Sustainability,
Journal Year:
2025,
Volume and Issue:
17(4), P. 1471 - 1471
Published: Feb. 11, 2025
In
the
face
of
global
climate
change,
accurately
predicting
carbon
dioxide
emissions
has
become
an
urgent
requirement
for
environmental
science
and
policy-making.
This
article
provides
a
systematic
review
literature
on
emission
forecasting,
categorizing
existing
research
into
four
key
aspects.
Firstly,
regarding
model
input
variables,
thorough
discussion
is
conducted
pros
cons
univariate
models
versus
multivariable
models,
balancing
operational
simplicity
with
high
accuracy.
Secondly,
concerning
types,
detailed
comparison
made
between
statistical
methods
machine
learning
methods,
particular
emphasis
outstanding
performance
deep
in
capturing
complex
relationships
emissions.
Thirdly,
data,
explores
annual
daily
emissions,
highlighting
practicality
predictions
policy-making
importance
providing
real-time
support
policies.
Finally,
quantity,
differences
single
ensemble
are
examined,
emphasizing
potential
advantages
considering
multiple
selection.
Based
literature,
future
will
focus
integration
multiscale
optimizing
application
in-depth
analysis
factors
influencing
prediction,
scientific
more
comprehensive,
real-time,
adaptive
response
to
challenges
change.
comprehensive
outlook
aims
provide
scientists
policymakers
reliable
information
promoting
achievement
protection
sustainable
development
goals.
Ecological Informatics,
Journal Year:
2024,
Volume and Issue:
82, P. 102661 - 102661
Published: June 3, 2024
Given
the
critical
urgency
to
combat
escalating
climate
crisis
and
continuous
rise
in
agricultural
carbon
emissions
(ACE)
China,
accurately
forecasting
their
future
trends
is
crucial.
This
research
employs
emission
factor
method
assess
ACE
throughout
mainland
China
from
1993
2021.
To
refine
our
approach,
both
statistical
neural
network
methodologies
were
utilized
pinpoint
key
factors
influencing
ACE.
We
crafted
models
incorporating
deep
learning
techniques
traditional
methods.
Notably,
Tree-structured
Parzen
Estimator
Bayesian
Optimization
(TPEBO)
algorithm
was
applied
optimize
Long
Short-Term
Memory
(LSTM)
networks,
culminating
creation
of
a
superior
integrated
TPEBO-LSTM
model
that
demonstrated
strong
performance
across
various
datasets.
The
outcomes
suggest
24
provinces
are
expected
reach
zenith
before
2030,
primarily
driven
by
farm
operations,
as
well
livestock
poultry
manure
management.
result
provides
significant
tool
for
assessing
different
regions,
offering
insights
crucial
targeted
mitigation
strategies.
Energy Strategy Reviews,
Journal Year:
2023,
Volume and Issue:
50, P. 101240 - 101240
Published: Oct. 18, 2023
The
industrial
sector
is
the
key
area
for
China
to
achieve
carbon
peaking
goals,
as
it
accounts
more
than
65
%
and
70
of
national
total
energy
consumption
emissions.
However,
discussion
on
time
route
peak
in
existing
literature
still
quite
different.
In
this
study,
we
establish
three
scenarios
comprehensively
used
Monte
Carlo
simulation
LSTM
Neural
Network
model
predict
evolution
trends
China's
emissions
during
2020–2030.
Firstly,
decomposition
results
Generalized
Divisia
Index
Method
shows
that
fixed
assets
investment
most
important
factor
promoting
intensity
reducing
Then,
basing
dynamic
simulation,
could
draw
kinds
will
2031
Baseline
scenario,
Green
Development
scenario
(environmental
policy
improvement)
Technological
Breakthrough
(green
technology
progress)
2027
2025,
under
model,
occur
2028.
Comparing
above
predictions,
by
2030(in
GD
2027;
TB
2025).
Finally,
discuss
path
reduction
provide
a
reference
rational
formulation
low-carbon
regulatory
policies
future
realization
sustainable
development.
