Research trends in innovation ecosystem and circular economy
Discover Sustainability,
Год журнала:
2024,
Номер
5(1)
Опубликована: Окт. 14, 2024
Understanding
innovation
ecosystems
and
the
circular
economy
is
crucial
for
systemic
change
in
business
industry,
fostering
eco-innovation
advancing
sustainable
development.
This
study
uses
bibliometric
analysis
to
uncover
research
trends,
patterns,
collaborations,
revealing
a
significant
gap
understanding
interactions
between
offering
potential
avenues
future
that
align
with
The
was
carried
out
help
of
Biblioshiny
VOSviewer
on
final
selected
documents
2981
from
Scopus
database
through
search
query
SPAR-4-SLR
stages
filtration.
key
findings
are
as
follows:
collaboration
among
countries
involves
accessing
countries'
resources,
knowledge,
markets,
location.
explores
trends
ecosystem
economy,
focusing
five
clusters:
resource
recovery,
models
bioeconomy,
sustainability
renewable
energy
sdgs,
model
enhancing
green
entrepreneurship,
Artificial
Intelligence
(AI)
Industry
4.0.
identifies
gaps,
exploration
industrial
symbiosis,
transition,
system
innovation.
analyzed
only
available
Scopus.
exclusion
papers
based
period,
language,
document
type,
incomplete
details
limitations
this
open
scope
research.
will
existing
researchers
field
well
new
interested
by
clearly
further
scopes.
also
offers
actional
recommendations
practices
policymakers.
Practices,
entrepreneurs
attainment
global
goals.
novelty
originality
rely
thorough
literature
review
describes
state
art
economy.
Язык: Английский
Navigating the Nexus of Artificial Intelligence and Renewable Energy for the Advancement of Sustainable Development Goals
Sustainability,
Год журнала:
2024,
Номер
16(21), С. 9144 - 9144
Опубликована: Окт. 22, 2024
The
integration
of
artificial
intelligence
(AI)
into
renewable
energy
and
sustainability
represents
a
transformative
approach
toward
achieving
sustainable
development
goals
(SDGs),
especially
SDG
7
(Affordable
Clean
Energy),
9
(Industry,
Innovation,
Infrastructure),
13
(Climate
Action).
This
study
utilized
the
PRISMA
framework
to
conduct
systematic
review,
focusing
on
role
AI
in
development.
research
Scopus’s
curated
area,
which
employs
text
mining
refine
concepts
unique
keywords.
Further
refinement
via
All
Science
Journals
Classification
system
SDG-mapping
filters
narrowed
focus
publications
relevant
SDGs.
By
employing
BERTopic
modeling
approach,
our
identifies
major
topics,
such
as
enhancing
wind
speed
forecasts,
performance
analysis
fuel
cells,
management
elective
vehicles,
solar
irradiance
prediction,
optimizing
biofuel
production,
improving
efficiency
buildings.
AI-driven
models
offer
promising
solutions
address
dynamic
challenges
energy.
Insights
from
academia-industry
collaborations
indicate
that
partnerships
significantly
accelerate
sustainable-energy
transitions,
with
storage,
grid
management,
renewable-energy
forecasting.
A
global
consensus
critical
investing
technology-driven
for
was
underscored
by
relationship
between
funding
data
R&D
spending
patterns.
serves
resource
practitioners
harness
technologies
energy,
where
example,
AI’s
accurate
predictions
can
increase
farm
efficiency,
highlighting
necessity
innovation
collaboration
Язык: Английский
Carbon Budget Assessment and Influencing Factors for Forest Enterprises in the Key State-Owned Forest Area of the Greater Khingan Range, Northeast China
Land,
Год журнала:
2024,
Номер
14(1), С. 56 - 56
Опубликована: Дек. 31, 2024
Analyzing
the
spatial
and
temporal
changes
in
carbon
budget
its
influencing
factors
is
basis
for
formulating
effective
measures
to
reduce
emissions
increase
sinks.
This
study
establishes
a
assessment
model
forest
enterprises,
calculating
stocks
enterprise
using
volume-derived
biomass
emission
factor
methods.
The
spatiotemporal
evolution
characteristics
of
budgets
enterprises
key
state-owned
area
(2017–2021)
were
analyzed
various
methods,
including
Mann-Kendall
(MK)
test
hotspot
analysis.
Influencing
are
identified
through
correlation
analysis
optimal
parameter
geographical
detector
(OPGD),
while
their
spatial-temporal
variations
causal
relationships
weighted
regression
(GTWR)
structural
equation
modeling
(SEM).
Greater
Khingan
Range
averaged
10.16
×
106
t
CO2-eq
from
2017
2021,
showing
gradual
upward
trend.
average
annual
was
1.02
CO2-eq,
which
highest
central
regions
lowest
periphery.
Soil
pH,
area,
elevation
primary
factors.
interaction
between
paired
enhances
explanatory
power
impact,
effects
different
exhibit
both
positive
negative
across
enterprises.
In
addition,
middle-aged
tending
precipitation
positively
influenced
soil
indirectly
enhancing
multifactor
interactions.
research
can
enhance
understanding
providing
scientific
support
ecological
protection
forests
contributing
development
sustainable
forestry
practices
that
benefit
societal
well-being
economic
resilience.
Язык: Английский