Journal of Water and Climate Change,
Год журнала:
2024,
Номер
15(8), С. 4016 - 4034
Опубликована: Июль 22, 2024
ABSTRACT
Climate
change,
driven
by
greenhouse
gas
(GHG)
emissions,
causes
extreme
weather
events,
impacting
ecosystems,
biodiversity,
population
health,
and
the
economy.
Predicting
GHG
emissions
is
crucial
for
mitigating
these
impacts
planning
sustainable
policies.
This
research
proposes
a
novel
machine
learning
model
forecasting.
Our
model,
Meta-Learning
Applied
to
Multivariate
Single-Step
Fusion
Model,
utilizes
historical
from
Brazil
over
past
60
years
predict
CO2
CH4
emissions.
Additionally,
employs
unique
combination
of
two
techniques
in
time
series
forecasting:
(i)
each
substance
individually
extracted
trained
based
on
specific
decision
task,
then
integrated
into
same
feature
space;
(ii)
allows
learn
prediction
tasks,
leading
better
generalization.
was
compared
with
state-of-the-art
models
using
dataset.
The
results
show
that
our
approach
reduces
mean
absolute
percentage
error
49.06%
95%
confidence
Transformer-based
TST
demonstrating
its
superior
performance
low
estimated
0.01
kg
CO2eq.
Furthermore,
model's
flexibility
it
be
adapted
various
environmental
studies
general
Energy Strategy Reviews,
Год журнала:
2023,
Номер
49, С. 101163 - 101163
Опубликована: Авг. 19, 2023
Many
challenges
should
be
tackled
in
transitioning
to
a
low-carbon
energy
system,
motivating
many
researchers
study
these
challenges.
In
this
context,
the
present
aims
identify
through
systematic
literature
review
of
studies
published
between
2006
and
2023
propose
comprehensive
framework
To
end,
PICOC
was
applied
set
research
scope
properly;
an
integrated
method
called
PSALSAR
protocol
used
find,
evaluate,
publications
Scopus
Web
Science.
As
result,
123
articles
were
reviewed,
seventeen
identified
classified
into
five
social,
economic,
environmental,
technical,
institutional
Results
indicated
that
international
agreements
on
climate
change
could
boost
number
transition.
Also,
it
is
qualitative
methods
more
earlier
studies,
topics
have
become
profound
over
years.
International Journal of Energy Economics and Policy,
Год журнала:
2024,
Номер
14(2), С. 367 - 382
Опубликована: Март 15, 2024
:
As
the
world
becomes
increasingly
aware
of
need
to
shift
towards
sustainable
energy
sources,
China
and
United
States
are
two
global
superpowers
leading
this
transition.
With
growing
populations
increasing
demand
for
energy,
these
countries
have
recognized
importance
renewable
in
meeting
their
needs
while
reducing
carbon
emissions.
The
motivation
study
is
evaluate
impact
trade
policy
uncertainty
on
renewal
demean
USA
period
2000-2021
by
employing
linear
nonlinear
assessment.
test
statistics
derived
through
cointegration
following
Bayer-Hancked
Makki's
established
a
long-run
tie
empirical
equation.
Moreover,
linkage
was
revealed
with
symmetry
asymmetry
investigation.
Referring
coefficients
assessment,
that
uncertainties
detrimental
role
clean
long-
short-run
asymmetric
association
between
REC
has
been
documented
execution
standard
weld
null
symmetry.
directional
causality
unidirectional
[TPU<-
->REC],
bidirectional
economic
[EPU<-
->REC].
Frontiers in Environmental Science,
Год журнала:
2024,
Номер
11
Опубликована: Янв. 8, 2024
Energy
structure
transformation
is
the
only
way
for
China
to
achieve
“dual
carbon”
goal,
and
one
of
difficulties
faced
by
energy
financing.
In
context
China’s
steadily
promoting
high-level
opening-up
financial
industry,
this
paper
uses
panel
data
provincial
level
from
2010
2019
systematically
examine
impact
on
structure.
The
results
show
that:
1)
Financial
openness
has
a
significant
positive
transition;
2)
different
stages
transformation,
as
main
driving
force
in
initial
stage
government’s
policy
support,
with
continuous
maturity
gradually
increases;
3)
With
levels
economic
development,
effect
also
different.
lower
development
is,
stronger
due
lack
financing
channels.
This
provides
theoretical
basis
rich
implications
industry
open
up
at
high
level.
