Journal of Economic Insights,
Год журнала:
2025,
Номер
2(1), С. 1 - 19
Опубликована: Март 25, 2025
The
global
climate
crisis
has
become
the
focus
of
attention,
and
China
vigorously
pursues
low-carbon
economic
development,
for
which
implementation
green
finance
is
continuously
enhanced,
so
does
development
contribute
to
development?
This
paper
constructs
a
low
carbon
index
system
containing
several
indicators,
measures
them
using
entropy
weight
method
spatio-temporal
extreme
difference.
conducts
an
empirical
study
based
on
data
30
provinces
cities
from
2005
2020,
results
find
that
policy
incentive
effect
institutional
effect,
can
significantly
positively
affect
economy,
findings
still
hold
after
endogeneity
test
robustness
test.
mechanism
shows
influence
by
promoting
industrial
structure
upgrading,
technology
innovation
FDI
inflow;
moreover,
positive
promotion
more
obvious
in
western
region
non-Yangtze
River
Economic
Zone
region;
finally,
moderating
reveals
environment
level
market
enhance
relationship
between
development.
Oeconomia Copernicana,
Год журнала:
2023,
Номер
14(2), С. 483 - 510
Опубликована: Июнь 30, 2023
Research
background:
As
an
outcome
of
a
global
consensus
on
combating
climate
change,
green
finance
is
expected
to
play
important
role
in
promoting
growth
and
innovation
progress.
Some
studies
note
that
credit
policy
yields
negative
influence
innovation,
while
how
affects
renewable
energy
has
received
scant
attention
academia.
This
study
focuses
the
impact
innovation.
Purpose
article:
research
investigates
economy's
by
using
bond
data
from
Climate
Bonds
Initiative.
further
tests
whether
it
varies
for
different
kinds
types
economic
development
levels.
Given
policies
are
key
technology
development,
this
checks
government
stability
changes
relationship
between
Methods:
Using
panel
fixed
effects
model
big-scale
64
economies
worldwide
during
period
2014-2019,
we
investigate
finance's
In
robustness
test,
dynamic
Tobit
employed.
Findings
&
value
added:
finds
positive
effect
prominent
non-OECD
as
well
middle-income
low-income
economies.
Government
enhances
Moreover,
results
indicate
mainly
promotes
progress
wind
produces
little
other
energies.
The
subsample
analysis
also
sheds
light
heterogeneity
Energies,
Год журнала:
2024,
Номер
17(2), С. 416 - 416
Опубликована: Янв. 15, 2024
The
use
of
renewable
energy
sources
is
becoming
increasingly
widespread
around
the
world
due
to
various
factors,
most
relevant
which
high
environmental
friendliness
these
types
resources.
However,
large-scale
involvement
green
leads
creation
distributed
networks
that
combine
several
different
generation
methods,
each
has
its
own
specific
features,
and
as
a
result,
data
collection
processing
necessary
optimize
operation
such
systems
become
more
relevant.
Development
new
technologies
for
optimal
RES
one
main
tasks
modern
research
in
field
energy,
where
an
important
place
assigned
based
on
artificial
intelligence,
allowing
researchers
significantly
increase
efficiency
all
within
systems.
This
paper
proposes
consider
methodology
application
approaches
assessment
amount
obtained
from
intelligence
technologies,
used
optimization
control
processes
operating
with
integration
sources.
relevance
work
lies
formation
general
approach
applied
evaluation
solar
wind
technologies.
As
verification
considered
by
authors,
number
models
predicting
power
using
photovoltaic
panels
have
been
implemented,
machine-learning
methods
used.
result
testing
quality
accuracy,
best
results
were
hybrid
forecasting
model,
combines
joint
random
forest
model
at
stage
normalization
input
data,
exponential
smoothing
LSTM
model.
