Gestion de l énergie Transport aérien et tourisme en Méditerranée

Transcription

1 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 TEC- 38 rue Sénac de Meilhan Marseille France Tel/ Fax : + 33 (0) accueil@tec-conseil.com Ingénierie et management du développement durable

2 Sommaire 2.1 INTRODUCTION TOURISM DATA International inbound International outbound Domestic tourism Distance Modal split Revenues en economics ELASTICITY TRANSPORT COST Road transport cost Air transport cost Other mode costs THE TRANSPORT CO2 EMISSION FACTORS INTRODUCTION THE GLOBAL TOURISM MARKET MODEL Baseline (global tourism market) BP scenario (global tourism market) COUNTRY MODEL Baseline (country) BP scenario (country) MODAL SPLIT Gestion de l énergie Transport aérien et tourisme en Méditerranée TEC /52

3 Error! For evaluation Style not defined. only. 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, , , , , >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. Gestion de l énergie Transport aérien et tourisme en Méditerranée TEC /52

4 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. Gestion de l énergie Transport aérien et tourisme en Méditerranée TEC /52

5 Error! For evaluation Style not defined. only. 1 Overview of the model Gestion de l énergie Transport aérien et tourisme en Méditerranée TEC /52

6 1.1 Model users interface 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 modelv 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) 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. Gestion de l énergie Transport aérien et tourisme en Méditerranée TEC /52

7 Error! For evaluation Style not defined. only. 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_v 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: Gestion de l énergie Transport aérien et tourisme en Méditerranée TEC /52

8 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 Gestion de l énergie Transport aérien et tourisme en Méditerranée TEC /52

9 Error! For evaluation Style not defined. only. 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). Gestion de l énergie Transport aérien et tourisme en Méditerranée TEC /52

10 National policies input file: Transport taxes Transport subsidies Transport speed Global tourism Model Global scenario (A1 & A2) UNWTO domestic & international tourism data WTTC economic Country Tourism Model National policy Transport cost data Modal split inbound (UNWTO) Output file: Trips Distances Emissions Economics 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. Gestion de l énergie Transport aérien et tourisme en Méditerranée TEC /52

11 Error! For evaluation Style not defined. only. 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 End year 2050 in time series but with a display of results for 2005, 2025 and Tourism will be evaluated for seven tourism markets (or distance classes ): a. Domestic b. International, one-way great circle distance: i km ii km iii km iv km v km vi. >= km The model consists of two basic sub-models: c. Global tourism market model d. Country model Gestion de l énergie Transport aérien et tourisme en Méditerranée TEC /52

12 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. Gestion de l énergie Transport aérien et tourisme en Méditerranée TEC /52

13 Error! For evaluation Style not defined. only. 2 Data sources Gestion de l énergie Transport aérien et tourisme en Méditerranée TEC /52

14 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 Inbound trips 2001 & 2005 per BP country and country of origin worldwide Outbound trips 2001 & 2005 per BP country and country of destination worldwide Domestic trips per BP country for 2001&2005 Distances between all countries worldwide (great circle distance between main airports) Number of trips for 2010 and 2020 from WTTC per country as input for long term growth rates Available with UNWTO 2007a, UNWTO 2007b UNWTO 2007a, UNWTO 2007b Estimates based on methods given in Dubois et al. 2010, Peeters et al Estimates based on UNWTO data and Swartz 2010, WebFlyer 2003 WTTC 2010 Transport travel cost See section 2.4 Air, car, other transport shares international Air, car, other transport shares domestic Tourism revenues per BP country 2001&2005 GDP per BP country 2001&2005 Emission factors per transport mode and distance class UNWTO 2007b Estimated based on international shares and domestic travel distance Data provided by BluePlan plus WTTC World bank World Development Indicators database (WDI). 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.) Gestion de l énergie Transport aérien et tourisme en Méditerranée TEC /52

15 Error! For evaluation Style not defined. only. 2.2 Tourism data 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 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 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 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,C cy + α cy GDP cap ) (1) Gestion de l énergie Transport aérien et tourisme en Méditerranée TEC /52

16 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 International OECD90 Developing GDP/Cap 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 E-05 6 non-oecd90 all trips E 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 km km km km 1.05 Gestion de l énergie Transport aérien et tourisme en Méditerranée TEC /52

17 Error! For evaluation Style not defined. only km 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 km 2 ) and fitted a power curve to these data (see Figure 3) the following equation was found: d i = ε q A i (2) with Ai in km 2 and the di the average domestic one-way distance in km. The factor ε q is an indicator for the quality of the road network. To be consistent with former work we take ε q =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 (km 2 ) 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 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). Gestion de l énergie Transport aérien et tourisme en Méditerranée TEC /52

18 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 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. Gestion de l énergie Transport aérien et tourisme en Méditerranée TEC /52

19 Error! For evaluation Style not defined. only y = x Revenues per trip (2005$/trip) World GDP/capita Lineair (GDP/capita) Lineair (World) y = x Figure 4: Different growth of tourism revenues per trip and GDP per capita. From the graph it can be seen that revenues per trip grow only at about 20% of the growth rate of total GDP/capita (both in 2005$). Therefore we correct the initial 2005 revenue data with 20% of GDP/capita growth as assumed in the SRES scenarios (global figures) 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. Gestion de l énergie Transport aérien et tourisme en Méditerranée TEC /52

20 Bosnia General country data Estimation method Air international (%) as average for countries same IMAGE region nr 10 Road international (%) as average for countries same IMAGE region nr 10 Rail international (%) as average for countries same IMAGE region nr 10 Sea international (%) as average for countries same IMAGE region nr 10 Lebanon General country data Estimation method Total personal domestic T&T spending (*10^9 current US $) Estimated assuming average of 300 $ per domestic trip Lybia General country data 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 Estimation method Total international revenues (*10^6 current US $) Total imports (*10^9 current US $) Total personal domestic ((*10^9 current US $) Int. overnight trips (WTTC projection (*1000) GDP (const US $) 1.84E E+09 Population According to m 25% of GDp is revenues of tourism. Based on average revs times two for France times Monaco trips Based on average revs times two for France times Monaco trips Data same as for from UNWTO pivot Took the data for GDP/cap for France times 1.3 Reference: population_wdi.xls for 2005, took 4% less in Gestion de l énergie Transport aérien et tourisme en Méditerranée TEC /52

21 Error! For evaluation Style not defined. only. Road international (%) Rail international (%) Sea international (%) Guestimated assuming km by car, remainder by air, no rail and ship. Palestine General country data Estimation method Total international revenues (*10^6 current US $) Estimated 2005 at just 6% higher then in 2001 (expert estimate) Total imports (*10^9 current US $) Expert guestimates Total personal domestic ((*10^9 current US $) 1 1 Expert guestimates Int. overnight trips (WTTC projection (*1000) Assumed 4% growth over which is much lower then measured (but that shows 50% growth and very erratic data). Number of nights (*1000) increased by 4% (expert guestimate) Air international (%) Market data International Estimation method Distance class (km) >8000 INBOUND Number of trips Number of trips Total distance 2001 (pkm*10^6 great circle) Total distance 2005 (pkm*10^6 great circle) Just filled in non-zero values tp keep model running. Serbia & Montenegro General country data Estimation method Total international revenues (*10^6 current US $) value estimate assuming rev per trip constant. Air international (%) estimated as average for countries same IMAGE region nr 10 Road international (%) estimated as average for countries same IMAGE region nr 10 Rail international (%) estimated as average for countries same IMAGE region nr 10 Sea international (%) estimated as average for countries same IMAGE region nr 10 Gestion de l énergie Transport aérien et tourisme en Méditerranée TEC /52

22 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 InterVISTAS Consulting Inc Pan-National EU-North America InterVISTAS Consulting Inc Pan-National EU-Asia InterVISTAS Consulting Inc Several definitions -0.5 to -1.0 Brons et al 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 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 and with 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 (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 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 Price car fuel -0.8 Price rail/bus ticket -0.5 Gestion de l énergie Transport aérien et tourisme en Méditerranée TEC /52

23 Error! For evaluation Style not defined. only. All modes travel speed 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 ticket prices per BP country Road transport cost Country Fuel cost (2009$/litre) Share of diesel (%) Cost per pkm (2005 /pkm) Petrol Diesel Albania % Algeria % Bosnia-Herzegovina % Croatia % Cyprus % Egypt % France % Greece % Israel % Italy % Lebanon % Libya % Malta % Monaco ? Morocco % Palestinian Authority ? Serbia and Montenegro % Slovenia % Spain % Syria % Tunisia % Turkey % All countries average Gestion de l énergie Transport aérien et tourisme en Méditerranée TEC /52

24 Table 7: Overview of fuel cost in the Blueplan countries (sources Ebert et al for fuel prices and WRI 2007 diesel shares) 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 that was designed to gather prices from the Dutch Air ticket website 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 3 rd of March and return 17 th of March 2010 while booking in the first week of February 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 Price class (per 50, starting ) 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). Gestion de l énergie Transport aérien et tourisme en Méditerranée TEC /52

25 Error! For evaluation Style not defined. only. SLO y = x R 2 = Minimum Average Maximum Minimum Average y = x R 2 = 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 Minimum Price, OECD Europe ALG EGY LIB MOR TUN FRA GRE ITA MAL SPA Distance (1-eay km) Distance (1-eay km) Minimum Price, Eastern Europe Minimum Price, Middle East ALB BOS CRO SAM SLO CYP ISR LEB SYR TUR Distance (1-eay km) Distance (1-eay km) Figure 7: Minimum prices as gathered for the four SRES-groups of the BP-countries. Gestion de l énergie Transport aérien et tourisme en Méditerranée TEC /52

26 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: P air = P air0 + α air 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 = 0.108x R 2 = Minimum Average Minimum Average y = x R 2 = Distance (1-way km) Figure 8: Overview of all results giving the air travel prices for the global market. Northern Africa Middle East y = x R 2 = Minimum Average Minimum Average y = x R 2 = Minimum Average Minimum Average y = x R 2 = y = x R 2 = Distance (1-way km) Distance (1-way km) OECD Europe Eastern Europe y = x R 2 = Minimum Average Minimum Average y = x R 2 = y = x R 2 = Minimum Average Minimum Average y = x R 2 = Distance (1-way km) Distance (1-way km) Figure 9: Overview of the final results grouped per IMAGE SRES region. Gestion de l énergie Transport aérien et tourisme en Méditerranée TEC /52

27 Error! For evaluation Style not defined. only. 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 OECD Europe Eastern Europe Middle East All countries 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 ( early February 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 ( ). 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: P rail = P rail0 + 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 Algeria Bosnia-Herzegovina Gestion de l énergie Transport aérien et tourisme en Méditerranée TEC /52

28 Croatia Cyprus (bus) Egypt France Greece Israel Italy Lebanon (fares from Syria) Libya (average EGI&ALG) Malta (only day tickets) Monaco - - Morocco Palestinian Authority - - Serbia and Montenegro Slovenia Spain Syria Tunisia Turkey Average all countries 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 ,000 km ,000 1,500 km ,500-2,000 km > 2,000 km World average Rail Car Coach Table 10: emission factors (Peeters et al. 2007a, UNWTO-UNEP-WMO 2008: Table 11-2). Gestion de l énergie Transport aérien et tourisme en Méditerranée TEC /52

29 Error! For evaluation Style not defined. only. 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: x E = a 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= , 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 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 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 % lower emissions when a large share of the air fleet is replaced by turboprop aircraft (based on Ryerson et al. 2010; see Figure 11). Gestion de l énergie Transport aérien et tourisme en Méditerranée TEC /52

30 Emission factor (kg CO2/skm) 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). Gestion de l énergie Transport aérien et tourisme en Méditerranée TEC /52

31 Error! For evaluation Style not defined. only. 3 Detailed description of calculations Gestion de l énergie Transport aérien et tourisme en Méditerranée TEC /52

32 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 Baseline (global tourism market) We created the global tourism market model for 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 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 Gestion de l énergie Transport aérien et tourisme en Méditerranée TEC /52