As
the
global
climate
situation
becomes
increasingly
severe,
rapid
development
of
robots
or
artificial
intelligence
(AI)
technology
to
achieve
zero
emissions
has
gradually
become
a
worldwide
consensus
among
major
countries.
However,
applications
AI
may
not
necessarily
lead
reduction
in
environmental
pollution,
as
outcomes
vary
depending
on
components
AI.
The
objective
this
study
is
examine
impacts
different
performance.
To
objective,
utilises
panel
regression
model
with
sample
52
countries
from
year
2019
2022.
seven
sub-pillars
considered
for
include
commercial,
development,
government
strategy,
infrastructure,
operating
environment,
research,
and
talent
aspects.
Additionally,
investigates
performance
two
groups
classified
advanced
developing
results
show
that
environment
pillars
are
positive
significant
carbon
all
samples.
Commercialisation
negative
Infrastructure
implication
demonstrates
policies
encouraging
sustainable
responsible
commercial
effective
reducing
impact
Developing
countries,
other
hand,
benefit
focus
building
enhancing
infrastructure.
novelty
lies
distinction
between
allowing
tailored
strategies
combat
hazardous
environments.
Advanced
managing
aspects
AI,
while
emphasise
infrastructure
development.
Journal of Innovation & Knowledge,
Год журнала:
2024,
Номер
9(3), С. 100498 - 100498
Опубликована: Май 19, 2024
Research
on
sustainable
manufacturing
is
currently
gaining
momentum
and
becoming
a
dynamically
developing
field
that
considers
green
innovations
(GI).
However,
rapid
dynamics
cause
the
entire
to
fragment
into
smaller
topics
with
different
research
interests,
impacts,
development
over
time.
This
study
aims
create
comprehensive
scientific
map
of
GI
in
by
systematically
processing
9376
documents
retrieved
from
Scopus
database.
The
results
show
this
domain
gained
significant
2019,
most
studies
published
engineering
business
subject
area.
Latent
Dirichlet
Allocation
was
used
identify
94
unique
all
abstracts.
We
classified
five
territories
regarding
their
level
systematization:
uncharted
(26
topics),
discovering
(23),
expanding
(15),
well-recognized
(19),
marginal
(11).
least
have
potential
for
systematization
are
Resource-based
Performance
Modeling,
Sustainability-oriented
Performance,
Supplier
Decision
Criteria
Fuzzy
Logic.
related
include
Smart
Technologies
Industry
4.0,
Green
Supply
Chain,
Carbon
Emission
Reduction,
Digital
Transformation,
last
two
having
dynamic
development.
offer
objective
information
wider
discussion
direction
concept
point
areas
may
represent
future
directions
concept.
Sustainability,
Год журнала:
2024,
Номер
16(17), С. 7466 - 7466
Опубликована: Авг. 29, 2024
The
purpose
of
this
study
is
to
investigate
the
role
AI
capability
(AIC)
on
organizational
creativity
(OC),
green
innovation
(GI),
and
sustainable
performance
(SP).
It
also
aims
mediating
roles
OC
GI,
as
well
moderating
knowledge
sharing
culture
(KNC).
This
used
quantitative
methodology
utilized
a
survey
collect
data
from
421
employees
in
different
organizations
Bangladesh.
We
structural
equation
modeling
(SEM)
technique
analyze
data.
finds
that
significantly
influences
OC,
SP.
GI
work
mediators,
KNC
serves
moderator
among
suggested
relationships.
notable
for
its
novelty
examining
multiple
unexplored
aspects
current
body
research.
research
provides
valuable
insights
policymakers
practitioners
regarding
effective
integration
enhance
competitiveness.
Purpose
This
paper
aims
to
empirically
test
the
impact
and
mechanisms
of
artificial
intelligence
(AI)
technology
on
innovation
performance
new
energy
vehicle
(NEV)
enterprises,
using
data
from
A-share
listed
companies
in
China’s
NEV
industry.
It
also
explores
role
dynamic
capabilities,
particularly
innovation,
absorptive
adaptive
capacities,
mediating
this
relationship.
Design/methodology/approach
The
study
establishes
indicators
measure
drive
AI
employs
empirical
analysis
examine
its
effect
enterprises.
research
heterogeneity
tests
assess
differentiated
macro-environmental
factors
micro-enterprise
characteristics
companies.
Findings
finds
that
significantly
enhances
Dynamic
capability,
play
a
crucial
Among
capability
has
most
significant
effect,
followed
by
capacity,
while
capacity
least
effect.
Heterogeneity
reveal
(e.g.
market
elements)
R&D
backgrounds
directors,
senior
management
property
rights
attributes)
differentially
enterprises
driven
AI.
Originality/value
provides
both
theoretical
explanations
evidence
how
offers
valuable
insights
for
policymakers
promoting
intelligent
transformation
achieving
high-quality
sustainable
development
within
Scientific Reports,
Год журнала:
2025,
Номер
15(1)
Опубликована: Апрель 12, 2025
As
a
critical
aspect
of
the
industry
4.0
era,
application
artificial
intelligence
(AI)
is
significant
to
environmental
governance.
It
serves
as
crucial
driving
force
in
assisting
enterprises
transition
toward
low-carbon
practices.
This
paper
examines
China's
A-share
industrial
from
2011
2022,
constructs
and
trains
word
vector
model
extract
AI-related
terms,
impact
AI
applications
on
carbon
emission
intensity
these
investigated.
The
findings
reveal
that
enhancing
level
can
effectively
decrease
intensity.
Specifically,
1%
increase
leads
reduction
0.0395%
Further
analysis
indicates
diminish
their
by
optimization
supply
chain
green
technology
innovation.
Heterogeneity
suggests
utilizing
beneficial
for
reducing
manufacturing,
high-tech,
high-pollution
enterprises.
results
this
study
enrich
micro-level
research
relationship
between
intensity,
offering
valuable
insights
aiming
achieve
sustainable
development.
Sustainability,
Год журнала:
2025,
Номер
17(1), С. 370 - 370
Опубликована: Янв. 6, 2025
The
realization
of
intelligent
transformation
is
an
important
path
for
the
industry
to
move
towards
low-carbon
development.
Based
on
panel
data
from
30
provinces
in
China,
this
study
utilizes
intermediate
effect
model
and
spatial
econometric
analyze
influence
industrial
intelligence
carbon
emissions.
research
reveals
that
helps
with
reduction,
result
still
valid
after
undergoing
various
tests.
Industrial
relies
green
technological
innovation,
structure
upgrading,
energy
intensity
realize
reduction.
There
a
spillover
role
emissions,
which
has
positive
reduction
local
adjoining
regions.
emissions
exhibits
heterogeneity
regional
dimension,
time
level
dimension.
provides
empirical
evidence
implications
using
artificial
achieve
