The Influencing Factors of Carbon Emissions in the Industrial Sector: Empirical Analysis Based on a Spatial Econometric Model
Sustainability,
Год журнала:
2024,
Номер
16(6), С. 2478 - 2478
Опубликована: Март 16, 2024
To
promote
the
low-carbon,
high-quality
development
of
China’s
industrial
sector
and
achieve
national
carbon
peak
goal
as
soon
possible,
this
study
explores
influencing
factors
emissions
among
sectors.
Based
on
panel
data
36
sectors
in
China
from
2009
to
2021,
spatial
effects
characteristics
are
examined
by
Durbin
model
(SDM)
based
analyzing
correlation
The
results
show
following:
(1)
Moran’s
I
statistical
that
have
a
strong
positive
correlation,
with
time,
between
gradually
increases.
(2)
empirical
whole
property
rights
structure,
capital
intensity,
energy
structure
main
driving
forces
promoting
emission
reduction;
grouping
analysis
impact
FDI
different
sample
groups
is
different.
Among
them,
research
play
role
reducing
each
group.
(3)
Therefore,
future,
reduce
sector,
it
necessary
inhibit
growth
reduction
factors;
optimizing
improving
rationality
effective
ways
conservation
reduction.
Язык: Английский
Evaluating Carbon-Emission Efficiency in China’s Construction Industry: An SBM-Model Analysis of Interprovincial Building Heating
Sustainability,
Год журнала:
2024,
Номер
16(6), С. 2411 - 2411
Опубликована: Март 14, 2024
In
the
pursuit
of
China’s
ambitious
carbon
neutrality
goals,
optimizing
carbon-emission
efficiency
within
construction
sector,
a
significant
emitter,
becomes
critical.
This
study
employs
super-Slacks-Based
Measure
(SBM)
model
and
Tobit
regression
to
analyze
buildings’
heating-related
emissions
across
China,
considering
urban
population
density,
electricity
usage,
building
energy
consumption
influencing
factors
that
cause
differences
in
difference.
The
results
this
show
average
30
provinces
China
is
0.789;
0.89
south,
higher
than
0.69
north.
After
excluding
centralized
heating
emissions,
value
northern
increases
by
0.01,
which
Jilin
Province
Ningxia
Hui
Autonomous
Region
shows
positive
growth,
respectively,
0.12
0.17.
terms
factors,
there
correlation
between
scientific
technological
levels,
regional
economic
scale,
efficiency;
however,
government
intervention
economy
has
negative
with
efficiency.
Renewable
utilization
green-policy
adoption
emerge
as
pivotal
enhancing
contribution
underscore
necessity
fostering
renewable
energy,
refining
energy-consumption
structures,
implementing
green
strategies
augment
Язык: Английский
Spatio‐Temporal Evolution Characteristics and Influencing Factors of Industrial Carbon Emission Efficiency in Chinese Prefecture‐Level Cities
Опубликована: Авг. 23, 2023
In
the
pursuit
of
China’s
dual
carbon
goals,
identifying
spatio-temporal
changes
in
industrial
emission
efficiency
and
their
influencing
factors
cities
at
different
stages
development
is
key
to
effective
formulation
countermeasures
promote
low-carbon
transformation
Chinese
national
industry
achieve
high-quality
economic
development.
this
study,
we
used
balanced
panel
data
270
from
2005
2020
as
a
research
object:
(1)
show
evolution
patterns
urban
efficiency;
(2)
analyze
aggregation
characteristics
using
Global
Moran's
I
statistics;
(3)
use
hierarchical
regression
model
for
assess
non-linear
impact
digital
economy
on
cities.
The
results
following:
exhibited
an
upward
trend
2020,
with
spatial
distribution
pattern
high
south
low
north;
China's
characterized
by
significant
autocorrelation,
increasing
stabilizing
correlation,
relatively
fixed
agglomeration;
there
inverted-U-shaped
relationship
between
increases
emissions
inhibits
carbon-emission
early
development,
but
promotes
mature
developmental
stages.
Therefore,
all
levels
should
reduce
pollution
high-energy-consuming
high-polluting
enterprises,
gradually
carbon-intensive
industries,
accelerate
upgrading
enterprises.
Western,
central
eastern
regions
especially
seek
sharing
innovation
resources,
strengthen
exchanges
interactions
relating
scientific
technological
innovation,
jointly
explore
coordinated
routes
economy.
Язык: Английский
The Impact and Mechanism behind the Effect of a Digital Economy on Industrial Carbon Emission Reduction
Sustainability,
Год журнала:
2024,
Номер
16(13), С. 5705 - 5705
Опубликована: Июль 3, 2024
Digital
technologies
hold
significant
potential
for
addressing
environmental
issues,
such
as
air
pollution
and
rising
global
temperatures.
China
is
focusing
on
accelerating
the
dual
transformation
of
industrial
greening
digitization
to
accomplish
UN’s
2030
Agenda
Sustainable
Development
sustainable
economic
growth.
By
combining
a
two-way
fixed
effect
model,
mediated
panel
threshold
this
research
endeavors
explore
that
expansion
digital
economy
has
level
carbon
emission
intensity
produced
by
industry.
The
yielded
following
primary
conclusions.
(1)
effectively
reduces
via
three
distinct
mechanisms:
enhancements
technological
innovative
capacities
China,
improvements
in
energy
efficiency,
country’s
overall
structure.
(2)
Regions
where
industrialization
are
highly
integrated
developing,
well
early
pilot
regions
Comprehensive
Big
Data
Pilot
Zones,
particularly
susceptible
inhibitory
effect.
This
offers
theoretical
backing
advancements
economy;
achievement
energy-saving
carbon-reducing
development
objectives;
establishment
green,
ecologically
friendly,
recycling
strategies.
Язык: Английский
Measurement and Spatial-Temporal Evolution of Industrial Carbon Emission Efficiency in Western China
Ruixia Suo,
Yangyuqing Bai
Sustainability,
Год журнала:
2024,
Номер
16(17), С. 7318 - 7318
Опубликована: Авг. 26, 2024
As
it
is
an
important
industrial
base
in
China,
of
great
significance
to
improve
the
carbon
emission
efficiency
western
region
promote
low-carbon
sustainable
development
region.
This
paper
selects
input–output
panel
data
11
provinces
China
from
2010
2021,
and
adopts
three-stage
DEA
model
measure
under
a
non-traditional
geographic
division
at
overall
regional
levels
analyze
its
influencing
factors.
The
Dagum
Gini
coefficient,
decomposition
method,
kernel
density
estimation
method
are
used
differences
dynamic
evolution
process
results
study
show
that
(1)
after
removing
environmental
random
factors,
has
been
improved,
but
there
inter-regional
differences,
characterized
by
“the
third
>
second
first
region”;
(2)
green
development,
shared
innovative
coordinated
have
positive
impact
on
improvement
while
level
industrialization
relatively
smaller
influence,
economic
government
support,
open
level,
energy
consumption
structure
not
yet
played
significant
role;
(3)
spatial
emissions
generally
increased
during
sample
period,
with
being
main
source;
(4)
improvements
time
space
stage
multi-polarization
differences.
certain
reference
value
for
improving
China.
Язык: Английский