Rete Montagna Interna,onal Congress Eurac Conven,on Center -‐ Bolzano/Bozen -‐ IT November, 6th -‐ 8th 2014
ESTIMATING THE CARBON FOOTPRINT OF TOURISM IN SOUTH TYROL MATTIA CAI
2014
The Alps in Movement – People, Nature and Ideas
ESTIMATING THE CARBON FOOTPRINT OF TOURISM IN SOUTH TYROL Mattia Cai
Bolzano/Bozen, November 6-8th, 2014
Overview • Aim: – Quantify the greenhouse gas (GHG) emissions associated with tourism in South Tyrol (ST)
• Methodological approach: – Input-Output (IO) analysis – Multi-regional model
4
Tourism in ST Population ≈ 505,000
Origin
Visitors (mln, 2008)
Nights (mln, 2008)
South Tyrol (ST)
1.4
0.6
Other
8.2
32.1
Total
9.6
32.6
5
Tourism and ST’s economy
Direct
Direct and indirect
Value added (% of total)
1,731 mln € (11.2%)
1,903 mln € (12.4%)
Employment (% of total)
–
41,300 FTE (15.3%)
Source of data: ASTAT (2012). Impatto economico del turismo. Astat Info nr. 15.
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Tourism and GHG emissions • A non-negligible emission sector • about 5% of global direct CO2 emissions (UNWTO 2008)
• One of the fastest growing economic sectors
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The carbon footprint of tourism • ‘Unfortunately, there has been little attempt to measure CO2 emissions associated with individual tourism destinations’(Dwyer et al. 2010) • Some notable exceptions • Wales: Jones and Munday (2007) • Australia: Dwyer et al. (2010) • South West England: Whittlesea and Owen (2012)
• Recent increase in interest (Gössling 2013)
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Carbon footprints and IO models • The use of IO models to analyze carbon footprints is well established • Hendrickson et al. (2006) • Minx et al. (2009)
• Applications to tourism using multiregional IO at a (very) local level?
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Main practical difficulties • No ‘tourism industry’ in official statistics. • Very limited data availability at the subnational level.
10
Outline of presentation • Model structure and data requirements • Preliminary results • the estimated carbon footprint of tourism in ST
• Concluding remarks • (Current) limitations, ongoing work, potential extensions
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MODEL STRUCTURE AND DATA REQUIREMENTS
IO modeling and carbon footprints
Tourists expenditure on different products in ST
Multi-regional IO model
Estimates of GHG emission (direct and indirect)
• 59 industries (NACE rev. 1.1) • 5 regions
Why multiple regions? 13
Emission factors: regional variation Agriculture, Italy (2005) V. d'Aosta Lombardia Piemonte Sardegna Friuli V.G. Molise Umbria Veneto Basilicata Marche Emilia R. Abruzzo Lazio Bozen Campania Toscana Puglia Sicilia Trento Calabria Liguria 0
500
1000
1500
2000
2500
3000
(direct) tons of CO2 equivalent per mln EUR of value added
Source of data: ISTAT (2009) NAMEA: emissioni atmosferiche regionali
14 Â
Emission factors: regional variation Energy production, Italy (2005) Liguria Puglia Sicilia Friuli V.G. Sardegna Umbria Veneto Lazio Calabria Toscana Emilia R. Lombardia Piemonte Abruzzo Marche Campania Molise Basilicata Trento Bozen V. d'Aosta 0
5
10
15
(direct) kt of CO2 equivalent per mln EUR of value added
Source of data: ISTAT (2009) NAMEA: emissioni atmosferiche regionali
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15 Â
The regions of the model South Tyrol Northwest
Northeast
+ REST OF WORLD Rest of country 16 Â
Data requirements 1. Final demand by tourists by industry 2. Interregional matrix of input coefficients 3. Regional emission factors by industry 4. Rest of the world region 17
1.Tourism demand by industry (2010) Agriculture 2%
Other 14%
Transport and storage 5% Food, beverages and tobacco 6%
3,460 mln €
Hotels and restaurants 62%
Trade 11%
Source of data: ASTAT (2013), Tavola Input-Output per l’Alto Adige 2010. Astat info nr. 70.
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2. Interregional input coefficients • Input coefficients estimated as: Product i from region r needed to produce one unit of product j in region s
i needed per unit of j in Italy (from national IO table)
rs IT ars = c ⇥ a ij i ij Share of all i used in s that comes from r (Laboriously estimated from Bank of Italy industry survey data)
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3. Emission factors • 2005 emission data at regional level from ISTAT • carbon dioxide (CO2) • methane (CH4) • nitrous oxide (N2O).
• ‘Updated’ to 2008 (base year of the model)
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4. The rest of the world • Assumed German technology and emission factors (WIOD data) • Working on improved representation • more foreign regions in the model
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PRELIMINARY RESULTS
ST’s tourism: direct emissions
Direct emissions (kt CO2e)
Tourism (2010, modeled)
All economic activities (2005, ISTAT)
169 174
1,892
23 Â
Direct emissions – by industry Other 13%
Hotel, restaurant and catering services 28%
Food, beverages and tobacco 6% Trade 10%
Transport and storage 21%
174 kt CO2e
Agriculture 22%
24 Â
Direct and indirect emissions Tourism (2010, modeled) Total emissions (% direct)
1,185 kt CO2e (15%)
Carbon intensity
350 t CO2e/mln â‚Ź of output
Emissions per visitor*
124 kg/visitor
Emissions per night*
36 kg/night
25 Â
Non−metal mineral prod. Agriculture Petroleum prod. Other mining Food, bev & tob. Electricity, gas, water Chemicals Metal prod. Other services Plastics and rubber Transport and storage Pulp and paper Leather Transport equipment Textiles Tourism Construction Other manufacturing Hotels and restaurants Machinery Wood Electrical equipment Trade Business activities Health Public adminstration Finance Real estate, renting Education
(direct + indirect) t of CO2e per mln EUR output
Carbon intensity of ST’s industries 800
600
400
200
0
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ST’s tourism supply chain emissions By industry and region: 29%
Agriculture 20%
Energy, gas, water 10%
Mining and quarrying 4%
Hotels and restaurants
5%
Transport and storage
6%
Food, beverage, tob.
4%
100
0
200
9%
Other
Carbon emission from tourism in ST (kt CO2e)
400
South Tyrol North−east North−west Rest of the country Rest of the world
13%
Other manufacturing
300
Comm., soc., pers. serv.
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CONCLUDING REMARKS
Issues with current analysis • Treatment of GHG emissions from transport • Choice of rest of the world technology? • Results obtained from preliminary version of the multi-regional IO model 29
Future extensions (data availability?) • Include other environmental externalities • Carbon footprint of more narrowly defined products or types of tourism • Easy to replicate in other parts of the country 30
mattia.cai@unibz.it
Familiar limitations of IO models • Linearity • Fixed interregional trade patterns • Underlying data quality issues
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2014