Can digital-real integration promote industrial green transformation: Fresh evidence from China's industrial sector
Journal of Cleaner Production,
Год журнала:
2023,
Номер
426, С. 139116 - 139116
Опубликована: Окт. 6, 2023
Язык: Английский
Does digital economy development reduce carbon emission intensity?
Frontiers in Ecology and Evolution,
Год журнала:
2023,
Номер
11
Опубликована: Апрель 4, 2023
Carbon
emissions
from
human
activities
are
the
main
cause
of
climate
warming.
Under
background
economic
and
social
digital
transformation,
accurately
assessing
carbon
emission
reduction
effect
development
economy
is
great
significance
for
countries
to
deal
with
warming
in
post-COVID-19
era.
This
paper
constructs
a
dynamic
evaluation
model
orthogonal
projection
measure
level
at
provincial
China
2007
2019.
On
this
basis,
panel
fixed
effects
mediation
used
empirically
test
impact
on
intensity
its
mechanism.
The
results
indicate
that:
(1)
China’s
unbalanced
among
regions,
showing
geospatial
pattern
decreasing
east
west.
(2)
has
trend
year
by
year,
there
differences
“high
west
low
east”
north
south.”
(3)
can
effectively
reduce
regional
through
industrial
structure
optimization
resource
allocation
effect,
suppress
more
obviously.
(4)
different
regions
degrees
reducing
intensity.
eastern
region
stronger
inhibitory
than
that
middle
western
economically
developed
more.
provides
enlightenment
policy
makers
Язык: Английский
Digital Economy and Low-Carbon Trade Competitiveness: A Multidimensional Analysis of China’s Manufacturing Sector
Sustainability,
Год журнала:
2025,
Номер
17(1), С. 274 - 274
Опубликована: Янв. 2, 2025
This
study
examines
the
mechanism
of
digital
economy
on
low-carbon
trade
competitiveness
in
China’s
manufacturing
sector
using
panel
data
from
30
provinces
2011
to
2022,
employing
dynamic
and
moderated
threshold
model.
The
findings
indicate
that
significantly
enhances
competitiveness.
Although
green
patents
contribute
overall
effect,
they
do
not
serve
as
primary
indirect
pathway
through
which
impacts
Among
these
patents,
utility
model
demonstrate
strongest
mediating
followed
by
invention
design
while
show
a
weaker
non-significant
effect.
Furthermore,
facilitates
transition
energy
consumption
structures,
indirectly
boosting
also
uncovers
heterogeneous
moderating
effects
environmental
regulations:
market-based
voluntary
regulations
positively
moderate
relationship,
command-and-control
moderation.
Environmental
regulation
exhibits
‘U-shaped’
non-linear
transitioning
negative
moderation
below
significant
positive
beyond
critical
value.
Язык: Английский
Impact of digital transformation on corporate sustainability: evidence from China’s carbon emissions
Energy Informatics,
Год журнала:
2025,
Номер
8(1)
Опубликована: Фев. 14, 2025
Climate
change
has
become
an
increasingly
pressing
issue,
underscoring
the
urgent
global
need
for
energy
conservation
and
emission
reduction.
As
one
of
largest
emitters,
China
is
actively
advancing
comprehensive
efforts
to
reduce
emissions
in
pursuit
sustainable
development,
with
enterprises
playing
a
key
role
aligning
economic
growth
environmental
sustainability.
Digital
Transformation
(DT)
emerged
as
crucial
enabler
low-carbon
development.
This
study
utilizes
data
from
publicly
listed
companies
China,
spanning
period
2000
2021,
employs
two-way
fixed-effects
model
assess
impact
corporate
DT
on
Carbon
Emissions
(CE).
The
findings
reveal
that:
First,
significantly
contributes
reduction
CE;
Second,
CE
varies
across
regions,
industries,
firm
characteristics;
Third,
positive
effect
driven
by
mechanisms
such
technological
advancement,
innovation
promotion,
resource
optimization,
improved
output
efficiency.
These
results
provide
both
theoretical
insights
empirical
evidence
supporting
fostering
green,
enterprise
Язык: Английский
The Development Status of the Manufacturing Industry and the Impact of Digital Characteristics from the Perspective of Innovation
Sustainability,
Год журнала:
2024,
Номер
16(3), С. 1009 - 1009
Опубликована: Янв. 24, 2024
From
the
perspective
of
innovation
manufacturing
links,
this
paper
conducted
research
on
current
situation
development
and
relationship
between
regional
economy
digital
transformation,
aiming
to
offer
suggestions
reference
for
relevant
policy
making.
Firstly,
taking
INCOPAT
patent
database
as
data
source,
a
quantitative
analysis
was
five
key
links
in
industry,
which
obtained
characteristics
industry
from
link
innovation.
Then,
based
economic
panel
regions
China,
coupling
coordination
investigate
transformation
coordinated
2017
2021.
The
level
characteristic
relations
31
provinces
or
cities
these
two
systems
were
analyzed.
On
whole,
China
is
steadily
rising
but
varies
among
different
regions,
that
is,
economically
developed
tend
have
better
development.
In
general,
highly
relates
Moreover,
speed
tends
be
stable
with
types
should
formulate
corresponding
policies
accelerate
Язык: Английский
Can the development of digital construction reduce enterprise carbon emission intensity? New evidence from Chinese construction enterprises
Frontiers in Ecology and Evolution,
Год журнала:
2023,
Номер
11
Опубликована: Сен. 29, 2023
Introduction
With
the
rapid
development
of
digital
technology
and
its
deep
integration
with
engineering
construction
field,
has
become
an
effective
way
for
low-carbon
transformation
in
industry.
However,
there
is
a
gap
empirical
research
between
carbon
emissions.
Methods
This
paper
empirically
investigates
impact
level
on
emission
intensity
mechanism
action
by
using
two-way
fixed
effects
model
testing
based
panel
data
52
Shanghai
Shenzhen
A-share
listed
companies
China’s
industry
from
2015
to
2021.
Results
The
findings
indicate
that
improvement
can
significantly
decrease
enterprises,
conclusions
still
hold
after
robustness
tests
discussions
endogeneity
issues
such
as
replacing
core
explanatory
variables,
models,
instrumental
variables
method,
system
GMM
difference
differences
model.
According
analysis,
curb
enhancing
R&D
innovation
capacity
total
factor
productivity
enterprises.
Furthermore,
heterogeneity
analysis
shows
state-owned
enterprises
well
civil
better
contribute
reducing
intensity.
Discussion
will
provide
reference
synergistic
optimization
emissions
reduction
are
going
promote
accelerate
achievement
peaking
neutrality
goals.
Язык: Английский
How enterprises' public welfare low-carbon behavior affects consumers’ green purchase behavior
Heliyon,
Год журнала:
2024,
Номер
10(8), С. e29508 - e29508
Опубликована: Апрель 1, 2024
The
Chinese
economy
has
undergone
high-speed,
high-quality
growth,
and
the
concept
of
low-carbon
technology
gained
popular
support.
Businesses
consumers
must
jointly
endeavor
to
achieve
economic
development.
Moreover,
it
is
important
investigate
whether
enterprises'
behavior
correlated
with
consumers'
green
consumption
behavior.
We
built
a
theoretical
model
depict
relationship
between
corporate
public
welfare
behavior,
purchase
intention,
then
divided
into
three
dimensions.
proposed
hypotheses,
collected
data
through
questionnaire
survey,
analyzed
using
statistical
analysis
software
SPSS
26.0
AMOS
24.0.
Public
was
significantly
participation
motivation
were
intention.
Finally,
we
suggestions
from
perspectives:
mechanism,
participation,
motivation.
results
provide
support
for
research
methods
related
growth
enterprises
consumption,
as
well
guidance
decision-making
in
carrying
out
cause
marketing.
Язык: Английский
Can Green Fiscal Policies Drive the Digital Transformation of Enterprises?
Journal of the Knowledge Economy,
Год журнала:
2024,
Номер
unknown
Опубликована: Ноя. 12, 2024
Язык: Английский
The Intersection of Digital Economy and Low-Carbon Development: A Meta-Analytic Review
Опубликована: Янв. 1, 2024
The
role
of
the
digital
economy
in
low-carbon
development
has
sparked
enormous
interest.
However,
magnitude
and
mechanisms
its
impact
on
remain
ambiguous.
This
article
begins
by
exploring
through
a
quantitative
synthesis
184
effect
sizes
reported
56
primary
literature
sources
from
China,
employing
meta-analytic
method.
It
then
identifies
driving
forces
heterogeneity
using
machine
learning
Finally,
it
delves
into
Sobel
test.
findings
this
study
are
as
follows:
(1)
potential
to
enhance
carbon
emission
efficiency
reduce
total
emissions,
intensity,
emissions
per
capita
most
cases;
(2)
Variations
selection
or
indices,
research
objects
(industries
regions)
specific
regression
models
may
lead
divergent
conclusions;
(3)
predominantly
influences
(energy
efficiency,
resource
allocation
urban
productivity),
scale
(economic
development,
consumption
expenditure,
tertiary
industry
ICT
industry),
structural
(industrial
structure,
energy
structure)
innovation
effect.
By
combining
review
with
machine-learning
methods,
offers
more
comprehensive
understanding
extent
economy's
development.
provides
valuable
scientific
evidence
for
researchers
guide
theoretical
decision-makers
formulate
high-efficacy
policy
decisions.
Язык: Английский