Sustainability,
Год журнала:
2024,
Номер
16(10), С. 4233 - 4233
Опубликована: Май 17, 2024
Analyzing
the
driving
factors
and
mechanisms
of
urban
carbon
emission
correlation
networks
can
provide
effective
reduction
decision-making
support
for
Shandong
Province
other
regions
with
similar
industrial
characteristics.
Based
on
data
from
various
cities
in
2013
to
2021,
spatial
network
was
established
by
using
a
modified
gravity
model.
The
characteristics
were
explored
Social
Network
Analysis
(SNA)
method,
significant
affecting
identified
through
Quadratic
Assignment
Procedure
(QAP)
analysis
motif
analysis.
mechanism
analyzed
Temporal
Exponential
Random
Graph
Models
(TERGMs).
results
show
that:
(1)
exhibits
multi-threaded
complex
correlations
relatively
stable
structure,
overcoming
geographical
distance
limitations.
(2)
Qingdao,
Jinan,
Rizhao
have
high
degree
centrality,
betweenness
closeness
centrality
network,
Qingdao
Jinan
being
central.
(3)
be
spatially
clustered
into
four
regions,
each
distinct
roles,
displaying
certain
“neighboring
clustering”
phenomenon.
(4)
Endogenous
structures
such
as
Mutual,
Ctriple,
Gwesp
significantly
impact
formation
evolution
while
Twopath
does
not
expected
impact;
FDI
promote
generation
reception
relationships
network;
IR
spillover
GS,
differences
GDP,
EI,
similarities
organic
within
temporal
level,
has
shown
stability
during
study
period.
Sustainability,
Год журнала:
2024,
Номер
16(7), С. 3086 - 3086
Опубликована: Апрель 8, 2024
From
2008
to
2021,
this
study
analyzed
the
spatial
correlation
characteristics
between
provincial
transportation
carbon
emission
intensity
and
explored
ways
reduce
emissions.
This
used
modified
gravity
model,
social
network
analysis
(SNA)
method,
temporal
exponential
random
graph
model
(TERGM)
analyze
evolution
driving
mechanism
of
China’s
intensity.
found
that
have
unbalanced
characteristics.
The
revealed
Shanghai,
Beijing,
Tianjin,
Guangdong,
Fujian,
other
provinces
were
at
center
network,
with
significant
intermediary
effects.
was
divided
into
four
functional
plates:
“two-way
spillover”,
“net
benefit”,
“broker”,
spillover”.
benefit”
plate
mainly
located
in
developed
regions,
spillover”
primarily
underdeveloped
regions.
Endogenous
structural
exogenous
variables
main
factors
affecting
Frontiers in Public Health,
Год журнала:
2025,
Номер
13
Опубликована: Март 13, 2025
Introduction
Biotechnology
has
significant
potential
in
public
health,
offering
critical
support
for
communicable
disease
control,
chronic
illness
management,
and
drug
development.
To
foster
biotechnology
innovation,
governments
increasingly
incentivize
cooperations
among
organizations,
resulting
more
interconnected
cooperation
networks.
However,
research
on
the
evolution
of
these
networks
rely
primarily
static
network
analysis
neglect
micromechanisms
under
evolution,
which
lead
to
deviations
policymaking.
Methods
Using
temporal
exponential
random
graph
model
(TERGM),
accounts
dynamic
correlations,
based
framework
consisting
agency,
opportunity
inertia,
this
study
analyzes
impacts
both
endogenous
exogenous
factors
Results
The
empirical
China’s
patent
data
from
2004
2023
reveals
following
findings
policy
recommendations.
First,
is
temporally
dependent,
highlighting
need
awareness
lags.
Second,
two
–
transitivity
convergence
emerge
implying
government
create
information
platforms,
establish
targeted
project
subsidies,
enforce
technical
confidentiality
policies.
Finally,
with
regard
factors,
exhibit
geographical
homogeneity,
needs
promote
cross-regional
by
establishing
innovation
centers
unified
standards
mitigate
lock-in
effects
barriers.
Systems,
Год журнала:
2025,
Номер
13(4), С. 279 - 279
Опубликована: Апрель 10, 2025
Under
the
rapid
advancements
in
information
technology,
complex
network
characteristics
of
agricultural
product
trade
relationships
among
global
economies
have
exhibited
increasing
prominence.
This
study
takes
soybean
market
as
an
empirical
case,
employing
a
combination
social
analysis
to
investigate
dynamic
evolution
structures;
then,
Temporal
Exponential
Random
Graph
Model
(TERGM)
is
adopted
analyse
factors
influencing
network.
Based
on
comprehensive
data
encompassing
126
from
2000
2022,
this
research
demonstrates
several
key
findings:
Firstly,
characterised
by
pronounced
agglomeration
effects
and
“small-world”
properties,
accompanied
heightened
substitutability.
Secondly,
network’s
structural
configuration
has
undergone
distinct
transformation,
shifting
traditional
single-core–periphery
structure
more
multi-core–periphery
architecture.
Thirdly,
response
external
shocks
impacting
topology,
core
exhibits
greater
resilience
stability,
whereas
periphery
displays
heterogeneous
responses.
Finally,
relations
governed
dual
mechanism
involving
both
endogenous
dynamics
exogenous
influences.
