RM Congress - Presentation - 06 Cai

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.

6

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

7

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)

8

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?

9

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?

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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

20

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