Measuring and Monitoring the Sustainability of Tourism at Regional Level in Spain

Chapter 4. Compilation guide with detailed indicator descriptions

Abstract

Regional indicators to monitor the sustainability of tourism provide a more granular picture of the societal, economic and environmental impacts of tourism, thus facilitating decision-making for more sustainable tourism development at both the regional and national level. This chapter provides detailed compilation guidance for the 30 indicators proposed in this measurement framework. For the 21 core and nine supplementary indicators, the chapter outlines their relevance and purpose as well as measurement considerations, including data sources, and calculation and recommended compilation frequency. It also provides a target direction for how the metrics should change to move to more sustainable models of tourism. Limitations and avenues for future development complement the compilation guidance.

This chapter provides detailed descriptions of the indicators in the monitoring framework to measure the sustainability of tourism in the four Spanish regions, as presented in Chapter 3. More specifically, the information provided in this chapter includes:

• A short description, detailing what each indicator intends to capture.

• A short discussion on the relevance and purpose of each indicator to illustrate how they respond to the specific priorities and needs identified by the four Spanish regions.

• An analysis of key measurement issues and limitations.

• A table with compilation details, namely: (i) Metrics and units, (ii) Calculation for computing the indicator, (iii) Target direction, to inform the interpretation of the indicator’s values, (iv) Data sources and (v) Data frequency.

This report including the indicator framework and compilation guidance is complemented by an Excel compilation tool to support indicator compilation (Annex 4.A.).

A. Governance dimension

Policy area: Sustainable tourism management

Indicator A.1: Sustainable tourism development strategy

Short description:

The indicator captures whether regions have current sustainable tourism strategies or plans in place. Quality criteria measured include the existence of a mid-term review, a participatory development process, the budget allocated for implementation as well as a monitoring framework to track progress. Regions would ideally address pressing issues of accessibility and climate change through dedicated action plans.

Relevance and purpose:

Sustainable tourism development requires dedicated strategies and plans able to provide the tourism ecosystem with a clear vision, framework and signals to shift practices towards greater sustainability. Recognising this, an increasing number of countries have developed or updated their tourism strategies and placing sustainability of the heart of it, including Croatia, New Zealand, Sweden and the United States (OECD, 2022[1]). Sustainable tourism strategies can also be developed at the regional level – all four Spanish regions have such strategies in place – as well as at the city level such as in Barcelona.

During the consultations, the four Spanish regions highlighted the importance of including a specific focus on climate in regional sustainable tourism strategies. The tourism sector contributes to greenhouse gas emissions and its transition towards models and practices that support the achievement of global climate objectives is urgent. Regional authorities may have limited agency to influence tourism-related emissions overall, but decisions taken at the regional level on market segmentation as well as incentives or licensing for air transport have the potential to affect emissions.

Several elements can enhance the effectiveness of regional sustainable tourism strategies. The indicator contains three metrics, including the existence of a framework to monitor progress against strategy objectives and budget allocated to implement the strategy. Budget constraints often remain a key challenge to achieving a sustainable transition (OECD, 2022[1]), and participating regions identified ‘adequate financial and human resources’ as a key success factor for effective implementation of sustainable tourism strategies. Additional factors for the effectiveness of sustainable tourism strategies include a participatory development process, and the regular update and review of the strategy. Translating long-term visions into short- to medium-term action plans can help adapt to evolving contexts (OECD, 2018[2]; OECD, 2021[3]).

Measurement considerations and limitations:

The indicator includes three metrics. The first metric evaluates the existence of a strategy for sustainable tourism development in the regions, including certain quality criteria. Defined as self-assessed score from 0 to 6, regions obtain one point for each of the following criteria that is met:

• Existence of a strategy for sustainable tourism development, with allocated budget

• Participatory development process

• Existence of mid-term review

• Existence of monitoring framework/indicators

• Action plan for accessibility measures in tourism

• Tourism-specific climate strategy or action plan

Metric A.1.2 is the year of publication to provide information on when the strategy has been adopted. Strategies usually cover the medium term – metrics capturing their characteristics are hence unlikely to change year-on-year and only need to be updated every 4-5 years. As budgets are often allocated for the full duration of the strategy, metric A.1.3 measures the yearly average for comparability. The annual budget is divided by the number of tourists in the region; tourist numbers are based on a five-year average to reduce metric fluctuation due to the denominator.

While strategies and plans are critical to building more sustainable tourism systems and guide policies and action, the existence of such strategies alone does not ensure better sustainability outcomes. Strategies or policy plans can, however, serve as a starting point (NBTC, CBS, CELTH, 2023[4]).

 

Compilation information: A.1 Sustainable tourism development strategy
Metrics and units	A.1.1	Existence of a strategy for sustainable tourism development, meeting certain quality criteria	Score 0-6
	A.1.2	Year of publication	YYYY
	A.1.3	Budget for strategy implementation	EUR / year / tourist
Calculation	A.1.1	Self-assessed score from 0 to 6 with one point for each of the following criteria that is met: - Existence of a strategy for sustainable tourism development, with allocated budget - Participatory development process - Existence of mid-term review - Existence of monitoring framework/indicators - Action plan for accessibility measures in tourism - Tourism-specific climate strategy or action plan
	A.1.2	n/a
	A.1.3	Total annual budget of sustainable tourism strategy5 year average of total number of tourists in the region
Target direction	A.1.1: Positive as higher values indicate the existence of a strategy for sustainable tourism development, meeting certain quality criteria. Such strategies are expected to facilitate enhanced sustainability outcomes for tourism in the region. A.1.2: n/a A.1.3: Positive as a higher budget may allow for more resources for policy implementation.
Data sources	A.1.1-A.1.2: Review of strategy documents and input from regional tourism departments A.1.3: Numerator based on information by regional tourism departments; Denominator based on INE surveys (FRONTUR/FAMILITUR)
Recommended frequency	Every 4-5 years, as strategies and plans usually cover the medium term

B. Economic dimension

Policy area: Benefits to the local economy

Indicator B.1: Tourism employment

Short description:

This indicator measures direct tourism employment as a percentage of total employment to capture the economic benefits of tourism development that are related to tourism for job creation.

Relevance and purpose:

One of the key benefits that tourism can bring to the local economy is employment, given the sector is labour intensive and creates jobs across skill levels as well as age groups (Stacey, 2015[5]). In Spain, the sector provided 13.5% of total employment in 2019 (OECD, 2022[1]). Following the COVID-19 pandemic, tourism’s total contribution to GDP more than halved to 5.5%. Tourism remains an important employment contributor, accounting for 12% of Spain’s total employment in 2021. However, a high percentage of tourism employment signals dependence and vulnerability to shocks and structural changes (Eurostat, 2006[6]), as the COVID-19 pandemic has demonstrated. Measuring tourism employment over time can help to identify trends and changes in the labour market, such as shifts in the types of jobs available or changes in the demand for certain skills or qualifications.

Measurement considerations and limitations:

Tourism employment is included in many existing sustainable tourism indicator frameworks, including ETIS (European Union, 2016[7]), the EU Tourism Dashboard (European Commission, 2022[8]), the Eurostat-commissioned report by Statistics Sweden (Eurostat, 2006[9]), UN Tourism’s INSTO framework (UNWTO, 2016[10]), Austria’s “Plan T” (Federal Ministry for Sustainability and Tourism, 2019[11]) and the indicator set developed jointly by VisitDenmark and CRT (Centre for Regional and Tourism Research, 2021[12]). However, metric definitions and measurement approaches vary. Denmark’s indicators include direct, indirect and induced effects. They capture the complexity of the tourism value chain with tight interlinkages to other sectors as well as the benefits derived from tourism employees’ spending. However, the calculation is complex and depends on the existence of supply and use tables which are not available for all regions. Similarly, the EU Tourism Dashboard, measures the “Contribution of tourism to employment” including direct, indirect, induced and catalytic effects in related activities (European Commission, 2022[8]). However, the regression model used is currently under revision and cannot be easily applied to the post-COVID-19 context. Difficulties also remain to isolate tourism-specific data; hence, indicators often focus on employment in accommodation and food services activities (Stacey, 2015[5]). The Eurostat-commissioned report by Statistics Sweden, for instance, includes accommodation only as this activity can be fully attributed to tourism (2006[6]).

At present, the indicator set for the four Spanish regions focuses exclusively on direct employment. Going forward, it is important to consider the indirect economic impacts of tourism employment as well as the potential for tourism to stimulate job creation and economic growth in other sectors. Regional employment data for tourism-characteristic economic activities, as defined by the IRTS (United Nations, 2008[13]), is available from INE’s Economically Active Population Survey (Encuesta de Población Activa or EPA), and obtained via regional statistical institutes. As the survey sample is too small to produce reliable results for small regions, Navarra will use end-of-year Social Security data to calculate the indicator.

 

Compilation information: B.1 Tourism employment
Metrics and units	B.1.1	Direct tourism employment as percentage of total employment	%
Calculation	B.1.1	Direct tourism employmentTotal employment
Target direction	Positive: High numbers of employees strengthen the economic development of an economy and contribute to public financing in terms of taxes on earned incomes.
Data sources	INE - Regional Accounts of Spain (Cuentas regioales de España): https://www.ine.es/dyngs/INEbase/es/categoria.htm?c=Estadistica_P&cid=1254735576581 INE - Structural Business Statistics in the Service Sector (IDESCAT): https://www.idescat.cat/pub/?id=emptur&lang=en INE - Active Population Survey (EPA): https://www.idescat.cat/indicadors/?id=conj&n=10215&lang=en Turespaña - Social Security Analysis: https://www.tourspain.es/es-es/ConocimientoTuristico/Paginas/Empleo.aspx
Recommended frequency	Annual

Indicator B.2: Tourism value-added

Short description:

This indicator measures the year-on-year change of direct tourism gross value-added (GVA) as well as the share of direct tourism GVA relative to total GVA to capture the economic contribution of tourism to the regional economy.

Relevance and purpose:

By tracking changes in tourism value-added over time, it is possible to identify areas where investment and policy changes may be needed to support the growth of the sector and ensure its economic viability. Measuring tourism value-added allows for comparisons between the tourism industry and other industries. This information can be used to identify the overall and relative importance of the tourism industry and to make informed decisions about resource allocation and policy measures to stimulate development.

Measurement considerations and limitations:

The indicator relies on INE’s Regional Accounts or data from regional statistical institutes including tourism-characteristic activities as defined by the IRTS (United Nations, 2008[13]). Some Spanish regions made progress in establishing Regional Tourism Satellite Accounts (TSA) that provide regional data on tourism value-added. Although TSA are published with a significant time lag, it provides the most robust and accurate data source available to estimate GVA (INE will publish 2022 data in July 2024). Moreover, the Structural Business Statistics in the Service Sector by INE includes data relating to tourism value-added but do not include all economic activities. However, the data needed for the calculation of the metrics is not fully public and has to be requested from regional statistical institutes. Going forward, it is important to consider the indirect economic impacts of tourism value-added as well.

 

Compilation information: B.2 Tourism value-added
Metrics and units	B.2.1	Year-on-year change in Tourism Direct Gross Value-Added (GVA)	%
	B.2.2	Tourism Direct GVA as % of total GVA	%
Calculation	B.2.1	TDGVAt-TDGVAt-1TDGVAt-1 Where TDGVAt is the regional Tourism Direct Gross Value Added in the year t.
	B.2.2	TDGVAtGVAt Where TDGVAt is the regional Tourism Direct Gross Value Added and GVAt is the total regional Gross Value Added, in the year t.
Target direction	Positive: If tourism value-added is high, it suggests that the region is heavily reliant on the tourism industry for economic growth and development. On the other hand, if the value is low, it indicates that the tourism industry has a relatively smaller impact on the region's economy. (N.B. the target direction could be region-specific depending on economic diversity)
Data sources	INE Regional Accounts of Spain (Cuentas regionales de España): https://www.ine.es/dyngs/INEbase/es/categoria.htm?c=Estadistica_P&cid=1254735576581 INE Structural Business Survey – Service Sectors: https://www.ine.es/dyngs/INEbase/es/operacion.htm?c=Estadistica_C&cid=1254736176865&menu=ultiDatos&idp=1254735576778 Data from regional statistical institutes (Catalonia relies on Idescat data based on structural business statistics and Annual Economic Accounts for Catalonia)
Recommended frequency	Annual if possible; however, frequency depends on the data source (TSA data for instance not available annually)

Indicator B.3: Tourism expenditure

Short description:

This indicator captures expenditures incurred by international and domestic tourists for and during their trip and stay at their destination. It covers a wide spectrum of expenditures, from the purchase of travel- and stay-specific consumer goods to the purchase of small consumer goods for personal use as well as souvenirs and gifts for friends and relatives.

Relevance and purpose:

Tracking tourism expenditure helps understand the spending behaviour and spending patterns in a region, including the diversity of tourism products consumed. Tourism expenditure is “the amount paid for the acquisition of consumption goods and services, as well as valuables, for own use or to give away, for and during tourism trips. It includes expenditures by visitors themselves, as well as expenses that are paid for or reimbursed by others” (United Nations, 2008[13]).

Measurement considerations and limitations:

International visitor expenditure and value added from the tourism sector are two common measures used to track economic sustainability in tourism sustainability frameworks. While Austria monitors both “Value-added” and “Tourism expenses” (Federal Ministry for Sustainability and Tourism, 2019[11]), New Zealand only includes value added (Ministry of Business, Innovation and Employment, New Zealand, 2022[14]); Denmark and Portugal focus on expenditures (Centre for Regional and Tourism Research, 2021[12]; Turismo de Portugal, 2017[15]). To measure tourism expenditure, the regions will rely on INE surveys FRONTUR/EGATUR (for international tourists’ expenditure) and ETR/FAMILITUR (for domestic tourists’ expenditure). Data is available for overnight visitors (i.e. tourists), but not for same-day visitors (excursionists). Going forward, options for capturing same-day visitors’ expenditure will be explored.

 

Compilation information: B.3 Tourism expenditure
Metrics and units	B.3.1	International tourism expenditure	EUR per tourist
	B.3.2	Domestic tourism expenditure	EUR per tourist
	B.3.3	International tourism expenditure per day	EUR per day per tourist
	B.3.4	Domestic tourism expenditure per day	EUR per day per tourist
Calculation	B.3.1	International tourism expenditure Number of international tourists
	B.3.2	Domestic tourism expenditure Number of domestic tourists
	B.3.3	International tourism expenditure Total number of nights spent by international tourists
	B.3.4	Domestic tourism expenditure Total number of nights spent by domestic tourists
Target direction	Positive: Increase visitor daily expenditure to achieve the highest possible added value in the destination.
Data sources	INE FRONTUR/EGATUR survey (international tourism expenditure) INE ETR/FAMILITUR survey (domestic tourism expenditure)
Recommended frequency	Quarterly

Indicator B.4: Accommodation occupancy

Short description:

The occupancy rate of tourist accommodation establishments rate is the percentage of time within a year that available bed-places (or available capacity in the case of campsites) within a tourist destination, are occupied by tourists.

Relevance and purpose:

Bed occupancy rates give an indication of the extent to which tourism supply and demand match and whether existing assets are used efficiently. The indicator is commonly referenced in indicator frameworks (European Commission, 2022[8]; European Union, 2016[7]; Turismo de Portugal, 2017[15]; UNWTO, 2004[16]). The indicator can assist in forward planning, management and regulation. Data on occupancy rates can for instance be used to cap new accommodation developments, only allowing new establishments above a certain region-specific occupancy threshold (UNWTO, 2004[16]). However, occupancy rates may also point to issues of seasonality, providing a basis for gearing marketing efforts towards lower seasons. For the indicator here included, regions follow the Eurostat definition to allow for comparability with data in the EU Tourism Dashboard, relying on data provided by INE. Going forward, the aim is to explore avenues to also include other smaller entities and short-term rentals.

Measurement considerations and limitations:

Bed occupancy rates can vary depending on the type of accommodation. It is important to distinguish between different types of accommodation when analysing bed occupancy rates, as they may have different trends and patterns. The indicator thus includes four metrics, measuring the net occupancy rate for hotels, apartments, rural accommodation and campsites based on INE survey data.

Bed occupancy rates can vary depending on the location within a tourism destination. Occupancy rates can also vary significantly depending on the season, with higher rates during peak periods and lower rates during off-peak periods. Lastly, small entities and grey accommodation market actors are not included in official tourism statistics. It is important to consider these factors and limitations when interpreting the metrics.

 

Compilation information: B.4 Accommodation occupancy
Metrics and units	B.4.1	Occupancy rate (Hotels)	%
	B.4.2	Occupancy rate (Apartments)	%
	B.4.3	Occupancy rate (Rural accommodation)	%
	B.4.4	Occupancy rate (Campsites)	%
Calculation	B.4.1-B.4.3	Number of nights spent during the monthBed-days available during the month	To note: regions directly adopt figures provided by INE rather than compute calculation
	B.4.4	Occupied capacity Capacity available during the month	To note: regions directly adopt figures provided by INE rather than compute calculation
Target direction	Positive: Higher values may signal an optimal use of capacity and demand.
Data sources	B.4.1 INE Hotel occupancy survey B.4.2 INE Apartments occupancy survey B.4.3 INE Rural accommodation occupancy survey B.4.4 INE Campsites occupancy survey
Recommended frequency	Annual & Monthly

Policy area: Reduced seasonality

Indicator B.5: Tourism seasonality

Short description:

This indicator measures seasonality of tourism flows as the share of the top three months relative to total annual nights spent in the region. At the request of the regions, a second metric was added to the core indicator: the share of municipalities in the highest decile of seasonality according to Gini index.

Relevance and purpose:

The vast majority of tourism destinations face issues stemming from the seasonality of tourist flows, often due to weather conditions in target and source markets (UNWTO, 2004[16]). The uneven distribution of visitor flows can lead to excessive pressure on infrastructure, the environment, local communities, other economic sectors, and wider society (OECD, 2021[3]). The regions highlighted that seasonality is a particularly pressing issue, especially in coastal areas, natural reserves, and around popular landmarks. Monitoring tourism seasonality is valuable for guiding policy measures to address overcrowding, pressure on resources and infrastructure, and negative impacts on the local environment and culture during peak periods. All regions refer to the need to reduce seasonality and spread the benefits from tourism more evenly across the year in their sustainable tourism strategies and plans.

Measurement considerations and limitations:

Seasonality is commonly measured in indicator frameworks to monitor the sustainability of tourism, albeit in different ways – the EU Tourism Dashboard first compiled the coefficient of variation of nights spent at tourist accommodation establishments per month, then switched to calculating the proportion of nights-spent in the three most visited months in relation to the total nights-spent over the year (European Commission, 2022[8]). Others also use a share of peak months to measure seasonality (Turismo de Portugal, 2017[15]; WEF, 2022[17]), while the set developed by CRT and VisitDenmark calculates the Gini coefficient (Centre for Regional and Tourism Research, 2021[12]).

In the indicator set presented here, the core metric is calculated as the share of the top three months relative to total annual nights spent in the region. This measure is easier to interpret than the Gini coefficient and is already used within the Spanish SDG Monitoring report. NUTS2 level data is available from the EU Tourism Dashboard.

However, regional data do not necessarily reflect the reality at more granular levels; regional averages may balance out certain visitor flows, hiding potential seasonality issues. The Tourism of Tomorrow Lab (ToT Lab), in collaboration with Andalusia, has thus explored the use of a Gini Index calculation that provides more in-depth information at the local level based on INE experimental data. At request of the regions, the resulting metric share of municipalities in the highest decile of seasonality has been added to the core set of indicators. It can help policymakers to focus efforts on the municipalities most affected by seasonality. However, compared to the core metric B.5.1, it may not be as easy to interpret and communicate by policymakers. Even if seasonality is reduced across regions and municipalities, the overall number of municipalities in the highest decile calculated for B.5.2 will remain stable. This can be improved by defining thresholds.

The overall Gini coefficient for the regions is included as a supplementary metric (S.2.1).

 

Compilation information: B.5 Tourism seasonality
Metrics and units	B.5.1	Share of the top 3 months relative to total annual nights spent in the region	%
	B.5.2	Share of municipalities in the highest decile of seasonality according to Gini index	%
Calculation	B.5.1	Nights in all means of accommodation for the three top monthsAnnual total nights in all means of accommodation
	B.5.2	First, the Gini index is calculated for each municipality in Spain: G=2∑j=1Mj yjM∑j=1Myj-M+1M Where G is the value for the Gini index; M is the number of time periods in a year (12 for monthly data); j is an index for time period (from 1 to 12); yi is the number of international tourists. Subsequently, the calculated Gini indices by municipality are sorted in descending order and the top decile is identified. Then, the number of municipalities in region X that fall within the top decile are counted and divided by the total number of municipalities in region X: Number of municipalities in the top decile of seasonalityTotal number of municipalities in the region
Data sources	B.5.1: Tourism seasonality (NUTS2-level data available from EU Tourism Dashboard) B.5.2: Calculation by ToT Lab with support from Andalusia based on INE experimental data https://www.ine.es/experimental/turismo_moviles/experimental_turismo_moviles.htm
Target direction	B.5.1: Negative. The lower the share, the better the overall distribution. The minimum would be 16.67%, as this would be an equal share amongst all months. B.5.2: Negative. The lower the share, the fewer municipalities in the region experience high levels of seasonality relative to other Spanish regions.
Recommended frequency	Annual (based on monthly data)

Policy area: Reduced vulnerability

Indicator B.6: Market dependency

Short description:

This indicator measures the dependence of regions on international tourism markets and suppliers, focusing on the share of nights spent by international tourists from the top three countries of origin, the share of nights spent by domestic tourists and the share of the passengers from the top three airlines relative to the total number of air travel passengers.

Relevance and purpose:

Overreliance on one or a small number of source markets can expose regional tourism to shocks and pose threats to longer term competitiveness (Dupeyras and MacCallum, 2013[18]). Regions highlighted the reliance on few source markets and on few international air travel companies as a key source of vulnerability for tourism. Decisions by any of the key airlines servicing the regions to shift to alternative destinations can have repercussions on the sector and may put regional authorities in a weak position when negotiating terms and conditions with these companies or defining regulations affecting them.

By measuring the dependence on international tourism markets as well as key international travel companies that serve the regions, this indicator provides insights into the susceptibility to shocks and unexpected developments in these key source markets and suppliers. The indicator was chosen to provide information that can guide the development of measures aimed at diversifying tourism products, managing capacities and reaching additional target groups.

Measurement considerations and limitations:

This is a partial indicator: B.6.1 and B.6.2 only refer to overnight stays, excluding same-day visitors, while B.6.3 does not relate to other modes of transport which might be more relevant in some destinations.

For small regions, such as Navarra, the FRONTUR sample is insufficient to identify the three top markets for B.6.1. It is thus preferable to use the INE Hotel Occupancy Survey which would deliver monthly data; however, one limitation is the time lag in data provision as results are only published nine months after the end of the year. For data on a monthly basis, INE experimental statistics would also be an option with data available for all regions. Metric B.6.2 relies on INE’s Hotel Occupancy Survey or alternatively on data from INE’s ETR/FAMILITUR domestic tourism survey. For B.6.3, private air traffic or airport data can be used as an indicative data source (e.g., from Aena, major airport operator in Spain). Catalonia’s tourism analytical system (SAT) includes data on flights that can also be used.

The top three countries of origin may vary depending on the season. For example, during the summer months, a tourism destination may have different top countries of origin compared to off-season months. It is important not to neglect the share of nights spent from other countries of origin, especially if there are significant changes in the composition of other countries below the top three markets.

Passenger numbers only relate to air travel as mode of transport. Some destinations might have significant tourist shares by other modes of transport (e.g., cars), which are not represented in this indicator. Some destinations might not have an airport or only a minor airport with little relevance for visitor flows, which can affect source-market shares and overall travel patterns. In Navarra, for instance, no international airport exists and the two common destinations from the domestic airport are Madrid and the Canary Islands; the indicator is, therefore, of limited relevance for Navarra. Regions without international airports could identify nearby airports in other regions that affect tourism demand.

 

Compilation information: B.6 Market dependency
Metrics and units	B.6.1	Share of nights spent from the top three inbound markets relative to total nights spent by international tourists in the destination	%
	B.6.2	Share of nights spent by domestic tourists relative to total nights spent	%
	B.6.3	Share of passengers from the top three airlines in relation to total air travel passengers	%
Calculation	B.6.1	Nights spent by tourists coming from the top three countries of originTotal nights spent by international tourists
	B.6.2	Nights spent by domestic touristsTotal nights spent
	B.6.3	Passengers from the top three airlinesTotal air travel passengers
Target direction	B.6.1 and B.6.3 – Negative: Lower values indicate lower dependence on the three top countries of origin and three top airlines, signalling high market diversification and thus a potentially lower vulnerability to short-term distresses. B.6.2 – Positive: Higher values indicate lower dependence on international source markets, thus reducing vulnerability to external shocks (N.B. the target direction could be region-specific)
Data sources	B.6.1 and B.6.2: INE – FRONTUR for international tourists and FAMILITUR for domestic tourists (alternative source would be INE Hotel Occupancy Survey) B.6.3: AENA: https://www.aena.es/es/estadisticas/inicio.html
Recommended frequency	Annual & Monthly

C. Social dimension

Policy area: Local community sentiment

Indicator C.1: Residents’ perception of tourism

Short description:

This indicator measures the average satisfaction of local residents with tourism development.

Relevance and purpose:

Satisfaction of the local community is central to the sustainable development of tourism (OECD, 2018[19]). Tourism can contribute to the attractiveness of places through diverse employment opportunities, opportunities for innovative small-scale business operations, improved infrastructure and support for the promotion of cultural authenticity and natural assets (OECD, 2021[3]). Promoting local satisfaction with tourism is about balancing these positive effects of tourism and its negative effects, often linked to overcrowding in destinations. The four Spanish regions see enhancing the local perception of tourism development and its management as a key policy priority.

However, not all regions have a local resident survey in place. While indicator C.2 evaluates tourism pressures as a proxy for local community sentiment and can be measured by all regions, this indicator faces limitations as higher tourism pressures may not necessarily translate into negative attitudes towards tourism. Therefore, indicator C.1 Residents’ perception of tourism was included in the core set, despite the current limitations in terms of data availability, highlighting the important progress made in some regions. Local sentiment in the regions is negatively affected by issues such as crime rates and housing price evolutions – as tourism-specific data is currently not available, these aspects are captured in the supplementary indicators.

Measurement considerations and limitations:

Currently, not all regions have surveys in place to measure local residents’ perception of tourism. However, Navarra has included a survey to measure how the local population perceives tourism flows and their management in the region’s statistical plan (Observatorio Turístico de Navarra, 2020[20]). This survey includes questions on the perceived impact of tourism on the level of crime as well as housing availability and affordability. Andalusia recently conducted a qualitative and quantitative study on residents’ perception of tourism, including focus groups, interviews and surveys (Junta de Andalucía, 2023[21]). Indicator C.1 builds on these efforts and addresses the limitations linked to metrics focused on pressures (C.2).

At national level, Turespaña is carrying out a study on attitudes and overall satisfaction towards tourism among Spanish residents (Turespaña, 2023[22]). This could be an alternative source to explore as consideration is given to minimum sample requirements for each Autonomous Community, islands, some municipalities, and specific provinces. However, the online panel survey may not be conducted on a continuous basis.

 

Compilation information: C.1. Residents’ perception of tourism
Metrics and units	C.1.1	Average satisfaction of local residents with tourism development	average score on scale X-X
Calculation	C.1.1	Σ of satisfaction scoresNumber of local residents
Target direction	Positive
Data sources	Regional surveys
Recommended frequency	Annual

Indicator C.2: Tourism pressures on local population

Short description:

This indicator measures the share of regional population living in municipalities with highest tourism density and intensity. The indicator uses the ratio of visitors relative to surface area (density) and local population (intensity). The indicator comprises one core metric and two descriptors. The core metric (C.2.3) measures the share of regional population that lives in the highest decile of municipalities according to tourism density and intensity. The two descriptors are tourism density (i.e. number of visitors per square kilometre) and tourism intensity (i.e. number visitors relative to number of residents) [C.2.1 and C.2.2].

Relevance and purpose:

Satisfaction of the local community is central to the sustainable development of tourism (OECD, 2018[19]). Spreading tourism flows geographically can contribute to reducing tourism pressures and enhancing tourism acceptance (OECD, 2021[3]). As not all regions have a local resident survey in place, ToT Lab with support by Andalusia developed a methodology to capture vulnerability to tourism saturation, i.e. the point at which the number of tourists might result in negative impacts on the quality of life of residents, local culture and the environment.

Measurement considerations and limitations:

While regions stressed the relevance of having a common measurement of local community sentiment to ensure comparability, this remains challenging at present. The approach for measuring the metric “Population living in municipalities with highest tourism intensity and density” is based on identifying municipalities with a high tourism intensity and density (i.e. placed within the highest decile) to then calculate the share of population in these municipalities compared to the total population of the region. Relying on granular data at municipal level for metric C.2.3., instead of at regional level, is valuable to avoid regional averages to hide issues in specific hotspots. The metric could be combined with other indicators such as prices for housing, local income, crime rates and others. C.2.3 is complemented by two descriptors, tourism intensity and density, which are also featured in other frameworks, such as the EU Tourism Dashboard and Portugal’s indicator set (note that the EU Tourism Dashboard indicators are based on nights spent rather than visitor numbers).

 

Compilation information: C.2. Tourism pressures on local population
Metrics and units	C.2.1 (descriptor)	Tourism density	Visitors per km2
	C.2.2 (descriptor)	Tourism intensity	Visitors per 1,000 residents
	C.2.3	Share of regional population living in municipalities with highest tourism intensity and density	%
Calculation	C.2.1 (descriptor)	Number of visitorsTotal area of the Region in km2
	C.2.2 (descriptor)	Number of visitorsTotal population of the Region (in thousands)
	C.2.3	Sum of population of municipalities in the top decile of density and intensityTotal population of the Region
Target direction	Negative: Alleviate resident stress due to an undesirable tourism development in local communities.
Data sources	INE - experimental statistics: Measurement of tourism based on the position of mobile phones: https://www.ine.es/experimental/turismo_moviles/experimental_turismo_moviles.htm Further data sources (e.g. from Regional Statistical Institutes) for total area and total population Development and calculation of C.2.3 has been carried out by ToT Lab with support from Andalusia
Recommended frequency	Monthly or Annual

Adopting an indicator focused on evaluating tourism pressures as a proxy for local community sentiment faces limitations. Higher tourism pressures may not necessarily translate into negative attitudes towards tourism if the benefits outweigh the negative effects and if adequate infrastructure exists to deal with visitor flows. A more robust and targeted measure could be based on dedicated survey data including questions on community sentiment toward tourism; an approach typically adopted although being resource-intensive (OECD, 2021[3]). Community sentiment can be measured through a dedicated survey with a potential focus on known tourism hotspots as undertaken by VISITFLANDERS; relevant questions could also be added to existing surveys such as the domestic tourism survey, a cost-efficient approach adopted by Statistics Austria. Regions can build on work undertaken in Andalusia, Navarra and at the national level in Spain as outlined under indicator C.1.

The two descriptors, and tourism density in particular, not only capture socio-cultural impacts but also give an indication of environmental pressures, pointing again to the interrelatedness of the dimensions of sustainability. To capture the pressures in particularly vulnerable natural ecosystems, a dedicated indicator on “Tourism pressure in protected areas” (D.6) is included in the environmental dimension.

Policy area: Attraction of visitors

Indicator C.3: Tourist satisfaction

Short description:

This indicator focuses on tourists’ perceived satisfaction regarding the tourism-related stay which can give an indication on the tourist experience’s impact on well-being and the attractiveness of destinations. It includes two metrics to measure domestic and international tourists’ average satisfaction with their overall experience during the trip.

Relevance and purpose:

Visitor satisfaction is explicitly mentioned in definitions of sustainable tourism (UNEP & UNWTO, 2005[23]). It is a key determinant of longer-term sustainability and competitiveness in destinations (Dupeyras and MacCallum, 2013[18]). Tourism experiences can positively influence quality of life and well-being of tourists (Uysal et al., 2016[24]). While placed under the social dimension, the indicator is also of economic relevance. If visitors are satisfied with their experience, they are more likely to return in the future, recommend the destination to others, and leave positive reviews and feedback. This can lead to increased tourism revenue and job creation. The regions consider tourist satisfaction as a key issue for destination management, aligned with the ETIS framework (European Union, 2016[7]) and the OECD Indicators for Measuring Competitiveness in Tourism (Dupeyras and MacCallum, 2013[18]).

Measurement considerations and limitations:

The indicator is often collated using costly survey data, but not all regions have a visitor survey in place. INE, however, regularly collects data on tourist satisfaction in their inbound Tourist Expenditure Survey (EGATUR), measured on a scale of 1-10. INE also collects data on domestic tourist satisfaction through their FAMILITUR survey, equally measured on a scale of 1-10. As data is not publicly available, regions need to file requests for microdata from INE which can result in compilation delays. The metric is defined as an average score (rather than the percentage of ‘satisfied’ tourists, defined as scores between 7 and 10) as satisfaction tends to be high and more nuanced changes would otherwise go unnoticed.

 

		Compilation information: C.3 Tourist satisfaction
Metrics and units	C.3.1	Satisfaction of international tourists with their overall experience during the trip	average score on scale 0-10
	C.3.2	Satisfaction of domestic tourists with their overall experience during the trip	average score on scale 0-10
Calculation	C.3.1	Σ of satisfaction scoresNumber of international tourists
	C.3.2	Σ of satisfaction scoresNumber of domestic tourists
Target direction	Positive: Keeping tourist satisfaction at a high level, as this is a key influencing factor for recommendations and return visits.
Data sources	C.3.1: INE – FRONTUR/EGATUR (data is not publicly available; regions need to file request for microdata from INE) C.3.2: INE – ETR/FAMILITUR (data is not publicly available; regions need to file request for microdata from INE)
Recommended frequency	Annual

EGATUR and FAMILITUR only capture information on overnight visitors (tourists); due to data availability the indicator thus excludes same-day visitors. Improvements in data availability to capture visitor satisfaction, both international and domestic, would provide a more holistic picture, enabling regions to distinguish between levels of satisfaction of different visitors including same-day or overnight.

Tourist satisfaction depends not only on the tourism experience, but also on contextual factors including situational, personal and cultural characteristics (Uysal et al., 2016[24]). While tourism policymakers have limited ability to influence these contextual factors, they need to take these into consideration when interpreting the data and designing policy responses.

Policy area: Inclusive tourism employment

Beyond numbers of tourism jobs, measured in the economic dimension, regions pointed to the quality of jobs in tourism as a crucial determinant of the acceptance of tourism as well as of regional social cohesion. Tourism demand has increased again in many countries after the COVID-19 pandemic, but the supply side is facing major constraints, including as the sector struggles to attract workers back to the industry (OECD, 2022[1]). This situation exacerbates pre-existing issues linked to labour shortages in the tourism sector and long-standing challenges in attracting both skilled and unskilled workers due to the pay levels, working hours and seasonality in the sector.

The OECD defines the quality of jobs in relation to key elements such as earnings quality, labour market security, and quality of the working environment (OECD, 2016[25]). No internationally accepted definition or measure exists of the quality of jobs specifically in tourism. However, evidence on the specific labour conditions in tourism and policy measures to enhance the attractiveness of tourism jobs exists (Stacey, 2015[5]). Interlinked factors such as seasonality, a high share of small and micro enterprises, working conditions, recruitment and retention difficulties, high turnover and vacancy rates, poor image and weak training culture present key challenges for the sector. Based on the key policy issues in the four Spanish regions, this indicator set focuses on gender inequality, youth unemployment and job insecurity in tourism. Data and measurement challenges have affected the choice of the specific metrics identified.

Indicator C.4: Gender equality

Short description:

This indicator measures gender equality through two metrics: The gender pay gap in tourism-characteristic industries (C.4.1) and the share of women employed in tourism jobs with high qualification requirements (C.4.2).

Relevance and purpose:

The share of women in the tourism workforce is higher than in the overall economy (Stacey, 2015[5]). In 2019, 54% of people employed in tourism were women compared to 39% in the broader economy (UNWTO, 2019[26]). The COVID-19 pandemic, however, has exacerbated existing inequalities in the tourism labour market, in particular affecting vulnerable groups including women (ILO, 2022[27]). A majority of tourism businesses are MSMEs, of which many are of smaller scale – these businesses are often run by female entrepreneurs; however, women are underrepresented in senior management positions in larger businesses (UNWTO, 2019[26]). Although the gender pay gap in tourism is slightly lower than in the broader economy (16.8%), women in tourism still earn 14.7% less than men (UNWTO, 2019[26]). Therefore, rather than measuring women’s participation in the tourism workforce, the indicator focuses on two key policy issues. Firstly, on the pay gap and secondly on the share of women employed in tourism jobs with high qualification requirements (as data on women in management positions is currently unavailable).

Measurement considerations and limitations:

While the relevance of the indicators was unanimously recognised by the regions, challenges remain with regard to the availability of robust and granular data. For measuring the gender pay gap (C.4.1), regions rely on INE’s Wage Structure Survey. However, the data for tourism-characteristic industries, as defined by the IRTS (United Nations, 2008[13]), is only published every four years: latest data available at the time of piloting dated back to 2018 while 2022 data is expected for the end of 2024. Navarra can only calculate the indicator for the services sector overall, not for tourism-characteristic activities; obtaining more granular data is an area for future development. Statistical Offices usually provide the "unadjusted" gender pay gap in their statistics, meaning that the difference in earnings is not corrected for differences in job roles, working hours, and other factors that may have an impact. When comparing the gender pay gap across organisations or industries, it is important to take these contextual factors into consideration.

A second metric measuring the share of women in management positions and/or the share of female business owners in tourism was discarded due to lack of data availability. However, as a proxy, Catalonia explored social security data on education requirements in tourism-characteristic industries; the data is not published, but available on request. The resulting metric measures the share of women employed in jobs with high education requirements, defined as engineers and graduates with higher education (Group 1) and Technical Engineers, Experts and Qualified Assistants with technical apprenticeships (Group 2).

 

Compilation information: C.4 Gender equality
Metrics and units	C.4.1	Gender pay gap in tourism-characteristic industries	%
	C.4.2	Share of women employed in tourism jobs with high qualification requirements	%
Calculation	C.4.1	wmen-wwomenwmen  Where wmen is the median earnings of men and wwomen median earnings of women
	C.4.2	Women employed within group 01 or 02 in the tourism sector Persons employed within group 01 or 02 in the tourism sector Classification is based on social security contribution categories according to education requirements. Group 01 corresponds to Engineers and graduates (higher education), group 02 to Technical Engineers, Experts and Qualified Assistants (technical apprenticeship)
Target direction	C.4.1: Negative C.4.2: Positive
Data sources	C.4.1: INE - Quadrennial Wage Structure Survey https://www.ine.es/en/metodologia/t22/meto_ees18_en.pdf (at time of piloting, latest data from 2018, waiting for 2022 results) C.4.2: Social security data
Recommended frequency	C.4.1: Every four years (due to limited data availability) C.4.2: Annual

Indicator C.5: Youth employment

Short description:

This indicator tracks the access of young professionals to tourism employment with the aim of increasing job opportunities for the youth. The indicator is measured by the share of youth employment (individuals aged 16-291) in tourism-characteristic industries, as defined by the IRTS (United Nations, 2008[13]), relative to total employment in tourism-characteristic industries.

Relevance and purpose:

According to ILO estimates, the youth unemployment rate in Spain in 2022 was 28.3% (World Bank, 2023[28]). Tourism can provide valuable opportunities for young people to enter the job market, especially relevant in countries and regions with high youth unemployment (WTTC, 2019[29]). Youth employment in turn can help to address labour shortages and support the growth of the tourism industry. However, youth employment in tourism has been disproportionally affected by the COVID-19 pandemic, dropping by 25% between 2019 and 2020, compared to a drop of 8% in the rest of the non-financial business industry (Eurostat, 2022[30]).

The ILO proposes a range of labour market indicators relevant to young people (ILO, 2018[31]). The starting point is measuring employment opportunities in terms of their quantity, using basic data such as the number (or rate) of employed young people. This is complemented by indicators on employment quality, employment access and employment skill. Although designed to measure progress at programme or project level and not tourism-specific, this gives an indication of relevant measurement dimensions. As a starting point, indicator C.5 measures the level of youth employment in tourism enterprises.

Measurement considerations and limitations

Two possible data sources exist for compiling the metric: INE data from the labour force survey (EPA) and social security data. To compile the indicator based on INE’s EPA survey, regions need to process microdata to obtain values for tourism-characteristic industries at NUTS2 level. However, the sample size is insufficient to produce robust results for smaller regions such as Navarra. Smaller regions hence have to rely on social security data for the compilation of the metric.

Factors such as seasonality, economic conditions, and the size and nature of the tourism industry in the destination may impact the level of youth employment in the sector and need to be considered in the interpretation of the indicator. Further, when comparing the number of youth employment in tourism-characteristic industries across different destinations, potential differences in industry size, structure, and labour market conditions should be taken into account.

 

Compilation information: C.5 Youth employment
Metrics and units	C.5.1	Share of youth employment in tourism-characteristic industries	%
Calculation	C.5.1	Youth employment in tourism characteristic industriesTotal employment in tourism characteristic industries
Target direction	Positive: High values indicate a good job opportunity for the youth.
Data sources	Social Security Data (as comparable source for which reliable data is available for small regions such as Navarra [EPA sample is insufficient]) For larger regions, INE - Economically Active Population Survey (Encuesta de población activa) would be an alternative: https://www.ine.es/dyngs/INEbase/en/operacion.htm?c=Estadistica_C&cid=1254736176918&menu=ultiDatos&idp=1254735976595
Recommended frequency	Annual

High numbers of youth employment in tourism are not necessarily purely positive, as it could mean that other age groups are excluded. While jobs in tourism can provide a steppingstone for youth career development, there is however a danger for some to get caught in a cycle of low paid, part-time and temporary work (Stacey, 2015[5]). This points to the limitations of the indicator as it only measures the quantity and not the quality of youth employment, which depends on adequate earnings and working time as well as access to social security and social dialogue (ILO, 2022[27]). Furthermore, training and skills are particularly important for young people just entering the workforce.

Indicator C.6: Job security

Short description:

This indicator assesses job security of employment in tourism-characteristic activities, as defined by the IRTS definition (United Nations, 2008[13]). The indicator includes three metrics: Share of employees with full-time employment in tourism-characteristic industries (C.6.1), Share of part-time employees with the wish for full-time employment (C.6.2) and Employment fluctuations throughout the year (C.6.3).

Relevance and purpose:

The OECD identifies job security as one of the key attributes of the quality of jobs (OECD, 2016[25]). However, while across sectors job security mainly relates to the probability of job loss and its economic cost for workers, measured by the risk of unemployment, in the tourism sector particularly relevant aspects of job security concerns the relatively large prevalence of part-time employment versus full time employment and the intermittency of labour due to seasonality.

About 23% of the workforce in the tourism industry works part time, and in 2023 the proportion of part-time employment in tourism was 8% points higher than that in the total non-financial business economy (15%) (Eurostat, 2022[30]). While the availability of part-time employment in the tourism industry may provide flexibility and suit specific segments of the workforce, it is important to capture part-time workers who seek full-time employment. Another key characteristic of tourism affecting employment conditions is seasonality, which may contribute to precarious working conditions and reduced attractiveness of jobs in the sector (Stacey, 2015[5]).

Measurement considerations and limitations:

The indicator relies on data from INE’s labour force survey (EPA) and social security. For Metric C.6.1 measuring the share of full-time employees, social security data is available across all regions; while the EPA sample is too small to produce robust results for small regions such as Navarra, it would be an alternative data source for larger regions. To measure the share of part-time employees with the wish of full-time employment, metric C.6.2 relies on surveying tourism employees and is not available from social security data. Metric C.6.3, capturing employment fluctuation, is compiled based on monthly social security data as EPA provides only quarterly data. Monthly social security data is available from June 2021 onwards.

Both data sources face constraints in relation to lengthy procurement processes as data are not publicly available but need to be requested. At the time of the piloting, regions were waiting for data input from INE. When interpreting the indicator, it is important to consider factors such as age, gender, education level, or experience as they may have an impact on the quality of jobs and working conditions in the sector. Employment fluctuations may affect different groups of employees in different ways. For example, part-time or temporary employees may be more vulnerable to fluctuations in demand than full-time or permanent employees.

Skills and training constitute a key factor to attracting people back to the sector, preparing the tourism workforce for the future demands of the industry, most notably with regard to the digital transition and improving overall job satisfaction (ILO, 2022[27]; Stacey, 2015[5]). Due to data limitations, the indicator on training was not included in the core set but is part of the supplementary indicators to encourage exploring avenues to measure this important element.

 

Compilation information: C.6 Job security
Metrics and units	C.6.1	Share of employees with full-time employment in tourism-characteristic industries	%
	C.6.2	Share of part-time employees with the wish of full-time employment in tourism-characteristic industries	%
	C.6.3	Employment fluctuation: Ratio of employment during the two top months to employment during the bottom two months in tourism-characteristic industries	% of low vs. high season
Calculation	C.6.1	Full-time employment in tourism characteristic industries Total employment in tourism characteristic industries
	C.6.2	Number of part-time employees with wish of full-time employmentin tourism characteristic industriesTotal employment in tourism characteristic industries
	C.6.3	Sum of employment in tourism characteristic ind. in the T top monthsSum of employment in tourism characteristic ind.  in the T bottom months
Target direction	C.6.1: Positive as high values indicate a high rate of full-time employment. C.6.2, C.6.3: Negative.
Data sources	C.6.1: Social Security Data (as comparable source for which reliable data is available for small regions such as Navarra [EPA sample is insufficient]); for larger regions, INE - Economically Active Population (EPA) Survey would be an alternative C.6.2: INE EPA Survey (social security data not available) [at time of piloting, regions were waiting for data input from INE] C.6.3: Social Security Data (as EPA provides only quarterly data)
Recommended frequency	Annual (C.6.3 based on monthly data)

Policy area: Accessibility in tourism

Indicator C.7: Accessibility in tourism

Short description:

The indicator provides an indication of efforts by public and private-sector actors to provide accessibility information and solutions for visitors, measured by the availability of information about accessibility on official tourism websites (C.7.1) and the share of accommodation businesses that have physical, visual or and/or auditory accessibility measures in place (C.7.2-C.7.4).

Relevance and purpose:

According to 2021 data, approximately 16% of the global population, or 1.3 billion people, experience disability (World Health Organization, 2022[32]). Removing barriers and improving accessibility not only benefits persons with mobility-related disabilities (UNWTO, 2016[33]). Measuring and monitoring accessibility also helps policymakers and the industry prepare for growing demand among elderly tourists due to an ageing population (OECD, 2018[19]). “Inclusiveness and accessibility, including for persons with disabilities” is recognised as a priority for tourism strategies in the EU Transition Pathway (European Commission, 2022[34]). The indicator aims at increasing the accessibility of tourism, enabling as many people as possible to participate in and enjoy tourism experiences. It highlights that people have different access needs to which the tourism sector needs to cater.

In addition to infrastructure improvements to better cater to people with mobility, sensorial and cognitive needs, making trusted information available on accessibility is crucial. Often, when a tourism facility or service is declared ‘accessible’, they do not fully cater to the diversity of accessibility needs. Rather, objective information on the characteristics of the destination’s tourism offer is needed to better respond to the actual needs of people with disabilities. Hence, the indicator focuses on a mix of information provision and infrastructure adaptation.

Measurement considerations and limitations:

The regions agreed that accessibility is a critical issue that needs to be included in the social dimension. However, quantification and objective evaluation of accessibility aspects are challenging. The first metric evaluates the availability of information on accessibility on the official tourism website of the region. Defined as self-assessed score from 0 to 3, regions obtain one point for each of the following criteria that is met:

• Regular updates (at least every 6 months)

• Covers aspects of accessibility beyond mobility (hearing/visual/cognitive/invisible or other)

• Existence of visual imagery (incorporation of people with disabilities in marketing imagery / showcasing people with accessibility needs)

The breadth/accuracy of information is difficult to rate in quantitative terms through a self-assessment. The three criteria above will hence be complemented by a qualitative section in which regions describe the extent of information available. Over time, as information availability improves, the criteria should become more ambitious.

As comprehensive data on accessibility measures implemented by accommodation businesses is difficult to obtain, the indicator will include proxy measures based on private-sector data. For metrics C.7.2-C.7.4, the ToT Lab with support by Andalusia developed a methodology based on web scraping of private-sector platform data. To consider different accessibility needs, metric C.7.2 focuses on physical, C.7.3 on visual and C.7.4 on auditory factors. Each metric captures the share of accommodation establishments with respective measures in place.

 

Compilation information: C.7 Accessibility in tourism
Metrics and units	C.7.1	Availability of information on accessibility on the official tourism website of the region	Score 0-3 + qualitative information
	C.7.2	Share of accommodation establishments where the entire unit is wheelchair accessible (physical)	%
	C.7.3	Share of accommodation establishments with braille and/or tactile signage (visual)	%
	C.7.4	Share of accommodation establishments with auditory guidance (auditory)	%
Calculation	C.7.1	Defined as self-assessed score from 0 to 3 with one point for each of the following criteria that is met: Regular updates (at least every 6 months) Covers aspects of accessibility beyond mobility (hearing/visual/cognitive/invisible or other) Existence of visual imagery (incorporation of people with disabilities in marketing imagery / showcasing people with accessibility needs) + Qualitative criterion in which regions describe the extent of information available
	C.7.2	Number of accommodation establishments where the entire unit is wheelchair accessibleTotal number of accommodation establishments
	C.7.3	Number of accommodation establishments with braille and/or tactile signage Total number of accommodation establishments
	C.7.4	Number of accommodation establishments with auditory guidance Total number of accommodation establishments
Target direction	Positive: High values indicate a strong commitment towards accessibility in tourism.
Data sources	C.7.1: Regional Tourism Departments C.7.2-4: Web scraping with support by ToT Lab, led by the region of Andalusia
Recommended frequency	Annual

This approach is a valuable starting point to analyse the sectors’ efforts to cater to different accessibility needs. However, users need to ensure they have lawful access to the content. In addition to ethical and legal considerations, web scraping faces technical challenges. The method relies on stability of websites, requiring changes to the code as websites change. Furthermore, online travel agency (OTA) data is based on self-declarations with potential information bias. Relying on OTA data can also have unintended effects, such as further incentivising consolidation under large, powerful platforms. Lastly, metrics C.7.2-C.7.4 only captures accommodation establishments although accessibility plays a role for all types of tourism infrastructure, including leisure facilities, attractions, food and beverage services and more (European Commission, 2015[35]).

D. Environmental dimension

Policy area: Climate change mitigation

Tourism both affects and is affected by climate change. Tourism-related transport, and air travel in particular, are a major contributor to global emissions. Recent estimates point to tourism emissions in the range of 8% -11% of global emissions (WTTC & UNEP, 2021[36]). At the same time, climate change directly affects tourism, as extreme weather events reduce the attractiveness of tourism destinations and rising temperatures shrink the scope of specific tourism segments. The impacts of climate change alter the terrestrial and marine ecosystems whose beauty tourism depends on. Reducing emissions from tourism directly contributes to the sustainability of the sector and responds to the needs of the global climate crisis.

The tourism sector is increasingly aware of the urgency to take climate action to reduce the climate risks facing to the sector. Signatories to the Glasgow Declaration on Climate Action in Tourism adopted at COP26 in 2021 commit to taking action to reduce emissions by 50% by 2030, and to reach net zero as soon as possible before 2050. In 2021, the World Travel and Tourism Council (WTTC), together with the UN Environment Programme (UNEP) put forward a net zero roadmap for travel and tourism businesses. The regions highlighted the need to align regional policies with greenhouse gas emissions targets set at the international and European level. This can be particularly relevant for meeting conditions or requirements of institutional funders, e.g., when applying for funding under the NextGenerationEU instrument. In order to measure the contribution of tourism towards climate change mitigation efforts, the indicators focus on the use of low-emission transport modes, green mobility infrastructure and the use of renewable energy.

Indicator D.1: Carbon emissions

Short description:

The indicator includes four metrics. Metric D.1.1 estimates greenhouse gas emissions from international tourists arriving by plane. The other three metrics represent response measures to achieve lower carbon tourism. D.1.2 captures the use of sustainable means of transport, proxied by the aggregated share of arrivals by sustainable transport modes including train, bus or non-motorised transport. D.1.3 and D.1.4 measure the average length of stay of domestic and international tourists.

Relevance and purpose:

Transport is a core element of the tourism value chain and a key contributor to greenhouse gas emissions (OECD, 2022[1]). Analysis by UN Tourism and the International Transport Forum (ITF) found that tourism-related CO2 emissions grew by at least 60% during the period 2005-16, with transport-related emissions from tourism representing 5% of global emissions in 2016. Tourism transport-related CO₂ emissions are expected to continue to rise by a further 25% or more by 2030 (UNWTO & ITF, 2019[37]). According to the ITF, current decarbonisation policies are considered insufficient to pivot passenger air transport onto a sustainable path (ITF, 2021[38]). Against this background, the International Civil Aviation Organisation (ICAO) Air Transport Action Group has recently adopted a long term plan to achieve net-zero carbon emissions by 2050, through accelerated efficiency measures, energy transition and innovation across the aviation sector and in partnership with governments (ICAO, 2022[39]). Hence, D.1.1 estimates air travel emissions for international arrivals.

The indicator is complemented by metrics capturing responses that contribute to emissions reduction (see next chapter for avenues for future development). A shift towards low-emission and human-powered transport options needs to be a central element of the green transition for tourism, and governments are increasingly investing into rail networks to enable this shift (OECD, 2022[1]). To monitor the adoption of more sustainable transport modes by tourists, metric D.1.2 measures the share of tourist arrivals by train, bus or non-motorised transport such as cycling or walking. This metric is closely related, but not identical, to the indicator “Share of trips by train” included in the EU Tourism Dashboard (European Commission, 2022[8]). The EU Tourism Dashboard relies on Eurostat data capturing transportation within a destination. The indicator presented here instead focuses on travel to the destination and includes several low-emission modes of transport.

Information on length of stay helps characterise tourism demand and provides insights into tourist behaviour. Overnight stays, in contrast to same-day visitors, tend to be associated with lower environmental footprint in relative terms and higher economic benefits for the destination. Hence, length of stay is commonly used in indicator sets, including by Austria (Federal Ministry for Sustainability and Tourism, 2019[11]), Portugal (Turismo de Portugal, 2017[15]), WEF (2022[17]) and in ETIS (European Union, 2016[7]). Whilst indirect, it helps to monitor trends of visitor behaviour that are relevant to improving carbon intensity. To be able to analyse differences in behavioural patterns of domestic and international tourists, metrics D.1.3 and D.1.4 separately capture the length of stay for either group.

Measurement considerations and limitations:

Focusing on the top 30 source markets, D.1.1 is calculated by multiplying the number of international arrivals by plane for the respective source market with the travel distance and average emission factors (see table below for further details on data sources). A factor of two is applied to account for return flights. The travel distance estimation can be based on the distance between the capital city of the source market and the capital city of the respective Spanish regions. For countries with a large surface area, it may be beneficial to choose two locations and use the average distance; in the United States, for example, the locations could be New York or Washington as an exemplary city on the East Coast and Los Angeles as an exemplary city on the West Coast.

The calculation makes no distinction between multi- and single-destination travellers. For example, a visitor from the United States could have travelled to multiple European countries, or Spanish regions for this matter, and more refined footprint analyses could apply some method of attributing air travel emissions, for example by nights spent in each location. This is not proposed at this stage as the main purpose here is to gain an understanding of the magnitude and trends of international travel emissions associated with a destination, irrespective of the broader itinerary.

Two different emission factors are applied for short-haul (i.e. within Europe) and long-haul (i.e. outside of Europe) flights. Assuming that the majority of tourists travelling to the Spanish regions travel economy class, the corresponding emission factor would be chosen to ensure that the emissions are not overestimated. Similarly, it is suggested to apply emission factors that exclude radiative forcing. Whilst it is important to acknowledge that aircraft emissions at altitude lead to numerous non-CO2 effects, including the risk of contrail and cirrus cloud generation, it is extremely challenging to determine the atmospheric impact on specific routes. Research in this area is progressing fast and the scientific certainty with which it is possible to estimate non-CO2 effects is improving. In the meantime, and to enable easier communication of findings, it is beneficial to focus on emissions of carbon dioxide equivalent. These can be calculated exactly as a direct function of fuel burn and average coefficient applied to air routes which are well understood and robust.

The measurement of the share of tourist arrivals by train, bus or non-motorised transport will rely on data from INE’s EGATUR/FRONTUR and ETR/FAMILITUR survey for international and domestic tourists respectively. The metric groups several modes of low-emission transport together to expand the sample size, allowing for more reliable results for small regions such as Navarra. Data users should take into consideration that tourists’ choice of transport modes depends on the availability and accessibility of infrastructure and service in the destination. Destinations with well-developed rail infrastructure and easy access to train stations may have a higher percentage of tourists arriving by train compared to destinations with limited train services. Also, the distance between the tourist's origin and the destination as well as geographical factors such as insularity (of both country of origin and destination) significantly affect the mode of arrival. Timeliness, costs and comfort of transportation can also affect tourists’ travel behaviour.

Possible data sources for measuring length of stay include INE’s Hotel Occupancy survey (for both international and domestic tourists), INE’s EGATUR/FRONTUR survey (for international tourists) and INE’s ETR/FAMILITUR survey (for domestic tourists). FRONTUR and FAMILITUR offer monthly data and a more complete view as they include all types of accommodation, not only hotel establishments. For a more complete picture of tourism-related greenhouse gas emissions, future development should consider emissions resulting from the consumption of goods and services at tourist attractions, accommodation establishments and in gastronomy (see next Chapter for details).

 

Compilation information: D.1 Carbon emissions
Metrics and units	D.1.1	Estimated greenhouse gas emissions from international arrivals by plane	t CO2e
	D.1.2	Share of tourist arrivals to destination by sustainable transport modes (train, bus or non-motorised transport – e.g. bike, walking…)	%
	D.1.3	Length of stay (domestic tourists)	days / tourist
	D.1.4	Length of stay (international tourists)	days / tourist
Calculation	D.1.1	∑ (International arrivals from country X * Travel distance to region [km] * Emission factor [kg CO2e / pkm] / 1000)
	D.1.2	Tourist arrivals by train, bus or non motorised transport (bike, walking…)Total tourist arrivals
	D.1.3	Total number of nights spent by domestic overnight visitors (tourists)Total number of domestic overnight visitors (tourists)
	D.1.4	Total number of nights spent by international overnight visitors (tourists)Total number of international overnight visitors (tourists)
Target direction	D.1.1: Negative: Emissions should be reduced. D.1.2-D.1.4: Positive: A higher share of tourist arrivals by train or other sustainable transport modes and longer length of stays contribute to carbon emissions reduction.
Data sources	D.1.1 - Number of international arrivals by plane: INE experimental data - Travel distance to region: Distance calculator, e.g. https://www.distance.to/ - Emission factor: Economy class emission factors without radiative forcing for short-haul and long-haul flights (kg CO2e per passenger-kilometre [pkm]), e.g. from DEFRA Conversion factors 2023: full set - updated 28 June 2023 D.1.2 INE – EGATUR/FRONTUR and ETR/FAMILITUR  D.1.3 INE – ETR/FAMILITUR [Catalonia calculated based on INE Hotel Occupancy Survey] D.1.4 INE – EGATUR/FRONTUR [Catalonia calculated based on INE Hotel Occupancy Survey]
Recommended frequency	Annual

Indicator D.2: Green mobility infrastructure

Short description:

This indicator tracks policy measures improving infrastructure to enable the use of sustainable and low-emission modes of transport, focusing on cycling routes and electric vehicle charging stations.

Relevance and purpose:

A key prerequisite for green and sustainable tourism is new and more efficient infrastructure (OECD, 2022[1]). During the consultations, the regions highlighted the importance to include an indicator that would capture information on tourism-related mobility infrastructure. To incentivise policy action and investment for more sustainable transport modes, metrics monitor the development of cycling routes and charging stations for electric vehicles. Well-planned mobility infrastructure reduces the environmental footprint, but also enhances the overall travel experience for tourists, making it easier and more enjoyable to explore the destination sustainably. For Navarra, in particular, non-motorised transport modes including hiking and cycling are an integral part of the tourism offer.

Measurement considerations and limitations:

Different data sources exist for estimating the length of cycling routes in the four Spanish regions. One of the main difficulties lies in defining and capturing tourism-specific cycling routes. Different types of routes exist including urban routes, cycling paths and thematic touristic routes. Data availability is limited to routes specifically designed for tourists to explore a destination, passing through scenic areas, cultural and historical sites, and local attractions. ToT Lab, with support from Andalusia, extracted data from OpenCycleMap. The map is based on data from the OpenStreetMap project and includes national, regional and local cycle routes as well as dedicated cycle tracks and lanes. Navarra can draw on information from their web platform Navarra Spatial Data Infrastructure (IDENA), which features a dedicated layer for cycling mobility. Catalonia has the option to rely on the length of EuroVelo routes; while it focuses on long-distance routes only, it can be a proxy to operationalise cycling routes with a tourism purpose. To enable comparison, even if relying on a different definitions of cycling routes or paths, the metric captures year-on-year change in length of tourism cycling routes rather than the absolute length in kilometres. For metric D.2.2, Electric vehicle charging stations, ToT Lab, with support by Andalusia extracted data from the European Alternative Fuels Observatory TENtec. Catalonia uses the public register of public and semi-public charging stations from Catalan Institute for Energy (ICAEN) to compile the metric. The metric is defined as the absolute number of electric vehicles charging stations as a simple count is easy to communicate and valuable from a messaging perspective for policymakers to show progress.

 

Compilation information: D.2 Green mobility infrastructure
Metrics and units	D.2.1	Year-on-year change in length of tourism cycling routes (km)	%
	D.2.2	Number of electric vehicle charging stations	count
Calculation	D.2.1	Length of cycling routes in current year (km)-Length of cycling routes in prior year (km)Length of cycling routes in prior year (km)
	D.2.2	n.a.- count number of electric vehicle charging stations
Target direction	Positive
Data sources	D.2.1: e.g. OpenCycleMap (proposal by ToT Lab), IDENA (Infraestructura de Datos Espaciales de Navarra), length of EuroVelo routes in Catalonia D.2.2: European Alternative Fuels Observatory – TENtec – ToT Lab to support with data extraction; Catalonia: public register of public and semi-public charging stations from Catalan Institute for Energy (ICAEN)
Recommended frequency	Annual

To further contextualise metric D.2.2, absolute numbers could be put in relation to the length of the road network, the surface area and/or the local population. While electric mobility can contribute to emissions reduction, promoting electrified private vehicles can be in conflict with the goal of promoting public transport over individual mobility.

Indicator D.3: Renewable energy use

Short description:

This indicator estimates the share of accommodation establishments that use 100% renewable electricity.

Relevance and purpose:

As outlined above, climate change mitigation constitutes a key policy issue for tourism. In addition to transport to, from and in destinations, energy use of fixed assets contributes to the tourism industry’s greenhouse gas emissions (OECD, 2003[40]). This includes energy use in tourism accommodation facilities for heating, lighting and air-conditioning. Reducing energy consumption and increasing the use of renewable energy (e.g., solar, wind, biomass, hydroelectric) provide avenues to help mitigate climate change (Simpson et al., 2008[41]). The EU has thus set binding renewable energy targets; the European Parliament and the Council have recently reached a provisional agreement to raise this target from 32% to 42.5% renewable energy share by 2030 (European Commission, 2023[42]). Voluntary schemes, building on private sector commitments, exist as well: The RE100 initiative by CDP (Carbon Disclosure Project) and Climate Action, for instance, set an ambitious target of reaching 100% renewable energy by 2031; however, significant progress is needed to close the gap by 2031 as member companies reported using 49% renewable energy in 2021 (Climate Group & CDP, 2023[43]).

Against this background, the Spanish regions consider it important to reflect the effort and progress the private sector is making to contribute to climate change mitigation. Promoting the use of renewable energy is one of the avenues the regions are taking to reduce emissions in the tourism sector; Andalusia has included a renewable energy target in their climate action plan.

Measurement considerations and limitations:

Several indicator frameworks include metrics to measure renewable energy use (European Union, 2016[7]; Federal Ministry for Sustainability and Tourism, 2019[11]; UNWTO, 2004[16]; WEF, 2022[17]). As isolating tourism-specific data can pose challenges, Austria for instance focuses on the share of renewable energy in gastronomy and accommodation. The present indicator set will initially focus on renewable electricity use of accommodation facilities as data can be almost fully allocated to the tourism sector. It includes a metric capturing the share of accommodation establishments that use 100% renewable electricity. ToT Lab with support by Andalusia developed a methodology based on web scraping of private-sector platform data.

This approach is a valuable starting point to analyse the sectors’ efforts to use renewable energy. However, it faces several limitations. First, users need to ensure they have lawful access to the content. In addition to ethical and legal considerations, web scraping faces technical challenges. The method relies on stability of websites, requiring changes to the code as websites change. Furthermore, OTA data is based on self-declarations with potential information bias. Relying on OTA data can also have unintended effects, such as further incentivising consolidation under large, powerful platforms. While the focus on one economic activity, namely accommodation, improves the feasibility of collecting data, it fails to provide information on other contributors including restaurants as well as energy intensive tourist attractions such as theme parks. While capturing the percentage of renewable electricity, the indicator does not allow insights on absolute electricity usage, thus not incentivising reduction measures. Going forward, regions may choose to elaborate on other potential metrics such as direct energy consumption or installed capacity of solar panels. Regional departments in charge of environmental affairs could be approached to further elaborate on potential alternative data sources.

When interpreting the indicator, it is important to note that different types of accommodation establishments may have different capacities to adopt renewable electricity sources, depending on factors such as their location or size. As accommodation establishments differ in size, future work could calculate the metric based on the number of beds or rooms for more comparable data weighted by size. Furthermore, government policies, incentives, and renewable energy infrastructure influence the adoption of renewable energy sources in the tourism industry. These may differ from region to region, impacting indicator results.

 

Compilation information: D.3 Renewable energy use
Metrics and units	D.3.1	Share of accommodation establishments with 100% renewable electricity	%
Calculation	D.3.1	Number of accommodation establishments with 100% renewable electricity Total number of accommodation establishments
Target direction	Positive: Higher values indicate higher shares of renewable energy.
Data sources	Web scraping by ToT Lab (from 2024 onwards)
Recommended frequency	Annual

Policy area: Water management

Indicator D.4: Tourism water use

Short description:

This indicator measures the expenditure on water by accommodation establishments as a proxy for water consumption (D.4.1) and the share of accommodation establishments with water saving measures in place (D.4.2).

Relevance and purpose:

Tourism accounts for approximately 1% of global water consumption compared to 70% consumed by agriculture (WeAreWater Foundation, 2022[44]). Even in tourism-intensive destinations such as Malta or Cyprus, the percentage remains below 5% (Eurostat, 2009[45]). Although these figures appear comparably low, tourism faces sector-specific issues regarding water use that need to be addressed by policymakers (Eurostat, 2009[45]). Due to seasonal effects, periods of high water usage in the summer coincide with periods of water shortage (an effect experienced in parallel in agriculture). Geographical concentration in areas facing high levels of water pressures is another distinctive factor, affecting especially coastal areas, islands and other sensitive yet popular natural sites. By using facilities such as swimming pools and golf courses, tourists disproportionately contribute to water consumption in a region. These factors contribute to pressure on the environment, also affecting the local population (Eurostat, 2006[6]).

Among industrialised countries, Spain experiences high levels of water stress: according to World Bank data, the freshwater withdrawal as a proportion of available freshwater resources amounts to 43.3% in Spain as of 2020, compared to 12.3% in Portugal, 20.5% in Greece 23.0% in France and 29.7% in Italy (World Bank, 2022[46]). Although agriculture is the major consumer of freshwater resources in Spain with 65% of freshwater withdrawals as of 2020 (World Bank, 2022[46]), tourism adds to the pressure on water resources, especially in the most popular tourism destinations. While residents consume on average 127 litres per day, tourists consume between 450 and 800 litres subject to seasonal and spatial differences (WeAreWater Foundation, 2022[44]). It is hence essential to monitor water use by tourists as well as efforts of the tourism sector to reduce consumption and increase efficiency.

Measurement considerations and limitations:

Measuring water consumption in tourism remains a challenge. A common approach is measuring water use in accommodation in litres per tourist per day (Gössling, Hall and Scott, 2015[47]). As outlined above, it is relevant to compare tourists’ consumption with that of the local population. The Eurostat-commissioned report carried out by Statistics Sweden (Eurostat, 2006[6]) as well as the ETIS framework (European Union, 2016[7]) relate tourists’ daily water use to that of the residential population. The former relies on Eurostat data for the total gross abstraction of total fresh water (ground + surface water) by public water supply, dividing it by the number of resident days and tourist overnights, then multiplied by the total number of overnight stays. The latter calculates two measures, comparing the yearly or monthly freshwater consumption by the general population to the tourism-related consumption per tourist. Using overall public water supply (as done by the Eurostat-commissioned report) and relating it to resident and tourist numbers relies on sector-agnostic data. ETIS relies on ‘water consumption related to tourism’; this data may be hard to obtain but makes the measure sector-specific.

To develop a sector-specific metric, Catalonia has developed a methodology for a proxy measure based on INE’s Structural Business Statistics in the Service Sector. The survey provides data on expenditure for water consumption by the accommodation industry (in Euros). Data is available for all regions, thus facilitating comparability. To eliminate inflationary fluctuations, the calculation takes price indices for water into account. For a larger sample size, regions pool together different types of accommodation. Tracking expenditure gives an indication for changes in water consumption over time and helps determine whether water consumption of accommodation establishments is increasing or decreasing. As a complementary metric to capture efforts of the private sector to implement water saving measures, metric D.4.2 measures the share of accommodation establishments with water saving measures in place. As accommodation establishments differ in size, future work could calculate the metric based on the number of beds or rooms for more comparable data weighted by size.

 

Compilation information: D.4 Tourism water use
Metrics and units	D.4.1	Expenditure on water consumption by accommodation establishments	EUR
	D.4.2	Share of accommodation establishments with water saving measures in place	%
Calculation	D.4.1	Σ water expenditure of accommodation establishments (activities 551-559)
	D.4.2	Number of accommodation establishments with all four water saving measures in placeTotal number of accommodation establishments Water saving measures: The accommodation only uses water-efficient toilets e.g., low-flow toilets, dual flush toilets Only using water-efficient showers (e.g., smart showers, low-flow showerheads) A towel reuse programme is available to guests Guests can opt out for room cleaning (e.g., bed linen laundry is reduced)
Target direction	D.4.1: Negative: The aim is to reduce water use by tourism businesses. D.4.2: Positive: The aim is to increase the share of accommodation establishments with water saving measures in place.
Data sources	D.4.1 INE Structural Business Statistics in the Service Sector (annual water expenses in EUR for 551 Hotels and similar accommodation, 552 Holiday and other short-stay accommodation, 553-559 Campsites and other type of accommodation activities) D.4.2 Web scraping by ToT Lab (from 2024 onwards)
Recommended frequency	Annual

Going forward, regions will explore whether data on unit price of water and the full water bill are available beyond Catalonia – this would allow to convert expenditure into consumption, using two metrics: litres per guest night and total volume.

The indicator faces limitations as expenditure focuses on direct water use of accommodation facilities. This excludes consumption by water-intensive attractions such as golf courses, indirect water use such as for the production of food and beverage nor systemic water use in the wider value chain such as through marketing activities (Gössling, Hall and Scott, 2015[47]). The interpretation of this indicator depends on the specific context of the tourism destination, including the availability and quality of water resources, the level of water stress in the region, and the local water management practices and policies. Thus, it is important to consider the availability and quality of water resources in the region. Pressures may not only vary geographically, but also temporally; yet annual data collection does not capture seasonal effects.

In addition to monitoring water consumption, proxied by expenditure, the indicator includes a response metric capturing the share of accommodation establishments with water saving measures in place. ToT Lab with support by Andalusia developed a methodology based on web scraping of private-sector platform data. While this provides a valuable starting point, the approach is subject to limitations as outlined above.

Indicator D.5: Bathing-water quality

Short description:

This indicator measures the quality of bathing waters, expressed as the percentage of bathing water sites classified as ‘excellent’. It monitors both fresh and coastal waters.

Relevance and purpose:

Water resources are integral to many tourist activities (Gössling, Hall and Scott, 2015[47]). To monitor and improve bathing water quality, the EU introduced a revised Bathing Water Directive in 2006. Since then, bathing water quality has risen; in 2021, 84.8% of bathing sites have been classified as ‘excellent’ across the EU (EEA, 2022[48]). However, challenges remain; bathing water management faces issues including microbiological pollution through faecal bacteria, extreme weather events, eutrophication and resulting cyanobacterial blooms which adversely affecting human health and aquatic life, plastic litter as well as increasingly common wild bathing (EEA, 2020[49]). Climate change and habitat alterations can exacerbate the effects, impacting aquatic ecosystems (EEA, 2022[48]). Tourist activities may contribute to negative impacts on the quality of water resources, with adverse effects on biodiversity, especially in fragile coastal areas, but also at inland natural sites.

Monitoring bathing water sites not only has environmental, but also social and economic implications. It helps ensure water quality poses no health hazards and water sites continue to attract tourists to destinations. This again shows the interrelation of the three sustainability dimensions and highlights the policy relevance of strengthening management practices that protect and improve water quality, counteracting the multiple pressures exerted.

The regions of Valencia, Catalonia and Andalusia have long coastal stretches for which it is important to monitor water quality; while Navarra is not a coastal region, quality of inland freshwater sites is relevant for and impacted by tourism. To cater to the needs of all four regions, it was important to identify an indicator that addresses both fresh and sea water.

Measurement considerations and limitations:

Data on bathing water quality is publicly available by Eurostat. The indicator, as adopted here, is included in the EU Tourism Dashboard and refers to both fresh and coastal waters which are monitored for polluting substances throughout the period of May to September (European Commission, 2022[8]). An alternative way to capture water resource aspects is the measurement of protected water areas relative to all water areas in tourist regions. However, this way of measuring was discarded as it does not evaluate the impact on the water resources.

The indicator only refers to bathing-water sites, not allowing for insights on other natural water resources/sites that may increasingly be used for ‘wild bathing’ (EEA, 2020[49]). Also, not all water sites may be of touristic interest; there is thus only an indirect tourism linkage. Furthermore, water samples are taken at a specific site at a specific point in time; their quality depends on a range of contextual factors, including weather patterns, water management practices, and human activities. However, given the limited available data on tourism impact on water resources (Eurostat, 2009[45]; Gössling, Hall and Scott, 2015[47]), this indicator provides a valuable starting point.

The classification "excellent" is a very broad estimate and data does not provide information on the other classifications. Furthermore, the proportion of bathing water sites classified as “excellent” is relatively high and stable in recent years across EU countries (EEA, 2022[48]) – based on piloting results, values for Andalusia, Catalonia and Valencia ranged between 91% and 98% in 2022; for Navarra, the share was 66.7%, possibly linked to the lack of coast. At levels above 90%, considerable improvements may be difficult.

 

Compilation information: D.5 Bathing-water quality
Metrics and units	D.5.1	Share of sampled bathing water sites that are classified as “excellent”	%
Calculation	D.5.1	Number of bathing water sites classified as “excellent” Total number of bathing water sites
Target direction	Positive: Higher values indicate higher quality of bathing waters in the tourist destination.
Data sources	EU Tourism Dashboard indicator available at NUTS2 level based on data by the European Environment Agency: Excellent bathing water – Green pillar – Regional
Recommended frequency	Annual

Policy area: Protected areas management

Indicator D.6: Tourism pressure in protected areas

Short description:

This indicator measures the pressure tourism places on protected areas based on tourist accommodation within these areas.

Relevance and purpose:

Given the significance of protected areas to the global tourism industry, measuring, monitoring, and managing tourism within these areas is critically important. Tourism can have negative and positive effects on protected areas. Tourism in and around protected areas can create employment opportunities, support local economies, and promote environmental and cultural preservation. If poorly managed, however, tourism can degrade fragile ecosystems through pollution related to human encroachment, resource consumption and waste, the introduction of non-native plants and animals as well as increase the risk of forest fires. Socio-culturally, unchecked tourism in and around protected areas may increase the prices of goods, services, and housing, and alter traditional modes of labour and social organisation (Belsoy, Korir and Yego, 2012[50]).

In Spain, visits to the National Parks network have increased by an estimated 77% over the past 20 years, with Parks closer to urban areas seeing higher numbers of visitors (González-Domingo, Fosse and Costa-Salavedra, 2021[51]). Given the increasing interest to visit protected areas in Spain, evidence-based policies are needed to ensure the negative impacts of tourism are minimised and the positive impacts encouraged.

Measurement considerations and limitations

The indicator measures tourism pressure in protected areas. It relies on the EU Tourism Dashboard indicator high nature-based tourism potential which measures the share of total accommodation capacity located in areas with high ecosystem quality and accessibility. JRC uses the Recreational Opportunity Spectrum map to classify relevant areas. Unlike the framing in the EU Tourism Dashboard, the metric will be interpreted as a risk measure of exposure of sensitive ecosystems to tourism activity and impact.

It should be kept in mind that tourist accommodations are often not allowed within protected areas but may be located directly outside or very close to them. Going forward, regions will explore if monitoring the number of rooms in surrounding areas would be a useful avenue to monitor pressures on protected areas. It is also important to recognise that the carrying capacities of protected areas also vary according to aspects such as the size of the area and the type or classification of protected area. By referring strictly to bed count, this is an indirect and partial indicator. Moreover, many protected areas see high levels of same-day visits, which are not covered with this indicator.

 

Compilation information: D.6 Tourism pressure in protected areas
Metrics and units	D.6.1	Share of the total accommodation capacity (no. of rooms) which is located in areas with “high nature-based recreational opportunities”	%
Calculation	D.6.1	Number of rooms in areas with “high nature-based recreational opportunities”Total number of rooms
Target direction	Negative: Higher values indicate a higher tourism pressure in the protected area. (N.B. the target direction could be region-specific)
Data sources	High nature-based tourism potential (EU Tourism Dashboard) – See link for more details on calculation
Recommended frequency	Annual

Indicator D.7: Management of natural parks

Short description:

This indicator identifies the adoption of management tools in natural parks based on EUROPARC data.

Relevance and purpose:

Monitoring and information systems in natural parks provide an evidence-based for decision making and evaluating the effectiveness of decisions. Such systems and processes are necessary for the sustainable management of natural parks, which require careful monitoring to track whether their ecosystems are being degraded, preserved, or enhanced. A first step is establishing a management plan that sets the foundation for natural resource protection.

When monitored and managed according to sustainable development principles, tourism can promote environmental values, and help finance the protection and management of protected areas and sensitive sites. Tourism can also play an important role in demonstrating the economic value of environmental conservation, primarily through the level of activity that it can stimulate in the local, regional, and national economy (OECD, 2021[3]).

Measurement considerations and limitations:

Implementing monitoring and information systems is a complex and ongoing process that requires sufficient resources, technical expertise, and stakeholder engagement. The quality and comprehensiveness of such systems can vary widely across natural parks, depending on factors such as their size, ecological complexity, personnel, financial resources and management objectives. This indicator relies on EUROPARC data to measure the share of natural parks awarded the European Charter for Sustainable Tourism (ECST).

Using natural parks may be too restrictive as it excludes a substantial number of the 3,705 protected areas, comprising of which 1,847 sites designated under national laws and 1858 recognized as Natura 2000 sites (EEA, 2022[52]). Furthermore, the metric’s development is likely static, providing limited information value for decisionmakers to design policy responses. does not capture management in ‘National Parks’ as a proxy for protected areas is restrictive. It should also be noted that the fulfilment of quality criteria does not guarantee effective conservation outcomes; future development should consider impact rather than process metrics.

 

Compilation information: D.7 Management of natural parks
Metrics and units	D.7.1	Share of natural parks awarded European Charter for Sustainable Tourism (ECST)	%
Calculation	D.7.1	Number of natural parks awarded European Charter for Sustainable Tourism Total number of natural parks
Target direction	Positive: Higher values indicate better planning and management in natural parks.
Data sources	EUROPARC and regional natural parks authorities
Recommended frequency	Every 2-4 years

S. Supplementary indicators

Policy area: Benefits to the local economy

Indicator S.1: Tourism tax revenue

Short description:

This indicator monitors tourism tax revenue. Measuring tourism tax contributions can be used to support public investment in tourism development.

Relevance and purpose:

Tourism taxation is a key issue in many tourism destinations and countries (OECD, 2014[53]). Such tax revenue can be and is used to fund environmental protection and infrastructure development to better manage the impacts of tourists in sensitive areas. It can also serve as a cost recovery instrument for international mobility administrations (e.g., customs, immigration, border security) and for funding international marketing promotion activities. Taxes can contribute to the competitiveness and attractiveness of tourism destinations. Monitoring tourism tax revenue can help policymakers with investment planning, providing information on potential needs to raise or reduce taxes.

Measurement considerations and limitations:

There is a lack of monitoring, evaluation and analysis of the impacts of tourism-related taxes to ensure that they are meeting their objectives without adversely affecting tourism competitiveness. As a starting point, metric S.1.1 measures the amount of tourism tax revenue generated, relative to the local population.

Regional Tourism Satellite Accounts (R-TSA) are one option for displaying tourism-tax revenues. However, not all regions in Spain compile R-TSAs. Furthermore, R-TSAs are not compiled on an annual basis; in Navarra, the most recent R-TSA dates back to 2014. Catalonia has a specific tourist tax that will be monitored by the metric; the tax covers overnight visitors who stay at commercial accommodation establishments. The other three regions do not have such taxes.

Different types of taxes are implemented in different municipalities and regions; regional development and environmental protection may also benefit from other fees or charges that could be relevant to monitor.

 

Compilation information: S.1. Tourism tax revenue
Metrics and units	S.1.1	Amount of tourism tax revenues per inhabitant	EUR per inhabitant
Calculation	S.1.1	Tourism tax revenue Number of residents in the region
Target direction	Positive: The higher the tax revenue per inhabitant. The higher are the potentials for public investments. However, they might be a threshold at which the amount of taxes can create negative effects in businesses (over-taxation).
Data sources	Regional Tourism Satellite Accounts; Regional Tourism Authorities (revenue generated from an existing tourism tax)
Recommended frequency	Annual

Policy area: Reduced seasonality

Indicator S.2: Tourism seasonality

Short description:

This indicator measures the Gini coefficient calculated based on monthly tourist numbers, complementing indicator B.5.

Relevance and purpose:

The regions highlighted that seasonality is a particularly pressing issue, especially in coastal areas, natural reserves and around popular landmarks. To complement indicator B.5, the Gini coefficient is included as a metric commonly used to measure seasonality, capturing distribution beyond the peak months.

Measurement considerations and limitations:

Metric S.2.1 relies on INE experimental data for international and domestic tourist numbers at municipal level for ‘puntos turisticos’. The Gini coefficient may not be easily interpretable by data users with limited statistical literacy. Metric B.5.1 may be better suited to capture the concentration in the peak season. Also, the data excludes same-day visitors who may add significantly to the pressures.

 

Compilation information: S.2 Tourism seasonality
Metrics and units	S.2.1	Gini coefficient of monthly tourists	Numerical between 0 - 1
Calculation	S.2.1	G=2∑j=1Mj yjM∑j=1Myj-M+1M Where G is the value for the Gini index; M is the number of time periods in a year (12 for monthly data); j is an index for time period (from 1 to 12); yi is the number of monthly international tourists.
Data sources	S.2.1: Calculation based on INE experimental data https://www.ine.es/experimental/turismo_moviles/experimental_turismo_moviles.htm
Target direction	S.2.1: Negative, ranging from 1 (perfect inequality) to 0 (perfect equality).
Recommended frequency	Annual (based on monthly data)

Policy area: Reducing tourism pressure on housing

Indicator S.3: Regulations on short-term rentals

Short description:

The indicator provides information on regulations on short-term rentals by regional governments.

Relevance and purpose:

Short-term rentals have developed rapidly in recent years. In 2022, they represented about one quarter of all tourist accommodation in the EU (Eurostat, 2023[54]). As such, short-term rentals have become an important source of accommodation, creating both opportunities and challenges for many regions. While they provide new forms of accommodation for tourists and additional incomes for hosts, they can also create concerns for communities with respect to the supply and affordability of housing.

The proposed indicators relate to in-progress EU legislation, regulating data collection and sharing for short-term accommodation rental services (European Commission, 2022[55]). Public authorities are in the need of reliable data to develop appropriate and proportionate policy responses to the increase in short-term rentals. An aim is increased transparency for local and regional authorities, which can allow effective and proportionate tourism policies related to the short-term rental sector. The metric captures region-specific regulation of short-term rentals.

Measurement considerations and limitations:

The proposed binary indicator is a starting point, closely linked to the EU proposal for a regulation of short-term accommodation rental services (Council of the European Union, 2022[56]). Additional data should be explored to provide further information of short-term accommodation and their impacts. For instance, the number of overnight stays in short-term rentals, the extent to which platforms are sharing data voluntarily and whether the accommodations are privately or professionally owned are important to understand.

Catalonia currently has data on short-term rentals and the share of municipalities where regulations are in place. Other regions do not have such data yet. The regions pointed out that INE has information about the price of apartments in tourism destinations. Such data should be explored further. An additional approach could be to calculate the average between short-term accommodation targeted at tourists and the total number of rental properties in the region. Issues linked to short-term rentals are often highly localised; regulations need to take contextual differences at local level into account.

 

Compilation information: S.3 Regulations on short-term rentals
Metrics and units	S.3.1	Existence of regional legal regulation for short-term rentals	yes/no
Calculation	S.3.1	n/a
Target direction	A positive value would indicate that there are legal regulations for short-term rentals in the region and/or that there is an obligation to maintain registration systems for short-term rentals as well as to share activity data. Such regulations would provide better guidance for the limitation of an excessive short-term rental development, which could lead to negative social impacts in the region.
Data sources	Regional government
Recommended frequency	Annually

Indicator S.4: Share of second homes

Short description:

The indicator includes two metrics, namely the ratio of second homes relative to all commercial accommodation establishments and the number of second homes per 100 resident homes.

Relevance and purpose:

Second home tourism creates positive as well as negative economic, environmental and socio-cultural impacts in destinations. The unbalanced development of second homes can create negative tourism perceptions and decrease the social wellbeing of the host population (Müller and Hoogendoorn, 2013[57]). Second homes directly and indirectly contribute to a significant number of domestic and international visitors to European destinations. Second home tourism reflects longer-term retirement, changing lifestyles, and amenity migration, creating positive as well as negative economic and social impacts on destinations (Hall, 2014[58]).

The second home concept originally pertained to non-commercial residences, but increasingly, the term is being applied to second residences that are also available for short-term holiday accommodation. This creates challenges in defining and differentiating between short-term rentals and second homes. Depending on the regional context, excessive second home developments can place pressures on housing supply, infrastructure, land availability and affordability, creating potential conflicts between permanent and temporary residents.

Measurement considerations and limitations:

The concept of second homes requires a common definition to establish a statistical baseline. The metrics relate the number of second homes to the total number of accommodation establishments, and total resident homes respectively. The metrics will rely on INE household survey data on second homes. Another option would be to approximate this indicator through demand surveys as the percentage of tourists staying in private dwellings.

It should be noted that the impacts of second homes can vary significantly among regions, depending on overall tourism development, population density and general regional aspects, such as housing prices and availability. Thus, interpretations around the proportion of second homes need to include such contextual factors, which are not covered by the metrics below. Issues linked to second homes are typically localised, more granular data at local level would be valuable for decisionmakers.

 

Compilation information: S.4 Share of second homes
Metrics and units	S.4.1	Ratio of second homes relative to all commercial accommodation establishments	ratio
	S.4.2	Number of second homes per 100 resident homes	ratio
Calculation	S.4.1	Number of second homes Total number of official accommodation establishments
	S.4.2	Number of second homes Number of resident homes / 100
Target direction	Negative: Higher values may signal an extensive development of second homes.
Data sources	INE data: https://www.ine.es/dynt3/inebase/es/index.htm?padre=9575&capsel=9579 Catalonia: Conventional family dwellings – IDESCAT
Recommended frequency	Annual

Indicator S.5: Long-term rental price evolution

Short description:

This indicator measures the evolution of the prices of long-term rentals. A strong increase long-term rental prices can create negative tourism perceptions and decrease the social well-being of the host population.

Relevance and purpose:

Short-term rentals and second homes used for tourism purposes are exerting pressure on housing supply and affordability in the regions, creating potential conflicts between permanent and temporary residents. The price evolution of long-term rentals can influence overall housing-prices, leading to an increased cost of living for local populations. Monitoring these developments can therefore be useful to inform tourism policies and housing regulations.

Measurement considerations and limitations:

INE currently measures resident rental housing prices as an experimental statistic and displays an index that gives information on the annual evolution of the prices of rented housing as the habitual residence of households. While data is generally available at regional level, Navarra is not included in this statistic.

Furthermore, the metric does not provide information on the causal mechanisms behind the price evolution. To analyse potential tourism-induced impacts, it would be necessary to cross this information with other data sources, potentially linking to tourism intensity or density. While tourism can impact housing prices and housing availability, other contextual factors such as regional housing policies equally play a role. Regional averages may hide localised increases in long-term rental prices; urban and rural areas are differently affected. More granular data at local level would be valuable for decisionmakers.

 

Compilation information: S.5. Long-term rental price evolution
Metrics and units	S.5.1	Rental Housing Price Index (RHPI)	Index
Calculation	S.5.1	Index calculation by INE based on prices of rental housing, weighted by the relative importance of each type of housing
Target direction	Negative: The higher the value, the higher is the price variation, a sign of fluctuating markets and possible instability.
Data sources	INE experimental statistics – Rental Housing Price Index (data table: National Institute of Statistics)
Recommended frequency	Annual

Policy area: Increasing security

Indicator S.6: Crime rate

Short description:

This indicator measures regional crime-rates as a proxy for safety in tourism destinations.

Relevance and purpose:

Safety is one of the main prerequisites for travelling (UNWTO, 1985[59]). The relationship between tourism and crime has long been acknowledged and researched (Chesney-Lind and Lind, 1986[60]; Mawby, Brunt and Hambly, 1999[61]; Stangeland, 1998[62]). Tourists are particularly at risk of suffering crime, especially in mass tourism destinations, but tourists can also be responsible for crime and disorder in regions. In general, the impact of crime in tourism destinations affects tourists, local communities, and the tourism industry itself (Mawby, 2017[63]). Little empirical evidence exists on the specific relationship between tourism development and potential crime rates. Some tourist areas experience low levels of crime, whereas others may have high crime-rates. Moreover, crime in tourism destinations can manifest itself in different ways. Tourists may experience high levels of property crimes, robbery, or vehicle-related crime. In other situations, local residents may suffer from increased levels of crime and disorder associated with tourists.

Measurement considerations and limitations:

Data on crime rates are available through official statistics from INE. Crime statistics record the number of total crimes committed (and reported) as well as further contextual details on geographic location and types of offenses. However, crime statistics do not register the residential status of victims and culprits, thus not allowing to distinguish between locals and visitors.

 

Compilation information: S.6 Crime rate
Metrics and units	S.6.1	Crime-rate variation in the last three years	%
Calculation	S.6.1	Current crime rate - Crime rate 3 years ago Crime rate 3 years ago *100
Target direction	Negative: The higher the value, the higher is the crime rate.
Data sources	INE – Conviction Statistics: https://www.ine.es/dynt3/inebase/en/index.htm?padre=8027&dh=99 https://www.ine.es/en/experimental/ipva/experimental_precios_vivienda_alquiler_en.htm
Recommended frequency	Annual & monthly (for seasonal variations)

A more sophisticated indicator would be needed to link tourism and crime rates. Despite the limitations, the regions agreed to start with the regional crime-rate variation as a proxy for potential crime risks to tourists as well as residents. It was also highlighted that monthly variations could be compared to the seasonality of a region, giving indications on the increasing number of tourists and the crime-rate respectively.

Policy area: Skills development

Indicator S.7: Training participation

Short description:

The indicator measures how many employees in tourism have participated in at least one training course within the regional training plan.

Relevance and purpose:

Tourism is a labour-intensive service industry that is highly dependent on the availability of trained and skilled personnel (Stacey, 2015[5]). It creates jobs for people of all ages and skill levels and provides opportunities to enter the labour market, gain experience, develop skills and move up the value chain into higher professional levels. Tourism’s specific characteristics, such as seasonality, high share of SMEs, demanding working conditions, recruitment and retention difficulties make it challenging to keep training and skill levels high of the tourism workforce.

Measurement considerations and limitations:

Training and upskilling in tourism take place in different contexts and by a multitude of stakeholders. While tourism businesses play a key role in training their employees, public-sector training plans can complement private-sector skill-building. Catalonia’s General Directorate for Tourism has data on the number of people trained under the regional training plan. However, other regions lack data sources. Institutions such as the European Institute for Education or the national Labour Force Survey could possibly provide further information. Other options could be to assess the number of existing education and training establishments as a first proxy for potential training opportunities, or to analyse the overall budget of regional training programmes. Importantly, merely monitoring the number of participants or the budget does not provide insights on the quality of training and its outcomes. Further development is needed to capture key policy issues to improve training and skills for a thriving tourism workforce.

 

Compilation information: S.7 Training participation
Metrics and units	S.7.1	Number of people that participate in the regional training plan for tourism	number
Calculation	S.7.1	n.a. - count number of employees trained
Target direction	Positive: The higher the value, the more people have participated in trainings.
Data sources	Catalonia: General Directorate for Tourism; sources for other regions to be explored
Recommended frequency	Annual

Policy area: Natural and cultural heritage

Indicator S.8: World Heritage sites under threat

Short description:

This indicator measures number of UNESCO World Heritage sites with threat intensity coefficient of 20 or above in the last three years.

Relevance and purpose:

Tourism and culture are closely interrelated (OECD, 2022[64]). Cultural and creative resources are important elements of the tourism product and key drivers of attractiveness for destinations. Tourism can help preserve cultural heritage (OECD, 2021[3]); however, it can also have adverse effects on cultural heritage. The same applies to environmental heritage sites.

The UNESCO World Heritage Sustainable Tourism Toolkit identifies three cultural dimensions in which benchmarks should be developed: 1) cultural conservation, 2) impacts on local communities and 3) economic value generated (UNESCO, 2015[65]). In existing indicator frameworks, typical cultural tourism measurements relate to recognised cultural and creative attractions (Dupeyras and MacCallum, 2013[18]). For instance, frameworks measure the expenditure to maintain or restore cultural and historical heritage sites (Eurostat, 2006[6]), the concentration of interest in cultural attractions (WEF, 2022[17]) or the number of UNESCO world heritage sites (European Commission, 2022[8]). The latter indicator is included as a basic descriptor in the EU Tourism Dashboard. This categorisation describes the tourism offer but does not measure the impact of tourism on cultural heritage more generally.

The regions highlighted the need to focus on local communities and on preventing the loss of cultural identity. The potential negative impacts of tourism on local culture were identified as the crowding out of traditional, local shops by souvenir shops, as well as an overcrowding of tourists during traditional folklore festivities, both of which are detrimental to the local population and tourists who seek authentic tourism experiences. ETIS includes an indicator that measures the percentage of residents that are satisfied with the impacts of tourism on the destination’s identity (European Union, 2016[7]). This indicator comes closer to measuring the policy issues the regions are facing but would likely rely on costly survey data.

Measurement considerations and limitations:

Measuring the influence of tourism on cultural identity is difficult. The most accurate, yet costly way may be through survey data. Although Navarra includes a question in their survey on local community sentiment regarding tourism, data is not currently available across all regions. Given these limitations, the indicator will rely on a proxy measure based on the number of UNESCO World Heritage sites with threat intensity coefficient of 20 or above in the last three years. This captures both natural and cultural heritage.

However, the piloting showed that the metric is quite static – in Catalonia the value was 1 for years from 2019 until 2022 – linked to a limited number of UNESCO World Heritage sites and longer-term changes. The metric should be complemented with additional data sources and stakeholder engagement to gain a more comprehensive understanding of tourism’s impact on the natural and cultural heritage of a destination. Although cost may be a barrier, one avenue could be collecting survey data relating to local community sentiment (cf. indicator C.1 above). Another option could be to focus on the destination’s initiatives and/or investments to preserve cultural heritage.

 

Compilation information: S.8 Threat intensity coefficient of UNESCO World Heritage sites
Metrics and units	S.8.1	Number of UNESCO World Heritage sites with threat intensity coefficient of 20 or above in the last three years	number
Calculation	S.8.1	n.a. - count number of sites under threat
Target direction	Negative
Data sources	https://whc.unesco.org/en/list/ https://whc.unesco.org/en/list/383/indicators/ https://whc.unesco.org/en/wh-gis/
Recommended frequency	Annual

Policy area: Sustainable business practices

Indicator S.9: Environmental labels and schemes

Short description:

This indicator tracks the number of accommodation establishments with environmental labels and schemes, focusing on those registered to the EU Eco-Management and Audit Scheme (EMAS) or awarded the EU Ecolabel.

Relevance and purpose:

The systemic changes urgently needed to achieve sustainability require action by all actors, involving both the public and the private sector. Private companies are increasingly pressured to adopt sustainable practices by regulators and investors. Certifications, labels and schemes are one way to signal and verify sustainability efforts, although they are no replacement for monitoring actual performance. Analysis by TUI, verified by UNEP, shows that hotels with sustainability certifications achieve 10% lower CO₂ emissions and 24% lower waste volume per guest night; on average, their share of ‘green’ energy is 23% higher than that of their non-certified counterparts. Hotels with sustainability certifications also received higher customer satisfaction scores compared to non-certified hotels (One Planet, 2021[66]). An increased uptake of reliable certification scheme by tourist accommodation establishments is an explicit aim of the EU Transition Pathway for Tourism (European Commission, 2022[34]). However, large differences exist in ambition and reliability of different schemes. Also, companies specifically gear their efforts to meeting particular certification criteria, potentially neglecting sustainable practices beyond the scored aspects.

Measurement considerations and limitations:

The regions agreed that monitoring certifications of accommodation establishments provides a valuable starting point, giving a first indication of the industry interest and implementation of sustainability practices. Several accommodation certifications exist and choosing which one to rely on is not straightforward. As no Spain-wide recognised and publicly administered certification scheme exists, regions will rely on data from the EU Tourism Dashboard with a focus on EMAS and the EU Ecolabel, instruments established under the EU policy framework for sustainable consumption and production. While many certification schemes also cover governance and/or social aspects, the metric has been placed under the environment dimension because of the emphasis on environmental factors in the considered certification schemes.

The metric faces several limitations. First, accommodation establishments are taken as a proxy for a wider range of private-sector tourism entities. Furthermore, the verification of data on certification is not always publicly available, raising issues around transparency and reliability of the data. Certification can be costly and small accommodation establishments, in particular, may not be able to afford it. Furthermore, the piloting showed that only a very limited number and small fraction of accommodation establishments are registered to EMAS or have been awarded the EU Ecolabel: 12 in Andalusia (2022), 61 in Catalonia (2022), 3 in Navarra (2022) and 9 in Valencia (2023). While one could still track progress over time, the indicator has been moved to the supplementary set due to its limited coverage. Lastly, as accommodation establishments differ in size, future work could consider weighting the metric based on the number of beds or rooms for more comparable data.

 

Compilation information: S.9 Environmental labels and schemes
Metrics and units	S.9.1	Number of accommodation establishments registered to EMAS or awarded the EU Ecolabel	number
Calculation	S.9.1	n.a. - count number of relevant accommodation establishments
Target direction	Positive: Increase the sustainability commitment of tourism businesses.
Data sources	Environmental labels and schemes (data at NUTS2-level available in EU Tourism Dashboard) – focus on EMAS and EU Ecolabel
Recommended frequency	Annual

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Annex 4.A. Compilation tool

This report including the indicator framework and compilation guidance is complemented by an Excel compilation tool to support indicator compilation. The compilation tool is set up as follows:

 

No.	Name	Action required	Content
1	Instructions	Review	Instructions for each indicator about the filling of this questionnaire.
2	Summary	Review	Partially customisable table of sustainability indicators.
3	Indicator compilation	Review	Complete compilation table of sustainability indicators.
4	Context	Input data	Number of visitors, number of tourists, duration of trips, nights spent, rooms and bed-days available, satisfaction of tourists, tourism establishments, accommodation establishments, region's area and population.
5	Economic dimension	Input data	Employment, GVA, expenditure, earnings.
6	Social dimension	Input data	Local residents' perception of tourism, population in tourism dense/intensive municipalities, gender pay gap and women in positions with high education requirements, youth employment, full-time and partial employment, variance in employment, accessibility.
7	Environmental dimension	Input data	Tourists arriving by sustainable transport modes, cycling routes, electric vehicle charging stations, use of renewable energy, expenditure on water consumption, bathing water quality, protected areas, monitoring of natural parks.
8	GHG emissions from international arrivals by plane	Input data	Template for estimation of GHG emissions from international arrivals by plane.
9	Monthly data	Input data	Monthly data for the total number of international visitors and the total number of nights spent.
10	Governance	Input data	Strategy for sustainable tourism development.
11	Supplementary	Input data	Tourism tax revenues, Regional regulation for short-term rentals, Second homes, Long-term rental price evolution, Crime-rate, Participants in regional training plans for tourism, Natural and cultural heritage, environmental labels and schemes.

Note

← 1.  The United Nations define youth as persons within the age range of 15-24 years; this definition is also used by the OECD. Depending on the indicator purpose, choosing 15-29 as an age range can also make sense to reflect societal changes of youth remaining in education for longer (ILOSTAT). Eurostat makes reference to both definitions in their Glossary. However, available data in the regions provides data on youth aged 16-29 years.