Frontiers in Marine Science,
Journal Year:
2025,
Volume and Issue:
12
Published: Feb. 10, 2025
This
paper
aims
to
provide
insights
into
the
future
trends
for
marine
industries
in
China,
by
forecasting
added
value
key
sectors
and
then
offering
tailored
policy
recommendations.
Those
economic
indicators
at
industry
level
are
characterized
small
sample
sizes,
sectoral
heterogeneity,
irregular
fluctuations,
which
require
a
specialized
methodology
handle
data
features
predictions
each
industry.
To
address
these
issues,
conformable
fractional
grey
model
(
CFGM
),
integrates
accumulation
with
model,
is
applied
proven
effective
through
accuracy
robustness
tests.
First,
results
from
multi-step
experiments
demonstrate
that
significantly
outperforms
traditional
statistical,
machine
learning
models,
models
context
of
predictions,
an
average
improvement
32.14%.
Second,
stability
predictive
values
generated
further
verified
Probability
Density
Analysis
PDA
)
multiple
comparisons
best
MCB
tests,
thereby
ruling
out
possibility
accurate
result
mere
chance.
Third,
used
estimate
across
industries,
accompanied
suggestions
ensure
sustainable
development
economy.
Journal of Cleaner Production,
Journal Year:
2024,
Volume and Issue:
451, P. 141946 - 141946
Published: March 25, 2024
Prior
research
has
primarily
concentrated
on
non-digital
and
process-oriented
methods
for
achieving
carbon
neutrality
(CN)
in
the
context
of
mitigating
climate
change
(CC),
while
potential
digital
technology
(DT)
hardly
been
investigated.
This
study
addresses
this
gap
by
answering
four
questions:
How
are
firms
utilizing
DT
to
achieve
CN?
What
drives
adoption
barriers
that
prevent
risk
mitigation
strategies
can
be
adopted
overcome
these
barriers?
An
inductive
method
using
open-ended
essays
gather
data
from
have
already
implemented
CN.
The
findings
revealed
distinct
dimensions.
Utilization
CN
includes
enhancing
business
value,
managing
one's
footprint,
enabling
smart
solutions,
efficiency.
drivers
adopting
included
driving
growth,
external
pressures,
competitive
advantage,
environmental
consciousness.
Key
financial
barriers,
technological
human
resource
barriers.
Risk
pre-implementation
strategies,
detailed
planning
evaluation,
ensuring
employees'
buy-in
readiness,
stakeholder
engagement.
offers
a
broad-based
foundation
designing
Business Strategy and the Environment,
Journal Year:
2024,
Volume and Issue:
33(5), P. 3986 - 4003
Published: Jan. 25, 2024
Abstract
Many
firms
have
established
formal
carbon
neutrality
(CN)
targets
in
response
to
the
increasing
climate
risk
and
related
regulatory
requirements.
Subsequently,
they
implemented
various
measures
adopted
multiple
approaches
attain
these
goals.
Academic
research
has
given
due
attention
firms'
efforts
this
direction.
However,
past
studies
primarily
focused
on
non‐digital
process‐oriented
achieving
CN,
with
potential
of
digital
technologies
such
as
artificial
intelligence
(AI)
remaining
less
explored.
Our
study
aims
address
gap
by
qualitatively
examining
use
AI
for
pursuing
drawing
insights
from
prior
experience
area.
We
analyzed
collected
qualitative
data
identify
four
key
dimensions
that
capture
different
nuances
applying
CN:
(a)
implementing
direct
indirect
control
emissions,
(b)
accepting
strategic
trade‐offs
funding,
systems
concerns,
social
priorities,
(c)
overcoming
organizational
human‐related
impediments,
(d)
acknowledging
significant
impact
terms
gains
business
model
efficiency
measurable
CN
target
attainment,
which
ultimately
contribute
CN.
Based
our
findings,
we
propose
a
convergence–divergence
encompassing
positive
aspects,
inhibiting
factors,
synergies,
offsets
necessary
leverage
achieve
net‐zero
emissions
effectively.
Overall,
contributes
discourse
utilization
comprehensive
manner.
