Rationally Designed High-Temperature Polymer Dielectrics for Capacitive Energy Storage: An Experimental and Computational Alliance
Progress in Polymer Science,
Год журнала:
2025,
Номер
unknown, С. 101931 - 101931
Опубликована: Фев. 1, 2025
Язык: Английский
AI Application in Climate-Smart Agricultural Technologies: A Synthesis Study
LatIA,
Год журнала:
2025,
Номер
3, С. 330 - 330
Опубликована: Март 25, 2025
Climate
change
poses
significant
challenges
to
global
agriculture,
necessitating
innovative
solutions
enhance
sustainability
and
productivity.
Artificial
intelligence
(AI)
has
emerged
as
a
key
enabler
in
climate-smart
agricultural
technologies
(CSAT),
offering
data-driven
approaches
optimize
resource
use,
mitigate
climate
risks,
improve
decision-making.
This
study
aims
evaluate
AI's
integration
into
CSAT,
focusing
on
its
applications,
benefits,
adoption
challenges,
particularly
climate-vulnerable
regions.
A
bibliographic
review
employing
machine
learning
(ML)
natural
language
processing
(NLP)
techniques
was
conducted
analyze
over
40,000
scientific
articles
from
academic
databases.
Topic
modeling
classification
algorithms
were
applied
identify
trends,
barriers,
implementation
pathways
for
AI-driven
CSAT.
The
also
incorporated
expert
validation
through
the
Delphi
method
refine
AI-generated
insights
ensure
their
alignment
with
real-world
challenges.
Findings
indicate
that
AI
enhances
decision-making
conservation
precision
farming,
water
management,
market
intelligence.
AI-powered
tools
facilitate
early
pest
detection,
irrigation
schedules,
provide
real-time
advisory
services,
significantly
improving
resilience
food
security.
However,
major
barriers
include
high
costs,
limited
digital
literacy,
inadequate
infrastructure,
low-income
Despite
these
CSAT
presents
potential
transform
especially
climate-affected
areas.
Strategic
investments
infrastructure
development,
supportive
policy
frameworks
are
essential
adoption.
Strengthening
interdisciplinary
collaboration
among
researchers,
policymakers,
farmers
will
be
crucial
advancing
sustainable
practices
ensuring
long-term
Язык: Английский
Climate Sustainability through AI-Crypto Synergies and Energy Transition in the Digital Landscape to Cut 0.7 GtCO2e by 2030
Environmental Science & Technology,
Год журнала:
2025,
Номер
unknown
Опубликована: Фев. 19, 2025
The
rapid
expansion
of
artificial
intelligence
(AI)-enabled
systems
and
cryptocurrency
mining
poses
significant
challenges
to
climate
sustainability
due
energy-intensive
operations
relying
on
fossil-powered
grids.
This
work
investigates
the
strategic
coupling
AI
data
centers
through
shared
energy
infrastructure
including
colocated
renewable
power
installations,
battery
storage,
green
hydrogen
infrastructure,
carbon
offsetting
measures
achieve
cost-effective
climate-neutral
operations.
Employing
a
novel
modeling
framework,
it
explores
synergistic
AI-crypto
with
detailed
scenario
design
along
an
optimization
framework
assess
decarbonization
potential
economic
implications,
enabling
transformative
shift
in
digital
landscape.
results
indicate
that
synergizing
while
achieving
net-zero
targets
can
avoid
up
0.7
Gt
CO2-equiv
2030.
Moreover,
reaching
these
strategies
globally
requires
90.7
GW
solar
119.3
wind
capacity.
findings
advocate
for
robust
policy
facilitate
credit
schemes
tailored
sector,
incentives
efficiency
improvements,
international
collaborations
bridge
disparities.
Future
research
should
focus
refining
interventions
across
different
geopolitical
contexts
enhance
global
applicability.
Язык: Английский
Smart Technologies in Enhanced Oil Recovery: Integrating AI, Nanotechnology, and Sustainable Practices
IntechOpen eBooks,
Год журнала:
2025,
Номер
unknown
Опубликована: Март 24, 2025
Enhanced
oil
recovery
(EOR)
is
a
critical
method
for
extracting
additional
from
mature
reservoirs,
but
it
faces
increasing
pressure
to
become
more
efficient
and
environmentally
sustainable.
This
chapter
explores
the
integration
of
smart
technologies
such
as
artificial
intelligence
(AI),
nanotechnology,
sustainable
practices
into
EOR.
AI
revolutionizing
EOR
operations
by
optimizing
reservoir
management,
improving
real-time
monitoring,
reducing
operational
costs.
Nanotechnology
enhances
through
use
functionalized
nanoparticles
fluids,
which
improve
mobility
reduce
chemical
consumption.
Additionally,
practices,
including
CO2-EOR,
water-efficient
techniques,
biodegradable
chemicals,
are
being
adopted
lower
environmental
impact
EOR,
especially
in
terms
carbon
emissions
water
use.
While
challenges
remain—such
high
cost
technology
fluctuating
prices—the
future
holds
promise
continuous
technological
innovation
growing
emphasis
on
sustainability.
Язык: Английский
AI‐Powered Sustainable Tourism: Unlocking Circular Economies and Overcoming Resistance to Change
Business Strategy and the Environment,
Год журнала:
2025,
Номер
unknown
Опубликована: Март 28, 2025
ABSTRACT
This
study
examines
the
integration
of
artificial
intelligence
(AI)
with
circular
economy
(CE)
principles
in
Thailand's
tourism
industry.
It
explores
interactions
between
AI‐Enhanced
Predictive
Waste
Analytics
(AI‐PWA),
Regenerative
Resource
Integration
(RRI),
Dynamic
Material
Flow
Optimization
(DMFO),
and
AI‐Induced
Resistance
to
Change
(AIRC).
Using
a
mixed‐methods
approach,
qualitative
insights
from
industry
stakeholders
are
combined
quantitative
analysis
via
Partial
Least
Squares
Structural
Equation
Modeling
(PLS‐SEM).
Findings
reveal
that
AI‐PWA
improves
real‐time
resource
management,
driving
DMFO
supporting
regenerative
practices
through
RRI.
However,
AIRC
moderates
AI's
effectiveness
sustainability
transitions,
concerns
such
as
job
displacement,
mistrust,
complexity
hindering
adoption.
provides
actionable
strategies
mitigate
resistance,
enhance
stakeholder
collaboration,
scale
AI
adoption
resource‐constrained
settings,
contributing
SDG
12
13.
The
findings
offer
practical
for
aligning
innovations
sustainable
development
high‐variability
industries.
Язык: Английский
Transformative Approaches in Photocatalytic CO2 Conversion: The Impact of AI and Computational Chemistry
Current Opinion in Green and Sustainable Chemistry,
Год журнала:
2025,
Номер
unknown, С. 101027 - 101027
Опубликована: Апрель 1, 2025
Язык: Английский
Geospatial Data Analysis for Mapping Carbon Sequestration Hotspots
IGI Global eBooks,
Год журнала:
2025,
Номер
unknown, С. 193 - 218
Опубликована: Апрель 11, 2025
Geospatial
measurement
of
carbon
is
required
for
hotspot
identification
and
precise
quantification
sinks
across
various
ecosystems.
The
evolution
GIS,
remote
sensing,
LiDAR,
spatial
modeling
using
AI
has
significantly
improved
the
precision
extent
monitoring.
chapter
describes
techniques
examining
forest
biomass,
soil
sequestration,
ocean
through
satellite
data,
geospatial
computation,
machine
learning
models.
Integration
big
data
enhances
flux
estimation
land-use
impact
assessment
on
sequestration
capacity.
Significant
challenges
such
as
resolution,
model
uncertainty,
computational
complexity
are
addressed,
along
with
new
solutions.
analysis
augmented
by
at
core
activities
maximization,
enabling
climate
change
mitigation,
sustainable
land
management,
transparent
credit
systems.
Язык: Английский