotemporal
changes
and
influencing
factors
of
carbon
emissions
generated
by
land
use
is
great
importance
for
improving
structure
promoting
regional
low-carbon
economic
development.
This
study,
based
on
remote
sensing
statistical
yearbook
data
from
1995
to
2020,
calculated
in
Jiangxi
Province,
China.
Multiple
spatial
analysis
methods
logarithmic
mean
Divisia
index
were
used
elucidate
evolution
driving
emissions,
findings
revealed
following:
(1)
The
Province
during
1995–2020
substantial
as
forest
accounted
65%
entire
area,
while
construction
increased
98.1%.
Cultivated
decreased
most,
followed
land.
(2)
There
was
a
fourfold
rise
driven
primarily
land,
northern
areas
produced
higher
compared
with
central
southern
regions.
Forest
main
sink.
(3)
Economic
development
(257.36%)
impact
proportion
(211.31%)
primary
contributing
increase
use,
other
had
inhibitory
effects.
study
transformed
macroscale
strategy
cities
into
targeted
local
policies,
research
theories
adopted
could
provide
scientific
reference
regions
urgent
need
reduction
worldwide.
Frontiers in Environmental Science,
Journal Year:
2024,
Volume and Issue:
12
Published: Aug. 26, 2024
The
global
climate
crisis
is
escalating,
and
urban
living
Space
(ULS)
a
significant
contributor
to
carbon
emissions.
How
improve
the
suitability
of
ULS
while
promoting
social
economic
development
issue.
This
study
aims
develop
an
evaluation
system
for
comparing
analyzing
inequality
spatial
differences
in
different
areas.
To
achieve
this
goal,
space
index
(ULS-CSI)
based
on
organizational
(SOI)
has
been
proposed.
ULS-CSI
was
calculated
at
area
scale
Tianjin
using
information
from
Land
Use
Database
2021.
emissions
coefficient
method
used
calculate
(ULSCE).
Moran’I
LISA
analysis
were
quantify
ULS-CSI.
results
showed
that
residential
(RLA)
highest
scale,
with
1.14
×
10
11
kg,
accounting
33.74%.
green
leisure
(GLA)
absorption
5.76
5
32.33%.
SOI
areas
have
heterogeneity
as
such
building
area,
road
network
density
land
use
characteristics
are
significantly
Areas
superior
CSI
primarily
situated
Heping,
Hexi,
Nankai,
Beichen,
83.90%.
Conversely,
under
basic
threshold
included
Xiqing,
Jinnan,
Dongli,
16.10%.
Spatial
portrayed
positive
correlation,
indicating
autocorrelation
degree
500
m,
Moran
’I
value
0.1733.
Although
these
findings
reflect
affecting
more
perfect
data
needed
complexity
structural
factors
scale.
helpful
planning
differentiated
reduction
strategies
promote
low-carbon
healthy
development.
Land,
Journal Year:
2024,
Volume and Issue:
13(12), P. 2121 - 2121
Published: Dec. 6, 2024
Urban
areas
are
significant
centers
of
human
activity
and
recognized
as
major
contributors
to
global
carbon
emissions.
The
establishment
urban
green
spaces
plays
a
crucial
role
in
enhancing
sinks
mitigating
emissions,
thereby
fostering
low-carbon
cycle
within
cities.
However,
the
existing
literature
on
sequestration
Chinese
cities
often
overlooks
water
bodies,
which
characteristic
wetland
Therefore,
it
is
necessary
investigate
potential
cities,
taking
into
account
contribution
bodies
sinks.
This
study
aims
analyze
quantitative
structure
through
lens
balance,
can
effectively
enhance
city’s
overall
capacity.
Utilizing
balance
theory,
this
research
first
assesses
offsetting
capability
(COC)
Wuhan
for
year
2019.
It
then
forecasts
future
sets
improvement
targets
COC,
calculates
required
area
standard
space
achieve
these
by
2030.
A
multi-objective
programming
(MOP)
model
developed
identify
optimal
solution
that
aligns
with
development
planning
constraints
while
maximizing
Lastly,
we
analyzed
rates
different
types
total
capacity
clarify
characteristics
absorption
Wuhan,
city.
findings
indicate
following:
(1)
In
2019,
Wuhan’s
emissions
from
activities
reached
approximately
38.20
Mt,
absorbing
around
5.62
Mt
carbon,
COC
about
14.71%.
(2)
Projections
2030
suggest
will
rise
42.64
Mt.
Depending
targeted
5%,
10%,
15%,
20%,
25%,
values
be
6.59
6.90
7.21
7.53
7.84
respectively.
(3)
results
MOP
projected
16.33%,
necessitates
6.97
(4)
Water
accounted
56.23%
2019
represent
45.37%
2030,
highlighting
distinctive
city
terms
its
sequestrations.
management
enhancement
body
Wuhan.
provide
evidence
recommendations
patterns
across
China.
Land,
Journal Year:
2024,
Volume and Issue:
13(12), P. 2160 - 2160
Published: Dec. 11, 2024
Land-use
change
is
an
important
cause
of
carbon
emissions
(CEs).
In
the
context
achieving
peaking
and
neutrality
goals,
understanding
coupling
mechanisms
between
land-use
CEs
great
significance
for
fostering
regional
low-carbon
sustainable
development.
this
study,
net
(LCN)
calculation
evaluation
model
was
built
based
on
perspective
change.
The
variation
matrix,
standard
deviation
ellipse,
spatial
autocorrelation
analysis
were
used
to
analyze
spatio-temporal
evolution
LCN
in
Chengyu
urban
agglomeration
(CUA)
from
2000
2020.
Meanwhile,
economic
contribution
coefficient
ecological
support
applied
evaluate
alignment
among
CEs,
socio-economic
development,
environment.
addition,
modified
Kaya
Logarithmic
Mean
Divisia
Index
(LMDI)
models
quantitatively
drivers
underlying
influence
LCN.
results
showed
following:
(1)
area
built-up
land
forest
expanded
rapidly,
mainly
transforming
grassland
farmland
CUA
during
study
period.
main
source
CEs.
changes
led
migration
center
variations
clustering.
(2)
growth
rate
decreased
after
2010,
disparities
productivity
compensation
cities
gradually
narrowed
environmental
governance
effectively
improved.
(3)
development
level
energy
consumption
intensity
primary
facilitator
inhibitor
LCN,
respectively.
could
offer
valuable
references
insights
formulating
reduction
strategies
policies.
