prognosis and scenarios of outdoor recreation...policy of use of natural resources planning and...
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Metsäntutkimuslaitos Skogsforskningsinstitutet Finnish Forest Research Institute www.metla.fi
Prognosis and scenarios of outdoor recreation
Pouta, E., Neuvonen, M . & Sievänen, T.
2
Need for outdoor recreation forecasts
Changing societyChanging populationChanging lifestylesChanging environment
Science-based information needed for Policy of use of natural resourcesPlanning and management of recreational areasPolicy of recreation service provision
3
Approach
three methods for predicting future recreation participation
1) extrapolation of past trends 2) regression techniques based on cross-sectional recreation inventory data3) scenario methods
an opportunity for comparison and discussion
4
Demographic and socio-economic trends
ageing populationincrease in …
level of educationpercentage of white collar workersdifference between high and low incomegroups
urbanizationincrease in …
amount of leisure timeprivate consumption in leisure goods
5
Measurement of outdoor recreation
Lack of time seriesSingle measurements of participation in outdoorrecreation:
Outdoor recreation survey, 1979 (Ministry of interior)Reittiharrastaminen Suomessa, 1992 (METLA)Time consumption (1979, 1987-88 and 1999-2000) and leisure time studies (1991, 2002) (StatisticsFinland)National Outdoor Recreation Demand Inventory(LVVI), 1998-2000 (METLA)
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METHOD 1: Extrapolation of past trends
Participation in activities
0
10
20
30
40
50
60
70
80
90
100
1979 1995 2000 2010 2020
% o
f pop
ulat
ion walking
berry pickingcross-country skiingboatinghiking
7
Alternative trends
cross-country skiing
2830323436384042444648
1979 1995 2000 2010 2020
timescale
part
icip
atio
n, % naive1
naive2SESDES
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METHOD 2: Model based prognosis
FACTORS AFFECTING
RECREATION
PARTICIPATION
MODEL:
PARTICIPATION=
F(POPULATION,
SUPPLY,
ENVIRONMENT)
FORECASTS OF POPULATION AND CLIMATE
PROGNOSIS
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Outdoor recreation models
DataData from National Outdoor RecreationInventory study (LVVI), n=10651
ParticipationLogistic regression
Participation frequencyCount data models, Neg.Bin regression
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Information of demographic and socio-economictrends
VariableAssummed
direction of change
Expected effect on outdoor recreation participation
Total population ↑(↓) + (-)
+/-
+/-
+ (-)
+/-
+
+
Age ↑
Education ↑
Percentage of employees ↑ (↓)
Percentage of urban population ↑
Leisure time private consumption
↑
Working time ↓
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Population forecast
0,40,60,8
11,21,41,61,8
22,22,4
2000 2020 2050 2080
popu
latio
n, m
illio
n
4
4,2
4,4
4,6
4,8
5
5,2
5,4
5,6
population 65+ years population 35-64 yearspopulation 0-34 years Total Finnish population
12
Participation forecast
Participation models
0
0,5
1
1,5
2
2,5
3
3,5
1998-2000
2020 2050 2080
Mill
ions
of p
artic
ipan
cross-country skiing
downhill skiing
snow-mobiling
boating
hiking
walking
Berry picking
Mushroom picking
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Climate change -effect
participation is dependenton climate
e.g. cross-country skiing
Building participationmodels with climatevariables
Cross-country skiing
Participation, % of population
> 4030-40< 30
+4
+2
0
-2
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Climate change -effect
Based on participation models Independent variables: 1) climate and socio-economic factors CCSDB2) only socio-economic factors SDB
00,20,40,60,8
11,21,41,61,8
1998-2000
2020 2050 2080
Part
icip
ants
, mill
ion
cross-countryskiing CCSDBdownhill skiingCCSDBsnowmobilingCCSDBcross-countryskiing SDBdownhill skiingSDBsnowmobilingSDB
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Predicted change in participation times
Based on frequency models Independent variables
1) socio-economic SDB 2) socio-economic and climate CCSDB
0
5
10
15
20
25
30
35
40
2000 2020 2050 2080
Parti
cipa
tion
days
, mill
ion
cross-countryskiing, SDB
cross-countryskiing, CCSDB
Downhill skiing,SDB
Downhill skiing,CCSDB
Snowmobiling,SDB
Snowmobiling,CCSDB
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Developing prognosis with information of skills
Effect of cohort
Measurement of recreationskills gives insight of cohort
downhill skiing skillschisq 166.99 (<.0001)
73,6
79,3
71,5
59,3
65,1
52,8
59,4
44,6
32,6
45,1
62,4
31,5
81,3
70,0
61,4
45,6
49,5
45,3
41,7
34,5
31,4
34,8
31,2
15,4
100 50 0 50 100
15-20
21-25
26-30
31-35
36-40
41-45
46-50
51-55
56-60
61-65
66-70
71-75
age
grou
p
skills, %
men women
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Example of cohort effect –downhill skiing
0
10
20
30
40
50
60
70
80
90
100
2000 2010 2020 2030
Time
Skill
s, %
0
10
20
30
40
50
60
70
80
90
100
Part
icip
atio
n ra
te, %
15-24
25-44
45-64
65-74
Skills, %
Participation rate corrected with skills
Participation rate
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METHOD 3:Recreation scenarios
Unified population* more leisure time for
everyone * equal income* whole country is populated
Divided population * lack of leisure time in some
population groups* disparity in income* population centralized in
large cities
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Environmental values and attitudes
Nature related and consumptive outdoor activitiesNatural environment is highly appreciated
Nature as recreational environment is replaced by built environmentNatural environment has a function of stage or scene
Traditional Modern
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Scenario combination
Technological,urban
Traditional, nature related
Divided
Unified
luxury natureactivities, walksin near forest
virtual activities, ”shoppingcentre walks”
VALUES
POPULATION
spending time at vacation home fishing, huntingand hiking trips
motorisedactivities
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Alternative futuresModel basedestimation
Trends Scenario/expertopionion
Walking -
Cycling
Jogging -
Hiking, backpacking
Hunting -
Fishing
Berry picking
Swimming in natural waters
Boating
Cross-country skiing
Downhill skiing -
Color codes: Stabile, Decrease, Increase
22
Future for outdoor recreation based on prognosis?
Future seems rather stabileAgeing is one of the key factorsClimate change has an effect on winter activities
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Different methods – different future alternatives Combination of different future alternatives gives a better general view
Need for methods of Future studies
As the forecasting is difficult it is very essential to monitor participation
Need for panel-data to identify the effect of age and generation
Discussion
24
THANK YOU!