Sustainability,
Год журнала:
2024,
Номер
16(21), С. 9338 - 9338
Опубликована: Окт. 28, 2024
Synergizing
air
pollution
control
and
climate
change
mitigation
has
been
of
significant
academic
policy
concern.
The
synergy
between
carbon
emissions
is
one
the
measures
to
understand
characteristics
process
pollution–carbon
synergistic
control,
which
will
also
provide
valuable
information
for
collaboratively
achieving
Sustainable
Development
Goals
(SDGs)
(such
as
SDGs
11
13).
This
study
establishes
a
systematic
framework
integrating
inventory
projection
models,
correlation
mining
typology
analysis
methods
predictively
evaluate
comprehensive
coordination
dioxide
(CO2)
in
Chinese
cities
by
2030,
2050,
2060
under
different
scenarios
CO2
control.
results
reveal
effects
synergistically
implementing
clean
aggressive
carbon-reducing
policies
on
mitigating
emissions.
Under
On-time
Peak-Net
Zero-Clean
Air
Early
scenarios,
total
reduction
be
more
significant,
particularly
2050
2060.
first
integrate
scenario
evaluation
research,
providing
novel
supplement
pollution–climate
methodology
based
co-benefit
estimation.
findings
contribute
measuring
achievement
analyzing
interaction
SDGs.
Ecological Indicators,
Год журнала:
2023,
Номер
156, С. 111198 - 111198
Опубликована: Ноя. 3, 2023
This
paper
is
based
on
the
construction
of
evaluation
system
urban
pollution
reduction
and
carbon
synergistic
governance
efficiency
indicators.
Firstly,
we
used
NDDF
model
to
measure
governance.
We
then
ArcGIS
technology
spatial
autocorrelation
method
explore
its
distribution
pattern,
econometric
driving
factors.
The
results
show
that
from
2006
2020,
Chinese
cities
in
terms
a
state
continuous
improvement,
with
an
increase
38.07
per
cent
2020
compared
2006,
overall
shows
pattern
development
which
east
high
west
low,
but
there
tendency
for
gap
narrow
one
year
next.
There
positive
aggregation
efficiency,
further
shown
by
Lisa
diagram
China
characterized
agglomerations,
zones
being
concentrated
central
eastern
such
as
agglomerations
middle
reaches
Yangtze
River
Guangdong-Fujian-Zhejiang,
low
western
Lanzhou-Western
Guanzhong
Plain
agglomerations.
SDM
indicate
can
significantly
promote
process
neighboring
areas.
six
drivers,
namely,
industrial
structure,
economic
level,
innovation
urbanization
rate,
population
size
foreign
direct
investment,
all
have
significant
impact
local
area.
spillover
effects
remaining
variables,
except
are
negative.
Based
this,
corresponding
countermeasures
proposed
improve
reduction.
