Buildings,
Journal Year:
2025,
Volume and Issue:
15(8), P. 1307 - 1307
Published: April 16, 2025
Buildings
account
for
39%
of
global
carbon
emissions,
making
the
construction
sector
a
pivotal
contributor
to
climate
change.
In
ecologically
fragile
plateau
regions,
tension
between
urban
development
and
environmental
sustainability
poses
significant
challenge.
This
study
examines
spatiotemporal
characteristics
influencing
mechanisms
building
emissions
(BCEs)
in
cities
using
an
empirical
analysis
13-year
panel
data
(2010–2022)
from
two
municipalities
six
prefectures
Qinghai
Province,
China.
By
employing
eXtreme
Gradient
Boosting
(XGBoost)
model,
we
comprehensively
assess
drivers
across
four
dimensions:
socioeconomic
structure,
demographic
factors,
expansion
patterns,
climatic
topographic
attributes.
Key
findings
include:
(1)
The
XGBoost
model
exhibits
robust
predictive
performance
(R2
>
0.9,
MSE
<
0.1,
RMSE
0.3),
validating
its
effectiveness
systems.
(2)
Socioeconomic
structure
significantly
positively
influence
with
GDP,
per
capita
built-up
areas
being
particularly
influential.
(3)
interaction
terrain
increases
buildings.
(4)
While
is
common
factor
affecting
BCEs
different
types
buildings,
other
such
as
population
density,
housing
area,
shape
index,
exhibit
variability.
These
insights
inform
policy
recommendations
cross-regional
flow
balancing
adaptive
low-carbon
planning
strategies
tailored
ecosystems.
Buildings,
Journal Year:
2025,
Volume and Issue:
15(8), P. 1307 - 1307
Published: April 16, 2025
Buildings
account
for
39%
of
global
carbon
emissions,
making
the
construction
sector
a
pivotal
contributor
to
climate
change.
In
ecologically
fragile
plateau
regions,
tension
between
urban
development
and
environmental
sustainability
poses
significant
challenge.
This
study
examines
spatiotemporal
characteristics
influencing
mechanisms
building
emissions
(BCEs)
in
cities
using
an
empirical
analysis
13-year
panel
data
(2010–2022)
from
two
municipalities
six
prefectures
Qinghai
Province,
China.
By
employing
eXtreme
Gradient
Boosting
(XGBoost)
model,
we
comprehensively
assess
drivers
across
four
dimensions:
socioeconomic
structure,
demographic
factors,
expansion
patterns,
climatic
topographic
attributes.
Key
findings
include:
(1)
The
XGBoost
model
exhibits
robust
predictive
performance
(R2
>
0.9,
MSE
<
0.1,
RMSE
0.3),
validating
its
effectiveness
systems.
(2)
Socioeconomic
structure
significantly
positively
influence
with
GDP,
per
capita
built-up
areas
being
particularly
influential.
(3)
interaction
terrain
increases
buildings.
(4)
While
is
common
factor
affecting
BCEs
different
types
buildings,
other
such
as
population
density,
housing
area,
shape
index,
exhibit
variability.
These
insights
inform
policy
recommendations
cross-regional
flow
balancing
adaptive
low-carbon
planning
strategies
tailored
ecosystems.