Atmosphere,
Journal Year:
2024,
Volume and Issue:
15(6), P. 641 - 641
Published: May 26, 2024
It
is
of
great
scientific
value
to
study
the
spatial
differences
and
influencing
factors
carbon
emission
intensity
(CEI)
in
urban
agglomerations
(UAs),
it
also
has
reference
significance
for
China
formulating
energy-saving
emission-reduction
policies
achieve
target
neutrality.
Taking
165
prefecture-level
cities
19
UAs
from
2007
2019
as
research
object,
this
investigated
CEI
using
exploratory
data
analysis
explored
via
Geodetector.
The
results
showed
following:
(1)
a
downward
trend.
(2)
typical
agglomeration
characteristics,
where
North
comprises
mainly
high-high
low-high
types,
whereas
South
primarily
high-low
low-low
types.
(3)
have
undergone
transformation
industrial
structure
population
urbanization.
Land,
Journal Year:
2025,
Volume and Issue:
14(4), P. 844 - 844
Published: April 12, 2025
Northeast
China,
a
traditional
heavy
industrial
base,
faces
significant
carbon
emissions
challenges.
This
study
analyzes
the
drivers
of
in
35
cities
from
2000–2022,
utilizing
machine-learning
approach
based
on
stacking
model.
A
model,
integrating
random
forest
and
eXtreme
Gradient
Boosting
(XGBoost)
as
base
learners
support
vector
machine
(SVM)
meta-model,
outperformed
individual
algorithms,
achieving
coefficient
determination
(R2)
0.82.
Compared
to
methods,
model
significantly
improves
prediction
accuracy
stability
by
combining
strengths
multiple
algorithms.
The
Shapley
additive
explanations
(SHAP)
analysis
identified
key
drivers:
total
energy
consumption,
urbanization
rate,
electricity
population
positively
influenced
emissions,
while
sulfur
dioxide
(SO2)
smoke
dust
average
temperature,
humidity
showed
negative
correlations.
Notably,
green
coverage
exhibited
complex,
slightly
positive
relationship
with
emissions.
Monte
Carlo
simulations
three
scenarios
(Baseline
Scenario
(BS),
Aggressive
De-coal
(ADS),
Climate
Resilience
(CRS))
projected
peak
2030
under
ADS,
lowest
fluctuation
(standard
deviation
5)
largest
reduction
(17.5–24.6%).
Baseline
indicated
around
2039–2040.
These
findings
suggest
important
role
de-coalization.
Targeted
policy
recommendations
emphasize
accelerating
transition,
promoting
low-carbon
transformation,
fostering
urbanization,
enhancing
sequestration
China’s
sustainable
development
achievement
dual-carbon
goals.
Indoor and Built Environment,
Journal Year:
2025,
Volume and Issue:
unknown
Published: April 16, 2025
Air
pollution
(PE)
and
carbon
emissions
(CE)
are
obstacles
to
achieving
the
Sustainable
Development
Goals
(SDGs)
globally.
The
development
intensity
spatial
form
of
urban
blocks
could
affect
PE-CE.
relationship
between
these
factors
PE-CE
was
investigated
in
this
study.
results
showed
that:
(1)
distribution
various
that
PE
consistent
with
normalized
difference
vegetation
index
(NDVI),
average
height
(AH)
building
density
(BD),
while
CE
BD,
floor
area
ratio
(FAR)
otherness
(BO).
Neighbourhoods
high
NDVI
have
low
CE.
(2)
Correlation
analysis
shows
general,
FAR,
BO
were
positively
correlated,
negatively
correlated.
correlation
other
different
functional
has
its
own
characteristics.
(3)
For
interpretation
random
forest
model,
FAR
a
strong
all
areas.
CE,
50%
public
blocks,
19%
residential
28%
industrial
blocks.
PE,
29%.
(4)
AH
most
frequently
judged
as
important
factors.
key
affecting
AH.
Public
block
(AREA).
Industrial
AREA,
BD
BO.
Atmosphere,
Journal Year:
2024,
Volume and Issue:
15(6), P. 641 - 641
Published: May 26, 2024
It
is
of
great
scientific
value
to
study
the
spatial
differences
and
influencing
factors
carbon
emission
intensity
(CEI)
in
urban
agglomerations
(UAs),
it
also
has
reference
significance
for
China
formulating
energy-saving
emission-reduction
policies
achieve
target
neutrality.
Taking
165
prefecture-level
cities
19
UAs
from
2007
2019
as
research
object,
this
investigated
CEI
using
exploratory
data
analysis
explored
via
Geodetector.
The
results
showed
following:
(1)
a
downward
trend.
(2)
typical
agglomeration
characteristics,
where
North
comprises
mainly
high-high
low-high
types,
whereas
South
primarily
high-low
low-low
types.
(3)
have
undergone
transformation
industrial
structure
population
urbanization.
