Journal of statistics and economics.,
Год журнала:
2024,
Номер
1(6), С. 24 - 33
Опубликована: Дек. 1, 2024
This
study
selects
carbon
emission
data
from
Daqing
City
2001
to
2023
as
the
subject
of
analysis,
employs
STIRPAT
model
and
ridge
regression
method
decompose
key
factors
affecting
emissions,
combines
scenario
analysis
construct
32
different
combined
scenarios
predict
emissions
peak
time
2024
2035.
results
show
that
are
generally
positively
correlated
with
City;
Under
baseline
scenario,
is
expected
reach
its
in
2030,
while
under
single
pathway
scenarios,
likely
achieve
early
2025.
Based
on
prediction
results,
propose
suggestions
both
industry
technology
aspects,
take
lead
achieving
peak.
Buildings,
Год журнала:
2023,
Номер
13(7), С. 1617 - 1617
Опубликована: Июнь 26, 2023
As
an
industry
that
consumes
a
quarter
of
social
energy
and
emits
third
greenhouse
gases,
the
construction
has
important
responsibility
to
achieve
carbon
peaking
neutrality.
Based
on
Web
Science,
Science-Direct,
CNKI,
accounting
prediction
models
emissions
from
buildings
are
reviewed.
The
emission
factor
method,
mass
balance
actual
measurement
method
analyzed.
top-down
bottom-up
their
subdivision
introduced
Individual
building
assessments
generally
adopt
physical
model,
while
urban
economic
input-output
model.
Most
current
studies
follow
path
“exploring
influencing
factors
then
putting
forward
based
factors”.
driving
mainly
use
Stochastic
Impacts
by
Regression
Population,
Affluence,
Technology
(STIRPAT)
Logarithmic
Mean
Divisia
Index
(LMDI)
grey
correlation
degree
other
models.
model
is
realized
regression
system
dynamics
mathematical
models,
as
well
Artificial
Neural
Network
(ANN)
Support
Vector
Machine
(SVM)
machine
learning
At
present,
research
individual
focuses
operational
consumption,
for
stages
should
become
focus
in
future
research.
International Journal of Information Management Data Insights,
Год журнала:
2024,
Номер
4(2), С. 100243 - 100243
Опубликована: Апрель 23, 2024
Environmental
stewardship
and
sustainability
have
become
critical
priorities
in
the
contemporary
business
environment.
Corporations
are
integrating
sustainable
practices
at
process
level
via
Sustainable
Enterprise
Resource
Planning
(S-ERP).
However,
a
recognised
shortfall
of
S-ERP
systems
lies
their
potential
inability
to
integrate
metrics
across
all
functions
holistically.
To
navigate
this
limitation,
our
study
introduces
application
Transaction
Cost
Theory
(TCT).
By
treating
processes
as
input-output
systems,
we
apply
theory
quantify
likelihood
overall
losses.
This
novel
approach
bridges
gap,
allowing
for
comprehensive
integration
processes.
The
essence
methodology
is
leverage
static
input
data
collected
by
regarding
environmental
impact
extrapolate
provide
broader
understanding
losses
gains.
We've
tested
validated
through
two
case
studies;
one
about
product
design
development,
other
evaluation
modular
versus
conventional
construction
methods.
results
inform
formulation
robust
policies
akin
S-ERP,
paving
way
more
practices.
Journal of Industrial Ecology,
Год журнала:
2025,
Номер
unknown
Опубликована: Янв. 8, 2025
Abstract
The
intersection
of
artificial
intelligence
(AI)
and
industrial
ecology
(IE)
is
gaining
significant
attention
due
to
AI's
potential
enhance
the
sustainability
production
consumption
systems.
Understanding
current
state
research
in
this
field
can
highlight
covered
topics,
identify
trends,
reveal
understudied
topics
warranting
future
research.
However,
few
studies
have
systematically
reviewed
intersection.
In
study,
we
analyze
1068
publications
within
IE–AI
domain
using
trend
factor
analysis,
word2vec
modeling,
top2vec
modeling.
These
methods
uncover
patterns
topic
interconnections
evolutionary
trends.
Our
results
71
trending
terms
selected
publications,
69
which,
such
as
“deep
learning,”
emerged
past
8
years.
analysis
shows
that
application
various
AI
techniques
increasingly
integrated
into
life
cycle
assessment
circular
economy.
suggests
employing
predict
optimize
indicators
related
products,
waste,
processes,
their
environmental
impacts
an
emerging
trend.
Lastly,
propose
fine‐tuning
large
language
models
better
understand
process
data
specific
IE,
along
with
deploying
real‐time
collection
technologies
sensors,
computer
vision,
robotics,
could
effectively
address
challenges
data‐driven
decision‐making
domain.
