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Research for TRAN Committee -

Overtourism: impact and

possible policy responses Policy Department for Structural and Cohesion Policies

Directorate

-General for Internal Policies

PE 629.184 - October 2018

EN STUDY

Requested by the TRAN committee

Abstract

This study addresses the complex phenomenon of overtourism in the EU. By focusing on a set of case studies, the study reports on overtourism indicators, discusses management approaches implemented within different destinations and assesses policy responses. It concludes that a common set of indicators cannot be defined because of the complex causes and effects of overtourism. Avoiding overtourism requires custom-made policies in cooperation between destinations' stakeholders and policymakers.

Research for TRAN Committee -

Overtourism: impact and

possible policy responses This document was requested by the European Parliament's Committee on Transport and Tourism (TRAN).

AUTHORS

Paul PEETERS, Stefan GÖSSLING, Jeroen KLIJS, Claudio MILANO, Marina NOVELLI, Corné DIJKMANS, Eke

EIJGELAAR, Stefan HARTMAN, Jasper HESLINGA, Rami ISAAC, Ondrej MITAS,

Simone MORETTI, Jeroen

NAWIJN, Bernadett PAPP and Albert POSTMA.

Research manager:

Beata Tuszyńska

Project and publication assistance:

Adrienn Borka

Policy Department for Structural and Cohesion Policies, European Parliament

LINGUISTIC VERSIONS

Original: EN

ABOUT THE PUBLISHER

To contact the Policy Department or to subscribe to updates on our work for the TRAN Committee please write to: Poldep-cohesion@ep.europa.eu

Manuscript completed in October 2018

© European Union, 2018

This document is available on the internet in summary with option to download the full text at: http://bit.ly/2srgoyg

This document is available on the internet at:

Further information on research for TRAN by the Policy Department is available at: https://research4committees.blog/tran/

Follow us on Twitter: @PolicyTRAN

Please use the following reference to cite this study:

Isaac, R., Mitas, O., Moretti, S., Nawijn, J., Papp, B. and Postma, A., 2018, Research for TRAN Committee -

Overtourism: impact and possible policy responses, European Parliament, Policy Department for

Structural and Cohesion Policies, Brussels

Please use the following reference for in-text citations:

Peeters et al. (2018)

DISCLAIMER

The opinions expressed in this document are the sole responsibility of the author and do not necessarily represent the official position of the European Parliament. Reproduction and translation for non-commercial purposes are authorized, provided the source is acknowledged and the publisher is given prior notice and sent a copy.

Overtourism: impact and possible policy responses

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CONTENTS

LIST OF ABBREVIATIONS 7

LIST OF FIGURES 11

LIST OF MAPS 12

LIST OF TABLES 14

EXECUTIVE SUMMARY 15

1 INTRODUCTION AND DEFINITION 19

1.1 Introduction, aim and objectives 19

1.2 Report outline 21

1.3 Definition of overtourism 21

1.4 Conceptual model 22

2 OVERTOURISM: CURRENT KNOWLEDGE 24

2.1 The origin of the term 'overtourism' 24

2.2 Causes of overtourism 27

2.3 Overtourism and destinations 29

2.4 Overtourism concerns from ETC members 36

2.5 Overview of the impacts of overtourism 37

3 MEASURING OVERTOURISM AND ITS RISKS 41

3.1 Introduction 41

3.2 Methods and data sources 42

3.3 The extent of overtourism in the world 45

3.4 Indicators of overtourism 49

3.5 Early warning tool 74

4 OVERVIEW OF CASE STUDIES 80

4.1 Introduction 80

4.2 The ETC survey list 81

4.3 Statistical analysis of case studies 83

4.4 Impacts of overtourism based on the case studies 88

4.5 Measures taken by local authorities 93

4.6 Best practices 98

IPOL | Policy Department for Structural and Cohesion Policies 4

5 POLICY RESPONSES TO OVERTOURISM 99

5.1 Introduction 99

5.2 Approach to the policy assessment 99

5.3 Policy inputs from the sector 100

5.4 EU policy response categories to overtourism 101

6 CONCLUSIONS AND RECOMMENDATIONS 107

6.1 Introduction 107

6.2 Overview of the overtourism situation in the EU 108

6.3 Overtourism indicators based on the data-study 108

6.4 Case study conclusions 109

6.5 Key issues likely to be of concern for EP TRAN Committee 110

REFERENCES 113

I OVERTOURISM MAPS 125

I.1 Test NUTS coding Airbnb and booking.com databases 125

I.2 Map overview global cases 126

I.3 Map tourism density 127

I.4 Maps tourism intensity 128

I.5 Map growth of number of bed-nights 129

I.6 Map combined bed-nights growth and intensity 130 I.7 Map share of Airbnb in total accommodation 131

I.8 Map distance to Airbnb accommodation 132

I.9 Map air transport intensity 133

I.10 Map tourism revenues share of GDP 134

I.11 Map growth of air transport 135

I.12 Map air transport seasonality 2016 136

I.13 Map air transport seasonality 2017 137

I.14 Map with airports, World Heritage Sites and OT destinations 138 I.15 Map of cases against global tourism density 139 I.16 Map of cases against global tourism intensity 140

II HEAT MAP NUTS 2 REGIONS 141

III CASE STUDY DETAILS 150

IV CASE STUDIES 153

IV.1 Ayia Napa, Cyprus 153

IV.2 Bagan, Myanmar 156

Overtourism: impact and possible policy responses

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IV.3 Bled, Slovenia 158

IV.4 Bruges Historic Centre, Belgium 160

IV.5 Bucharest, Romania 162

IV.6 Budapest, Hungary 164

IV.7 Byron Bay, Australia 166

IV.8 Cinque Terre, Italy 168

IV.9 Copenhagen, Denmark 170

IV.10 Dublin, Ireland 173

IV.11 Echternach, Luxembourg 176

IV.12 Geirangerfjord Area, Norway 178

IV.13 Giethoorn, the Netherlands 180

IV.14 Grand Canyon, the United States 182

IV.15 Isle Of Skye, United Kingdom 184

IV.16 Juist Island, Germany 186

IV.17 Plitvice Lakes, Croatia 188

I

V.18 Lisbon, Portugal 190

IV.19 Lucerne, Switzerland 192

IV.20 Machu Picchu, Peru 194

IV.21 Mallorca, Spain 196

IV.22 Maya Bay - Phi Phi Leh, Thailand 199

IV.23 Parc Naturel Régional des Monts d'Ardèche, France 201

IV.24 Prague Old Town, the Czech Republic 203

IV.25 Reykjavik, Iceland 205

IV.26 Riga - Historic Centre, Latvia 207

IV.27 Rio de Janeiro, Brazil 209

IV.28 Rovaniemi (Lapland), Finland 211

IV.29 Salzburg Historical Centre, Austria 213

IV.30 Santorini, Greece 215

IV.31 Stockholm, Sweden 218

IV.32 Sunny Beach, Bulgaria 220

IV.33 Tallinn Old Town, Estonia 223

IV.34 Tatranská Lomnica, Slovakia 225

IV.35 Turkish Riviera, Turkey 227

IV.36 Valletta, Malta 229

IPOL | Policy Department for Structural and Cohesion Policies 6

IV.37 Vatican City, Vatican 231

IV.38 Venice, Italy 233

IV.39 Vilnius Old Town, Lithuania 235

IV.40 Warsaw Historic Centre, Poland 237

IV.41 Yellowstone, United States 239

V POLICY MEASURES OVERVIEW 241

VI VIEWS FROM THE WORLD TRAVEL AND TOURISM COUNCIL 254

Overtourism: impact and possible policy responses

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LIST OF ABBREVIATIONS

ABTS Assembly of Neighbourhoods for Sustainable Tourism

ADB Asian Development Bank

ANOVA Analysis of variance, a statistical test to compare differences between groups

AT Austria

AU Australia

B&B Bed and Breakfast (accommodation type)

BE Belgium

BG Bulgaria

BNGP Bed Night Growth Percentile Rank

BR Brazil

CELTH Center of Expertise for tourism and Hospitality

CH Switzerland

CIGS Combined Intensity Growth Score

CLIA Cruise Lines International Association

CY Cyprus

CYSTAT Statistical Service of Cyprus

CZ Czechia

DE Germany

DK Denmark

DMO Destination Marketing Organisation

DV Day Visitors

EC_XXX Economic impact of tourism (see Table 1)

ECST European Charter for Sustainable Tourism in Protected Areas IPOL | Policy Department for Structural and Cohesion Policies 8

EE Estonia

EL Greece

ENV_XXX Environmental impact of tourism (see Table 1)

ES Spain

ETC European Travel Commission

FI Finland

FIT Free Independent Traveler or Free Independent Tourist

FR France

GDP Gross Domestic Product

HR Croatia

HU Hungary

HUT Habitatges ús turísticuse (housing for tourist use)

ICT Information and Communication Technologies

IE Ireland

IREFREA Instituto Europeo de Estudios en Prevención

IS Iceland

IT Italy

JICA Japan International Cooperation Agency

LAC Limits of Acceptable Change

LT Lithuania

LU Luxembourg

LV Latvia

M_02_BND Tourism density

M_03_BNP Tourism intensity

Overtourism: impact and possible policy responses

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M_04_GAT Growth air transport (2016)

M_05_SDG Tourism share GDP

M_06_SAB Airbnb share of the total of Booking and Airbnb M_08_DAB Airbnb average shortest distance to booking.com

M_09_AND Air transport intensity

M_10_NUW Number of UNESCO World Heritage Sites

M_11_ALL

AV

Percentile Average significant indicators

MICE Meetings, Incentives, Conferences and Exhibitions

MSP Member of the Scottish Parliament

MT Malta

NL Netherlands

NO Norway

NSW Australian state of New South Wales

NTO National Tourism Office

NUTS Nomenclature of Territorial Units for Statistics

OD Overtourism Drivers

OT Overtourism

OUV Outstanding Universal Value

PL Poland

PT Portugal

RO Romania

ROS Recreation Opportunity Spectrum

SE Sweden

IPOL | Policy Department for Structural and Cohesion Policies 10 SET Southern European Cities against Touristification

SI Slovenia

SK Slovakia

SOC_XXX Social impact of tourism (see Table 1)

STB Slovenia Tourism Board

SURS Statistical Office of the Republic of Slovenia

TALC Tourism Area Life Cycle

TC Tourism Capacity

TDR Tourism Density Rate

TI Tourism Impacts

TIntP Tourism Intensity Percentile Rank

TN Tourist Nights

TPR Tourism Penetration Rate

TR Turkey

TRAN European Parliament Committee on Transport and Tourism

UK United Kingdom

UNESCO United Nations Educational, Scientific and Cultural

Organization

UNWTO United Nations World Tourism Organisation

US United States

VERP Visitor Experience and Resource Protection

VUM Visitor Use Management Framework

WHS World Heritage Sites

WTCF World Tourism Cities Federation

WTTC World Travel and Tourism Council

Overtourism: impact and possible policy responses

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LIST OF FIGURES

Figure 1: Growth in arrivals and receipts, 2017 compared to 2016 20

Figure 2 : Conceptual model of overtourism 23

Figure 3: Distribution of the values for tourism intensity (bed-nights/resident) 43 Figure 4: Monthly overview of 'overtourism' social media mentions on social media channels 48 Figure 5: Sentiment about Airbnb mentions on social media channels 49

Figure 6: Number of overtourism (OT) cases per country in the world as a function of some indicators

51
Figure 7: Number of overtourism (OT) cases as a function of tourism density (tourist arrivals/km 2 and intensity (tourist arrivals per inhabitant). 53 Figure 8: Impact of Airbnb bed-capacity share of all accommodation and distance to commercial accommodation on the number of destinations in state of overtourism (OT cases) 60 Figure 9: Overview of number of Airbnb listings and search counts for 'Airbnb' between December

2014 and summer 2018 for the city of Amsterdam, the Netherlands 61

Figure 10: Share of destinations in state of overtourism within a certain distance of at least one airport

64
Figure 11: Number of overtourism (OT) cases as a function of air transport indicators 65

Figure 12: Share of destinations in a state of overtourism within a certain distance to a cruise port 68

Figure 13: Overview of the local and NUTS 3-based cruise passenger density for all cruise ports and those close to a destination in a state of overtourism 68

Figure 14: Share of destinations in state of overtourism within a certain distance of a World Heritage

Site 71

Figure 15: Number of overtourism (OT) cases as function of tourism's share in regional (NUTS 2) GDP. 72
Figure 16: Relationship between the city-based method by McKinsey & Company and World Travel & Tourism Council (2017) and the regional method of this study, for all indicator percentiles averaged and only the comparable (equal) indicator percentiles 77 Figure 17: Overview of shares of overtourism case study destination types (left) and regional distribution (right) 83 Figure 18: Overview of the impacts occurring in all 41 overtourism cases 92 Figure 19: Overview of frequency of occurrence of measures found in the 41 case studies 96 Figure 20: Occurrence of measures in all 41 cases 97 Figure 21: Shares of measures per source per policy response 105 IPOL | Policy Department for Structural and Cohesion Policies 12

LIST OF MAPS

Map 1: Global tourism density (upper map; tourist arrivals per km 2 ) and tourism intensity (lower map; tourist arrivals per inhabitant) for international plus domestic tourism in 2016 46

Map 2: Tourism density (5

th percentile ranks of bed-nights/km 2 ) for the EU28+. 55

Map 3: Tourism intensity (5

th percentile ranks of bed-nights/resident) for the EU28. 56quotesdbs_dbs24.pdfusesText_30

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