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Ingénierie et management du développement durable

Plan Bleu

Gestion de l’énergie Transport aérien et tourisme en Méditerranée

Modélisation: méthodologie et sources de données

Paul Peeters

NHTV university of applied sciences, Breda, Netherlands Version 02-08-2010

Generated by Foxit PDF Creator © Foxit Softwarehttp://www.foxitsoftware.com For evaluation only.

Gestion de l’énergie Transport aérien et tourisme en Méditerranée – TEC – 2010 2/52

Sommaire

2.1 INTRODUCTION.................................................................................................................... 14

2.2 TOURISM DATA .................................................................................................................... 15

2.2.1 International inbound ............................................................................................ 15

2.2.2 International outbound ......................................................................................... 15

2.2.3 Domestic tourism..................................................................................................... 15

2.2.4 Distance.................................................................................................................... 16

2.2.5 Modal split ................................................................................................................ 18

2.2.6 Revenues en economics....................................................................................... 18

2.3 ELASTICITY ........................................................................................................................... 22

2.4 TRANSPORT COST ................................................................................................................ 23

2.4.1 Road transport cost ................................................................................................ 23

2.4.2 Air transport cost ..................................................................................................... 24

2.4.3 Other mode costs ................................................................................................... 27

2.5 THE TRANSPORT CO2 EMISSION FACTORS ............................................................................. 28

3.1 INTRODUCTION.................................................................................................................... 32

3.2 THE GLOBAL TOURISM MARKET MODEL.................................................................................. 32

3.2.1 Baseline (global tourism market) ......................................................................... 32

3.2.2 BP scenario (global tourism market) ................................................................... 34

3.3 COUNTRY MODEL................................................................................................................ 35

3.3.1 Baseline (country) ................................................................................................... 35

3.3.2 BP scenario (country) ............................................................................................. 37

3.4 MODAL SPLIT....................................................................................................................... 38


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− Introduction

This report provides a technical description of the MEDTOUR model developed for BluePlan. With MEDTOUR the user may evaluate the impacts of climate mitigation policies on the development of tourism and specifically (air) transport for the 22 countries around the Mediterranean. The model is based on definitions for tourism as given by the UNWTO (UNWTO 2008a: Annex-21):

• Tourism “comprises the activities of persons travelling to and staying in places outside their usual environment for not more than one consecutive year for leisure, business and other purposes”. All persons active in tourism are called ‘visitors’.

• Tourists are all overnight visitors thus all visitors that stay at least 1 night outside their usual environment (excluding same-day visitors); all numbers in the model refer to tourists!

• Inbound Tourism, involving the non-residents received by a destination country from the point of view of that destination;

• Outbound Tourism, involving residents travelling to another country from the point of view of the country of origin;

• Domestic tourism, involving residents of a given country travelling within that country.

Furthermore all international tourism is divided over six tourism markets (i.e. distance classes): 0-999, 1000-1499, 1500-3499, 3500-549, 5500-7999, >8000 km. These markets are used throughout the model for inbound tourism to the BP countries, form the base for the average distance of outbound tourism, but do not apply to domestic tourism, which is taken as one market with a single specific average distance that depends on the geographical size of the country.

The model is able to evaluate the impacts of three groups of policies:

• Global mitigation policies (emission trading, emission taxes translated to a path for carbon cost per ton of CO2)

• National economic measures (tourism and transport taxes and subsidies)

• National supply side measures with an impact on travel speed (these are based on assumptions like investments in ports, airports, rail, road, and ITC, for which the model user has to estimate first the impacts on annual changes of average transport speed per mode, which is the only input for the model for supply side measures).

All policies are - as stated above - translated to two variables: transport cost transport travel time. The model has been programmed for Excel 2003 for Windows. The main properties are that the model user can define different scenarios for global and for national policies and easily switch between these scenarios to evaluate a range of impacts. Furthermore the results can be saved in excel files at the choice of the user of the model. Such files are available for specific countries and for all countries together. The policies contain a general indication of global carbon prices and the impacts of country specific infrastructure investments and. The model does not translate specific infrastructure investments or taxes/subsidies into the consequences for the cost of carbon or the speed of travel. The user has to prepare this beforehand. This makes the model more flexible and less complex.



Based on the ‘current situation’ (2005) the model calculates the future situation for each year up to 2050 using a travel speed and travel cost constant elasticity model. This means that the model evaluates for each year the changes in cost and travel speed and calculates the number of trips and distances covered for the next year. Furthermore the model takes into account that fuel efficiency and CO2 emission factors will change over time. Also these technological changes can be defined by the user.

Output of the model consists of overview tables of the number of trips, distances covered, CO2 emissions and economics for the years 2005, 2025 and 2050 and for 6 distance class markets, domestic and outbound tourism. Also for specific countries time series for the period 2005 to 2050 produced for trips, distances, CO2 emissions and net tourist economy per country and market segment are calculated.

In this technical description we will first describe the data sources used, then proceed with a global description and a detailed description of the assumptions and mechanisms programmed in the model.




1 Overview of the model



1.1 Model users interface

1.1.1 Installation

The model consists of 5 spreadsheets that should be contained in directory with the following set of sub file directories:

The root model directory (in this case ‘Actual Model A1’, but you may choose any file you want) contains just the model file. The DATA directory and sub file directories ‘country’ and ‘scenario_results’ are empty before the first run (after running you can save results for a single country run in ’country’ and results of full scenario runs in ‘scenario_results’. In ‘national policies’ standard one file is added, NO_national_policy.XLS, which gives input for zero policies. For the full set of scenarios developed in this project we have created the following files in this directory:

Finally, the TEMPL sub file directory contains the following files, without which the model cannot run:

Installation (steps 1 and 2 only apply in case you receive the model in a .txt file because of mail-box restrictions):

1. Store the ‘Actual modelv00.txt’ file at any place on your disk you find convenient.

2. Rename the ‘Actual modelv00.txt’ to ‘Actual modelv00.0.0.zip’ ignoring warnings.

3. Double click the zip file and unzip the contents to the place you chose to run the model from keeping the sub file directory structure as described above (normally that is default).

1.1.2 Running the model

The following steps show how to run the model:

1. This file has been made with Microsoft Excel 2003 and it is recommended to run it in this version. Open Excel.




2. First check if the security settings of your excel version allow to run macro’s. The most secure setting is set in the option menu, macro security under tools:

3. Go to the sub file directory where you unloaded the contents of the zip-file to (e.g. ‘Actual model’); open it and open the single excel file in it (e.g. BP_MEDTOUR_SRES_A1_v1.0.05.xls), press ‘enable macros’ if asked for (see example below.

4. After you open the file three other files are opened automatically and you are directed to the “control” sheet. The control buttons look as follows:



5. Working of the buttons:

a. Before you run the model make sure the global climate mitigation policy scenario (which determines the cost of carbon development for ETS countries or all countries) and the national policy scenario (that determines per country and transport mode taxes and subsidies and changes in average transport speed) are the right ones. Use the buttons in the in the green box to check and choose

b. You may add new global mitigation scenarios or national policies using the two yellow box buttons. However, this has not been automated but just brings you to some instructions at the place where you can change the values.

c. Running the model is very simple: just hit one of two the buttons in the blue ‘Run the model!’ box:

i. The country model asks you to choose a country and then runs the model for just that country and stores results in a specific country results file of your choice. If you want to evaluate the results of the country run, just have a look at the worksheet Ctry_output for a summary and Ctry_model for all data and results.

ii. The Run a full scenario button first asks for a pre-defined scenario and to open a national policy file and then loads these national policy data and runs the model for all BP counties, while sting an overview of data to a template file. This file can be stored with a name of choice by the user at the end of the run. These full scenario runs may take some time so be patient! At the end of the full




scenario run you will be asked to store the result in a file for future use.

d. The two purple boxes contain buttons normally not necessary. The upper one changes a few basic parameters and the lower one just checks if a country is calibrated (this needs only be done when the baseline data for a country are changed, preferably in cooperation with the programmer).

6. Do not close any files while running the model, unless you are asked for by the program.

7. In some case a macro will fail and ask you to debug. Choose ‘End’ (no debugging) and then always close the model (choose to save or save it to a new name if you want) and reopen the saved file; if you fail to do so otherwise you may be plagued by more ‘error messages’ during the same session and with erroneous results.

8. When you want to see more results of a country then press the country model button and follow the instructions; after that you may find detailed calculation information at the ctry_model worksheet.

9. When you want to see more of the global tourism model hit the ‘select a global scenario’ button, follow the instructions and go to the sheet global tourism market, where you will find all detailed data.

General warning: do not change data or cells in the model except through the buttons or when asked to do so by the model (and only in cells that are highlighted in green).

1.2 Model scheme

Figure 1 gives an overview of the MEDTOUR model. The model consists of two calculating units: the global tourism market model and the country model. The global tourism market model estimates global travel for seven markets: 6 distance classes for international tourism flows and domestic tourism. The country model calculates all missing data for 2005 (like average distance for outbound, CO2 emissions, average travel and trip cost per distance class inbound and modal split for outbound and domestic).



Figure 1: MEDTOUR model overview.

The data generated for 2005 are based on data from UNWTO, WTTC and several other sources (see 2), which have been indicated by grey blocks in Figure 1 and do not need

to be handled by the model user. When a scenario is calculated the country model loads all these values per country into the country model, adds to this the choices for global and national policies, calculates the data and writes these data to a country specific worksheet in the scenario output file. This procedure is repeated 22 times to calculate all countries. Then the user is asked to save the scenario output file. This file has a worksheet for each country and several worksheets with overview data and graphs.

National policies input file: • Transport taxes • Transport subsidies • Transport speed

Country Tourism Model

UNWTO domestic & international tourism data

WTTC economic data

Global scenario (A1 & A2)

National policy

Global tourism Model

Output file: • Trips • Distances • Emissions • Economics

Transport cost data

Modal split inbound (UNWTO)




1.3 Basic assumptions

The countries to be evaluated are listed in Table 1 including an indication if the country falls within the EU Emission Trading Scheme for aviation (ETS), if it belongs to the OECD90 countries (because of the different coefficients for OECD90 and non-OECD90 countries for the trip generation equations and to what SRES region it belongs on which the IPCC national GDP and population growth is based.

Country IMAGE (SRES) region OECD90 ETS zone Mediterranean

zone

Albania 10 Eastern Europe No No PNM Algeria 5 Northern Africa No No PSEM Bosnia-Herzegovina 10 Eastern Europe No No PNM Croatia 10 Eastern Europe No Yes PNM Cyprus 12 Middle East Yes Yes? PNM Egypt 5 Northern Africa No No PSEM France 9 OECD Europe Yes Yes PNM Greece 9 OECD Europe Yes Yes PNM Israel 12 Middle East No No PSEM Italy 9 OECD Europe Yes Yes PNM Lebanon 12 Middle East No No PSEM Libya 5 Northern Africa No No PSEM Malta 9 OECD Europe Yes Yes PNM Monaco 9 OECD Europe Yes Yes PNM Morocco 5 Northern Africa No No PSEM Palestinian Authority 12 Middle East No No PSEM Serbia and Montenegro 10 Eastern Europe No No PNM Slovenia 10 Eastern Europe No Yes PNM Spain 9 OECD Europe Yes Yes PNM Syria 12 Middle East No No PSEM Tunisia 5 Northern Africa No No PSEM Turkey 12 Middle East Yes No PSEM

Table 1: Overview of Blueplan countries (sources IMAGE-team 2006, IPCC 2000: Appendix III)

Note that for Monaco, Palestine, Serbia-Montenegro and Bosnia-Herzegovina data is incomplete (modal split is missing for both Serbia-M and Bosnia-H; Mon and Pal have more deficiencies, so if the Modal split for the first two can be estimated we need only to leave out Monaco and Palestine and the model will be almost complete).

Some important basic assumptions for the model are:

− Base year 2005.

− End year 2050 in time series but with a display of results for 2005, 2025 and 2050.

− Tourism will be evaluated for seven tourism markets (or distance ‘classes’):

a. Domestic b. International, one-way great circle distance:

i. 0-999 km ii. 1000-1499 km iii. 1500-3499 km iv. 3500-5499 km v. 5500-7999 km vi. >=8000-9999 km

− The model consists of two basic sub-models:

c. Global tourism market model d. Country model



− Input for the model are carbon cost time series for the global tourism market model, and country specific tax/subsidy and transport speed changes at the national level.

− The country sub-models basically are travel price and time elasticity models that give additional growth (normally negative in case of carbon cost increases) per class (domestic and the five international distance groups) plus an additional growth for short haul markets given by the Blue Plan total market.




2 Data sources



2.1 Introduction

The biggest challenge in developing this model is the lack of des-aggregated data. What we need is data per BP country about number of trips and distances covered per tourism market (domestic, inbound distance class and outbound), and each divided over transport mode. However the data just give us trips from a range of specific countries of origin (inbound) and destination (outbound), no domestic trips, nor overall inbound (international) transport modal split for arrivals. The data are therefore insufficient to directly feed the model.

For emission and transport cost calculations we need trips as a calculation base, where distance is defined as the total travel from the tourists home to the destination and back (return trip). Within each trip several stages may occur like train to airport, flight to main hub, connecting flight to airport at destination, rent car trip to final destination. We will take the main transport mode for the whole distance. As UNWTO data define ‘arrivals’ we have in some cases corrected for the difference caused by people travelling through and staying a night in one country on their way to another country and thus counted as a 1-night visitor t that country. This double counting are not very numerous, but may have a strong impact on distances travelled (resulting in an over estimate).

Table 2 gives a summary of data and the sources used. The main problem with the data was the high level of aggregation; overall numbers of tourist trips per destination or per transport mode are available, but the model needs information about the distribution of trips for all source markets per transport mode and preferably per distance class. Therefore we have developed modules and methods in the model to redistribute the trips to a level of aggregation that allows running an elasticity-based algorithm. In this chapter we will describe the raw data sources used.

Data description Available with

Inbound trips 2001 & 2005 per BP country and country of origin worldwide

UNWTO 2007a, UNWTO 2007b

Outbound trips 2001 & 2005 per BP country and country of destination worldwide

UNWTO 2007a, UNWTO 2007b

Domestic trips per BP country for 2001&2005 Estimates based on methods given in Dubois et al. 2010, Peeters et al. 2010.

Distances between all countries worldwide (great circle distance between main airports)

Estimates based on UNWTO data and Swartz 2010, WebFlyer 2003

Number of trips for 2010 and 2020 from WTTC per country as input for long term growth rates

WTTC 2010

Transport travel cost See section 2.4

Air, car, other transport shares international UNWTO 2007b

Air, car, other transport shares domestic Estimated based on international shares and domestic travel distance

Tourism revenues per BP country 2001&2005 Data provided by BluePlan plus WTTC

2010.

GDP per BP country 2001&2005 World bank World Development Indicators database (WDI).

Emission factors per transport mode and distance class

Peeters et al. 2007a, UNWTO-UNEP-WMO 2008

Table 2: Overview of raw data requirements for the model (this excludes other data required for the project regarding historical developments etc.)




2.2 Tourism data

2.2.1 International inbound

The international inbound number of trips (foreign visitors to each BP-country) are based on the data supplied by BluePlan which originate from the UNWTO Tourism Fact and year books (UNWTO 2007a, UNWTO 2007b). UNWTO gives different types of data:

(1) Arrivals of non-resident tourists in all types of tourism accommodation establishments

(2) Arrivals of non-resident tourists in hotels and similar establishments

(3) Arrivals of non-resident visitors at national borders

In most case option (3) is available and has been chosen as best representing the number of international visitors. All data were critically evaluated and in some cases corrected, for example the number of visitors to France from The Netherlands has been corrected for the millions of Dutch that stay just one night in France while actually on a trip to Spain or Italy. The main importance to remove such data is the double counting in travel distances that would occur as obviously, those just passing a country like France do not travel back first to their homes (in the Netherlands) before continuing to Spain. Keeping the visits to France would mean an the additional distances caused by a return between The Netherlands and France, that did not occur. Another correction looked at was the elimination of same-day trips between neighbouring countries.

2.2.2 International outbound

The CSTT of NHTV Breda University developed a global database filled with the Tourism Factbook data from UNWTO. This database, the CSTT World Tourism Database or CWTD, contains all data from 2001 up to 2005. As for each country the arrivals from other countries are given per country of residence it is possible to gather these data for each country of the world and thus get the data for outbound tourism. So essentially the outbound data are also based on the UNWTO factbook data.

2.2.3 Domestic tourism

Domestic tourism has been calculated using the method described in (Peeters et al. 2010). This method is based on the educated assumption that the total number of trips per capita of a population (e.g. of the population of France or Tunisia) is a linear function of GDP per capita. The coefficients of this function roughly differ for OECD and non-OECD countries. This total number of trips comprises of the sum of domestic and outbound trips. As the number of outbound trips is know as well as the GDP per capita and the population per BP country we can simply calculate domestic trips by calculating the total trips by the BP-country population and subtract the outbound trips from this total. The data for GDP and population were partly delivered by BluePlan and partly drawn from the World Bank World Development Indicators database (WDI).

For the global number of tourist trips for a country’s population (outbound plus domestic):

T

C= min T

Cmax

,Ccy

+ αcy

⋅GDPcap( ) (1)



TCmax represents the maximum number of trips per capita (based on Mulder et al. 2007), Ccy and αcy are the two coefficients that resulted after fitting the 2005 and the 2035

numbers of tourists for international, respectively domestic in OECD90 and in non-OECD90 – developing - countries). Compared to a similar relationship published previously (Bigano et al. 2004), our estimated number of trips per capita is slightly more shallow.

0

1

2

3

4

5

6

0 20000 40000 60000 80000 100000

GDP/Cap

International

OECD90

Developing

2035

2005

2035

2005

Figure 2: The number of trips/cap/year as based on UNWTO scenarios (UNWTO 2008b) and a certain maximum (derived from data by Mulder et al. 2007).

We combined each of the two domestic relations with the international one to create two overall trip generating sets for total trips. The coefficients used for total trips for OECD and non-OECD countries is presented in Table 3.

Table 3: Baseline values for the parameters determining trip generation.

Tourism market Ccy αcy Tmax

OECD90 all trips 0.5340 7.43E-05 6

non-OECD90 all trips 0.2502 7.329E-05 6

2.2.4 Distance

The distances for inbound and outbound tourists are based on the distances between all main airports of the BP-countries and the main airports of the countries of origin (inbound) and destination (outbound) as calculated using a ‘great circle distance’ calculator (e.g. Swartz 2010, WebFlyer 2003). The distances are corrected for detours, as given in Table 4.

Table 4: Overview of detour factors per distance class.

Distance classes Detour factor

Domestic 1.2

0-999 km 1.2

1000-1499 km 1.1

1500-3499 km 1.1

3500-5499 km 1.05




5500-7999 km 1.05

8000- km 1.05

We have compared the distances to and from EU countries with an earlier study for European tourism transport (Peeters et al. 2007a) and corrected some of the neighbouring country distances, but found generally not too large discrepancies.

The distance travelled by domestic tourists is much less easily determined as the number of domestic trips. For the EU we have a well established table of average domestic distances from the MuSTT study (Peeters et al. 2004). Based on these data we drafted a relation with the size (the area of land surface in km2) and fitted a power curve to these data (see Figure 3) the following equation was found:

di = εq ⋅1.679 ⋅ Ai0.4387 (2)

with Ai in km2 and the di the average domestic one-way distance in km. The factor εq is

an indicator for the quality of the road network. To be consistent with former work we take εq =1.0 for OECD90 countries and 0.8 for all other countries (see Table 1 for an

overview of OECD90 countries around the Mediterranean).

Figure 3: relation between land area (km2) and average one-way distance for domestic tourism (based on data from Peeters et al. 2004)

Using equation (2) and country areas based on data from Bigano et al. (2004) and the CIA database (country-geographic data) on https://www.cia.gov/library/publications/the-world-factbook/index.html we have calculated the average distance of domestic tourism for all 22 countries in the model and used that for generating the total distance travelled in 2005 in the country input data worksheets of the model (those labelled with the country three letter code).



2.2.5 Modal split

The modal split (shares of trips per transport mode air, road and other) has been derived as follows:

• Inbound: for 2001 and 2005 these were gathered by BluePlan based on the UNWTO statistics.

• Outbound: the inbound data were used to estimate coefficients for an equation calculating air share as function of distance; these coefficients are also used for outbound air share taking the average distance of outbound as input for this function, but correcting by a factor because the relation’s non-linearity (i.e. using the average would over-estimate the share of air transport; the factor is based on the real distribution of outbound trips over distance classes and the air share following from that in 2005 and assumed to be constant for the whole forecast period). The split between road and other has been assumed to be equal to that for measured for inbound.

• Domestic: here the international coefficients are used based on domestic average distance.

A detailed description of the prognoses of modal split is given in section 3.4. Some

specific notes:

1. Italy, Croatia, Turkey, Egypt and Syria: BP modal split data included same day trips so corrected by subtracting difference of total arrivals according to BP mode data and WTTC overnight stays which are corrected for same day (WTTC 2010), from the road based trips.

2. Albania: here same day is included in both datasets (the CWTD and the BP mode data). So we leave that as it is.

2.2.6 Revenues and economics

For the economics we used the UNWTO data as gathered by BluePlan for the total revenues of inbound tourism. For outbound we used ‘Import’ from the data given by WTTC (WTTC 2010). For domestic tourism we used the sum for ‘Personal domestic’ given by the WTTC data (WTTC 2010).

For calculating the economic impacts of tourism in e country we basically multiplied the number of trips with the country-specific revenues per trip. However, from historical data it becomes clear that revenues per trip do increase over time (though in an irregular way) but much less fast as GDP/cap. For revenues data we used the UNWTO historical overview (UNWTO 2008a: Annex-3, Annex-10). These data are converted to 2005$ using the data given by Sahr (2009). GDP/capita data are the data for ‘World’ taken from the Groningen Growth and Development Centre (Maddison 2010). The result is given in Figure 4 as an index of 1950 values.




y = 0.0397x - 76.487

y = 0.0074x - 13.597

0

0.5

1

1.5

2

2.5

3

3.5

4

1940 1950 1960 1970 1980 1990 2000 2010 2020

Re

ve

nu

es p

er

trip

(2

005

$/t

rip

)

World

GDP/capita

Lineair (GDP/capita)

Lineair (World)

Figure 4: Different growth of tourism revenues per trip and GDP per capita.

From the graph it can be seen that revenues per trip grow only at about 20% of the growth rate of total GDP/capita (both in 2005$). Therefore we correct the initial 2005 revenue data with 20% of GDP/capita growth as assumed in the SRES scenarios (global figures).

2.2.7 Missing data

For several countries data were missing and have been estimated to allow the model to be able to run full scenarios (just one number missing invalidates almost of the country’s calculations and makes it impossible to give for example averages for all countries). The two text boxes below give an overview.



Bosnia General country data 2001-2005 2001 2005 Estimation method

Air international (%) 13.22 18.83 as average for countries same IMAGE region nr 10

Road international (%) 81.22 72.00 as average for countries same IMAGE region nr 10

Rail international (%) 3.63 3.29 as average for countries same IMAGE region nr 10

Sea international (%) 1.93 5.88 as average for countries same IMAGE region nr 10 Lebanon General country data 2001-2005 2001 2005 Estimation method Total personal domestic T&T spending (*10^9 current US $) 0.1823 0.1705 Estimated assuming average of 300 $ per domestic trip Lybia

General country data 2001-2005 2005 Estimation method

Number of nights (*1000) 599

Reference: UNWTO through recette_nuite.xls file, sheet Nuitées, BluePlan; 2005 scaled based on constant LOS

Monaco

General country data 2001-2005 2001 2005 2001 2005 Estimation method Total international revenues (*10^6 current US $) 461.1 497.1

According to http://www.state.gov/r/pa/ei/bgn/3397.htm 25% of GDp is revenues of tourism.

Total imports (*10^9 current US $) 0.01779 0.03024

Based on average revs times two for France times Monaco trips

Total personal domestic ((*10^9 current US $) 0.03584 0.04971

Based on average revs times two for France times Monaco trips

Int. overnight trips (WTTC projection (*1000) 267 285.7

Data same as for 2001 2005 from UNWTO pivot

GDP (const. 2005 US $) 1.84E+09 1.99E+09 Took the data for GDP/cap for France times 1.3

Population 31200 32500 Reference: population_WDI.xls for 2005, took 4% less in 2001.




Palestine General country data 2001-2005 2001 2005 2001 2005 Estimation method Total international revenues (*10^6 current US $) 35 37

Estimated 2005 at just 6% higher then in 2001 (expert estimate)

Total imports (*10^9 current US $) 0.1 0.2 Expert guestimates Total personal domestic ((*10^9 current US $) 1 1 Expert guestimates

Int. overnight trips (WTTC projection (*1000) 29.82 31.06

Assumed 4% growth over 2001-2005 which is much lower then measured (but that shows 50% growth and very erratic data).

Number of nights (*1000) 217 254 2015 increased by 4% (expert guestimate)

Air international (%) 43.00 43.0

0

Road international (%) 57.00 57.0

0

Rail international (%) 0.00 0.00

Sea international (%) 0.00 0.00

Guestimated assuming 0-1500 km by car, remainder by air, no rail and ship.

Market data International Estimation method

Distance class (km) 0-999 1000-1499

1500-3499

3500-5499

5500-7999 >8000

INBOUND

Number of trips 2001 16930 1 1 1 1 12891

Number of trips 2005 17635 1 1 1 1 13430 Total distance 2001 (pkm*10^6 great circle) 0.6592 0.001 0.005 0.009

0.0135 258

Total distance 2005 (pkm*10^6 great circle) 3.633 0.001 0.005 0.009

0.0135 268

Just filled in non-zero values tp keep model running.

Serbia & Montenegro General country data 2001-2005 2001 2005 Estimation method Total international revenues (*10^6 current US $) 54 55 2005 value estimate assuming rev per trip constant.

Air international (%) 13.22 18.83 estimated as average for countries same IMAGE region nr 10

Road international (%) 81.22 72.00 estimated as average for countries same IMAGE region nr 10

Rail international (%) 3.63 3.29 estimated as average for countries same IMAGE region nr 10

Sea international (%) 1.93 5.88 estimated as average for countries same IMAGE region nr 10



2.3 Elasticity

The elasticities have been divided over air transport, road (private car) and rail (public transport). It is difficult to measure price and travel speed elasticities (Seetaram 2010), but there is no simple and established alternative to determine the impacts of cost and speed changes. Where available we will use long term elasticities because we are evaluating scenario’s that run over decades. Most data exist for air transport elasticities. Table 5 shows general values gathered.

Table 5: Overview of some air fare price elasticities found in the literature

Description Price elasticity Source

Overall own-price air fare -0.7 Njegovan 2006

Pan-National Intra Europe -0.92 InterVISTAS Consulting Inc. 2007

Pan-National EU-North America -0.72 InterVISTAS Consulting Inc. 2007

Pan-National EU-Asia -0.54 InterVISTAS Consulting Inc. 2007

Several definitions -0.5 to -1.0 Brons et al. 2002

As most travel in the MEDTOUR model is short haul and related to EU (or even intra-EU like to France, Italy, Greece and Spain) or in a market comparable to intra EU (to some of the Middle East and North African countries) we decided to take -0.85 as a general value for air transport.

For road transport the fuel cost elasticity has been taken as base, because most research concentrates on this. From several meta studies it appears that the long term (more than one year) elasticity for fuel consumption is found to be between rather narrow bounds of -0.71 and -0.84 with -0.80 a good average (Sterner 2007). Therefore we will use fuel cost as a proxy for car travel cost and -0.8 as price elasticity.

Information about (long distance) rail transport elasticities is rare. One estimate based on a range of mainly European railway companies data between 1972 and 1999 results in a own price elasticity of only -0.22 (Couto et al. 2008). Much higher elasticities are reported for the French TGV on average -0.7 but up to -1.5 (Sánchez-Borràs et al. 2010). Therefore we choose an intermediate value of -0.5.

For travel speed we assume an elasticity of +1 based on the idea that travel time is more or less a constant, so a change in travel speed of +1% will result in a change of +1% of distance on average covered.

Table 6 gives an overview of the default elasticities used in the MEDTOUR model.

Table 6: Overview elasticities in the MEDTOUR model.

Elasticity

Price air ticket -0.85

Price car fuel -0.8

Price rail/bus ticket -0.5




All modes travel speed +1.0

2.4 Transport cost

One of the most difficult aspects of elasticity based models for tourism transport is the lack of accurate data about travel cost. In this study we have overcome this by using fuel cost for the car from a recent report issued by GTZ (Ebert et al. 2009), taking national railway online published ticket prices to find the per pkm cost for rail and other transport (in countries without rail transport long distance coach was used) and for air transport we automated online data mining for all countries gathering about 700-1000 ticket prices per BP country.

2.4.1 Road transport cost

Country Fuel cost (2009$/litre) Share of diesel (%) Cost per pkm

(2005€/pkm)

Petrol Diesel

Albania 1.36 1.31 73% 0.0449

Algeria 0.34 0.20 56% 0.0093

Bosnia-Herzegovina 1.13 1.18 61% 0.0384

Croatia 1.27 1.37 56% 0.0438

Cyprus 1.28 1.25 50% 0.0422

Egypt 0.49 0.20 62% 0.0126

France 1.52 1.45 71% 0.0500

Greece 1.23 1.41 35% 0.0449

Israel 1.37 1.70 32% 0.0532

Italy 1.57 1.63 60% 0.0532

Lebanon 0.76 0.76 1% 0.0254

Libya 0.14 0.12 58% 0.0044

Malta 1.66 1.56 57% 0.0540

Monaco 1.64 1.55 ? 0.0517

Morocco 1.29 0.89 32% 0.0340

Palestinian Authority 1.34 1.25 ? 0.0417

Serbia and Montenegro 1.28 1.18 48% 0.0410

Slovenia 1.18 1.26 49% 0.0407

Spain 1.23 1.28 75% 0.0415

Syria 0.85 0.53 53% 0.0233

Tunisia 0.96 0.84 69% 0.0308

Turkey 1.87 1.63 69% 0.0599

All countries average 0.0382



Table 7: Overview of fuel cost in the Blueplan countries (sources Ebert et al. 2009 for fuel prices and WRI 2007 diesel shares).

2.4.2 Air transport cost

Data for air transport cost are not readily available unless against very high cost through IATA or ICAO. To have an indication of transport cost per country we have developed an automatic data mining model (based on Automation Anywhere software, see www.automationanywhere.com) that was designed to gather prices from the Dutch Air ticket website Vliegtickets.nl (http://boeking.vliegtickets.nl). We chose this website because it gives generally an overview of between 50 and 150 price offers per city air, has the ability to show all found flights in one overview which eases automation, gives prices in Euros and has a standard presentation of the data including number of changes and total flight time. For each BP-country we selected two city-pairs per distance class (total 12) with the highest number of visitors. In some cases no or only 1 flight was available. Also some relation did not generate reasonable connections, in which case we selected the next city-pair. Same for connection at less then 250 km distance.

Per city-pair this delivered a distribution of prices (these prices include all taxes and service/booking costs, see Figure 5) for the dates 3rd of March and return 17th of March 2010 while booking in the first week of February 2010. For each city-pair we gathered the minimum, the average and the maximum price, the total return travel time and price and the great circle distance between the two airports.

Prices for Milano-Ljubjana

0

5

10

15

20

25

30

35

1 2 3 4 5 11

Price class (per €50, starting €300-€350)

Figure 5: example of price distributions for flights between Italy and Slovenia.

For Slovenia we found 12 valid city-pairs and calculated the linear regression for both the minimum and the average price (see Figure 6).




SLO

y = 0.0911x + 122.56

R2 = 0.9643

y = 0.0931x + 260.99

R2 = 0.9481

0

500

1000

1500

2000

2500

3000

3500

0 5000 10000 15000

Minimum

Average

Maximum

Minimum

Average

Figure 6: example for Slovenia of the results of all city-pairs.

For all BP countries we found a rather wide range of results, that however seems to be giving more or less consistent results per IMAGE SRES region as shown in Figure 7 for minimum prices. The largest scatter occurs at the Northern Africa and Middle Eastern countries probably due to all sorts of (political) restrictions causing sometimes long detours through other airports.

Minimum Price, Northern Africa

0

500

1000

1500

2000

2500

0 5000 10000 15000

Distance (1-eay km)

ALG

EGY

LIB

MOR

TUN

Minimum Price, OECD Europe

0

200

400

600

800

1000

1200

1400

0 5000 10000 15000

Distance (1-eay km)

FRA

GRE

ITA

MAL

SPA

Minimum Price, Eastern Europe

0

200

400

600

800

1000

1200

1400

1600

1800

0 5000 10000 15000

Distance (1-eay km)

ALB

BOS

CRO

SAM

SLO

Minimum Price, Middle East

0

200

400

600

800

1000

1200

1400

1600

1800

0 5000 10000 15000

Distance (1-eay km)

CYP

ISR

LEB

SYR

TUR

Figure 7: Minimum prices as gathered for the four SRES-groups of the BP-countries.



Therefore we have taken the overall regressions per IMAGE SRES region as a base for the per country price levels. These price levels have the form of:

Pair = Pair0

+ αair ⋅ d (3)

where Pair is the total ticket cost per return, Pair.0 a basic ticket cost and αair the cost per 1-

way great circle distance d.

All city pairs

y = 0.0772x + 145.82

R2 = 0.7635

y = 0.108x + 467.92

R2 = 0.4374

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

0 5000 10000 15000 20000Distance (1-way km)

Minimum

Average

Minimum

Average

Figure 8: Overview of all results giving the air travel prices for the global market.

Northern Africa

y = 0.0771x +

235.24

R2 = 0.526

y = 0.1366x +

615.73

R2 = 0.3954

0

500

1000

1500

2000

2500

3000

3500

4000

4500

0 5000 10000 15000Distance (1-way km)

Minimum

Average

Minimum

Average

OECD Europe

y = 0.0695x + 80.453

R2 = 0.9509

y = 0.1097x + 321.72

R2 = 0.4973

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

0 5000 10000 15000 20000Distance (1-way km)

Minimum

Average

Minimum

Average

Eastern Europe

y = 0.0914x + 119.62

R2 = 0.9468

y = 0.0967x + 319.94

R2 = 0.7388

0

500

1000

1500

2000

2500

3000

0 5000 10000 15000Distance (1-way km)

Minimum

Average

Minimum

Average

Middle East

y = 0.0648x + 178.28

R2 = 0.7318

y = 0.1007x +

577.84

R2 = 0.3866

0

500

1000

1500

2000

2500

3000

0 5000 10000 15000Distance (1-way km)

Minimum

Average

Minimum

Average

Figure 9: Overview of the final results grouped per IMAGE SRES region.




Finally Table 8 gives the data as used in the model for the BP countries and the global world. The overview of IMAGE SRES regions can be found in Table 1. Thesxe numbers are calculated by taking the minimum price as starting point and adding 20% of the difference between average and minimum. This low share of maximum has been taken as the data mining system does just collect offers for tickets, not the number of sales. Therefore we suspect that most of the high tariffs will almost never be sold.

Table 8: Overview of the cost factors used for air transport (cost per 1-way ticket in 2010€).

Region Base cost

Cost per km (great circle)

Northern Africa 142 0.0406

OECD Europe 59 0.0354

Eastern Europe 73 0.0422

Middle East 118 0.0329

All countries 96 0.0381

2.4.3 Other mode costs

Railway fares are available through national railway company sites for most of the 16 Mediterranean countries with a railway network. For Greece and Syria, secondary Websites had to be consulted. For each country, an average per km price was calculated by checking the fares and distances for 5 to 10 different itineraries/journeys, if possible/available. National currencies were converted to Euros with an online converter (http://www.xe.com/ucc/) early February 2010. Few railway companies supply track distance information. In most cases, these were estimated with the lineal tool in Google Earth. Fares were for second class, where available. For some countries with a frequent service, average fares were calculated for each itinerary. Where possible, long distance itineraries were used for the calculation (>200 km). Only for Albania, Bosnia-Herzegovina, Israel, and Serbia and Montenegro the average distance for the itineraries used stayed below 200 km. The average price/km for all countries is estimated at 5.1 Eurocent (€ 0.0512). Costs are highest in Italy and Spain, followed by Israel and France.

For the remaining countries long distance bus fares were taken as base for the data. Only in case of Lebanon and Libya the average of the neighbouring countries was taken because of lack of data. For Malta we could only find prices for day tickets on the full network. These price levels have the same form as for air transport:

Prail = Prail0

+ α rail ⋅ d (4)

where Prail is the total ticket cost per return, Prail.0 a basic ticket cost and α rail the cost per

1-way great circle distance d.

Table 9: Overview of the results for rail transport cost per BP country.

Country Ticket cost

Prail.0 (€) α rail

(€/pkm)

Albania 0.528 0.0076

Algeria 0.221 0.0262

Bosnia-Herzegovina 0.822 0.0323



Croatia 0.000 0.1367

Cyprus (bus) 3.62 0.0568

Egypt 0.980 0.0089

France 19.62 .0467

Greece 0.000 .0736

Israel 3.472 0.0367

Italy 6.719 0.1232

Lebanon (fares from Syria) 0.0237 0.0056

Libya (average EGI&ALG) 0.601 0.0176

Malta (only day tickets) 3.49 0.0000

Monaco - -

Morocco 0.452 0.0296

Palestinian Authority - -

Serbia and Montenegro 0.091 0.0282

Slovenia 2.734 0.0469

Spain 0.000 0.1475

Syria 0.0237 0.0056

Tunisia 1.622 0.0186

Turkey 6.359 0.0094

Average all countries 2.569 0.0429

2.5 The transport CO2 emission factors

All emissions have been based on the MuSTT study (Peeters et al. 2007a) and UNWTO (UNWTO-UNEP-WMO 2008). The emission factors are given in Table 10.

Mode CO2 factor (kg/pkm) Occupancy rate/load factor (%)

Air < 500 km 0.206 -

500 - 1,000 km 0.154 -

1,000 – 1,500 km 0.130 -

1,500 - 2,000 km 0.121 -

> 2,000 km 0.111 -

World average 0.129 75

Rail 0.027 60

Car 0.133 50

Coach 0.022 90

Table 10: emission factors (Peeters et al. 2007a, UNWTO-UNEP-WMO 2008: Table 11-2).




Emissions of air transport are more complicated as these are a function of distance. Based on the original data from UNWTO-UNEP-WMO 2008 and fitting e regression with FindGraph (Vasilyev 2004) resulted in a best fit for a ‘Double Hyperbola’ function:

E = a ⋅x

b + x+ c ⋅

x

d + x+ g ⋅ x (5)

with E the emission factor (kg/pkm), x the stage distance flown and parameters a=0.1672, b=-129.5, c=-0.08361, d=498.9, g=2.837*10-6

Figure 10 shows the original points and the results of equation (5), which are used in the model.

Figure 10: relation between flight distance and CO2 emission factor.

Equation (5) gives the emission factor for the global fleet technology level in 2005. Of course the fuel efficiency improves with time and the development of new aircraft technology and therefore this value is multiplied with a technological development factor based on an equation given in Peeters et al. 2007b. As this reference gives two sets of possible development there is a choice in the model (IPCC based on data given by Penner et al. 1999 or LEE given by Lee et al. 2001). Furthermore the model is prepared such that two more sets can be added to also assume faster or slower technological development. One such set has been developed based on the assumption of a large share of turbo-prop aircraft use. This IPCC-turboprop scenario assumes in 2050 10% lower emissions when a large share of the air fleet is replaced by turboprop aircraft (based on Ryerson et al. 2010; see Figure 11).



0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

2000 2010 2020 2030 2040 2050 2060

Em

issio

n f

acto

r (k

g C

O2/s

km

)

IPCC LEE IPCC_turboprop

Figure 11: Overview of the development of emission factor for air transport for the three different scenario assumptions (LEE, IPCC and IPCC_turboprop).




3 Detailed description of calculations



3.1 Introduction

The calculations comprise two steps: a ‘business-as-usual’ development, the Baseline scenario, and the combined impact of global and national policies on this baseline development, the BP-Scenario. The Baseline development is always the same in every run and cannot be changed. The BP Scenario depends on the assumptions for carbon cost changes in the future and taxes and subsidies as well as changes in the speed of transport systems at the national level. The calculations are made in two modules; the global tourism market model and the country model. The aim of the calculations is of course to find the impacts on tourism for each BP country. This is performed in following steps:

1. The baseline for both global tourism market and each country is calculated based on current short term trends, the UNWTO 2035 (see UNWTO-UNEP-WMO 2008) and the IPCC A1F long term scenario’s.

2. The impact of global climate mitigation policies on the global carbon cost with a distinction between an emission trading system just for Europe (ETS) and a global emission trading system determines the BP scenario for the global tourism market giving shifts between the different tourism markets (domestic and the six distance classes).

3. The impact on tourism within, to and from the BP countries is determined by a mix of the shifts in the global tourism markets (if a specific distance class grows faster then average, then a BP country that depends much on this market will benefit from this growth), and a combination of the impact of national cost and global cost on ticket prices and of changes in transport infrastructure on national travel speed changes assumed.

In this section we will detail the two calculation modules and the Baseline and BP Scenario’s. One of the main challenges of the model is the projection of the transport modal split. Therefore we added a specific section ( 3.4) with detailed information.

3.2 The global tourism market model

3.2.1 Baseline (global tourism market)

We created the global tourism market model for 2005-2050 for seven transport market segments (domestic and 6 different distance classes for international). The model is calibrated to the UNWTO scenario for 2035 (UNWTO-UNEP-WMO 2008) but extended to 2050. The international trips for 2005 in this global model are based on the arrivals data from UNWTO, but of course also represent the departures per country (these must be equal in a global model of international tourism). Therefore the global model provides both inbound and outbound for all countries in the world, totalling in 2005 at 4750 million trips this includes the 750 million international trips). The data have been calibrated to the data provided by BluePlan, which are enriched UNWTO data. Sometimes the datasets differ slightly, caused by corrections we sometimes applied because UNWTO apparently included same day trips in some relations that we excluded in this study.

The development of the total number of trips in the world is defined based on an IPCC scenario. We have created two different models one using the A1F scenario, that up till now best represents the current development of the economy and emissions (IPCC 2000), and one the A2 scenario that represents a low economic growth future. The only variables taken from the IPCC scenario are GDP per capita and growth of the population (both as given by IMAGE-team 2006). The idea behind this is that people start to travel as soon as they have a chance to do so. This chance is determined by a general common urge to travel and if the ticket can be afforded with respect to (dispensable) income. Therefore the number of trips per year taken by individuals is a




function of GDP/capita. The cost do not determine the number of trips but only the transport mode chosen and the distance travelled. The latter choice enables customers to adjust spending on travel to the amount of money available.

The overall average annual growth rate of global international tourism is found to be 4.4% per annum. The initial growth rates per tourism market (distance class) deviates from this long term number. In some cases the growth rate is very low (-2.7% for the 0-999 km distance market or very high (+17% for the medium haul 3500-5499 km market). Over a period of 45 years such growth rates would mean a 70% decrease of the short haul and a growth by a factor 1170% for the medium growth market. Specifically the latter is simply impossible. Therefore we have developed a method to reduce the extreme growth rates to become nearer to the long term growth rate as found for the UNWTO 2035 scenario, while retaining a difference between markets with strong and weak growth over the whole period until 2050. For domestic tourism the results of the GDP and population method are followed as no consistent empirical data exist to base a global trend on.

Then the growth has to be distributed over the inbound distance classes. We presume that the growth factors converge smoothly by 2020-2025 and stay constant after that. To accomplish this we used a ‘sigmoidal Boltzmann’ equation:

δn = δ0' +δ N − δ0'

1+ expc − n

d

(6)

In this equation δn is the growth factor for year n, δ0’ is an ‘uncorrected’ initial value at year n=0 (2005 in our case), δN is the growth factor at the final year N (the one based on the WTTC growth) and c and d are two constants determining the shape of the line between n=0 and N. The final growth factors per distance class are chosen by assuming all growth rates to converge to 20% of the original difference with the overall long term growth rate given by WTTC, thus keeping the initial order of growth is kept (so highest initial growth stays highest, etc).

The uncorrected initial value is calculated with equation (7)

δ0 ' = δN + φ ⋅ δm − δ N( )+ µ (7)

where δm is the Initial growth rate of the market (domestic or distance class international), φ the fraction of the difference between δm and δN (the afore mentioned 20%) and µ a

correction factor to adjust the final 2050 number of trips as calculated as the sum of the seven tourism markets to the number of trips estimated with δN. Figure 12 gives the

resulting growth factors over time.



Global tourism market smooth function

0.950

1.000

1.050

1.100

1.150

1.200

2000 2010 2020 2030 2040 2050 2060

year

Gro

wth

facto

rs p

er

yea

r

Domestic 0-999 km 1000-1499 km

1500-3499 km 3500-5499 km 5500-7999 km

8000- km

Figure 12: Growth rate conversion for the global tourism markets in the baseline scenario.

The modal split (air, car, other) is given for (international) inbound tourism and calculated in two steps for domestic and outbound tourism. First the share of air transport is determined as function of the average distance per distance class - domestic or outbound (international) - using the method detailed in section 3.4. Then the shares of

car are calculated based on:

Scar = 1− Sair( )⋅Scaroriginal

Snon _ air

(8)

The share of other transport modes is based on the same equation (8) replacing subscript ‘car’ by ‘other’.

Total distance is calculated by multiplying the number of trips with the average distance of the specific tourist market in 2005 (so no growth of distances within the markets, which essentially are distance classes) is assumed. The total CO2 emissions are found by multiplying distance with emission factors.

3.2.2 BP scenario (global tourism market)

The global tourism market is solely governed by the cost development for carbon as submitted by the model user. The calculation follows a two-step procedure. First the initial impact of policies is translated to a fractional cost change per year n. The cost for transport is calculated as follows:

Pm = P0 + 2 ⋅γ ETSm⋅CCO2

⋅ eCO2⋅ dtripm

(9)




with Pm the final cost of transport for tourism market m, P0 the ticket cost in 2005, CCO2 the cost of carbon per kg, eCO2 the emission CO2 factor (kg/pkm) and dtrip.m the average distance of the tourism market for which the price is calculated. γETS.m gives the tourism

market specific fraction of trips that are affected when only the EU ETS emission trading system is installed. These fractions have been calculated using the CWTD.

Then the volume is calculated with:

VnSc= max 1,Vn0

1+ εG

pn − p0

p0

(10)

where Vn.Sc is the scenario number of trips for a specific market segment m and transport mode v (for clarity the indexes m and v have been left out) in year n resulting from a ticket price Pn in year n, which is higher then P0 in the Baseline scenario due to the assumed carbon cost. εG is the global (world) elasticity for the specific transport mode.

The second step involves correcting all trips back to the total number of trips in the Baseline, because we assume that the number of trips does not depend on transport cost but on GDP and the size of the population. The destination (tourism market) and transport mode choice are dependent on transport cost as calculated with equation (10). The correction now is given by

Cn =Vn0

VnSc( )

mm=1

7

∑ (11)

where Cn is the correction factor applied to all Vn.Sc, Vn0 is the total number of trips in the Baseline (international plus domestic) in year n and m denotes the market (domestic or one of the 6 distance classes). After this step all trips are known per transport mode and distance class and thus the distance and CO2 emissions can be calculated as in the Baseline.

3.3 Country model

3.3.1 Baseline (country)

For the baseline scenario the projections for inbound tourism are based on equation (6) and (7) with growth data for each tourism market as defined by the change in number of trips δ0 between 2001 and 2005 (UNWTO 2007a, UNWTO 2007b) and overall trip growth of inbound tourism δN is defined by the growth given by WTTC over the years 2015-2020 (WTTC 2010). The values of φ and µ are specific for each BP-country. See Figure 13 for an example of the growth decay for Italy.

For outbound trips the initial trips growth δ0’ between 2001 and 2005 is found from the

CWTD database. This initial growth is then reduced using the same parameters and equations (6) and (7) as described above for inbound tourism. Domestic tourism is calculated using the GDP and population method. For each BP country the GDP and population for 2005 are taken from the WDI (World development Indicators; see section 2.2), while the annual growth of population and GDP is taken from the projection per

IMAGE SRES region to which the BP country belongs (IMAGE-team 2006). Domestic



tourism is now the total number of trips by the BP-country’s citizens minus the known outbound trips.

0.8

0.85

0.9

0.95

1

1.05

1.1

1.15

2000 2010 2020 2030 2040 2050 2060

0-999 km 1000-1499 km

1500-3499 km 3500-5499 km

5500-7999 km 8000- km

Outbound

Figure 13: example of growth factor development per distance market for Italy.

Then we apply the method described in section 3.4 to determine the share of air

transport in each tourism market both (inbound distance classes, domestic and outbound) and the method described by equation (8) to find the shares of car and other. All other parameters are based on the number of trips per tourism market (domestic, inbound distance classes and outbound), the average distance and the global emission factors to calculate transport distances and CO2 emissions.

The total contribution of tourism of each BP-country is calculated using following general equation (assuming constant 2005 € and constant prices:

GDPTn= Vdomn

⋅ Rdom + Vinbn,m⋅ Rinbm

( )m=1

6

∑ − Voutbn⋅ Routb (12)

where GDPTn is the national contribution of tourism to GDP in year n, V the volume and R the revenues in year n for respectively domestic (dom), inbound (inb) and outbound (outb). The index m give the tourism market (i.e. the 6 distance classes for inbound). The price of inbound varies with distance class due to the larger cost for travelling over larger distances. This has been calculated as follows:

rinbm=

Rtot − Pmm=1

6

∑

Vtot

+ Pm (13)




where rinbm is the revenue per trip for distance class m, Rtot the total inbound tourism revenues in 2005 (based on UNWTO 2007a, UNWTO 2007b), Pm the cost per trip for transport (weighted for modal split) in 2005 and Vtot the total number of trips in 2005. For domestic and outbound tourism we defined only one tourism distance class (the average) and used Personal domestic and Imports from the WTTC database (WTTC 2010) as total revenues for domestic and outbound respectively.

3.3.2 BP scenario (country)

The BP country specific tourism market is governed by changes in the global tourism market (as determined by global climate mitigation policies) and national policies for tourism transport taxes, subsidies and investments affecting transport speed. The impact of the global mitigation policies (ETS, global trading system) is calculated by directly applying the market specific (outbound, domestic and inbound per distance class) annual additional growth factor (additional with respect to Baseline growth) of the global tourism market. Because not all BP countries are part of the EU zone that is covered by the European emission trading system ETS, the impacts of an ETS only mitigation policy will differ much for each BP country. Therefore we multiply the global market growth factor with the ratio between the global ETS fraction and the national ETS fraction, thus accounting for the amount of dependence of a BP-country’s tourism market of the EU. When a global emission trading system is assumed this factor is by definition 1.0.

Now there are two mechanisms that may impact the number of tourists going to a specific BP country. The first is a change in the global market, that will change the inbound tourism for each BP country in a specific way. This change could be calculated by imposing the rate of change of the global market for each year to the volume (trips) per market segment of the specific BP country. However, there is also a direct impact of the price on the competitiveness of the specific BP country. This could be calculated by just adding the price changes due to global climate change mitigation policies (global or European ETS) to the cost of each trip. However, in that case all impacts will be negative and total number of trips would be reduced, while the shift from high carbon intensity markets to low carbon intensity ones would disappear. Therefore we use a factor α (0.0 to 1.0) that will divide the impact caused by the global mitigation policy between fully from the cost increase on transport to the specific BP country (α = 0.0) and the whole effect to be from the chances in the global market (α = 1.0). Default value for α is 0.5.

Apart from this global market impact there is also an impact of national taxes and subsidies that change the price of transport. The national taxes and subsidies are defined as a percentage of total ticket price per tourism market and per year, from which a change of price per year is calculated. Finally the impacts of changes in travel speed are calculated from the % diversion of travel speed with respect to the Baseline. Overall we have now:

vn+1 = vn ⋅ 1+ α ⋅δ Basn+1

− δBasn

δ Basn

⋅γ ETScntrym

γ ETSm

⋅ 1+ ε tc ⋅

pn+1 − pn

pn

⋅ 1+ ε ts ⋅

sn+1 − sn

sn

(14)

where vn+1 is the number of trips to a specific BP country for a specific market segment and transport mode in year n+1 resulting after changes in global market (the left most part of the equation), the impact of national policies on the price of transport (middle part of the equation) and impact of national policies on travel speed (right part of the



equation). ε tc is transport cost elasticity and εts travel speed elasticity, p denotes prices in

a specific BP country and s denotes travel speeds. The overall cost price is corrected with α as follows:

pn = 1− α( )⋅ccglobn+ ccnatn

+ pnatn (15)

where Pnat.n is the national transport price, ccglob.n the global cost of carbon and ccnat.n the cost of national policies. Default value has been chosen at 0.5.

With all trips known, the distance and CO2 emissions are calculated in the same way as for the Baseline. Also tourism’s contribution to GDP can now be calculated for each BP country using basically equation (12).

3.4 Modal split

The modal split per market segment is unknown and has been estimated using the known overall modal split for international transport. First we have estimated the share of air transport based on a relation between average distance and from the MuSTT study (Peeters et al. 2004). The share of air transport can be fitted by a Sigmoidal Hill function (Vasilyev 2004):

Sair =a ⋅ xd

cd + xd (16)

with Sair the share of air transport in %, x the 1-way distance in km and the parameters a=117.2, c=1623 and d=2.503. Figure 14 gives the measured points and the fitted curve.

0

20

40

60

80

100

120

0 500 1000 1500 2000 2500 3000 3500 4000

Stage distance (km 1-way)

Original points

Fitted line

Figure 14: Relation between 1-way distance and share of air transport between EU countries; measured points and fitted curve (based on Peeters et al. 2004)

This function has a very useful property as it is possible to shift it substantially from low to high air transport shares at short distances while retaining the starting (0%) and ending




(100%) point. Because we have overall air transport shares for international arrivals we need to fit the overall data to this number. For this we developed the following function using a ‘fit factor’ FF that shifts the function left or right and stretches it:

Sair =

a ⋅ xFF⋅d

(FF ⋅ c)(FFgd ) + x(FFgd ) (17)

where Sair is the share of air transport for all trips at average distance of x; x is the class average of one-way distance in km. FF is the ‘fit factor’ that will shift the share to the upper left or lower right to fit the curve to the data for each specific BP country as given by the UNWTO data. Figure 15 shows the effect of the fit factor FF. The FF is fully determined by the measured situation, so for example an isle will have a very low FF, giving high shares of air transport.

The shares of car and other transport modes are created using equation (8).

0

20

40

60

80

100

120

0 500 1000 1500 2000 2500 3000 3500 4000

Distance 1-way (km)

0.5

0.75

1

1.25

1.5

MuSTT points

Figure 15: shows the effect of the fit factor FF as given in equation (17).

The next problem of developing the scenario’s is the development of modal shares over time. For all BP countries there are differences between the modal split in 2001 and 2005. The solution is to take the change of FF between 2001 and 2005 as an initial growth factor for FF. However, in many cases this rate of change is rather high (as is in the case of individual tourism market growth rate) leading to very unrealistic values of FF and air transport mode shares. Therefore we have created a method that slows down rate of change of FF (δFF) to a minimum value FFmin defined by the model user that will be reached in a year Ymax (but no later then 2050) also chosen by the model user. These two values are determined once for the Baseline scenario per BP country and for the global tourism market in such a way that a smooth development of FF occurs (see Figure 16 for an example for Egypt). To accomplish this we need that δFF itself changes by a rate ρFF:

FFmin = FF0 ⋅ δFF0⋅ ρFF

n( )n

(18)

where n is the number of year from the base year (in our case 2005), FF0 the initial (2005) value of FF, FFmin the minimum value set by the model user and δFF0

the initial average



rate of change as calculated over the period 2001 and 2005. Equation (18) can be re arranged to give the value of ρFF :

ρFF = exp

lnFFmin

FF0

− n ⋅ ln δFF0

( )

n2

(19)

However, the above form resulted is somewhat flawed and non-smooth developments of FF, that were caused by choosing different time spans (values of n). After some experimenting we found that introducing a time span correction factor Cts solved this:

ρFF = exp

lnFFmin

FF0

− n ⋅ ln δFF0

( )

nCts

(20)

with

Cts = a + b ⋅ Ymax − Y0( )+ c ⋅ Ymax − Y0( )2+ d ⋅ Ymax − Y0( )3

(21)

where a = 1.684, b = 0.007662, c = -0.0001571 and d = 1.239E-06.

To calibrate the model we first fit the values of FF for 2001 and for 2005 such that the input share of air transport (all market segments for the global international tourism model and international inbound air transport share for the country specific model) equal the average given by the UNWTO data. From this the initial change per year of FF (δFF0

) is

found. Then both FFmin and Ymax are chosen such that a smooth development of FF occurs as shown in Figure 16.




0

0.2

0.4

0.6

0.8

1

1.2

1.4

2000 2020 2040 2060

Year

Corr

ecte

d a

ir t

ransprt

mode s

hare

fit

facto

r FF

Figure 16: the change of the air transport share fit factor FF for the case of Egypt.



4 Final remarks




The model has been calibrated to historical data about tourism trips, economic growth and tourism revenues. The programmer has gone systematically through all parts, modules and equations of the model and all team members of the project have evaluated the model and its results.

This resulted in many remarks, clarifications and modifications of the model. Still some arbitrary choices regarding data and method remain, for example the precise way to use elasticities can be varied taking total price and travel time change with respect to the same values in the baseline scenario or by using incremental changes in price and travel time per year and adding its effect to the Baseline growth. We used the latter because then automatically negative growth is avoided, but sacrificing a bit of the effectiveness of the measures.

4.1 The impact of carbon cost on travel behaviour

In many publications the required cost of carbon has been projected for the next 100 year or more in order to assess the economic impacts (e.g. Böhringer et al. 2009, Calvin et al. 2009, van Dender 2009, IPCC 2007, Kyle et al. 2009, MartÌn-Cejas 2009, Nordhaus 2008, Russ et al. 2009, Stern 2006, Tol 2009, Tuladhar et al. 2009, van Vliet et al. 2009). Unfortunately these costs differ over a wide range and even with more or less equal mitigation goals (e.g. levels of CO2 concentrations, temperature rises or emissions) the numbers given in the literature differ by up to an order of magnitude. The highest values given are up to $500/ton CO2 in 2050 (e.g. Calvin et al. 2009), but in most cases the cost is limited to $20/ton CO2 or even less. At such low values the impact on the cost of transport and travel is almost negligible (see Anger 2010, Mayor et al. 2009, Mayor et al. 2007, Mayor et al. 2010, Tol 2007). It is not difficult see this also from the data in the MEDTOUR model: take for example relatively high emission trips over distances larger then 8000 km by airplane: the average cost of such a trip is in the order of €2000-2500 per return ticket, while the emissions are almost 3000 kg of CO2. This means a €20/ton CO2 cost of carbon would increase the ticket price by less then 3% and only at the introduction of the carbon cost. This means that only at one time some 2.5% of the air transport market will be lost, which is even generally lower than average annual growth. Even at the much higher cost of €500/ton CO2 the ticket cost would not increase by more then 75%. This might induce a long term loss of volume of just over 60% by 2050, which would be compensated by intrinsic growth (caused by GDP/cap and population growth) between 25 and 30 years of assumed growth (so there will still be strong growth by 2050, even in this most disadvantaged long haul market). Another impact is the improvement of fuel efficiency that significantly reduces the extra cost imposed by a certain carbon cost level. Finally the volume of air transport does not only depend on air ticket cost, but also on the cost development of alternatives. For the long haul market this is not vey relevant, but in the short and medium haul market there will be competition by car and other transport modes. However, certainly in the first 1 or 2 decades, car (fuel!) cost will increase by larger margins then air ticket cost. The car fuel elasticity is only slightly smaller then for air transport, causing initially the car to be not a very viable second choice. This hampers the shift to other modes, though car fuel efficiency at the end of the period 2005-2050 will improve more then for air transport, finally levelling off this compensating effect.

Another problem in PSEM countries, hampering modal shift away from air, is caused by a very strong increase of car transport cost because we assumed that current subsidies, on car fuel, existing is some of the PSEM countries, will be removed. This of course does not help to decrease the competitive edge of air transport with respect to ca transport and



even may cause a shift from short haul car based travel) to medium haul (air based) travel.

Considering the vey strong growth caused by the global economic and population growth, specifically in the PSEM countries, makes it very difficult in 2050 to even keep total tourism transport emissions at the 2005 level by economic measures only. Making absolute reductions almost impossible. Fr such emission reductions to happen, policies might be needed that limit for example airport and road capacity. Such a policy seems to be adopted by the French government (The Rail Engineer 2010), where almost all investments will be directed at rail and none at new highways. The main idea is to offer a better alternative to car and air transport within France. Furthermore efforts might be taken to change the market by marketing domestic and short haul tourism (specifically within the region) and by de-marketing long haul tourism both in- and outbound; e.g. Peeters et al. 2009).

4.2 Economic impacts

Another difficult issue to assess in the model is the impact of tourism on the economy of the countries. First a simple approach had to be chosen which is that just the revenues per trip times the number of trips are taken as the total economic value. Furthermore the 2005 revenues per trio are increased over time proportionally to average global GDP/capita growth as given by the IPCC SRES scenarios, but corrected by a factor 0.2 as explained in section 2.2.6. Second it is assumed that only part of spending on outbound

tourism is spent in the home country (a share of 40% has been chosen by default). Overall the contribution to GDP thus equates as inbound revenues plus domestic revenues minus 60% of outbound revenues minus global carbon cost minus national taxes. Furthermore taxes (both global carbon cost and national taxes) are withdrawn from the total revenues. Therefore the results of economic impacts should be evaluated with some care and common sense.




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Annexes

Table 11: Overview of some general data for the BP countries.

Country IMAGE (SRES) region

OECD90 ETS zone Country Nr Country abbr.

Albania 10 0 0 1 ALB Algeria 5 0 0 2 ALG Bosnia-Herzegovina 10 0 0 3 BOS Croatia 10 0 1 4 CRO Cyprus 12 1 1 5 CYP Egypt 5 0 0 6 EGY France 9 1 1 7 FRA Greece 9 1 1 8 GRE Israel 12 0 0 9 ISR Italy 9 1 1 10 ITA Lebanon 12 0 0 11 LEB Libya 5 0 0 12 LIB Malta 9 1 1 13 MAL Monaco 9 1 1 14 MON Morocco 5 0 0 15 MOR Palestinian Authority 12 0 0 16 PAL Serbia and Montenegro 10 0 0 17 SAM Slovenia 10 0 1 18 SLO Spain 9 1 1 19 SPA Syria 12 0 0 20 SYR Tunisia 5 0 0 21 TUN

Turkey 12 1 0 22 TUR

Table 12: Overview of trips used for calculating the cost of air tickets per BP country.

Origin Destination Origin airport code

Dest. airport code

Distance (1-way km)

Serbia and Montenegro Albania BEG TIA 381

Greece Albania ATH TIA 529

Italy Albania MXP TIA 1003

Germany Albania FRA TIA 1300

France Albania PAR TIA 1586

United Kingdom Albania LON TIA 1902

Iceland Albania KEF TIA 3689

India Albania DEL TIA 5329

United States Albania JFK TIA 7432

Canada Albania YYZ TIA 7622

Japan Albania NRT TIA 9543

Australia Albania AKL TIA 17823

Tunisia Algeria TUN ALG 626

Spain Algeria MAD ALG 726

Libyan Arab Jamahiriya Algeria TIP ALG 1014

France Algeria PAR ALG 1370

Germany Algeria FRA ALG 1540

United Kingdom Algeria LON ALG 1669

United States Algeria JFK ALG 6494

Canada Algeria YYZ ALG 6799

Japan Algeria NRT ALG 10848

Australia Algeria SYD ALG 17110

Croatia Bosnia and herzegovina ZAG SJJ 278





Dest. airport code

Distance (1-way km)

Serbia and Montenegro Bosnia and Herzegovina ATH SJJ 807

Germany Bosnia and Herzegovina FRA SJJ 1014

France Bosnia and herzegovina PAR SJJ 1350

United Kingdom Bosnia and Herzegovina LON SJJ 1624

Spain Bosnia and herzegovina MAD SJJ 1841

United States Bosnia and Herzegovina JFK SJJ 7188

Canada Bosnia and herzegovina YYZ SJJ 7364

Japan Bosnia and herzegovina NRT SJJ 9430

Australia Bosnia and Herzegovina SYD SJJ 15846

Italy Croatia MXP ZAG 572

Germany Croatia FRA ZAG 735

France Croatia PAR ZAG 1082

United Kingdom Croatia LON ZAG 1371

Sweden Croatia ARN ZAG 1553

Russian Federation Croatia DME ZAG 1871

United States Croatia JFK ZAG 6924

Canada Croatia YYZ ZAG 7092

Japan Croatia NRT ZAG 9396

Australia Croatia SYD ZAG 16021

Israel Cyprus TLV NIC 379

Greece Cyprus ATH NIC 890

Bulgaria Cyprus SOF NIC 1196

Romania Cyprus BUH NIC 1199

Germany Cyprus FRA NIC 2596

United Kingdom Cyprus LON NIC 3239

Ireland Cyprus DUB NIC 3683

Portugal Cyprus LIS NIC 3764

China Cyprus PEK NIC 7096

South Africa Cyprus CPT NIC 7803

United States Cyprus JFK NIC 8790

Australia Cyprus SYD NIC 14420

Israel Egypt TLV CAI 393

Jordan Egypt AMM CAI 473

Greece Egypt ATH CAI 1106

Saudi Arabia Egypt JED CAI 1215

Italy Egypt MXP CAI 2618

Germany Egypt FRA CAI 2910

United Kingdom Egypt LON CAI 3536

India Egypt DEL CAI 4412

South Africa Egypt CPT CAI 7220

China Egypt PEK CAI 7549

United States Egypt JFK CAI 9035

Japan Egypt NRT CAI 9612

United Kingdom France LON PAR 348

Germany France FRA PAR 449

Netherlands France AMS PAR 742

Portugal France LIS BOD 969

Italy France MXP LYN 1107

Sweden France ARN PAR 1791

United States France JFK PAR 5849




Dest. airport code

Distance (1-way km)

Canada France YYZ PAR 6037

Japan France NRT PAR 9734

Australia France SYD PAR 16942

Albania Greece TIA ATH 529

Turkey Greece IST ATH 538

Romania Greece BUH ATH 744

Bulgaria Greece SOF SKG 750

Israel Greece TLV ATH 1195

Germany Greece FRA ATH 1818

United Kingdom Greece LON ATH 2431

United States Greece JFK ATH 7952

South Africa Greece CPT ATH 7979

Canada Greece YYZ ATH 8149

Australia Greece SYD ATH 15306

Egypt Israel CAI TLV 393

Jordan Israel MED TLV 952

Turkey Israel IST TLV 1133

Greece Israel ATH TLV 1195

Germany Israel FRA TLV 2957

France Israel PAR TLV 3288

Spain Israel MAD TLV 3551

United Kingdom Israel LON TLV 3593

South Africa Israel CPT TLV 7500

Singapore Israel SIN TLV 7970

United States Israel JFK TLV 9137

Canada Israel YYZ TLV 9316

Austria Italy VIE VCE 433

Germany Italy FRA MXP 491

France Italy PAR TRN 574

Switzerland Italy ZRH ROM 692

United Kingdom Italy LON MXP 938

Spain Italy MAD MXP 1151

Iran, Islamic Republic of Italy THR MXP 3718

Saudi Arabia Italy JED MXP 3833

United States Italy JFK MXP 6430

Canada Italy YYZ MXP 6630

Japan Italy NRT MXP 9772

Australia Italy SYD MXP 16590

Jordan Lebanon CAI BEY 563

Iraq Lebanon BGW BEY 830

Saudi Arabia Lebanon JED BEY 1393

Iran, Islamic Republic of Lebanon THR Beirut 1462

Germany Lebanon FRA BEY 2842

France Lebanon PAR BEY 3192

India Lebanon DEL BEY 3983

Sri Lanka Lebanon CMB BEY 5427

China Lebanon PEK BEY 7001

Malaysia Lebanon KUL BEY 7655

United States Lebanon JFK BEY 9040

Canada Lebanon YYZ BEY 9202





Dest. airport code

Distance (1-way km)

Malta Libyan Arab Jamahiriya MLA TIP 375

Italy Libyan Arab Jamahiriya MXP TIP 1489

Morocco Libyan Arab Jamahiriya AGA TIP 2156

Yemen Libyan Arab Jamahiriya SAH TIP 3666

India Libyan Arab Jamahiriya DEL TIP 6056

Thailand Libyan Arab Jamahiriya BKK TIP 8982

Iran, Islamic Republic of Libyan Arab Yamahiriya THR TIP 3512

Tunisia Lybian Arab Yamahiriya TUN TIP 536

Algeria Lybian Arab Yamahiriya ALG TIP 1014

Egypt Lybian Arab Yamahiriya CAI TIP 1756

India Lybian Arab Yamahiriya DEL TIP 6056

Philippines Lybian Arab Yamahiriya MNL TIP 10755

Libyan Arab Jamahiriya Malta TIP MLA 375

Greece Malta ATH Malta 874

Italy Malta MXP MLA 1188

Austria Malta VIE MLA 1372

Germany Malta FRA MLA 1646

United Kingdom Malta LON MLA 2104

United States Malta JFK MLA 7402

Canada Malta YYZ MLA 7662

Australia Malta SYD MLA 16095

New Zealand Malta AKL MLA 18259

Italy Monaco MXP MCM 236

United Kingdom Monaco LON MCM 1045

Russian Federation Monaco DME MCM 2530

United States Monaco JFK MCM 6435

Japan Monaco NRT MCM 10003

Portugal Morocco LIS TNG 443

United Kingdom Morocco LON AGA 2463

Germany Morocco FRA AGA 2654

Sweden Morocco ARN AGA 3849

Canada Morocco YYZ AGA 6234

Australia Morocco SYD AGA 18155

Israel Palestine TLV GZA 103

United States Palestine JFK GZA 9153

Slovenia Serbia and Montenegro LJU BEG 483

Germany Serbia and Montenegro FRA BEG 1050

Netherlands Serbia and Montenegro AMS BEG 1413

United Kingdom Serbia and Montenegro LON BEG 1706

Russian Federation Serbia and Montenegro DME BEG 1715

United States Serbia and Montenegro JFK BEG 7260

Canada Serbia and Montenegro YYZ BEG 7418

Canada Serbia and Montenegro YYZ BEG 7418

Japan Serbia and Montenegro NRT BEG 9241

Australia Serbia and Montenegro SYD BEG 15690

Italy Slovenia MXP LJU 449

Germany Slovenia FRA LJU 610

France Slovenia PAR LJU 946

Netherlands Slovenia AMS LJU 976

United Kingdom Slovenia LON LJU 1238




Dest. airport code

Distance (1-way km)

Israel Slovenia TLV LJU 2356

United States Slovenia JFK LJU 6789

Canada Slovenia YYZ LJU 6960

Japan Slovenia NRT LJU 9439

Australia Slovenia SYD LJU 16141

Portugal Spain LIS MAD 514

France Spain PAR MAD 1062

United Kingdom Spain LON MAD 1244

United States Spain JFK MAD 5775

Iraq Syrian Arab Republic BGW DAM 735

Saudi Arabia Syrian Arab Republic JED DAM 1326

Bahrain Syrian Arab Republic BAH DAM 1575

USSR (former) Syrian Arab Republic DME DAM 2447

United Kingdom Syrian Arab Republic LON DAM 3586

United Kingdom Syrian Arab Republic LON DAM 3586

Indonesia Syrian Arab Republic CGK DAM 8572

United States Syrian Arab Republic JFK DAM 9140

Libyan Arab Jamahiriya Tunisia TIP TUN 536

Algeria Tunisia ALG TUN 626

Spain Tunisia MAD TUN 1265

Germany Tunisia FRA TUN 1470

France Tunisia PAR TUN 1487

United Kingdom Tunisia LON TUN 1832

Qatar Tunisia DOH TUN 4114

United States Tunisia JFK TUN 7022

Canada Tunisia YYZ TUN 7295

Brazil Tunisia GIG TUN 8664

Japan Tunisia NRT TUN 10465

Bulgaria Turkey SOF IST 488

Iran Turkey THR VAN 773

Ukraine Turkey KBP IST 1054

Austria Turkey VIE IST 1252

Russian Federation Turkey DME IST 1738

Germany Turkey FRA IST 1870

Kyrgyzstan Turkey FRU IST 3742

Kazakhstan Turkey ALA IST 3938

China Turkey PEK IST 7089

Korea, Republic of Turkey SEL IST 7974

United States Turkey JFK IST 8072

Japan Turkey NRT IST 9017


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