Land,
Год журнала:
2024,
Номер
13(9), С. 1421 - 1421
Опубликована: Сен. 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.
Urban Science,
Год журнала:
2024,
Номер
8(3), С. 104 - 104
Опубликована: Авг. 1, 2024
Artificial
intelligence
(AI)
has
become
a
transformative
force
across
various
disciplines,
including
urban
planning.
It
unprecedented
potential
to
address
complex
challenges.
An
essential
task
is
facilitate
informed
decision
making
regarding
the
integration
of
constantly
evolving
AI
analytics
into
planning
research
and
practice.
This
paper
presents
review
how
methods
are
applied
in
studies,
focusing
particularly
on
carbon
neutrality
We
highlight
already
being
used
generate
new
scientific
knowledge
interactions
between
human
activities
nature.
consider
conditions
which
advantages
AI-enabled
studies
can
positively
influence
decision-making
outcomes.
also
importance
interdisciplinary
collaboration,
responsible
governance,
community
engagement
guiding
data-driven
suggest
contribute
supporting
carbon-neutrality
goals.
Scientific Reports,
Год журнала:
2024,
Номер
14(1)
Опубликована: Авг. 24, 2024
Yellow
River
Basin
(YRB)
is
a
pivotal
region
for
energy
consumption
and
carbon
emissions
(CEs)
in
China,
with
cities
emerging
as
the
main
sources
of
regional
CEs.
This
highlights
their
critical
role
achieving
sustainable
development
China's
neutrality.
Consequently,
there
pressing
need
detailed
exploration
urban
spillover
effects
an
in-depth
analysis
complex
determinants
influencing
CEs
within
YRB.
Remote
sensing
data
provide
optimal
conditions
conducting
extensive
studies
across
large
geographical
areas
extended
time
periods.
study
integrates
DMSP/OLS
NPP/VIIRS
nighttime
light
datasets
longitudinal
Using
harmonized
dataset
from
2007
to
2021,
this
quantifies
58
prefecture-level
By
combining
ESDA,
STIRPAT
model
spatial
econometric
model,
investigation
further
clarifies
empirically
driving
factors
The
delineates
phase-wise
augmentation
CEs,
converging
towards
distinct
distribution
characterized
by
"lower
reach
>
middle
upper
reach".
autocorrelation
tests
unravel
interplay
between
agglomeration
differentiation
patterns
underscored
pronounced
lock-in
phenomena.
Significantly,
demonstrates
that
urbanization,
economic
development,
structure,
green
coverage
rate,
industrial
population,
technological
progress,
FDI
each
exhibit
varied
direct
indirect
effect
on
Furthermore,
it
elaborates
potential
policy
implications
future
research
directions,
offering
crucial
insights
formulating
mitigation
strategies
advance
development.
Land,
Год журнала:
2024,
Номер
13(9), С. 1421 - 1421
Опубликована: Сен. 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.
