Prognosis and scenarios of outdoor recreation...Policy of use of natural resources Planning and management of recreational areas Policy of recreation service provision. 3 Approach

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)

6

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

8

METHOD 2: Model based prognosis

FACTORS AFFECTING

RECREATION

PARTICIPATION

MODEL:

PARTICIPATION=

F(POPULATION,

SUPPLY,

ENVIRONMENT)

FORECASTS OF POPULATION AND CLIMATE

PROGNOSIS

9

Outdoor recreation models

DataData from National Outdoor RecreationInventory study (LVVI), n=10651

ParticipationLogistic regression

Participation frequencyCount data models, Neg.Bin regression

10

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 ↓

11

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

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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

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THANK YOU!