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Tourism in Maine: Visitation and Economic Impacts of Weather

Emily Wilkins, M.S. Student

Advised by Dr. Sandra De Urioste-Stone

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Overview

  • Introduction
  • Statistical modeling
  • Visitors’ perceptions
  • Conclusions

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Introduction Models Perceptions Conclusion

Tourism

  • 9.8% of world GDP

​

​

(World Travel and Tourism Council, 2015)

  • U.S.: $2.1 trillion economic impact

​

  • U.S.: $141.5 billion in tax revenue

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Introduction Models Perceptions Conclusion

Maine

  • 17.3 million overnight visits (2014)
  • 20.6 million day visits (2014)

(Maine Office of Tourism, 2015; Maine Office of Tourism, 2016)

Image from http://s211.photobucket.com/user/photomnt/media/me-3081-jd_01.jpg.html

  • $5.2 billion direct expenditure
  • If tourism 15%, taxes $152 per house

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(Explore Maine, 2014)

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Weather and Tourism

  • Highly dependent 1

​

  • Weather: “atmospheric condition at any given time or place” 2

​

  • Climate: “average weather across a period of over 30 years” 2

​

​

1 (i.e. Becken & Hay, 2007) 2 (US EPA, 2013) 3 (Becken & Wilson, 2013; Falk, 2014)

Image: http://www.nationalgeographic.com/adventure/0510/photos/Jpegs/NewZealand.jpg

Image: https://drupal-internationsgmbh.netdna-ssl.com/cdn/article/public/moving_to_austria.jpg

Austria

New Zealand 3

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Introduction Models Perceptions Conclusion

Climate Change in Maine

  • 1.7 °C from 1895 to 2014

(Fernandez et al., 2015)

  • 13% increase in precipitation
  • 1.1-1.7 °C increase by 2050

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Introduction Models Perceptions Conclusion

Climate Change and Tourism in Maine

  • Projected visitation to Acadia up by 2041-2060 1

​

  • Visitors to MDI perceive negative consequences 2

1 (Fishichelli, Schuurman, Monahan, & Ziesler, 2015) 2 (De Urioste-Stone, Scaccia, Howe-Poteet, 2015)

3 (Scott, Dawson, & Jones, 2013) 4 (Dawson & Scott, 2013) 5 (Burakowski & Magnusson, 2012)

  • Snowmobile season 21-29% by 2050s 3

​

  • Alpine skiing: 50-57% maintain season length of 100 days by 2050s 4

​

  • 14% decrease in skier visits low snowfall years 5

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Introduction Models Perceptions Conclusion

Objectives

1. To investigate the impacts of past weather on tourism spending in Maine

​

2. Better understand how weather affects different tourist groups in Maine and tourists’ perceptions

​

3. Explore how climate change could impact tourism in Maine

​

​

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Introduction Methods Results Conclusions

Introduction: Modeling

  • Spending vs. visitation

​

​

  • Daily weather affects daily spending 1

​

​

  • Past limitations: Temperature as a proxy for climate 2

Models:

1 (Murray, Di Muro, Finn, & Leszcyc, 2010) 2 (Gössling, Scott, Hall, Ceron, & Dubois, 2012)

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Introduction Methods Results Conclusions

Study Site

Models:

  • Diversity of outdoor recreational opportunities

​

  • Differing regions

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Introduction Methods Results Conclusions

Mount Desert Island (MDI)

  • Bar Harbor ESA
  • Downeast & Acadia
  • Acadia National Park
  • Summer tourism

Models:

Image: www.visitmaine.net/page/76/bar-harbor-restaurants

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Introduction Methods Results Conclusions

Bethel

  • Rumford ESA
  • Maine Lakes & Mountains
  • Ski resorts
  • Winter tourism

​

Models:

Image: www.nytimes.com/2009/01/09/travel/escapes/09ski.html?_r=0

Image: www.rostay.com/bethelmesummer.htm

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Introduction Methods Results Conclusions

Millinocket

  • Millinocket ESA
  • Maine Highlands
  • Baxter State Park
  • Winter and summer tourism

Models:

Image: http://maineanencyclopedia.com/millinocket/

​

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Introduction Methods Results Conclusions

Data Used

  • Secondary: Jan 2004 – Dec 2014
  • Monthly taxable restaurant, lodging, and retail sales (Maine Revenue Services)
  • Weather variables (NOAA)

Models:

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Introduction Methods Results Conclusions

Data Collection

  • Dependent variable
      • Tourism Spending (inflation-adjusted)

​

  • Independent variables
    • 18 NOAA weather measurements
    • 4 created weather categories
    • Statewide consumer spending (economy)
    • Month
    • Year

​

Models:

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Introduction Methods Results Conclusions

Data Analysis

  • Nonparametric: Boosted regression trees 1
  • Summer, fall, winter analyses
  • Mixed-effects ANCOVAs
      • Linear and curvilinear
  • Projected spending at higher temperatures

​

​

​

Models:

1 (Elith, Leathwick, & Hastie, 2008)

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Introduction Methods Results Conclusions

Mean Spending

Models:

​

Season

Mean Monthly Tourism Spending

(million $)

MDI

Summer

29.9

Fall

17.3

Winter

2.2

Bethel

Summer

3.5

Fall

3.3

Winter

6.1

Millinocket

Summer

1.4

Fall

1.1

Winter

0.9

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Boosted Regression Trees

Models:

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Models:

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ANCOVA

Models:

R2 = 0.9247

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Predicted vs. Actual Spending

Models:

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Introduction Methods Results Conclusions

Future Projections

  • Increase spending in summer and fall under higher temperatures

​

​

  • Winter lowest at 4.6 °C (40.3 °F)

​

Models:

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Introduction Methods Results Conclusions

Results

Models:

​

​

Summer

Fall

Winter

MDI

Avg temp (°C)

22.7

15.0

4.5

Spending ($)

26,650,885

12,585,588

2,104,499

1.1°C increase ($)

28,796,466

13,227,384

2,108,047

1.7°C increase ($)

30,096,744

13,526,096

2,114,060

Low % change

8.1

5.1

0.2

High % change

12.9

7.5

0.5

Bethel

Avg temp (°C)

23.5

14.3

2.6

Spending ($)

3,211,413

2,776,174

4,629,649

1.1°C increase ($)

3,481,527

2,896,619

4,600,929

1.7°C increase ($)

3,645,346

2,970,231

4,594,194

Low % change

8.4

4.3

-0.6

High % change

13.5

7.0

-0.8

Millinocket

Avg temp (°C)

23.1

13.5

1.6

Spending ($)

1,302,910

958,210

796,887

1.1°C increase ($)

1,410,149

996,459

788,655

1.7°C increase ($)

1,475,162

1,019,928

785,715

Low % change

8.2

4.0

-1.0

High % change

13.2

6.4

-1.4

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Introduction Methods Results Conclusions

Does this make sense?

Models:

  • Warmer temperatures expected to benefit parts of northern U.S. 1

​

  • Increase in visitation to Acadia 2

​

  • Non-temperature variables irrelevant 3

​

  • Visitors perceived climate change to have a negative impact on tourism on MDI 4

1 (Scott, McBoyle, & Schwartzentruber, 2004) 2 (Fisichelli, Schuurman, Monahan, & Ziesler, 2015)

3 (Rosselló-Nadal, 2014) 4 (De Urioste-Stone, Scaccia, & Howe-Poteet, 2015)

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Introduction Methods Results Conclusions

Conclusions

Models:

  • New methodology for tourism: Boosted Regression Trees
  • Still only temperature significant
  • Increase in summer and fall tourism spending
  • Likely decrease in winter spending

​

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Introduction Models Perceptions Conclusion

Next Steps

  • Temperature is influential
  • Inconclusive about other weather variables
  • Do visitors’ perceptions agree?
  • What are visitors’ thoughts on climate change?

​

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Introduction Methods Results Conclusions

Intro: Perceptions

Perceptions:

​

  • Perceptions influence behavior 1

​

  • Intended visitation not significantly different from revealed preferences 2

​

  • Perceptions influence policy 3

​

1 (Denstadli, Jacobsen, & Lohmann, 2011) 2 (Loomis & Richardson, 2006)

3 (Brownlee, Hallo, & Krohn, 2013; Brownlee, Powell, & Hallo, 2012) 4 (Leiper, 1979)

  • Tourists: Away from home 24+ hours 4

Image: http://theodysseyonline.com/suny-binghamton/10-signs-you-know-you-grew-up-in-coastal-town/341677

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Perceptions:

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Sampling Procedure

Perceptions:

  • May-November 2015

​

  • Two-stage cluster probability sampling design

​

​

  • Tailored design 1

1 (Dillman, Smyth, and Christian, 2009)

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Introduction Methods Results Conclusions

Self-Administered Questionnaire

Perceptions:

  • Online
  • Paper

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Introduction Methods Results Conclusions

Questionnaire

Perceptions:

1. Basic trip info

2. Impacts of weather

3. Accommodations and spending

​

4. Activities, climate change beliefs, and place attachment

5. Demographics

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Introduction Methods Results Conclusions

Response Rate

Perceptions:

  • 1712 intercept surveys with tourists
  • 704 self-administered
  • 41.1% response
  • Compared for non-response bias
    • Chi-square test of independence
    • No significant differences

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Introduction Methods Results Conclusions

Data Analysis: Segmentation

Perceptions:

1 (Rastogi, Harikrishna, & Patil, 2015; Chiang, Wang, Lee, & Chen, 2015; Bicikova, 2014; Hall, Seekamp, & Cole, 2010)

  • Differences in tourist behavior, travel motivations, and management opinions 1

Image: www.enterrasolutions.com/media/Segmentation-clear.png

  • Multivariate two-step cluster analysis

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Data Analysis

Perceptions:

  • Segmented on:
    • Number of nature-based activities
    • If the primary activity was nature-based

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Data Analysis

Perceptions:

  • 3 segments
  • Levene’s statistic for equal variances
    • ANOVA with Tukey’s post hoc

​

    • Welch stat with Games-Howell post hoc

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Results

Perceptions:

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Non-Nature-Based Tourists

Perceptions:

(Image by Katherine Du, NPR)

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Nature-Based Generalists

Perceptions:

(Image by Katherine Du, NPR)

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Nature-Based Specialists

Perceptions:

(Image by Katherine Du, NPR)

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Visitor Profile

Perceptions:

54.2

$$$

Images: www.enchantedlearning.com/usa/statesbw/northeast/northeastbw.GIF

www.onlineeducationincanada.com/wp-content/uploads/2014/02/Difference-Between-GED-and-Online-High-School-Diploma.jpg

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Introduction Methods Results Conclusions

Spending Behavior

Perceptions:

  • All in US$ per person, per night

​

  • a,b Represent significant differences

Weather

Avg

Non-nature

Generalists

Specialists

P-value

Lodging

50.91

59.96a,b

43.29a

41.31b

.00

Transportation

15.51

20.45a,b

11.41a

10.25b

.00

Food/drinks

33.38

38.01a,b

28.27a

29.06b

.00

Recreation

6.32

5.68

6.93

6.98

.74

Retail/other

13.05

16.63a,b

9.07a

9.71b

.00

Total spending

119.03

141.08a,b

98.98a

96.78b

.00

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Weather Impacts

Perceptions:

Weather

Avg

Non-nature

Generalists

Specialists

P-value

Precipitation

2.14

2.01a

2.29

2.26a

.02

Sunshine

2.32

2.28

2.52

2.28

.19

High temperature

2.19

2.15

2.40

2.14

.12

Low temperature

1.95

1.93

2.17

1.88

.08

Wind speed

1.67

1.65

1.70

1.68

.85

  • 13.7%: Weather unimportant

Scale: 1 (not influential) – 5 (extremely influential)

  • Changed plans:
    • Non-nature-based: 22.2%
    • Generalists: 35.1%
    • Specialists: 28.2%

​

Unimportant

Neutral

Important

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Introduction Methods Results Conclusions

Climate Change Perceptions

Perceptions:

Statement

Avg

Non-nature

Generalists

Specialists

P-value

I believe climate change is happening

3.71

3.58a

3.76

3.89a

.04

I am concerned about climate change

3.65

3.45a

3.77

3.90a

.00

I am concerned about the impacts of climate change to tourism in Maine

3.15

2.97a,b

3.37a

3.31b

.00

The recreational activities that I enjoy in Maine would be at risk if local climate conditions were to change

3.30

3.19a

3.35

3.45a

.03

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Introduction Methods Results Conclusions

Civic Engagement Behavior

Perceptions:

Statement

Avg

Non-nature

Generalists

Specialists

P-value

I am interested in learning more about the impacts of local climate change in Maine

2.77

2.62a,b

3.02a

2.88b

.00

I would be willing to educate others about local climate change

2.65

2.50a

3.11a,b

2.67b

.00

I would be willing to donate $ to reduce my carbon footprint when traveling to Maine

2.66

2.50a,b

2.96a

2.75b

.00

I would be willing to donate $ to help deal with the impacts from climate change in Maine

2.58

2.39a,b

2.89a

2.71b

.00

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Introduction Methods Results Conclusions

Conclusions

Perceptions:

  • Non-nature-based tourists spend over $40 more per person per night

​

​

​

(Leones, Colby, & Crandall, 1998; Mehmetoglu, 2007)

Image: http://cdn2.drprem.com/travel/wp-content/uploads/2013/08/arizona-to-doin.jpg

Image: www.tourist-destinations.com/wp-content/uploads/2012/01/2-Preikestolen.jpg

Arizona

Norway

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Introduction Methods Results Conclusions

Conclusions

Perceptions:

  • Impact of individual weather variables relatively small, but overall weather is influential

Images by Katherine Du, NPR

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Conclusions

  • Models show increasing spending in summer and fall

​

  • Maine tourists think the weather is important
  • Non-nature-based tourists spend more and are least concerned about climate change

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Future Research

  • Models based on daily data

​

  • Embed choice experiments in surveys

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  • Segment tourists on intended visitation changes

​

  • How place attachment impacts intended future visitation

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Acknowledgements

  • Committee: Sandra de Urioste-Stone, Aaron Weiskittel, and Todd Gabe

​

  • Lydia Horne, Ashley Cooper, Matt Scaccia

​

  • Maine Office of Tourism, Maine Tourism Association, Acadia National Park, Bethel Chamber of Commerce, Maine Bureau of Parks and Lands, Baxter State Park Authority, BIA

​

  • Washington State University

​

​

​

​

​

Based upon work that is supported by the National Institute of Food and Agriculture, U.S. Department of Agriculture, under award number 1003857, and by funding provided by the University of Maine Office of the President

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Questions?

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References

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  • Becken, S., & Wilson, J. (2013). The impacts of weather on tourist travel. Tourism Geographies, 15(4), 620-639. doi: 10.1080/14616688.2012.762541
  • Bicikova, K. (2014). Understanding student travel behavior: A segmentation analysis of British university students. Journal of Travel & Tourism Marketing, 31(7), 854–867. doi:10.1080/10548408.2014.890154
  • Brownlee, M. T.J., Hallo, J. C., & Krohn, B. D. (2013). Botanical garden visitors’ perceptions of local climate impacts: awareness, concern, and behavioral responses. Managing Leisure, 18(2), 97-117. doi: 10.1080/13606719.2013.752209
  • Brownlee, M. T.J., Powell, R. B., & Hallo, J. C. (2012). A review of the foundational processes that influence beliefs in climate change: opportunities for environmental education research. Environmental Education Research, 19(1), 1-20. doi: 10.1080/13504622.2012.683389
  • Burakowski, E., & Magnusson, M. (2012). Climate Impacts on the Winter Tourism Economy in the United States.
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  • Dawson, J., & Scott, D. (2013). Managing for climate change in the alpine ski sector. Tourism Management, 35, 244–254. doi:10.1016/j.tourman.2012.07.009
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  • De Urioste-Stone, S. M., Scaccia, M. D., & Howe-Poteet, D. (2015). Exploring visitor perceptions of the influence of climate change on tourism at Acadia National Park, Maine. Journal of Outdoor Recreation and Tourism, 11, 34–43. doi:10.1016/j.jort.2015.07.001
  • Denstadli, J. M., Jacobsen, J. K. S., & Lohmann, M. (2011). Tourist perceptions of summer weather in Scandinavia. Annals of Tourism Research, 38(3), 920–940. doi:10.1016/j.annals.2011.01.005

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  • Dillman, D. A., Smyth, J. D., & Christian, L. M. (2009). Internet, Mail, and Mixed-Mode Surveys: The Tailored Design Method (3rd ed.). New Jersey: John Wiley & Sons Inc.
  • Elith, J., Leathwick, J.R., & Hastie, T. (2008). A working guide to boosted regression trees. Journal of Animal Ecology 77, 802-813.
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  • Falk, M. (2014). Impact of weather conditions on tourism demand in the peak summer season over the last 50 years. Tourism Management Perspectives, 9, 24–35. doi:10.1016/j.tmp.2013.11.001
  • Fernandez, I.J., Schmitt, C.V., Birkel, S.D., Stancioff, E., Pershing, A.J., Kelley, J.T., Runge, J.A., Jacobson, G.L., and Mayewski, P.A. (2015). Maine’s Climate Future: 2015 Update. Orono, ME: University of Maine
  • Fisichelli, N. A., Schuurman, G. W., Monahan, W. B., & Ziesler, P. S. (2015). Protected area tourism in a changing climate: Will visitation at US national parks warm up or overheat? PloS One, 10(6), e0128226. doi:10.1371/journal.pone.0128226
  • Gössling, S., Scott, D., Hall, C. M., Ceron, J.P., & Dubois, G. (2012). Consumer behaviour and demand response of tourists to climate change. Annals of Tourism Research, 39(1), 36–58. doi:10.1016/j.annals.2011.11.002
  • Hall, T. E., Seekamp, E., & Cole, D. (2010). Do recreation motivations and wilderness involvement relate to support for wilderness management? A segmentation analysis. Leisure Sciences, 32(2), 109–124. doi:10.1080/01490400903547096
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  • Rosselló-Nadal, J. (2014). How to evaluate the effects of climate change on tourism. Tourism Management, 42, 334–340. doi:10.1016/j.tourman.2013.11.006
  • Scott, D., Dawson, J., & Jones, B. (2008). Climate change vulnerability of the US northeast winter recreation - tourism sector. Mitigation and Adaptation Strategies for Global Change, 13(5-6), 577-596. Scott, D., McBoyle, G., & Schwartzentruber, M. (2004). Climate change and the distribution of climatic resources for tourism in North America. Climate Research, 27, 105-117.
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Location Surveyed

Number

%

Acadia

224

31.8

Bangor Airport

26

3.7

Hampden North

60

8.5

Hampden South

28

4.0

Kittery

91

12.9

Calais

6

0.9

Yarmouth

52

7.4

Fryburg

3

0.4

Houlton

8

1.1

Bethel Chamber

38

5.4

Baxter

16

2.3

Popham Beach

11

1.6

Camden Hills

69

9.8

Mt. Blue

14

2.0

Sebago Lake

16

2.3

Rangeley Lake

6

0.9

Wolfe's Neck Woods

20

2.8

Reid

16

2.3

TOTAL

704

 

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