Public health impacts of air pollution from the spatiotemporal heterogeneity perspective: 31 provinces and municipalities in China from 2013 to 2020
Yizhong Ye,
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Qunshan Tao,
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Hua Wei
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et al.
Frontiers in Public Health,
Journal Year:
2024,
Volume and Issue:
12
Published: Aug. 2, 2024
Air
pollution
has
long
been
a
significant
environmental
health
issue.
Previous
studies
have
employed
diverse
methodologies
to
investigate
the
impacts
of
air
on
public
health,
yet
few
thoroughly
examined
its
spatiotemporal
heterogeneity.
Based
this,
this
study
investigated
heterogeneity
in
31
provinces
China
from
2013
2020
based
theoretical
framework
multifactorial
decision-making
and
combined
with
spatial
durbin
model
geographically
temporally
weighted
regression
model.
The
findings
indicate
that:
(1)
as
measured
by
incidence
respiratory
diseases
(IRD)
exhibit
positive
correlation
local
aggregation.
(2)
demonstrates
noteworthy
spillover
effects.
After
controlling
for
economic
development
living
environment
factors,
including
disposable
income,
population
density,
urbanization
rate,
direct
indirect
IRD
are
at
3.552
2.848,
correspondingly.
(3)
China's
is
primarily
influenced
various
factors
such
pollution,
development,
conditions,
healthcare,
degree
influence
an
uneven
distribution
trend.
hold
considerable
practical
significance
mitigating
safeguarding
health.
Language: Английский
An Analysis of the Spatial–Temporal Evolution and Influencing Factors of the Coupling Coordination Degree Between the Digital and Real Economies in China
Xiaoya Li,
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Zhao Min,
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Guang Yang
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et al.
Sustainability,
Journal Year:
2025,
Volume and Issue:
17(8), P. 3384 - 3384
Published: April 10, 2025
The
digital
economy
(DE)
and
real
(RE)
are
dual
pillars
of
the
modern
economic
system.
deep
integration
(IDR)
has
emerged
as
a
pivotal
strategic
trend.
IDR
not
only
can
enhance
international
competitiveness
but
also
contributes
to
sustainable
development
goals.
This
work
collects
DE
RE
data
from
30
provinces
in
China
between
2012
2022.
entropy
weight
method
coupling
coordination
degree
(CCD)
model
employed
measure
level
IDR.
Furthermore,
Dagum
Gini
coefficient,
Kernel
density
estimation,
spatial
autocorrelation
model,
geographically
temporally
weighted
regression
(GTWR)
utilized
analyze
spatial–temporal
evolution
influencing
factors
CCD.
following
conclusions
drawn:
(1)
During
study
period,
CCD
shows
an
upward
trend,
value
is
relatively
low.
(2)
There
significant
differences
CCD,
inter-regional
difference
primary
cause.
(3)
regional
continuously
widening.
(4)
obvious
global
agglomeration
feature,
been
enhanced
(5)
policy
intensity,
infrastructure,
industrial
structure,
human
capital,
technological
innovation,
market
environment
have
impacts
on
obtained
findings
provide
important
theoretical
support
for
coordinated
RE.
Language: Английский
Mechanism of risk perception diffusion in public health emergencies: Based on the dual perspectives of cross‐evolution and emotional difference
Applied Psychology Health and Well-Being,
Journal Year:
2024,
Volume and Issue:
17(1)
Published: Dec. 16, 2024
Abstract
The
high‐level
risk
perception
diffusion
caused
by
public
health
emergencies
seriously
threatens
mental
and
social
stability.
Much
scholarly
attention
focused
on
the
traditional
epidemic
models
or
simply
combined
content
attributes,
overlooking
differences
in
individual
characteristics.
This
paper
proposes
an
S
1
2
EI
pos
I
neu
neg
R
model
of
innovatively
subdividing
susceptible
people
infectious
people.
Then,
taking
Xi'an
as
example
(
N
=
105,417),
this
employs
sentiment
analysis
Word2Vec
Bi‐LSTM
to
calculate
emotional
value
microblog
text
quantify
perception.
Finally,
numerical
experiments
are
conducted
explore
effects
cross‐evolution
difference
under
different
scenarios.
Findings
reveal
that
a
larger
initial
density
accelerates
diffusion,
with
negative
emotions
playing
dominant
role.
In
addition,
higher
level
lower
heterogeneity,
greater
maximum
impact
final
scale
diffusion.
When
emergency
deteriorates,
tends
shift
high‐risk
Otherwise,
it
tilt
low‐risk
These
findings
provide
critical
insights
for
developing
precise
guidance
strategies
enhancing
governance
capabilities.
Language: Английский