Journal of Computer and Communications,
Год журнала:
2024,
Номер
12(07), С. 105 - 119
Опубликована: Янв. 1, 2024
Real
estate
has
been
a
dominant
industry
in
many
countries.
One
problem
for
real
companies
is
determining
the
most
valuable
area
before
starting
new
project.
Previous
studies
on
this
issue
mainly
focused
market
needs
and
economic
prospects,
ignoring
impact
of
natural
disasters.
We
observe
that
disasters
are
important
selection
because
they
will
introduce
considerable
losses
to
enterprises.
Following
observation,
we
first
develop
self-defined
indicator
named
Average
Loss
Ratio
predict
caused
by
an
area.
Then,
adopt
existing
ARIMA
model
After
that,
propose
integrate
TOPSIS
Grey
Prediction
Model
rank
recommendation
levels
candidate
areas,
thereby
assisting
their
decision-making
process.
conduct
experiments
datasets
validate
our
proposal,
results
suggest
effectiveness
proposed
method.
Energy & Environment,
Год журнала:
2025,
Номер
unknown
Опубликована: Янв. 15, 2025
Carbon
emission
efficiency
(CEE)
reflects
the
interplay
between
carbon
emissions
and
economy,
which
refers
to
achieving
more
economic
benefits
lower
while
considering
energy,
labor,
capital
inputs.
Assessing
regional
CEE
is
crucial
for
evaluating
level
of
China's
low-carbon
development.
Thus,
this
paper
proposes
a
scenario-based
hybrid
model
with
foresight
perspective
game
cross-efficiency
(GCE)
analysis.
It
measures
future
41
Yangtze
River
Delta
(YRD)
cities
from
2023
2030.
The
improved
gray
forecasting
models
generate
input
output
datasets
GCE
analysis,
assurance
region
constraint
simulates
energy
consumption
dual-control
policy.
results
show
that:
(1)
CEEs
are
generally
low,
an
average
0.2142.
Shanghai
has
highest
CEE,
0.8089,
Tongling
lowest,
0.0307,
under
current
policy
constraint.
(2)
Under
four
control
scenarios,
YRD
urban
agglomeration
follows
U-shaped
trend.
indicates
that
may
lead
short-term
decline
in
YRD,
but
long
term,
it
gradually
increase
2025
or
2026.
(3)
Spatial–temporal
analysis
reveals
government
should
flexibly
optimize
update
intensity
value
based
on
development
differences
focus
consumption.
These
provide
forward-looking
guidance
high-quality
Abstract
Estimating
the
characteristics
of
CO
2
emission
peaks
through
decoupling
relationships
is
crucial
for
understanding
global
emissions
and
mitigating
climate
change.
This
study
investigated
spatiotemporal
patterns
primary
sources
from
1990
to
2020.
Using
Mann–Kendall
test
index,
we
identified
peak
across
countries
worldwide.
Furthermore,
geographically
temporally
weighted
regression
(GTWR)
model
was
employed
examine
effects
various
factors
on
emissions.
The
results
indicate
that
increased
steadily
over
period,
with
power
industry
contributing
most
(34.54%–38.62%).
Countries
were
categorized
into
four
groups:
no-declined,
plateau,
passively
declined,
proactively
peaked,
comprising
99,
48,
20,
26
countries,
respectively.
Notably,
65.4%
peaked
developed
nations,
while
65%
declined
developing
nations.
exhibited
positive
correlations
carbon
intensity,
GDP
per
capita,
secondary
but
negative
population
density,
tertiary
industries.
These
findings
provide
valuable
insights
dynamics,
highlighting
relationships.
also
offers
robust
scientific
support
policymakers
effectively
design
tailored
strategies
reducing
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