The
cooling
effect
of
urban
parks
(PCE)
is
widely
recognized
as
an
effective
way
to
improve
the
thermal
environment.
While
influence
various
background
meteorological
factors
(BMFs)
on
PCE
has
been
increasingly
documented,
further
understanding
these
impacts,
particularly
across
different
types
parks,
remains
insufficient.
Here,
we
selected
two
typical
fifteen
with
green
space
(PG)
and
blue-green
(PB&G)
in
Shanghai,
China
for
comparative
studies.
impact
BMFs
threshold
value
efficiency
(TVoE)
sampled
under
six
dates
were
quantified
through
descriptive
statistics
correlation
analysis.
results
showed
that:
(1)
Compared
PG,
PB&G
had
a
stronger
effect,
stable
conditions
(BMCs).
(2)
enable
significant
PCE,
both
PG
better
higher
air
temperature
(Ta).
(3)
blue
ratio
(RBS)
presented
than
(RGS)
BMCs,
especially
BMCs
lower
relative
humidity
(Rh);
thus,
regulation
RBS
should
be
firstly
considered
PCE.
(4)
TVoE
fitting
was
also
less
affected
by
area
0.93
ha
encouraged
optimal
from
cost-benefit
perspective
study
area.
These
findings
are
essential
decision-makers
can
provide
actionable
knowledge
climate
adaptation
planning.
Sustainable Cities and Society,
Journal Year:
2024,
Volume and Issue:
112, P. 105597 - 105597
Published: June 20, 2024
Climate
changes
have
led
to
increasing
global
energy
consumption,
detrimental
the
sustainable
development
of
society.
Urban
blue-green
infrastructure
(UBGI)
can
improve
urban
microclimate.
However,
influence
intensity
UBGI
on
microclimate
has
not
been
quantified
deeply
use
efficiency
water
and
greenery
resources.
To
solve
research
deficiencies,
this
study
numerically
simulated
for
44
scenarios
with
different
resource
configurations
(various
body
areas
coverages)
in
summer.
Based
simulations,
developed
novel
mathematical
models
thermo-environment
(BGTE)
quantify
UBGI.
The
results
indicated
that
daytime
synergies
first
increased
then
decreased
time.
significance
time
(t),
area
(Sw),
tree
coverage
rate
(TCR),
shrub
(SCR),
grassland
(GLCR)
synergy
was
by
artificial
neural
network:
t
(39.4%),
Sw
(22.6%),
TCR
(22.0%),
SCR
(13.2%),
GLCR
(2.8%).
make
overall
effect
relatively
efficient,
should
be
less
than
10000
m2,
greater
65%,
close
15%.
This
provides
practical
ideas
efficient
Land,
Journal Year:
2024,
Volume and Issue:
13(11), P. 1735 - 1735
Published: Oct. 23, 2024
Understanding
and
recognizing
urban
morphology
evolution
is
a
crucial
issue
in
planning,
with
extensive
research
dedicated
to
detecting
the
extent
of
expansion.
However,
as
development
patterns
shift
from
incremental
expansion
stock
optimization,
related
studies
on
meso-
microscale
face
limitations
such
insufficient
spatiotemporal
data
granularity,
poor
generalizability,
inability
extract
internal
patterns.
This
study
employs
deep
learning
meso-/microscopic
form
indicators
develop
generic
framework
for
extracting
describing
meso-/microscale
morphology.
The
includes
three
steps:
constructing
specific
datasets,
semantic
segmentation
form,
mapping
using
Tile-based
Urban
Change
(TUC)
classification
system.
We
applied
this
conduct
combined
quantitative
qualitative
analysis
Binhai
New
Area
2009
2022,
detailed
visualizations
at
each
time
point.
identified
that
different
locations
area
exhibited
seven
distinct
patterns:
edge
areal
expansion,
preservation
developmental
potential,
industrial
land
pattern,
rapid
comprehensive
demolition
construction
linear
mixed
evolution,
stable
evolution.
results
indicate
phase,
high-density
areas
exhibit
multidimensional
characteristics
by
region,
period,
function.
Our
work
demonstrates
potential
grid
providing
scalable,
cost-effective,
quantitative,
portable
approach
historical
understanding.
