Green finance: The catalyst for artificial intelligence and energy efficiency in Chinese urban sustainable development
Energy Economics,
Journal Year:
2024,
Volume and Issue:
139, P. 107883 - 107883
Published: Sept. 3, 2024
Language: Английский
Artificial intelligence of robotics and green transformation: evidence from Chinese manufacturing firms
Environment Development and Sustainability,
Journal Year:
2025,
Volume and Issue:
unknown
Published: March 20, 2025
Language: Английский
Research on Smart City Construction in the Context of Public Culture
Yuhang Zhang,
No information about this author
Jiaji Gao
No information about this author
Telematics and Informatics Reports,
Journal Year:
2025,
Volume and Issue:
unknown, P. 100187 - 100187
Published: Jan. 1, 2025
Language: Английский
The Role of Artificial Intelligence Technologies in Sustainable Urban Development: A Systematic Survey
Lecture notes in computer science,
Journal Year:
2025,
Volume and Issue:
unknown, P. 217 - 230
Published: Jan. 1, 2025
Language: Английский
Digital Technology and AI for Smart Sustainable Cities in the Global South: A Critical Review of Literature and Case Studies
Urban Science,
Journal Year:
2025,
Volume and Issue:
9(3), P. 72 - 72
Published: March 5, 2025
Many
countries
across
the
Global
South
strive
to
align
their
urban
development
with
sustainability
goals.
Consequently,
notion
of
smart
sustainable
cities
has
emerged,
integrating
ideas
and
sustainability.
The
region
faces
diverse
challenges,
including
rapid
population
growth
financial
constraints.
Infrastructural
deficiencies,
especially
in
digital
infrastructure
AI
adoption,
add
these
challenges.
Therefore,
exploring
technologies
is
essential
for
developing
smart,
South.
This
paper
examined
both
potential
barriers
AI.
It
also
explored
policy
implications
proposes
a
framework
cities.
A
qualitative
methodological
approach
used,
systematic
literature
review
case
studies.
study
demonstrates
how
various
challenges
can
be
addressed
AI,
alongside
adoption.
conceptual
three
key
pillars:
adopting
as
pivotal
element,
overcoming
barriers,
identifying
application
areas
transform
into
Moreover,
discusses
suggests
future
directions
research.
Language: Английский
AI in business operations: driving urban growth and societal sustainability
Frontiers in Artificial Intelligence,
Journal Year:
2025,
Volume and Issue:
8
Published: March 24, 2025
Approximately
30%
of
smart
city
applications
will
use
artificial
intelligence
(AI)
by
the
end
2025,
thereby
radically
altering
urban
sustainability
landscape
in
future
(Yan
et
al.,
2023).
The
advent
AI
reshaping
traditional
businesses
into
sustainable
operations
is
evident.
Whenever
brought
to
forefront,
it
considered
a
cornerstone
business
domain,
enabling
transition
towards
more
innovative
and
practices
(Appio
2024).
Incorporating
has
many
facets.
According
Grand
View
Research
(2023),
global
market
size
was
anticipated
at
USD
196.63
billion
2023
expected
grow
CAGR
36.6%
from
2024
2030.
recent
fanfare
surrounding
elevated
key
enabler
development,
prompting
companies
prioritize
integrate
their
operations;
hence,
there
stark
difference
between
new
practices.
In
tandem
with
this
evolution,
growth
societal
dynamics
are
experiencing
profound
changes
as
AI-driven
solutions
come
fore
various
aspects
modern
society
(Shahidi
Hamedani
government,
transforming
conventional
cities
efficient
ones
(Ortega-Fernández
2020),
have
significantly
shifted
functional
systems
intelligent
ones.
Furthermore,
another
perspective,
role
optimizing
processes
surpassed
comparison
its
implication
for
improving
logistics
operational
capabilities
reducing
environmental
impacts
(Jorzik
2024a)
till
manufacturing
reduces
downtime,
all
which
contribute
economics.
meantime,
speedy
pace
adoption
operations,
also
imperative
amalgamate
Acting
on
matter
requires
thoughtful
approach
that
aligns
social,
economic,
sustainability.The
intersection
recently
gained
widespread
attention.
Some
studies
(Chen
2024;Shahzadi
2024)focused
AI's
supply
chain
management,
highlighting
minimizing
inefficiencies
utilizing
often;supply
chains
become
leaner
reduced
carbon
footprints,
paving
path
operations.
It
estimated
2026,
60%
adopt
AI-powered
warehouse
instead
just
10%
2020
(MHI,
2024).In
line
shift,
(Dilmegani
&
Ermut,
2025)
note
invest
heavily
robots
enhance
management
through
technology.
Robots
can
manage
efficiently
accurately
automating
picking,
packing,
sorting,
inventory
thus
saving
labor
costs
accelerating
order
processing.
Amazon,
instance,
deployed
than
200,000
warehouses
optimize
operations.AI
be
used
resource
utilization,
automate
improved
efficiency,
enable
real-time
monitoring
goals
(Waltersmann
2021).
As
focuses
waste
enhancing
traceability,
technologies
such
machine
learning
big
data
analytics
been
pivotal
achieving
these
goals.
(Tsolakis
2023)
Companies
like
eBay
leverage
translation,
decision-making
efficiency
.
Similarly,
Vodafone
employs
personalize
services,
exemplifying
transformative
impact.
2024a).These
help
reduce
forecasting
errors,
minimize
excess
inventory,
lower
energy
consumption.
(Sharma
2020)
Likewise,
Smart
grid
protection
sensors
detect
defects
up
80%
sensors,
losses
system's
reliability
adjusting
conditions
dynamically
(Mahadik,
Sheetal
2025).
These
economic
fostering
technological
innovation.
leverages
advanced
techniques
deep
reinforcement
(DRL)
dynamic
(Shuford,
DRL
improves
adaptive
routing
optimization,
demand
logistics;
DRL,
researchers
develop
adapt
changes,
facilitate
multi-objective
(Dehaybe
addition,
enables
prevent
equipment
failures
streamlining
workflows
(Mohan
Moreover,
centers,
advancements
catalyze
foster
other
words,
contribution
development
allocation,
ensuring
resilient
economically
prosperous
(Li
developing
cities,
impact
urbanization
trends.
Through
application
AI,
infrastructure
optimized
transportation,
managing
housing
needs;
makes
possible
traffic
congestion
advance
mobility
transportation
systems,
prescriptive
autonomous
vehicles
(Regona
Singapore,
manages
monitors
consumption,
setting
benchmarks
(Padhiary
On
similar
note,
Tennet
TSO,
German
transmission
system
operator,
AI-based
IBM
Watson's
cognitive
computing
platform
anticipate
renewable
generation
real
time,
allowing
adjustments
maximizing
clean
use.
3Nowadays,
debatable
topic,
inevitable.
Reducing
food
print,
circular
economy
sprout
assists
environment
(Onyeaka
2023);
example,
agriculture
industry,
automation,
prediction
model
total
agricultural
output
value
(Sachithra
Subhashini,
2023),
yields
while
apparent
regarding
implications
IoT
due
ability
improve
sustainability.
Agriculture
leads
way
35%
technologies,
followed
precision
farming
irrigation
16%
each.Farming
becoming
smarter
innovations,
increase
yields,
waste,
conserve
resources
(Market.Us,
2024).Similarly,
distribution,
lowering
footprints
(Bhattacharya
2022).
increasingly
automated,
consumption
minimized
aligned
(Garrido
2024).By
placing
heart
sustainability,
industries
solving
social
issues.
shift
linear
circular,
innovative,
models
(Pathan
paradigm
contributes
aspects;
site
surveying
progress
monitoring,
power
drones
decision-making,
green
finance
sector
cultivation
harvesting
phases
(Fuentes-Peñailillo
While
crucial
implementing
brings
several
challenges,
including
ethical
privacy
concerns
(Fan
2023).In
planning
infrastructure,
notable
examples;
using
knowledge
acquired
level
potential
revolutionize
over
solutions,
welfare,
vitality
(Herath
Mittal,
AI-enabled
hospitality
provide
personalized
services
seamless
guest
experiences
(Szpilko
2023).Similarly,
healthcare
predict
diseases
rapidly
(Rashid
Kausik,
For
PRAIM
study
Germany
assessed
AI-supported
mammography
screening
versus
standard
double
reading.
Out
463,094
women
screened,
260,739
were
assisted
AI.
With
screening,
6.7
cancers
detected
out
1,000,
17.6%
higher
screening.
(Eisemann
Policies
needed
protect
individual
settings
solve
(Dong
Liu,
gain
momentum
but
present
significant
security
concerns.
Data
an
urgent
concern
(Saura
Acknowledging
consequences,
particularly
when
shifting
employment
patterns
consumer
behaviors,
important
(Yu
rise
automation
displaced
jobs
created
AIspecialized
workers
(Betts
2024).AI's
personalizing
underscores
responsibility
mitigate
algorithmic
biases,
maintaining
public
trust
equity.
Governments
must
work
together
implement
reskilling
programs
seamlessly
AIdriven
world.
Ethical
concerns,
digital
divide,
underscore
need
transparent
inclusive
(Bouhouita-Guermech
challenges
amplified
areas,
where
disparities
access
marginalize
vulnerable
populations.
issues
solved
only
collaborative
efforts
design
prioritizing
wellbeing
inclusiveness.Several
exist,
integration
issues,
literacy
resistance
change,
availability,
reliance
(Uwaoma
industries,
getting
actionable
time-consuming
costly.
result,
cannot
produce
satisfactory
results
without
robust
data,
undermining
complicated
Ensuring
equal
technology
addressed
so
benefits
sectors
society.
Additionally,
within
organizations
utmost
importance.
Many
resist
change
lack
understanding,
making
difficult
them
future.Moreover,
(availability
quality)
remains
hurdle
accessing
opaque
2024b);
training
DRL's
models,
quality
datasets
critical,
contexts
both
sparse
expensive
collect
(Saliba
2020).On
hand,
concern;
according
Choudhuri,
30
%
unreliable
or
poor
quality;
having
said
that,
incomplete
fail
any
method
analysis
affect
process
words
data-especially
high-quality
data-sustainable
doomed
falter.
A
further
equitable
since
marginalized
communities
often
face
barriers
taking
advantage
developments
(Kasun
highlighted
here
highlight
balanced
deployment.Without
prospects
adopting
bleak.
However,
Sustainable
demands
involvement
government
sector.Governments
establish
policies
regulations
promote
transparency
collaboration
ensure
transfer
private
sector.
kind
cooperation
facilitating
responsibly
addressing
broader
goals.The
advancement
however,
hindered
limitations,
unwillingness
difficulty
integrating
pre-existing
HR
(Madanchian
Taherdoost,
hampered
algorithms
common
sense
interpret
properly,
resulting
flawed
decisions
(Nishant
clinicians'
negatively
impacted
(Dratsch
prescribing
antidepressants,
clinicians
less
accurate
following
incorrect
recommendations
compared
baseline
correct
advice
condition
(Jacobs
high
cost
resource-intensive
reach
broad
audience
(Sommer
organizational
creates
barrier
HRM
employees
reluctant
about
security,
privacy,
job
(Hassan
2024).Businesses
witnessing
driver
growth,
profoundly
sectors.
context
implemented
scheduling
livable
development.
means
resources,
inefficiencies,
embrace
practices,
tackle
full
realized
if
align
clearly
defined
targets.
Achieving
strategic
not
technical
tool
generating
short-term
benefits.Policymakers
reliable
fairly
equitably
fosters
range
would
beneficial
create
same
time.As
becomes
integral
presents
opportunities
challenges.
costs;
McKinsey
(
2022),
reported
engines
15%
competitive
position
50%
workforce
tasks.
policymakers
planners
creating
innovations
thrive
inclusively
overstated.
Integrating
balancing
considerations.
powerful
force
stakeholders
atmosphere,
address
barriers,
transparency.
businesses,
societies,
benefit.
By
examining
manner,
article
introduces
fresh
perspective
literature
because
comprehensively
covered
current
literature.
valuable
synthesize
existing
trends
strong
foundation
understanding
business.
Language: Английский
Assessing the impact of artificial intelligence on the transition to renewable energy? Analysis of U.S. states under policy uncertainty
Renewable Energy,
Journal Year:
2025,
Volume and Issue:
unknown, P. 122969 - 122969
Published: March 1, 2025
Language: Английский
Can Artificial Intelligence Effectively Improve China’s Environmental Quality? A Study Based on the Perspective of Energy Conservation, Carbon Reduction, and Emission Reduction
Ke Zhao,
No information about this author
Chao Wu,
No information about this author
Jinquan Liu
No information about this author
et al.
Sustainability,
Journal Year:
2024,
Volume and Issue:
16(17), P. 7574 - 7574
Published: Sept. 1, 2024
The
“technological
dividends”
brought
by
AI
development
provide
a
new
model
for
the
country
to
achieve
green
governance,
enhance
enterprises’
ability
manage
pollutant
emissions
during
production
and
operations,
create
driving
force
improving
environmental
quality.
In
this
regard,
paper
systematically
examines
impact
of
on
quality
in
China
employing
provincial
panel
data
spanning
from
2000
2020.
Focusing
energy
conservation,
carbon
reduction,
mitigation,
analysis
is
conducted
through
application
two-way
fixed-effects
mediation
effects
explore
both
mechanisms
AI’s
influence
findings
indicate
that
implementation
contribute
positively
China’s
efforts
ultimately
leading
an
enhancement
This
conclusion
remains
valid
after
multiple
robustness
checks.
Mechanism
tests
reveal
optimization
regional
structures,
advancements
technological
innovation,
upgrades
industrial
structures
serve
as
crucial
pathways
which
facilitates
mitigation.
Heterogeneity
uncovers
notable
“path
dependence”
effect
development;
regions
characterized
higher
material
capital
investment,
more
advanced
market
development,
greater
levels
marketization
experience
relatively
pronounced
study
offers
direct
references
practical
insights
countries
globally
foster
quality,
advance
high-quality
economic
growth
amid
ongoing
wave
digital
intelligent
transformation.
Language: Английский
The service trade with AI and energy efficiency: Multiplier effect of the digital economy in a green city by using quantum computation based on QUBO modeling
Da Huo,
No information about this author
Wenjia Gu,
No information about this author
Dongmei Guo
No information about this author
et al.
Energy Economics,
Journal Year:
2024,
Volume and Issue:
140, P. 107976 - 107976
Published: Nov. 2, 2024
Language: Английский
Energy Consumption Analysis of Batch Runs of Evolutionary Algorithms
Proceedings of the Genetic and Evolutionary Computation Conference Companion,
Journal Year:
2024,
Volume and Issue:
16, P. 87 - 88
Published: July 14, 2024
Language: Английский
