E3S Web of Conferences,
Год журнала:
2024,
Номер
491, С. 02042 - 02042
Опубликована: Янв. 1, 2024
The
ecology
and
all
of
its
components
are
suffering
greatly
as
a
result
the
unchecked
speed
development.
At
this
rate,
environmental
degradation
will
have
an
impact
on
humanity
associated
fields.
In
order
to
prevent
consequences
expansion
from
pushing
environment
into
situation
which
it
is
incapable
recovering,
there
should
be
ongoing,
earnest
efforts
made
towards
sustainable
three
pillars
ecodevelopment
environment,
humanity,
economy.
A
stable
growth
rate
necessary
attain
just
balance
between
these
pillars.
Since
agriculture
employs
majority
population,
also
has
ecosystem.
Because
every
unplanned
step
progress
puts
us
back
in
front,
we
must
thus
mindful
boundaries
challenges
achieve
equitable
economic
growth.
hope
for
development
lies
decreased
deforestation,
greater
food
security,
conservative
agricultural
practices,
use
biopesticides,
prudent
natural
resources.
To
effective,
policy
probably
needs
employ
variety
tools,
each
addressing
distinct
aspect
issue
attempting
minimise
redundancies
pointless
regulations.
Appropriately
pricing
inputs
facilitates
resource
provision
management.
Long-term
corporate
investment
new
technology
innovation
encouraged
by
consistent
clear
policy,
increases
certainty.
Environmental
success
interdependent.
Economic
activity
advancement
depend
because
provides
resources
needed
produce
goods
services
processes
absorbs
waste
pollution,
unwanted
byproducts.
This
paper
focuses
how
assets
assist
control
risks
with
social
activities,
flood
risks,
local
climate
regulation
(temperature
air
quality),
availability
clean
water
other
Urban Climate,
Год журнала:
2024,
Номер
53, С. 101806 - 101806
Опубликована: Янв. 1, 2024
The
urban
heat
island
(UHI)
phenomenon,
a
well-documented
consequence
of
urbanization
and
industrialization,
is
one
significant
anthropogenic
alteration
to
the
Earth
system.
surface
UHI
(SUHI)
has
been
subject
extensive
study
in
recent
decades
owing
easy
access
spatially
continuous
satellite
data
observations.
However,
there
lack
comprehensive
SUHI
studies
understand
possible
underlying
mechanisms
drivers
SUHI's
spatial
variation
over
Taiwan.
Therefore,
we
aim
investigate
diurnal,
seasonal,
patterns
intensity
(SUHII)
its
driving
factors
eleven
cities
Taiwan
from
2003
2020.
We
employed
Stepwise
multiple
regression,
Pearson's
correlation
technique,
land
temperature
(LST)
Aqua/Terra
MODIS
explore
relationship
between
SUHII
factors.
Our
findings
reveal
that
was
more
intense
daytime
(from
2.21
6.78
°C)
than
at
night
0.52
1.63
°C),
intensive
SUHIIs
were
observed
northern
(day
night:
4.99
1.09
southern
(3.35
1.01
°C).
exhibited
seasonal
variation,
with
greater
day
night.
pattern
highly
correlated
normalized
difference
latent
index
(NDLI),
vegetation,
built-up
intensity,
emissions.
In
contrast,
nighttime
closely
related
light,
vegetation.
considered
this
work
explained
fraction
(79.5
89.0%)
(44.9
77.0%),
indicating
mechanism
complicated,
especially
spring
vs.
81.5%
50.3%)
winter
seasons
(85.3%
44.9%).
This
provides
crucial
information
on
spatio-temporal
forces
can
aid
developing
mitigation
strategies.
Environmental Monitoring and Assessment,
Год журнала:
2025,
Номер
197(2)
Опубликована: Янв. 3, 2025
Abstract
In
recent
decades,
global
climate
change
and
rapid
urbanization
have
aggravated
the
urban
heat
island
(UHI)
effect,
affecting
well-being
of
citizens.
Although
this
significant
phenomenon
is
more
pronounced
in
larger
metropolitan
areas
due
to
extensive
impervious
surfaces,
small-
medium-sized
cities
also
experience
UHI
effects,
yet
research
on
these
rare,
emphasizing
importance
land
surface
temperature
(LST)
as
a
key
parameter
for
studying
dynamics.
Therefore,
paper
focuses
evaluation
LST
cover
(LC)
changes
city
Prešov,
Slovakia,
typical
European
that
has
recently
undergone
LC
changes.
study,
we
use
relationship
between
Landsat-8/Landsat-9-derived
spectral
indices
Normalized
Difference
Built-Up
Index
(NDBI),
Vegetation
(NDVI),
Water
(NDWI)
derived
from
Landsat-8/Landsat-9
Sentinel-2
downscale
10
m.
Two
machine
learning
(ML)
algorithms,
support
vector
(SVM)
random
forest
(RF),
are
used
assess
image
classification
identify
how
different
types
selected
years
2017,
2019,
2023
affect
pattern
LST.
The
results
show
several
decisions
made
during
last
decade,
such
construction
new
fabrics
roads,
caused
increase
evaluation,
based
RF
algorithm,
achieved
overall
accuracies
93.2%
89.6%
91.5%
2023,
outperforming
SVM
by
0.8%
2017
4.3%
2023.
This
approach
identifies
UHI-prone
with
higher
spatial
resolution,
helping
planning
mitigate
negative
effects
increasing
LSTs.
Scientific Reports,
Год журнала:
2024,
Номер
14(1)
Опубликована: Март 15, 2024
Abstract
Urban
areas
worldwide
are
experiencing
escalating
temperatures
due
to
the
combined
effects
of
climate
change
and
urbanization,
leading
a
phenomenon
known
as
urban
overheating.
Understanding
spatial
distribution
land
surface
temperature
(LST)
its
driving
factors
is
crucial
for
mitigation
adaptation
So
far,
there
has
been
an
absence
investigations
into
spatiotemporal
patterns
explanatory
LST
in
city
Addis
Ababa.
The
study
aims
determine
temperature,
analyze
how
relationships
between
vary
across
space,
compare
effectiveness
using
ordinary
least
squares
geographically
weighted
regression
model
these
connections.
findings
showed
that
show
statistically
significant
hot
spot
zones
north-central
parts
area
(Moran’s
I
=
0.172).
relationship
variables
were
modelled
square
thereby
tested
if
dependence
Koenker
(BP)
Statistic.The
result
revealed
non-stationarity
(p
0.000)
consequently
was
employed
performance
with
OLS.
research
that,
GWR
(R
2
0.57,
AIC
1052.1)
more
effective
technique
than
OLS
0.42,
2162.0)
studying
selected
variables.
use
improved
accuracy
by
capturing
heterogeneity
Statistic.
((p
Consequently,
Localized
understanding
formulated.
Urban Climate,
Год журнала:
2023,
Номер
49, С. 101557 - 101557
Опубликована: Май 1, 2023
Comprehensive
and
objective
assessment
methods
need
to
be
developed
create
inclusive,
safe,
resilient
sustainable
cities.
Monitoring
the
evolution
of
sustainability
well-being
in
cities
is
important
for
researchers
implementing
UN
2030
Agenda.
This
research
explores
analyzes
climate
change
hazards,
adaptation-
mitigation
actions
their
implementation
776
located
84
different
countries.
The
action
co-benefits
are
supporting
achievement
development
goals,
which
comprehensively
elaborated
this
methodological
development.
carried
out
based
on
continuously
updated
Carbon
Disclosure
Project
database.
An
open
source
algorithm
has
been
that
represents
CDP
database
as
a
bit
table
use
frequent
itemset
mining
identification
global
patterns
mitigation-
adaptation
co-benefits,
therefore,
paper
offers
an
exploratory
analysis
tool
suitable
monitoring
actions.
most
frequently
identified
were
energy
planting
(1444
actions),
on-site
renewable
production
(644),
while
common
tree
(283)
flood
mapping
(267).
Regarding
city
size,
41%
large
metropolitan
areas
plan
develop
mass
transit
actions,
separate
collection
recyclables
typical
85%
towns.
56.2%
support
access
communities
goal
(SDG11),
54.2%
(SDG13),
emergence
affordable
clean
(SDG7)
gender
equality
(SDG5)
below
5%.
Urban Climate,
Год журнала:
2024,
Номер
56, С. 102045 - 102045
Опубликована: Июнь 28, 2024
High
land
surface
temperatures
(LST)
have
emerged
as
crucial
threats
to
urban
ecosystems
and
sustainable
development.
To
better
understand
mitigate
their
impacts,
it
is
essential
analyze
the
contributing
features.
Against
this
background,
we
developed
a
random
forest
model
enhanced
by
Explainable
Artificial
Intelligence
(XAI)
impact
features
of
LST
in
Beijing,
China.
By
applying
XAI
method,
our
results
suggest
that
major
Beijing
are
elevation
(44.19%),
compactness
impervious
(17.27%),
Normalized
Difference
Vegetation
Index
(11.12%),
proportion
area
(8.04%),
tree
height
(3.83%).
Compactness
exhibited
an
overall
cooling
effect,
which
became
weaker
at
high
values.
increased
with
building
height,
trend
reached
5
m.
The
most
important
impacting
inner
city
buildings,
whereas
outer
these
surfaces.
study
applies
explain
non-linear
interactions
between
features,
offering
innovative
insights
policy-makers
develop
planning
strategies.
Our
findings
increasing
green
spaces
water
bodies
well
controlling
density
can
effectively
heat
dense
areas
enhance
effects.
