1.
CESAR
WORKING
DOCUMENT
SERIES
Project
1,
working
document
no.1
Climate
change
effects
on
destination
choices
for
daily
activities
in
the
Randstad
Holland
Second
climate
change
analysis
on
Dutch
National
Travel
Survey
(MON)
data
L.
Böcker,
J.Prillwitz
and
M.Dijst
19
March
2012
This
working
document
series
is
a
joint
initiative
of
the
University
of
Amsterdam,
Utrecht
University,
Wageningen
University
and
Research
centre
and
TNO
The
research
that
is
presented
in
this
series
is
financed
by
the
NWO
program
on
Sustainable
Accessibility
of
the
Randstad:
http://www.nwo.nl/nwohome.nsf/pages/nwoa_79vlym_eng

2. CESAR
Project
1
working
document
series
no.1
Climate
change
and
destination
choices
Page
2
TABLE
OF
CONTENT
1.
INTRODUCTION................................................................................................................ 3
2.
RESEARCH
DESIGN........................................................................................................... 3
3.
ANALYSIS.......................................................................................................................... 5
4.
REFERENCES................................................................................................................... 12

3. CESAR
Project
1
working
document
series
no.1
Climate
change
and
destination
choices
Page
3
1. INTRODUCTION
In
the
light
of
a
growing
societal
interest
for
climate
change
adaptation,
various
recent
studies
have
looked
into
the
relationship
between
climate/weather
and
a
variety
of
daily
travel
choices,
such
as
choices
for
transport
modes,
departure
times
and
routes
(see
for
an
overview
of
the
literature
Koetse
and
Rietveld,
2009
and
Böcker,
et
al,
submitted),
as
well
as
on
long
term
preferences
for
tourism
destinations
(Nicholls
and
Amelung,
2008;
Amelung
and
Viner,
2006;
Hamilton
et
al.,
2005;
Bigano
et
al.,
2006;
Matzarakis
and
De
Freitas
2001).
However,
the
impact
on
daily
destination
choices
has
largely
been
neglected
by
these
contributions.
This
is
remarkable,
since
the
role
of
changing
weather
patterns
for
daily
destination
choices
is
highly
relevant
from
a
geographical
point
of
view.
One
can
think
of
citizens
escaping
inner-­‐city
heat
to
recreational
sites
and
shopping
complexes
outside
cities,
or
a
switch
from
active
outdoor
to
inactive
indoor
activities
with
increasing
periods
of
precipitation.
Consequently,
this
study
analyses
the
effects
of
projected
climate
change
on
the
demand
for
different
types
of
activity-­‐destinations
(like
indoor/outdoor
and
recreational/maintenance)
in
different
urban,
suburban
and
rural
residential
environments
in
the
Dutch
Randstad.
This
working
document
presents
the
research
design
and
preliminary
analyses
of
seasonal
climate
change
effects
on
destination
choices
in
the
Randstad
Holland.
First
the
research
design
will
be
outlined.
Thereafter
an
analysis
will
be
provided
of
the
effects
of
climate
change
on:
the
balance
between
leisure
and
utilitarian
activities;
the
participation
into
various
activities;
destination
locations;
and
travelled
distances
in
the
Randstad
Holland.
2. RESEARCH
DESIGN
This
research
is
located
in
the
Randstad
Holland.
The
densely
populated
region
is
located
in
the
west
of
the
Netherlands,
spanning
the
area
around
the
four
largest
cities
Amsterdam,
Rotterdam,
The
Hague
and
Utrecht.
This
region
forms
the
study
area
of
the
CESAR-­‐project
(Climate
and
Environmental
change
and
Sustainable
Accessibility
of
the
Randstad)
on
sustainable
urbanisation
and
accessibility
in
which
this
study
is
embedded
(http://www.nwo.nl/nwohome.nsf/pages/NWOP_7YUHV3_Eng).
This
study’s
research
design
is
similar
to
an
earlier
research
on
climate
change
effects
on
mode
choices
and
travelled
distances
(Böcker
et
al.,
submitted).
Based
on
Randstad
meteorological
records
(KNMI,
2011)
and
four
regional
climate
change
scenarios
reflecting
variations
in
global
temperature
rise
(+1
to
+2˚C)
and
prevailing
wind
patterns
(KNMI,
2009),
we
estimate
present
as
well
as
2050
seasonal
averages.
In
order
to
analyse
climate
change
effects
we
select,
from
the
last
decade,
seasons
with
average
weather
conditions
for
the
climate
at
present
as
well
as
seasons
with
weather
conditions
projected
to
be
average
in
2050
(KNMI,
2009).
Selected
seasons
represent
precipitation
and
temperature
patterns
as
accurately
as
possible.
To
address
not
only
amounts
but
also
distributions
of
precipitation,
we
include
seasonal
precipitation
sums
as
well
as
numbers
of
wet
days
(≥0.1mm).
With
regard
to
temperature,
seasons
at
the
higher
end
of
the
projected
2050-­‐
bandwidth
are
preferred,
as
underlying
climate
scenarios
for
these
are
more
likely
to
occur
(KNMI,
2009).
If
necessary,
precipitation
is
valued
over
temperature
as
a
selection
criterion,
because
of
its
higher
significance
for
travel
behaviour
in
the
literature
(e.g.
Cools
and
other,
2010).
Table
1
presents
shows
the
selected
seasons.
At
present,
the
Randstad
Holland
is
subjected
to
a
maritime
climate
characterised
by
warm
summers,
mild
winters
with
moderate
but
relatively
stable
year-­‐round
precipitation.
For
2050,
winters
are
projected
to
become
much
milder
and
wetter;
springs
warmer
and
wetter;
summers
hotter
with
at
periods
heavier
precipitation
as
well
as
more
intensive
drought;
and
autumns
will
become
warmer
with
also
at
periods
intensified
precipitation
as
well
as
drought,
although
less
than
in
summer.

4. CESAR
Project
1
working
document
series
no.1
Climate
change
and
destination
choices
Page
4
Table 1: Overview of changing climate patterns present-2050 in the Netherlands, for the selected seasons
Temperature Precipitation
Selected season Average in ˚C Seasonal sum in mm # of days ≥0.1mm
Present 2050 Present 2050 Present 2050 Present 2050
Winter 2004/05 2007/08 3.6 5.1 176 218 50 47
Spring 2005 2008 9.8 10.2 152 197 57 49
Summer 2009 2006 17.4 18.5 180 263 43 38
Autumn 2008 2005 10.2 12.0 267 241 50 43
Source: Böcker et al., forthcoming
From
2004-­‐2009
Dutch
National
Travel
Survey
data
(Mobiliteitsonderzoek
Nederland)
we
analyse
activity
data
for
the
selected
seasons.
The
total
annual
number
of
respondents
varies
from
around
66,000
in
2004
to
40,000
in
2009.
From
a
sub-­‐sample
of
participants
living
in
the
Randstad
region
with
the
age
of
18
years
and
older,
we
select
heads
of
households
and
their
partners
only.
For
different
activity
destinations
–
work/study,
maintenance
(including
shopping
under
30
minutes),
picking
up
persons,
social
visit,
leisure-­‐shopping
(30
minutes
or
longer),
leisure-­‐touring
and
leisure-­‐
other
–
we
analyse
seasonal
climate
change
effects
on
demand
and
location
choice
in
terms
of
travelled
distance
and
urbanization
degree.
Unfourtunately,
an
exact
subdivision
between
indoors
and
outdoors
leisure
activities
could
not
be
made
from
the
existing
data.
Generally,
however,
the
leisure
touring
category
comprises
activities
with
a
more
outdoors
character
(recreational
trips,
including
walking/cycling
tours),
whereas
the
leisure
other
category
includes,
in
addition
to
some
activities
that
could
be
either
indoors
or
outdoors
(hobby,
sports),
a
lot
of
typically
indoors
activities
(cultural
activities,
church,
community
center,
etc).
In
the
multivariate
part
we
control
for
various
independent
individual/household
attributes
and
spatio-­‐temporal
attributes
in
which
trips
are
situated.
As
individual
attributes
we
include
age,
gender,
education
level
and
workweek
duration.
We
include
the
household
attributes
car
availability,
household
income,
and
household
type.
The
latter
is
a
typology
based
on
household
size,
presence
of
children
under
the
age
of
12,
and
the
number
of
adults
participating
in
the
labour
market.
As
spatial
attributes
we
include
address
densities
of
the
destination
and
the
place
of
residence
and
as
temporal
attributes
we
include
activity
timing
in
view
of
day/night,
peak/off-­‐peak
and
weekday/weekend.
Figure
1
summarizes
all
variables
into
a
conceptual
framework.
Figure
1:
Conceptual
framework
of
variables
used

5. CESAR
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Climate
change
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destination
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Page
5
Activity
demand
is
estimated
in
terms
of
the
number
of
trips
per
person
per
day.
Hereby
use
is
made
of
negative
binomial
regression
models,
which,
unlike
Poisson
regression,
can
deal
with
over-­‐
dispersed
count-­‐data
with
excess
zeros
generated
by
the
large
number
of
people
not
participating
certain
activities
on
a
day.
Travelled
distance
is
estimated
per
trip
by
regression
analyses.
Activity
location
is
estimated
with
binary
logistic
regressions
in
terms
of
whether
or
not
people
on
the
day
of
enquiry
made
a
trip
towards
locations
of
varying
urbanization
degrees
subdivided
into
five
classes.
For
all
analyses
separate
models
are
estimated
for
the
different
activity
types.
In
order
to
address
seasonal
climate
change
effects,
they
are
conducted
for
the
full
sample
and
thereafter
repeated
for
the
four
separate
seasons.
3. ANALYSIS
3.1 Recreational
and
utilitarian
trip
generation
In
the
literature
we
have
encountered
that
on
a
daily
level
under
dry
and
moderately
warm
weather
conditions,
people
generally
perform
more,
or
cancel
less,
recreational
trips,
than
under
wet,
cold
or
very
hot
weather
conditions,
whereas
utilitarian
trips
remain
more
or
less
unaffected
(Aaheim
and
Hauge,
2005;
Sabir,
2011;
Cools
et
al,
2010).
Projected
for
the
Randstad
climate
change
generates
warmer
weather
in
all
seasons
in
2050.
Especially
in
winter
the
temperature
effect
may
have
positive
effects
on
recreational
activities,
whereas
an
extra
increase
in
summer
temperature
will
not,
and
may
on
the
contrary
at
days
have
a
negative
effect.
However
in
winter
and
spring
also
precipitation
increases,
which
could
counter
the
positive
effect
on
recreational
trips.
In
order
to
address
whether
people
adjust
their
number
of
recreational
and
utilitarian
trip
to
changing
climate
conditions,
we
descriptively
analyse
the
number
of
recreational
and
utilitarian
trips.
Hereby
recreational
trips
include
trips
made
for
social
and
leisure
purposes
including
shopping
trips
longer
than
half
an
hour.
Utilitarian
trips
include
trips
with
the
purpose
of
work/study,
errands
and
the
bringing
or
picking
up
of
people.
It
appears
that
in
winter,
spring
and
summer,
as
well
as
year-­‐round,
people
approximately
make
as
many
recreational
trips
as
utilitarian
trips,
and
that
this
ratio
remains
relatively
stable
when
we
compare
2050
to
present
seasons.
In
autumn
a
slight
increase
in
the
share
of
recreational
over
utilitarian
trips
can
be
observed
from
44%
at
present
to
47%
in
2050.
Although
these
figures
do
not
point
at
clear
climate
change
effects,
we
perform
a
multivariate
analysis
to
see
whether
effects
appear
when
controlled
for
various
background
variables.
The
five
binary
logistic
regressions
–
one
for
each
season
and
one
for
the
full
year
–
presented
in
Table
1,
show
the
effects
for
various
independent
background
variables,
including
climate
change,
on
whether
a
trip
is
recreational
or
utilitarian.
The
impacts
of
socio-­‐demographic,
household
and
temporal
attributes
are
as
could
be
expected,
with
for
instance
more
recreational
trips
for
elderly,
couples
and
singles,
especially
those
who
work
less,
and
for
trips
off-­‐peak
and
in
the
weekend.
Table 1: Determinants for the ratio between recreational and utilitarian trips
Binary logistic regression: Recreational trip generation (ref. = utilitarian trips)
Winter Spring Summer Autumn All
B S.E. B S.E. B S.E. B S.E. B S.E.
Constant 1,461*** ,160 1,521 *** ,160 1,394 *** ,168 ,791 *** ,160 1,289 *** ,080
Age (ref.=30-49)
18-29 -,110 ,086 ,068 ,090 ,060 ,090 ,028 ,090 ,007 ,044
50-64 ,107* ,065 -,002 ,065 ,022 ,063 ,155 ** ,063 ,061 * ,032
65-75 ,442*** ,105 ,055 ,104 ,053 ,103 ,539 *** ,108 ,255 *** ,052
75+ ,421*** ,129 ,037 ,128 ,008 ,130 ,502 *** ,129 ,222 *** ,064

6. CESAR
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Climate
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Gender (ref.=female)
male -,295*** ,052 -,210 *** ,054 -,218 *** ,051 -,214 *** ,053 -,229 *** ,026
Education (ref.= higher)
lower_education -,068 ,059 -,046 ,060 ,033 ,059 -,019 ,059 -,020 ,029
middle_education -,152*** ,054 -,056 ,056 -,046 ,055 -,126 ** ,056 -,096 *** ,027
Work duration (ref. <12h/w)
>30 hours/week -,621*** ,082 -,520 *** ,082 -,564 *** ,082 -,565 *** ,081 -,556 *** ,040
12-30 hours/week -,630*** ,084 -,416 *** ,087 -,307 *** ,088 -,313 *** ,084 -,410 *** ,043
Household type
(ref.= family. 2 workers)
family 1 or no worker -,321*** ,109 -,225 ** ,107 -,094 ,117 -,097 ,113 -,167 *** ,055
couple 1 worker ,250** ,109 ,414 *** ,115 ,409 *** ,115 ,510 *** ,113 ,407 *** ,056
couple 2 workers ,282*** ,081 ,130 ,088 ,373 *** ,085 ,448 *** ,084 ,324 *** ,042
couple no worker ,436*** ,122 ,685 *** ,125 ,559 *** ,125 ,527 *** ,126 ,574 *** ,062
single and worker ,111 ,104 ,254 ** ,104 ,340 *** ,103 ,389 *** ,108 ,303 *** ,052
single no worker ,433*** ,141 ,601 *** ,145 ,561 *** ,143 ,528 *** ,142 ,553 *** ,071
other ,209*** ,080 ,218 *** ,083 ,273 *** ,083 ,252 *** ,081 ,251 *** ,041
Household income
(ref.<15K)
15,000 to 29,999 euros ,141 ,096 -,162 ,099 -,112 ,100 ,302 *** ,097 ,048 ,049
30,000 euros or more ,015 ,098 -,022 ,103 -,085 ,103 ,165 * ,098 ,033 ,050
unknown ,066 ,101 ,055 ,109 -,195 * ,107 ,052 ,103 ,006 ,052
Car ownership (ref.=no car)
2 cars or more ,062 ,093 -,034 ,090 ,096 ,092 ,049 ,095 ,036 ,046
1 car and main driver ,019 ,081 ,044 ,078 ,044 ,080 -,028 ,083 ,018 ,040
1 car. not main driver ,011 ,095 ,111 ,092 ,207 ** ,093 ,146 ,097 ,121 ** ,047
Geographical context
address density residence ,006 ,016 -,023 ,017 -,015 ,017 ,000 ,017 -,009 ,008
address density destination -,014 ,013 -,015 ,014 -,020 ,014 ,006 ,014 -,011 ,007
Temporal context
weekend (ref. = weekday) -1,440*** ,054 -1,286 *** ,055 -1,284 *** ,055 -1,336 *** ,056 -1,329 *** ,027
night (ref. = day) ,545*** ,054 ,224 *** ,085 ,165 ,129 ,416 *** ,061 ,318 *** ,032
peak (ref. = off-peak) -2,570*** ,094 -2,118 *** ,087 -1,917 *** ,082 -2,099 *** ,084 -2,143 *** ,043
2050 Climate change ,019 ,044 -,097 ** ,047 ,047 ,047 ,131 *** ,047 ,012 ,022
Goodness of fit
Pseudo R2 (Nagelkerke) .342 .302 .271 .314 .304
*p<0.10; **p<0.05; ***p<0.01
When
tested
multivariately,
in
spring
a
significant
decrease
in
recreational
trips
can
be
observed,
which
may
have
to
do
with
the
fact
that
in
spring
2050
weather
conditions
not
only
got
warmer
but
also
wetter.
In
line
with
the
descriptives
a
positive
effect
can
be
observed
in
autumn,
which
could
be
explained
by
the
combination
of
warmer
weather
with
an
increasing
number
of
dry
days
in
the
2050
autumn
season:
conditions
under
which
we
expected
people
to
participate
more
in
recreational

7. CESAR
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Climate
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destination
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trips.
During
the
other
seasons,
temperature
also
increases
but
this
is
accompanied
by
an
increase
in
(heavy)
precipitation.
As
with
the
descriptives,
no
significant
climate
change
effects
have
been
found
on
the
ratio
between
utilitarian
and
recreational
trips
in
winter
and
summer
as
well
as
year-­‐
round.
Overall,
therefore,
seasonal
climate
change
effects
on
the
ratio
between
recreational
and
utilitarian
trips
may
seem
quite
marginal.
When
put
in
perspective,
this
is
however
not
entirely
surprising,
as
it
may
be
questioned
whether
substitution
between
leisure
and
utilitarian
activities,
as
observed
in
the
literature
on
a
daily
level,
may
actually
take
place
on
a
seasonal
level.
3.2 Trip
generation
and
travelled
distances
for
different
activity
types
In
section
4.1
we
observed
a
relative
decrease
in
leisure
over
utilitarian
activities
in
spring
and
a
relative
increase
in
autumn.
However
from
this
ratio
we
cannot
conclude
which
changes
in
absolute
terms
take
place.
Neither,
it
becomes
clear
exactly
which
different
types
of
utilitarian
and
recreational
trips
are
affected
by
climate
change.
In
this
section
we
will
therefore
subdivide
within
recreational
as
well
as
utilitarian
trips
between
different
activity
types.
Based
on
the
literature
we
expect
that
the
participation
in
different
types
of
recreational
activities
is
more
subjected
to
changing
weather
conditions
than
that
in
utilitarian
activities
(e.g.
Cools
et
al.,
2010;
Brandenburg
et
al.,
2004).
In
the
literature
we
have
also
encountered
that
physical
activities
(e.g.
Chan
and
Ryan,
2009)
outdoor
leisure
activities
(Spinney
and
Millward,
2010)
and
walking/cycling
trips
(e.g.
Keay,
1992;
Aultman-­‐Hall,
2010)
are
positively
affected
by
warm
and
dry
weather
conditions
and
negatively
by
wet,
cold
or
very
hot
weather
conditions.
Hence
our
expectation
is
to
observe
within
the
recreational
sphere
an
increase
in
leisure-­‐touring
activities
in
the
slightly
wetter
but
much
milder
2050-­‐winters,
and
an
opposed
effect
in
the
hot
2050-­‐summers
with
increased
heavy
precipitation
and
drought.
For
the
generally
more
indoor
and
less
active
leisure-­‐other
and
leisure
shopping
categories,
which
are
competing
within
the
same
leisure
time
budget
as
leisure-­‐touring
and
partially
satisfy
the
same
needs
(Nijland
et
al.,
2011),
we
expect
reversed
effects
due
to
potential
substitution.
Figure
2
presents
the
relative
impact
of
seasonal
climate
change
effects
on
various
activity
types
expressed
in
per
cent
changes.
The
activities
are
ordered,
based
on
the
size
(not
direction)
of
climate
change
impact
summed
up
for
the
different
seasons,
with
maintenance
activities
on
the
left
resembling
the
smallest
impact
and
leisure
touring
activities
showing
the
highest
impact.
In
line
with
the
literature
and
our
expectations
Figure
2
clearly
demonstrates
that
the
participation
into
recreational
activities,
such
as
the
leisure
other
and
leisure
touring
categories,
is
much
more
sensitive
to
climate
change
than
the
participation
into
utilitarian
activities
such
as
work/study
and
maintenance.
Figure 2: Seasonal climate change effects on per cent changes in number of trips per person per day for
different activity types

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Two
exceptions
here
are
the
relatively
higher
climate
change
impact
on
more
or
less
utilitarian
category
of
bringing
and
picking
up
persons,
and
the
relatively
lower
climate
change
impact
on
the
more
or
less
recreational
category
of
social
visits.
An
explanation
for
the
first
could
be
that
bringing
or
picking
up
persons
may,
in
some
cases,
be
a
more
voluntary
or
even
recreational
event.
Explanations
for
the
latter
could
be
that
social
visits
cannot
easily
be
substituted
for
by
other
activities
(regardless
of
the
weather
in
a
season
one
wants/needs
to
meet
friends
and
family),
that
social
visits
may
need
to
be
planned
far
in
advance,
and
that
social
home
visits
may
often
be
flexibly
located
indoors
or
outdoors
(as
they
may
be
situated
inside,
in
the
garden
or
on
the
terrace),
and
for
all
these
potential
reasons
are
less
subjected
to
the
weather.
Again
we
will
first
turn
to
the
multivariate
part
before
discussing
into
detail
the
results
in
the
context
of
seasonal
climate
change.
In
order
to
analyse
trip
generation
multivariately,
we
estimated
35
negative
binomial
regression
models:
for
each
activity
type
one
model
per
season
and
one
for
the
full
year.
In
these
models,
climate
change
effects
are
analysed
along
with
the
effects
of
various
individual
and
household
background
predictors.
Table
2
summarizes
only
the
effects
for
climate
change;
we
will
not
go
into
detail
into
the
effects
of
the
other
predictors,
but
upon
checking
their
respective
effects
seemed
logical.
Table 2: Climate change effects on frequencies for various activities
Negative binomial models: Climate change effects on # trips/person/day
Winter Spring Summer Autumn All seasons
B B B B B
Work/study -,045 -.019 .085 -,056 -,012
Maintenance -.091 -..099 -.019 .000 -.036
Picking up .294 *** .220 ** -.142 ,190 ** ,071 *
Social visit .089 -.019 -.076 .127 * .061 **
Leisure shopping -.032 -.171 *** .188 *** .024 -.018
Leisure touring .475 *** .277 *** -.327 *** -.129 * .097 ***
Leisure other -.321 *** -.243 *** .210 *** .207 *** -.088 ***
All trips .012 -.025 .020 .008 .005
Goodness of fit: Unscaled deviance/df lies between .43 and .63 and unscaled Pearson Chi2
/df between .71
and 1.66. All full models are significant improvement over intercept-only models (Omnibus-test). In most
models the majority of predictors is significant.
*p<0.10; **p<0.05; ***p<0.01
In
line
with
the
descriptives
utilitarian
trips
remain
largely
unaffected
by
climate
change.
Work
trips,
remain
largely
unaffected
by
climate
change,
and
so
do
errands
trips.
Climate
change
does
seem
to
strongly
increase
trip
for
bringing
and
picking
up
persons
in
winter.
Additional
analysis
(not
included
in
this
paper)
shows
that
this
is
mostly
an
increase
of
trips
by
active
transport
modes,
indicating
that
it
may
often
involve
people
(parents)
who,
with
the
milder
2050-­‐winter
weather,
more
often
bring
or
pick
up
others
(their
children)
by
foot
or
bicycle.
Also
in
spring
and
autumn
this
category
increases
significantly,
whereas
in
summer
a
non-­‐significant
decrease
is
observed.
Under
recreational
trips
more
significant
climate
change
effects
can
be
found.
In
line
with
the
decriptives
social
visits
are
an
exception.
For
social
visits
we
observe
non-­‐significant
effects
for
all
seasons
except
for
autumn
and
full
year,
when
significant
positive
effect
can
be
identified.
In
line
with
the
decriptives
and
our
expectations,
leisure
touring
trips
increase
highly
significantly
in
the
warmer
and
wetter
2050-­‐winter
and
–spring,
whereas
highly
significant
declines
are
observed
in
the
hotter/warmer
2050-­‐summer
and
-­‐autumn
with
intensified
precipitation
and
drought.
These
effects
on
leisure-­‐touring
coincide
with
the
higher
use
of
active
open-­‐air
transport
modes
in
the
Randstad-­‐Holland
in
2050-­‐winter
and
spring
seasons
in
contrast
to
the
lower
use
of
these
in
summer
and
autumn,
found
in
an
earlier
publication
(Böcker
et
al.,
submitted).
As
expected,
leisure-­‐shopping
and
leisure-­‐other
trips
are
subjected
to
seasonal
climate
change
in
the
exact

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opposite
directions.
Both
decrease
in
winter
(although
shopping
insignificantly)
and
spring,
while
increasing
significantly
in
summer.
In
autumn,
shopping
non-­‐significantly
decreases
and
leisure-­‐
other
significantly
increases.
Although
comparison
between
the
activities
should
be
made
carefully
(as
of
the
separate
models)
and
substitution
effects
cannot
directly
be
derived,
there
seems
a
strong
indication
that
people
substitute
between
on
the
one
hand
the
more
active
and
outdoors
leisure-­‐touring
activities
and
on
the
other
hand
the
leisure-­‐shopping
and
leisure-­‐other
activities
with
a
more
mixed/indoors
character.
For
travelled
distances
our
expectations
are
less
clear.
Based
on
one
earlier
Norwegian
study
(Aaheim
and
Hauge,
2005),
an
increase
in
leisure
trip
distance
may
be
expected
when
weather
conditions
get
warmer
and
dryer,
whereas
decreases
may
be
expected
when
weather
conditions
get
colder
and
wetter.
This
supports
the
intuitive
way
of
reasoning
that
climate
change
effects
on
trip
frequencies
would
be
more
or
less
in
line
with
the
effects
on
trip
generation.
However,
climate
change
effects
could
also
work
their
way
through
on
travelled
distances
indirectly
via
the
choice
for
transport
modes
–
a
problem
recognised
but
not
accounted
for
by
the
earlier
Norwegian
study
(Aaheim
and
Hauge,
2005)
–
rising
uncertainty
in
our
expectations
about
its
net
effects.
In
order
to
analyse
the
seasonal
climate
change
effects
on
travelled
distance,
for
each
season
and
the
full
year
we
run
separate
regression
models
for
each
of
the
activity
types
and
all
trips
combined.
A
summary
of
these
models
with
regard
to
the
effects
of
climate
change
is
given
in
table
3
and
will
be
compared
to
the
results
on
trip
generation
in
table
2.
Table 3: Summary of seasonal climate change effects on trip distance for different leisure activities
OLS regression: Climate change effects on travelled distance (in 0.1 km) per trip
Winter Spring Summer Autumn All
B Beta B Beta B Beta B Beta B Beta
Work/study .004 .003 -.030 -.023 .039 .030 .030 .024 .012 .010
Maintenance -.042 -.036 .005 .004 -.049 * -.041 .026 .022 -.021 -.018
Picking up -.001 -.001 -.090 ** -.072 -.029 -.022 .009 .007 -.007 -.006
Social visit .018 .013 -.084 ** -.057 -.033 -.022 .035 .024 -.011 -.007
Leisure shopping -.037 -.035 -.111 *** -.096 .029 .026 .071 *** .065 -.02 -.01
Leisure Touring -.059 * -.049 .112 *** .087 -.058 -.045 -.011 -.008 .012 .010
Leisure Other -.043 -.034 -.067 ** -.052 -.003 -.002 .076 ** .061 -.015 -.013
All trips -.022 * -.016 -.033 ** -.023 -.001 -.001 .049 *** .035 -.001 -.001
Goodness of fit: R2
values lie between .05 and .15
Notes: For travelled distances the log is taken. *p<0.10; **p<0.05; ***p<0.01
In
line
with
the
effects
on
trip
generation,
travel
distances
for
recreational
trips
are
more
strongly
influenced
by
seasonal
climate
change
those
for
utilitarian
trips.
When
looked
at
the
shoulder
seasons
Tables
2
and
3
show
many
similarities.
Trip
distances
seem
to
be
mostly
influenced
in
the
warmer
and
wetter
2050
spring
season.
Trip
distances
significantly
increase
for
touring
and
significantly
decrease
for
shopping
and
leisure
other,
as
well
as
for
some
of
the
other
activity
types
and
the
average
for
all
trips
combined.
It
seems
that
with
the
increase
in
the
participation
into
the
active
and
outdoors
oriented
leisure-­‐touring
activities
(Table
2),
people
are
also
willing
to
travel
further
for
these
(Table
3).
For
autumn
we
observe,
also
in
line
with
climate
change
effects
on
trip
generation,
a
decrease
in
leisure-­‐touring
trips
(although
non-­‐significant)
and
highly
significant
increases
in
distances
for
shopping
and
leisure-­‐other,
as
well
as
in
the
average
distance
for
all
trips
combined.
However,
when
looked
at
winter
and
summer,
a
comparison
between
Table
2
and
3
reveals
much
dissimilarity.
In
contrast
to
the
number
of
trips,
trip
distances
in
warmer
2050-­‐winters
significantly
decrease
for
touring
trips.
At
the
same
time
we
do
not
observe
a
significant
decrease
in
distances
travelled
in
summer.
Above
all,
climate
change
effects
on
travelled
distance
in
winter
and
summer
seem
to
be
rather
limited,
rising
our
expectation
of
the
interference
of
second
process:
mode
choice.
In
a
previous
study
on
mode
choice
in
the
Randstad
Holland,
we
found
that
the

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choices
for
active
transport
modes
increase
slightly
in
spring
and
largely
in
winter
conditions
whereas
they
decrease
slightly
in
autumn
and
largely
in
2050-­‐summer
weather
conditions
(Böcker
et
al.,
submitted).
Consequently,
for
instance
warmer
winter
weather
may
on
the
one
hand
enhance
further
travelling
for
leisure
touring,
but
on
the
other
hand
increase
the
use
of
active
transport
modes
–
typically
used
for
shorter
distances
–counteracting
the
former
effect.
Interference
of
the
indirect
effect
via
mode
choices
could
explain
why
in
contrast
to
the
clear
climate
change
effects
on
trip
generation,
its
effects
on
trip
distance
are
less
clear,
especially
in
winter
and
summer
when
climate
change
effects
on
mode
choice
are
strongest
(Böcker
et
al.,
submitted).
3.3 Degree
of
urbanization
of
selected
destinations
for
leisure
activities
For
our
analysis
of
activity
destination
locations
in
terms
of
urbanization
degree,
we
will
focus
our
analysis
on
the
recreational
activities
shopping,
leisure-­‐touring
and
leisure-­‐other
(excluding
social
visits)
for
two
reasons.
First,
in
contrast
to
the
other
activities,
these
leisure
activities
are
generally
more
voluntary,
flexible
and
occassional,
and
as
such
are
expected
to
be
less
fixed
in
time
and
space
and
more
strongly
subjected
to
weather
conditions,
for
which
evidence
has
been
found
throughout
the
literature
(e.g.
Cools
et
al.,
2010;
Brandenburg
et
al.,
2004)
and
which
we
have
seen
in
section
4.2.
Second
these
leisure
activities
are
directly
competing
with
each
other
within
the
same
leisure
time
budget
as
found
in
the
literature
(Nijland
et
al
2011)
and
encountered
in
section
4.2.
Based
on
the
literature
(e.g.
Nikopoulou
and
Lykoudis,
2007)
and
intuitive
reasoning,
our
expectation
is
that
people
stick
to
more
sheltered
inner-­‐city
locations
for
leisure
activities
when
the
weather
conditions
are
colder
or
wetter,
to
benefit
from
the
urban
heat
island
(against
cold)
or
to
be
less
exposed
to
precipitation
or
heavy
wind.
In
contrast,
with
warmer
and
dryer
weather
conditions
we
may
expect
people
to
enjoy
more
weather-­‐
exposed
destinations
outside
the
city.
During
very
hot
weather
conditions,
such
as
in
the
selected
2050-­‐summer,
we
may
–
as
a
result
of
an
escape
of
inner-­‐city
heat
–
also
expect
people
to
select
destinations
outside
cities,
although
we
doubt
whether
this
effect
will
show
on
the
aggregated
seasonal
level.
A
descriptive
overview
of
the
seasonal
climate
change
effects
on
selected
destination
locations
of
various
degrees
of
urbanization
for
the
different
leisure
activities
is
presented
in
figure
3.
Figure 3: Seasonal climate change effects on attendances of destinations of different density for leisure
activities, in per cent changes of the number of trips per person per day.

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Figure
3
in
broad
lines
echoes
the
climate
change
effects
on
activity
participation
found
earlier
in
Figure
2,
with
clear
increases
in
touring
destinations
in
winter/spring
and
decreases
in
summer
and
to
a
lesser
extent
autumn,
against
opposed
effects
for
leisure-­‐
other
and
shopping.
But
when
looked
into
more
detail
it
appears
that
for
these
different
leisure
activities
under
changing
weather
conditions
different
locations
are
preferred.
For
instance
in
winter,
very
clearly
it
appears
that
the
increases
in
touring
(found
earlier
in
Figure
2)
are
mostly
taking
place
in
the
more
rural/
suburban
locations
and
not
in
inner-­‐city
areas.
Before
discussing
these
effects
on
leisure
destination
locations
in
the
context
of
seasonal
climate
change,
we
will
first
turn
to
the
multivariate
analysis.
For
all
seasons,
we
modelled
the
effects
of
climate
change,
along
with
various
individual
and
household
background
predictors,
on
the
number
of
trips
per
person
per
day
for
the
different
leisure
activities’
destinations
of
varying
address
density.
Table
4
presents
a
summary
of
the
effects
of
seasonal
climate
change.
Table 4: Summary of seasonal climate change effects on location in terms of urbanization degree
Binary logistic regression: Climate change effects on #trips/person/day towards different densities
Winter Spring Summer Autumn All
B
Bet
a B
Bet
a B Beta B
Bet
a B
Bet
a
Leisure shopping
<700 -0,277 0,209 -0,731 *** 0,223 0,028 0,216 0,504 ** 0,222 -0,105 0,097
700-1400 0,057 0,131 -0,146 0,139 0,343 ** 0,143 -0,080 0,149 0,022 0,066
1400-2000 -0,079 0,108 -0,054 0,129 0,000 0,127 0,132 0,135 -0,037 0,059
2000-3500 -0,102 0,094 -0,068 0,104 0,367 *** 0,112 -0,151 0,103 -0,028 0,049
>3500 -0,176 0,125 -0,402 *** 0,150 0,263 * 0,149 0,250 * 0,144 -0,040 0,065
Leisure touring
<700 0,346 ** 0,164 0,052 0,173 -0,401 *** 0,140 -0,281 0,174 -0,049 0,078
700-1400 0,640 *** 0,168 0,239 0,173 -0,411 *** 0,142 0,145 0,171 0,170 ** 0,079
1400-2000 0,541 *** 0,174 0,417 ** 0,166 -0,278 * 0,163 0,046 0,189 0,165 * 0,084
2000-3500 -0,029 0,172 0,131 0,164 -0,269 * 0,153 0,156 0,164 0,053 0,078
>3500 -0,047 0,224 0,493 ** 0,206 -0,888 *** 0,192 -0,534 ** 0,211 -0,162 0,101
Leisure other
<700 -0,279 * 0,168 -0,400 ** 0,164 0,397 ** 0,182 0,188 0,174 -0,069 0,079
700-1400 -0,607 *** 0,149 -0,051 0,132 0,295 * 0,164 -0,033 0,144 -0,171 ** 0,068
1400-2000 -0,022 0,142 -0,138 0,150 0,342 ** 0,171 0,061 0,148 0,032 0,072

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2000-3500 -0,511 *** 0,138 -0,306 ** 0,147 -0,145 0,158 0,581 *** 0,152 -0,105 0,067
>3500 -0,332 ** 0,157 -0,375 ** 0,177 0,153 0,206 0,263 0,186 -0,158 * 0,082
Goodness of fit: Pseudo R2 (Nagelkerke) range from .015 to .232 and average .085
*p<0.10; **p<0.05; ***p<0.01
In
the
slightly
wetter
but
much
milder
2050-­‐winters
leisure
touring
trips
increase
highly
significantly
towards
locations
with
lower
address
densities,
whereas
towards
higher-­‐density
destinations
this
is
not
the
case
(non-­‐significant
decreases).
According
to
our
expectations
and
the
descriptives
it
seems
that
the
milder
2050
weather
conditions
favour
the
visiting
of
more-­‐exposed
lower
density
areas,
which
are
less
attractive
in
colder
present-­‐day
winters.
At
the
same
time
the
more
indoors
leisure-­‐other
trips
towards
all
urbanization
degrees
decrease
significantly,
with
the
exception
of
medium
density
locations,
which
remain
unaffected.
Also
shopping
trips
are
not
significantly
impacted.
According
to
the
descriptives,
in
the
warmer
and
wetter
2050
spring
seasons
we
can
see
significant
increases
in
touring
trips,
towards
medium
density
locations
(including
many
of
the
cities’
fringes),
as
well
as
in
inner-­‐city
environments
(including
urban
parks).
Touring
in
rural
areas
seems
to
be
less
affected.
Leisure-­‐other
and
shopping
trips
are
negatively
affected,
but
decreases
are
only
significant
for
the
higher
density
locations
and
the
very
rural
locations.
In
hotter
2050-­‐summers
with
increased
heavy
precipitation
and
drought,
leisure
touring
activities
significantly
decrease
for
all
degrees
of
urbanization.
As
in
the
descriptives,
the
decrease
in
general
seems
to
be
stronger
for
lower
density
areas,
which
could
be
related
lack
of
shelter
in
these
areas
to
heavy
rain.
But
the
decrease
in
touring
is
also
exceptionally
high,
and
highly
significant,
for
the
highest
density
areas,
which
could
be
a
result
of
the
unattractiveness
of
these
areas
for
physical
activity
during
heat.
With
regard
to
the
more
indoors/mixed
recreational
alternatives,
leisure-­‐other
activities
increase
mostly
in
lower
density
areas
whereas
leisure
shopping
increases
mostly
in
higher
density
areas.
Of
all
seasons,
in
autumn
location
in
terms
of
urbanization
degree
seems
to
be
least
clearly
affected.
Leisure
touring
seems
to
decrease
for
the
most
rural
(near-­‐to-­‐significant)
and
urban
areas
(significant),
whereas
towards
locations
of
more
medium
density
non-­‐significant
increases
can
be
observed.
Leisure
shopping
significantly
increases
in
very
urban
and
very
rural
areas,
whereas
leisure-­‐other
increases
only
significantly
in
moderately
urban
areas.
As
of
opposite
seasonal
climate
change
effects,
over
the
whole
year
the
net
climate
change
effect
on
destination
location
for
leisure
activities
is
mostly
marginal:
shopping
remains
entirely
unaffected;
touring
seems
to
increase
significantly
only
for
medium
density
destinations;
and
leisure
other
decreases
in
moderately
rural
and
very
urban
areas.
In
this
section
it
became
clear
that
climate
change
highly
affects
the
choices
for
recreational
activities
on
the
seasonal
level,
but
that
in
addition
to
what
we
have
seen
in
section
4.2,
considerable
differences
exist
between
the
generation
of
trips
in
different
geographical
contexts.
References
Amelung,
B.
and
Viner,
D.
(2006)
Mediterranean
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pp.
349–366.
Bigano,
A.,
Hamilton,
J.M.
and
Tol,
R.S.J.
(2006)
The
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Hamilton,
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13. CESAR
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Climate
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Page
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Matzarakis,
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