Land,
Journal Year:
2024,
Volume and Issue:
13(9), P. 1421 - 1421
Published: Sept. 3, 2024
Exploring
the
low-carbon
transition
in
China
can
offer
profound
guidance
for
governments
to
develop
relevant
environmental
policies
and
regulations
within
context
of
2060
carbon
neutrality
target.
Previous
studies
have
extensively
explored
promotion
development
China,
yet
no
completely
explained
mechanisms
from
perspective
per
capita
emissions
(PCEs).
Based
on
statistics
data
367
prefecture
level
cities
2000
2020,
this
study
employed
markov
chain,
kernel
density
analysis,
hotspots
spatial
regression
models
reveal
spatiotemporal
distribution
patterns,
future
trends,
driving
factors
PCEs
China.
The
results
showed
that
China’s
2000,
2010,
2020
were
0.72
ton/persons,
1.72
1.91
respectively,
exhibiting
a
continuous
upward
trend,
with
evident
regional
heterogeneity.
northern
eastern
coastal
region
higher
than
those
southern
central
southwestern
regions.
obvious
clustering,
hot
spots
mainly
concentrated
Inner
Mongolia
Xinjiang,
while
cold
some
provinces
exhibited
strong
stability
‘club
convergence’
phenomenon.
A
analysis
revealed
urbanization
latitude
had
negative
effects
PCEs,
economic
level,
average
elevation,
slope,
longitude
positive
PCEs.
These
findings
important
implications
effective
achievement
“dual
carbon”
goal.
Ecological Indicators,
Journal Year:
2024,
Volume and Issue:
165, P. 112092 - 112092
Published: May 27, 2024
As
an
important
economic
growth
pole
and
ecological
area
in
China,
the
urban
agglomeration
of
Yangtze
River
Economic
Belt
(YREB)
is
key
to
carbon
emission
reduction.
Exploring
spatial–temporal
evolution
driving
variables
its
efficiency
(CEE)
crucial
for
realizing
goals
peaking
neutrality.
The
super-efficiency
SBM
model,
nuclear
density
method,
spatial
autocorrelation
method
were
used
discuss
CEE
characteristics
105
cities
YREB.
On
factors
emissions,
geographic
detector
Tobit
model
combined
explore
differentiation
from
perspective
heterogeneity,
concurrently
analyze
single-factor's
effecting
intensity
impacting
direction,
as
well
dual-factors'
interaction
effects.
findings
indicated
that
YREB
generally
showed
a
slow
upward
trend
during
2006–2021.
From
time
dynamic
evolution,
intensified,
overall
development
was
toward
high
level.
Furthermore,
results
pattern
presents
"downstream
areas
>
midstream
upstream
areas",
"high
east
low
west",
"hot
cold
while
clustering
effect
significant,
showing
distributions
low-low
or
high-high
clustering.
Moreover,
government
intervention,
growth,
technological
progress
main
factors.
In
addition,
interactions
intervention
other
significantly
detected.
regression
advancement
had
favorable
impact
on
CEE,
but
foreign
investment,
urbanization,
involvement
negative
impacts.
future,
correlations
between
provinces
should
be
strengthened
amplified
promote
integrated
green
development,
improve
environments.
