Nature-based tourism accounting.
Date: 2024-10-09
Institutions: Joint Research Centre
Subjects: ecological tourism, ecosystem, environmental economics, environmental impact, environmental law, fire, natural resources, nature-based solution, protected area, regulation (EU), report, statistical method, tourism statistics

ISSN 1831-9424 

Nature-Based Tourism Accounting 

Zulian, G., La Notte, A., Grammatikopoulou, I., Zurbaran 
Nucci, M. 

2024 

 EUR 32029 

 
  
 
This  document  is  a  publication  by  the  Joint  Research  Centre  (JRC),  the  European  Commission’s  science  and  knowledge 
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Contact information  
Name: Grazia Zulian 
Email: graziaz@live.com 

EU Science Hub 
https://joint-research-centre.ec.europa.eu 

JRC138538 

EUR 32029 

PDF  

ISBN 978-92-68-20096-4   ISSN 1831-9424   doi:10.2760/762803  

KJ-NA-32-029-EN-N 

Luxembourg: Publications Office of the European Union, 2024  

© European Union, 2024  

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How to cite this report: European Commission, Joint Research Centre, Zulian, G., La Notte, A., Grammatikopoulou, I. and Zurbaran Nucci, M., 
Nature-Based Tourism Accounting, Publications Office of the European Union, Luxembourg, 2024, https://data.europa.eu/doi/10.2760/762803, 
JRC138538. 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Contents 

Abstract ....................................................................................................................................................................................................................................................................... 1 

Acknowledgements .......................................................................................................................................................................................................................................... 2 

Executive summary .......................................................................................................................................................................................................................................... 3 

1.  Introduction..................................................................................................................................................................................................................................................... 6 

1.1.  Outline .................................................................................................................................................................................................................................................... 8 

2.  Nature-based tourism: what it is and how to measure it ............................................................................................................................... 10 

2.1.  What is nature-based tourism? ................................................................................................................................................................................... 10 

2.2.  The SEEA EA and INCA approaches to accounting for nature-based tourism ............................................................... 11 

3.  Updated version of the ESTIMAP recreation model .............................................................................................................................................. 16 

3.1.  The basics of the ESTIMAP model ............................................................................................................................................................................ 16 

3.1.1. 

Suitability of land to support recreation .......................................................................................................................................... 21 

3.1.2. 

Protected areas ........................................................................................................................................................................................................ 22 

3.1.3.  Water-related elements................................................................................................................................................................................... 26 

3.1.4.  Human inputs map ............................................................................................................................................................................................... 29 

3.1.5.  Additional parameter: extent of burnt areas in Europe ..................................................................................................... 30 

4.  An overview of tourism in Europe .......................................................................................................................................................................................... 35 

5.  EU nature-based tourism accounting ................................................................................................................................................................................. 40 

5.1.  How to compute supply and use tables to account for nature-based tourism ............................................................ 40 

5.2.  EU-level actual flow between 2000 and 2018 ........................................................................................................................................... 43 

6.  Nature-based tourism accounting in 2021: demonstrative multiscale applications ............................................................ 55 

6.1.  Actual flow and use of services at the EU level: comparison of results obtained with data gathered at 
the NUTS 2 and NUTS 3 levels .................................................................................................................................................................................................. 55 

6.2.  Case study of Italy: service use evaluated with data gathered across scales .............................................................. 58 

6.3.  The case of the Veneto region explored across scales ........................................................................................................................ 65 

7.  Conclusions .................................................................................................................................................................................................................................................. 68 

References ............................................................................................................................................................................................................................................................. 71 

Glossary.................................................................................................................................................................................................................................................................... 76 

List of figures ..................................................................................................................................................................................................................................................... 77 

List of tables ........................................................................................................................................................................................................................................................ 79 

Annexes .................................................................................................................................................................................................................................................................... 80 

Annex 1: Cultural ecosystem services, as defined by the Common International Classification of Ecosystem 
Services ........................................................................................................................................................................................................................................................... 80 

Annex 2: Recreation opportunity spectrum data preparation ...................................................................................................................... 84 

i 

 
 
Abstract 

The purpose of ecosystem service accounting is to quantify the main contributions of ecosystems 
to society and the economy and to report them in accounting tables that are compatible with the 
structures and practices used in traditional economic accounting. 

In this report, we assess and value nature-based tourism (NBT), one of the mandatory services EU 
Member States are requested to quantify according to the amended Regulation (EU) No 691/2011 
on European environmental economic accounts. NBT is a cultural ecosystem service in which people 
visit and enjoy destinations characterised by the presence of natural resources. 

The proposed methodology was implemented using an updated version of a biophysical map that 
considers the impact of major fires. The approach was applied across different scales to estimate 
the impact of statistical data aggregation on the final results. At the EU level, statistical data on 
tourism were gathered at the NUTS (nomenclature of territorial units for statistics) 2 level (for 
2000–2018) and at the NUTS 3 level (for 2021). In addition, a case study of Italy was conducted to 
apply the methodology at the municipal level. 

Overall, from 2000 to 2018, Europe registered an increase in total overnight stays of 47.5 %, and 
an increase in NBT of 46.7 %; this growth was mainly caused by an increase in activities in the 
tourism sector over time. However, when assessing NBT in relation to the total number of overnight 
stays, we register a loss of 0.5 %. 

The level of aggregation of overnights stays affects service use differently across the Member 
States. The impact appears extremely relevant when assessing the spatial pattern of NBT in Italy at 
the municipal level. The use of aggregated statistical data affects the results differently, depending 
on the dominant ecosystem types. Notably, among the regions where NBT was underestimated at 
the NUTS 2 level are those recognised as having significant tourism activity linked to nature, such 
as Valle d’Aosta (very important for mountain tourism) and Trentino-Alto Adige. 

This study reduced the complexity of valuing cultural services and demonstrated the importance 
not only of creating specific national biophysical maps but also of using more specific statistical 
data. 

1 

Acknowledgements 

We acknowledge the support provided by colleagues from the Joint Research Centre’s Disaster Risk 
Management Unit (Mainanti Pieralberto) and Territorial Development Unit (Riccardo Barranco and 
Filipe Bastista Silva). 

Authors 

—  Grazia Zulian (Seidor SRL (Italy)), 

—  Alessandra La Notte (European Dynamics SA (Marousi, Greece)), 

—  Ioanna Grammatikopoulou and Mayra Zurbaran Nucci (Joint Research Centre). 

2 

Executive summary 

Policy context 

In July 2022, the Commission adopted Regulation (EU) No 692/2011, a technical proposal to 
include ecosystem accounts in European environmental economic accounts. Reporting on nature-
based tourism (NBT), as part of cultural ecosystem services, was made mandatory by the proposal. 
EU Member States are required to report annually on the supply and use tables for NBT in line with 
the System of Environmental Economic Accounting Ecosystem Accounting, a global standardised 
system for ecosystem accounting. 

In this study, a methodology for assessing NBT is tested and implemented at the EU level. The 
approach is based on biophysical spatially explicit data and on statistical information. An updated 
version of the biophysical map is presented. The new version is based on the most up-to-date data 
available and allows for the exclusion of burnt areas from the service-providing units. 

The methodology for evaluating NBT from an accounting perspective was applied in the EU; the 
approach uses official statistics and open-source data available at the continental scale. The 
assessment has been conducted every 6 years since 2000 and analyses the effects of major fires 
in Europe on NBT. 

In a case study of Italy, the method was applied using data gathered for various territorial units 
(NUTS (nomenclature of territorial units for statistics) 2 and NUTS 3 municipalities), to evaluate the 
effect of spatial accuracy on the results. The Veneto region (Italy) was assessed to demonstrate 
the pros and cons of the methodology and discuss challenges and next steps. 

Key conclusions 

The methodology provides a good estimate of the contribution of ecosystems to NBT; nevertheless, 
it must be applied carefully, considering the following. 

The rationale of the method. In line with the Commission’s proposal (European Commission, 
2022a), the approach allows for the estimation of the contribution of ecosystems to the tourism 
sector in a given area. 

—  The method enables the estimation of the ecosystem service potential, or, in other words, the 

suitability of land for supporting nature-based recreation activities. 

—  It can be used to estimate what proportion of participation in the accommodation industry can 
be attributed to the presence and quality of ecosystems. If present, ecosystems can have a role 
in the direct or indirect enjoyment of nature – that is, they ‘enable people to use and enjoy the 
environment’ (European Commission, 2022b). 

—  Importantly, the method does not predict the behaviour of individuals, which remains an 

extremely complex subject of study. 

The precision and accuracy of the biophysical map of ecosystem services. The EU-level 
map provides a good estimate of the ecosystem service potential at the continental level. At the 
national level, the European biophysical map, or at least the current version, cannot fully capture all 
details across scales. Therefore, the supply and use tables constructed by Member States in this 
report have to be considered a preliminary overview and not a final account. 

—  Each Member State should implement a national biophysical map, using the methodology 

described in this report but identifying the local physical and cultural peculiarities, selecting 
precise and accurate local data and, most importantly, using knowledge of local areas to set up 
the scoring system. 

—  The level of aggregation of tourism statistics has a significant impact on the values 

reported in the supply and use tables. Member States should first work at the municipal level 
and then aggregate the data to determine the situation at the national level. 

3 

The approach to producing the biophysical map must be further developed to better consider: 

—  the role of extremely important ecosystem types, such as marine transitional ecosystems; 

lakes; rivers; coastal beaches, dunes and wetlands; and wetlands; 

—  travellers’ behaviours, to analyse their destinations and choices of activities. 

Main findings 

Tourism is an important industry in Europe. In 2019, Europe registered 2 874.8 million overnight 
stays for tourism. Between 2005 and 2019, this figure increased by 36.05 % (0.5 % per decade 
from 2010 onwards). The COVID-19 pandemic (in 2020–2021) caused a decrease by 50.5 % in 
overnight stays. The tourism sector experienced a slow recovery: there were 36 % fewer overnight 
stays in 2021 and 4 % in 2022 than in 2019. In 2018, when absolute values are considered, the 
number of overnight stays was highest in France, Germany, Italy and Spain. When overnight stays 
are reported in relative terms, the picture changes. When the size of the country is considered, 
Malta has the highest number (followed by the Netherlands); when overnight stays are reported in 
relation to the size of the population, Greece has the highest number (followed by Germany). These 
indicators represent the impact of tourism on the territory and on the local population, respectively. 

The presence of nature has a direct and indirect impact on tourism activities and on the selection 
of a destination. This assumption was supported by the results of the 2021 Flash Eurobarometer 
survey on the preferences of Europeans for tourism activities. In 2021, 43 % of respondents from 
the EU-27 included the natural environment of their destinations among the most important 
reasons for their selection, and 18 % reported the natural environment as the most important 
reason. Europe hosts several world heritage sites. Spain, France, Italy and Germany rank highest in 
terms of the number of world heritage sites by country. Out of the 364 sites in Europe, 24 (6.65 %) 
are codified as natural and 5 (1.4 %) as mix sites. Nevertheless, natural sites cover 96.2 % of 
United Nations Educational, Scientific and Cultural Organization land (97.7 % if both the natural 
and the mix category are considered). The 24 natural sites have 156 features and cover 
72 million ha. They are an outstanding resource for tourism. 

Overall, from 2000 to 2018, Europe registered an increase in total overnight stays of 47.5 % and 
an increase in NBT of 46.7 %; however, when NBT is considered in relation to the total number of 
overnight stays, we register a loss of 0.5 %. 

At the EU level in 2018, NBT was estimated to equate to 1 560.38 million overnight stays in the 
tourism sector, which represents 58.1 % of all overnight stays. This figure can be interpreted as the 
number of people who spent time in locations where ecosystems provide opportunities for the 
direct or indirect enjoyment of nature and related activities. Across the different Member States, 
the value varies from less than 40 % (in Belgium) to 70 % (in Malta). France, Germany, Italy and 
Spain rank highest in terms of overnight stays for NBT, with more of 200 million nights in areas 
with opportunities for NBT. 

In 2018, major fires affected NBT, with a decrease in service use of 1.82 %. The impact was not 
uniform across Europe: Portugal was most affected (15.9 %), followed by Greece, Croatia, Cyprus, 
Spain, Italy and Bulgaria. 

The effect of aggregating statistical data on tourism was tested using 2021 data. At the European 
level, data aggregation slightly affects the findings on the use of services. At the NUTS 3 level, 
service use decreases by 0.89 % (9.22 million overnight stays’ allocated to nature) compared with 
the use determined from data gathered at the NUTS 2 level. Nevertheless, the level of aggregation 
has different effects on service use across Member States. Across 14 Member States, the average 
difference was estimated to be less than ±2 %, going from –6 % in Denmark to –3 % in France. 
Belgium and Croatia experienced a larger difference (+9 % in Belgium and +7.9 % in Croatia).  

The effect of statistical data aggregation is more evident when analysing the difference between 
the NUTS 2 and the municipal levels. This effect was investigated in Italy using data gathered at 

4 

the municipal level. The actual flows increase in ecosystem types characterised by the presence of 
water and in settlements. The differences are not uniform across the country. In 12 Italian regions, 
data aggregated at the NUTS 2 level underestimate service use. Among the regions with significant 
tourism activity linked to nature are Valle d’Aosta, Trentino-Alto Adige and Veneto. 

Related and future Joint Research Centre work 

Future developments in the approach to producing biophysical maps to evaluate NBT should aim to 
overcome the limitations of the current work, which does not: 

—  fully elucidate the opportunities available in all ecosystem types, including highly modified ones 

and details related to coastal areas, offshore areas and wetlands; 

—  include additional elements to create a human inputs map, for instance including features 

enabling visitors to reach and enjoy destinations; 

—  quantify the opportunities available within a given distance; 

—  expand the assessment to marine ecosystems, by including the ecological features that enable 
associated recreational activities (e.g. snorkelling, diving and whale watching) and therefore 
attract visitors. 

In addition, it would be interesting to move from a simplified integrated system for natural capital 
accounting approach to a full accounting approach, where the ecosystem services’ demand for 
tourism is identified and mapped considering all the economic activities (related to tourism) that 
depend on nature to attract tourists. 

Future work on NBT could embed these elements to generate a broader and richer set of 
information for policymakers. 

5 

1.  Introduction 

Ecosystem services are defined as the ‘contributions of ecosystems to the benefits that are used in 
economic and other human activity’ (United Nations, 2021). Within ecosystem accounting, 
ecosystem services play a crucial role in linking ecosystem types with the economic activities of 
businesses, households and governments. The measurement of ecosystem services is of great 
importance in understanding the diverse contributions that ecosystems make to both people and 
the economy. These contributions go beyond traditional marketable goods such as crops, timber 
and fish, encompassing services such as air purification, water filtration, climate regulation, and 
nature-based recreational activities and tourism. 

In July 2022, the Commission adopted Regulation (EU) No 692/2011 (European Commission, 
2022a), a technical proposal to amend Regulation (EU) No 691/2011 on European environmental 
economic accounts to include ecosystem accounts. Reporting on nature-based tourism (NBT), as 
part of cultural ecosystem services, was made mandatory by the proposal. Ecosystem accounts 
present data in line with the System of Environmental Economic Accounting Ecosystem Accounting 
(SEEA EA) (United Nations, 2021), which is a global standardised system for ecosystem accounting 
(United Nations, 2021). EU Member States are required to annually report the supply and use 
tables (SUTs) for the ecosystem service of NBT in line with the SEEA EA. 

NBT is a cultural ecosystem service (1), which, in the legal proposal, is defined as ‘the ecosystem 
contribution, through the biophysical characteristics and qualities of ecosystems, that enable 
people to use and enjoy the environment through direct, in-situ, physical and experiential 
interactions with the environment. These contributions shall be reported in number of overnight 
stays in hotels, hostels, camping grounds, etc. that can be attributed to visits to ecosystems’ 
(European Commission, 2022a). This definition is perfectly aligned with the definition provided in 
the SEEA EA global framework (United Nations, 2021).  An extension to this definition reported in 
the SEEA EA refers to the use of the service by ‘both locals and non-locals (i.e. visitors, including 
tourists)’ and states that the service should be recorded as a stand-alone’ ecosystem service. No 
distinction is made between nature-based daily recreation and NBT. 

Cultural ecosystem services are too complex to define and evaluate. The latest version (i.e. 
version 5.1) of the Common International Classification of Ecosystem Services (CICES (2)) provides 
an exhaustive definition of cultural ecosystem services, organised in 11 classes (see Table 14 in 
Annex 1). NBT, as defined by the legal proposal and in the SEEA EA and used across this report, 
can be placed in the following classes: 

—  elements of living systems that enable activities promoting health, recuperation or enjoyment 

through active or immersive interactions (CICES 3.1.1.1); 

—  elements of living systems that enable activities promoting health, recuperation or enjoyment 

through passive or observational interactions (CICES 3.1.1.2). 

Other classes of interest that nature-based recreation-related services in general (and tourism in particular) 
do not include are: 

—  elements of living systems that enable education and training (CICES 3.2.1.2); 

—  elements of living systems that linked to culture or heritage (CICES 3.2.1.3); 

—  elements of living systems that enable aesthetic experiences (CICES 3.2.1.4). 

For these services, other features may need to be investigated and other conceptual approaches 
may be needed to fully elucidate the purposes they serve. 

(1)  Cultural services are the experiential and intangible services related to the perceived or actual quality of ecosystems whose existence 

and functioning contributes to a range of cultural benefits (United Nations, 2021). 
https://cices.eu/. 

(2) 

6 

                                                        
Notably, NBT encompasses the direct, active enjoyment by visitors of ecosystems through very 
intense activities that require a high level of fitness (these visitors are defined as ‘hard 
recreationists’) and more relaxed, indirect types of enjoyment that do not require a high level of 
fitness (these visitors are defined as ‘soft recreationists’) (Dick et al., 2022). Examples of soft 
recreation include bird watching or easy walking, whereas rough hiking, river rafting, or rock 
climbing are usually considered hard recreation. In addition, the direct enjoyment of nature can be 
a component of the overall touristic experience, which has very complex dynamics (Cutler and 
Carmichael, 2010). Tourism is a complex psychological process. Individuals experience similar 
settings in different ways. Therefore, as the tourist experience is highly subjective, it can only be 
interpreted by reflecting on the specific individuals involved and the specific settings in which their 
experiences take place (Cutler and Carmichael, 2010). These affect the tourists’ experience in 
general and their experiences related to nature in particular, because human–nature relations are a 
social construction with deep and complex cultural roots (Raatikainen et al., 2023). 

It remains extremely difficult to effectively respect the CICES classification, because often the 
service captured by the ecosystem service accounting falls into several or even all classes, 
experiences and perceptions are individual and the cultural dimension must be considered. Benefits 
enjoyed by the users are mixed and very often multiple. Cutler and Carmichael (2010) provide an 
interesting summary of motivations for tourist experiences, identifying 13 categories that can 
relate to a direct or an indirect connection with nature. In 7 out of 13 (54 %) motivational 
categories, the CICES classes related to NBT are present (shown in bold in Table 1). 

Table 1. Motivations for tourist experiences and links to cultural ecosystem services 

Motivation 

Escape 

Education 

Relaxation 

Adventure 

Enhancement of relationships 

Exploration of self 

Prestige 

Link to cultural ecosystem services 

CICES 3.1.1.2, CICES 3.2.1.3 

CICES 3.2.1.2 

CICES 3.1.1.2, CICES 3.2.1.3, CICES 3.2.1.4 

CICES 3.1.1.1 

CICES 3.1.1.2, CICES 3.2.1.4 

Interpersonal interactions 

CICES 3.1.1.1 

Novelty 

Recreation 

Health 

Regression 

Mastery 

CICES 3.1.1.1, CICES 3.1.1.2 

CICES 3.1.1.1, CICES 3.1.1.2, CICES 3.2.1.4 

NB: The CICES classes related to NBT are in bold. 

Source: Authors’ own preparation, based on Cutler and Carmichael (2010). 

7 

 
 
 
 
 
People visit and enjoy nature for many reasons, making it difficult to decide whether the service is 
used for health/well-being-related reasons (CICES 3.1.1.1 and 3.1.1.2); because of landscape 
amenities, including cultural elements (CICES 3.2.1.3 and 3.2.1.4); because they have an 
attachment to the place (CICES 3.4.1.1); or purely for bequest value-related reasons (CICES 3.4.2.1 
and 3.4.2.2). In addition, people may enjoy nature as an indirect effect of a particular location, for 
instance when visiting a world heritage cultural landscape. The dynamics are very complex, and 
therefore it is important to stress that each cultural ecosystem service should be assessed 
separately. 

This report extends and complements the work done on cultural ecosystem services in the 
integrated system for natural capital accounting (INCA) project (Vallecillo et al., 2018), focusing on 
nature-based recreation-related services (CICES 3.1.1.1 and CICES 3.1.1.1) with a specific interest in 
their use for tourism. 

Firstly, the report presents an updated version of the ecosystem service mapping tool (ESTIMAP) 
recreation model (Zulian et al., 2013; Paracchini et al., 2014; Zulian et al., 2017; Vallecillo et al., 
2018).  This biophysical map has already been used for nature-based daily recreation accounts 
(Vallecillo et al., 2018, 2019a)  and can also be used for NBT accounts. The updated model was 
implemented with the most recent datasets, and a new methodology was introduced to consider 
changes in the extent of natural protected areas over time. In addition, a new dataset was included 
that reports the extent of burnt areas, which may have an impact on tourism. 

Secondly, the implementation at the EU level of a method, in line with the SEEA EA, of reporting the 
supply and use of NBT is presented. Official data available at the EU level were used and supply 
and use tables were produced, as requested in Regulation (EU) No 692/2011. The analysis was 
conducted over time (between 2000 and 2018, every 6 years) using tourism data gathered at the 
NUTS (nomenclature of territorial units for statistics) 2 level. 

Thirdly, the report explores the effect of data collected and analysed at different levels of 
aggregation using a case study of Italy. The aim of the experiment was to show that results may 
change if data are gathered from different reporting units. Italy was selected because the national 
statistical office makes data available at the municipal level. The case study was useful for 
demonstrating, on the one hand, the validity of the model and, on the other hand, the challenges 
that remain, concerning, for instance, the level of aggregation of statistical data required for 
reporting on tourism activities, data gaps and the need to adapt the biophysical mapping approach 
to local conditions. 

This work provides for the first time at the EU level an account of NBT. This is particularly 
important, as it provides an initial overview of the contribution of nature to a key economic sector. 
This initial implementation of the method used enabled the provision of an exhaustive description 
of NBT, a pragmatic solution for accounting to be determined and the identification of challenges 
that have yet to be overcome and research gaps that must still be filled. 

1.1.  Outline 

Following this introduction, Chapter 2 provides the definition of NBT and describes the SEEA EA 
and INCA approaches to its analysis. Chapter 3 describes the new version of the ESTIMAP 
recreation model. A detailed description of the rationale and data used for the European map is 
provided. 

Chapter 4 provides an overview of tourism in Europe, focusing on European statistics, on the 
distribution of world heritage sites in Europe and on the role of the environment in the choices 
made by Europeans with regard to tourist destinations. 

Chapter 5 presents an analysis of NBT from 2000 to 2018 at the EU level, using touristic data 
gathered at the NUTS 2 level. The first section introduces the approach implemented in this study 
to derive the SUTs. The methodology is explained in detail, with a dedicated section describing the 

8 

differences between nature-based daily recreation and NBT. The second section presents the SUTs 
compiled at the EU level every 6 years and the impact of major fires on NBT across years. SUTs for 
2018, together with the impact of burnt areas on NBT, are presented at the Member State level. 

Chapter 6 includes an analysis of NBT in 2021 from a multiscale perspective. Data are gathered 
and analysed at the NUTS 2 and NUTS 3 levels, with the objective of exploring the impact of data 
aggregation on the computation of supply use tables. The exercise was implemented at the EU 
level, and at the national level by conducting a case study of Italy. In Italy, data were also analysed 
at the municipal level. In addition, a more detailed case study was conducted evaluating NBT in the 
Veneto region. 

Chapter 7 presents the overall conclusions, along with challenges and next steps. 

9 

2.  Nature-based tourism: what it is and how to measure it 

Tourism is one of the largest and fastest-growing sectors of the global economy. According to 
Weston et al. (2019), tourism ‘creates a surplus for the European Union (EU) economy, with 
international tourism receipts exceeding EU residents spending on international tourism by 
27 billion US$ in 2016’ (Weston et al., 2019). In 2018, the top five EU destinations were France, 
Germany, Italy, Spain and the United Kingdom (UNWTO, 2018), with a total of 309 million arrivals 
and USD 285 billion spent. In 2018, the tourism sector comprised over 2 million businesses 
employing over 13 million people. Over 60 % of these businesses were in the food and beverage 
industry, followed by the accommodation (20 %) and transport (15 %) sectors. Three industries rely 
almost entirely on tourism: accommodation, travel agencies / tour operators and air transport 
account for around 28 %, employing 3.6 million people (Weston et al., 2019). 

2.1.  What is nature-based tourism? 

Tourism refers to the activity of visitors taking a trip to a destination outside their usual 
environment, for less than a year. It can be for any main purpose, including business, leisure or 
other personal reasons (Eurostat, 2021). NBT is the section of tourism in which people travel with 
the purpose of visiting and enjoying destinations characterised by the presence of natural 
resources (Kuenzi and Mcneely, 2008). NBT covers various types of activities, ranging from hard 
adventure activities, such as climbing, trekking and mountain biking, to soft adventure activities, 
such as walking and bike tours, canoeing and camping (Fennell, 2000; Buckley and Coghlan, 2012), 
or any other practice that is complemented by the experience of nature, such as observation. 

Relevant subcategories of NBT are ecotourism, defined as ‘responsible travel to natural areas that 
conserves the environment and sustains the well-being of local people’ (Christ et al., 2003) and 
sustainable tourism, defined as a way of travelling that ‘seeks to minimize the negative footprint 
of tourism developments and at the same time contribute to conservation and community 
development’ (Christ et al., 2003). In other words, sustainable tourism refers to ‘an aspiration for 
the impacts of all forms of tourism to be sustainable for generations to come’ (UNWTO, 2005). 

The presence of the natural environment is crucial in these types of tourism and drives the industry 
that relies on them. However, it is difficult to appropriately quantify and record the importance of 
nature for many reasons, including the following. 

—  The information officially reported on the wealth of a country is conventionally limited to that 
included in traditional economic reporting methods, such as the contribution of tourism to the 
gross domestic product and the number and share of jobs supported by tourism (Jus and 
Misrahi, 2022). These two measurements do not provide any details related to the natural 
aspects of the locations of visited. 

—  Very often it is extremely complex to analyse the multifaceted issue of the contribution of 
nature. Notably, most NBT sites contain both natural cultural features and other attractions 
(Räikkönen et al., 2023). Nowadays, almost everywhere in the world it is nearly impossible to 
identify landscapes that are completely untouched, or ‘pristine’. The ‘natural’ and ‘cultural’ 
landscapes are intrinsically combined and provide different types of direct or indirect 
opportunities for tourism and recreation. According to the World Heritage Convention, cultural 
landscapes are ‘combined works of nature and humankind, [and] they express a long and 
intimate relationship between peoples and their natural environment’ (3). 

Natural and cultural heritage sites give rise to unique attractions, such as the Tuscan countryside 
(an Italian cultural landscape), which is officially an agroecosystem but in fact is a unique 
combination of nature, culture and history (see Figure 1A). Another example is the Venetian 

(3)  World Heritage Centre - Cultural Landscapes (unesco.org) 

10 

                                                        
Lagoon (another Italian cultural heritage site (4), in Venice), which is undoubtably a historical and 
cultural hotspot of worldwide importance but whose attractiveness and uniqueness is also linked to 
its coastal lagoon ecosystem. This ecosystem is recognised as important for cultural ecosystem 
services, as demonstrated by Rodrigues-Filho et al. (2023). They determined that cultural 
ecosystem services represented 38 % of the ecosystem services associated with coastal lagoons 
(see Figure 1B). 

Figure 1. Examples of the landscapes of (A) Tuscany and (B) the Venetian Lagoon 

(A) 

(B) 

Source: © Grazia Zulian. 

In tourism, destination choice is considered a critical, complex and contextual decision (Pandey and 
Joshi, 2021). Tourists look for experiences provided by leisure holidays, which are complex bundles 
of products, services and activities. The destination itself is characterised by a combination of 
elements, including accessibility, attraction, amenities, activities and ancillary services (Pandey and 
Joshi, 2021). Nevertheless, the final choice of destination is strongly related to the objective 
characteristics of the locations and, above all, the characteristics, needs and preferences of the 
individuals. 

2.2.  The SEEA EA and INCA approaches to accounting for nature-based tourism 

Integrated accounting systems are meant to consistently report relevant data and information that 
are not part of the traditional system of economic accounts. The United Nations Statistics Division 
coordinates the SEEA, which specifically considers environmental data and information. The central 
framework (United Nations, 2014) of the SEEA was adopted as a standard framework by the 
United Nations Statistical Commission in 2012 as the first international standard framework for 
environmental–economic accounting. The EA module (United Nations, 2021) was adopted as the 
standard framework by the United Nations Statistical Commission in 2021. Cultural ecosystem 
services, related to recreation, are part of the SEEA EA (United Nations, 2021).  

In the SEEA EA, recreation-related services are defined as ‘the ecosystem contributions, in particular 
through the biophysical characteristics and qualities of ecosystems, that enable people to use and 
enjoy the environment through direct, in-situ, physical and experiential interactions with the 
environment. This includes services to both locals and non-locals (i.e., visitors, including tourists)’ 
(Table 6.3 in UN, 2022). Recreation-related services are part of the cultural ecosystem services, 
defined as the ‘experiential and intangible services related to the perceived or actual qualities of 
ecosystems whose existence and functioning contributes to a range of cultural benefits’ (UN, 2021, 

(4)  https://whc.unesco.org/en/list/394/. 

11 

 
 
                                                        
p. 130). It is important to consider that, according to the SEEA EA, the label ‘cultural services’ is a 
‘pragmatic choice and reflects its longstanding use in the ecosystem services measurement 
community. It is not implied that culture itself is a service, rather it is a summary label intended to 
capture the variety of ways in which people connect to, and identify with, nature and the variety of 
motivations for these connections’ (United Nations, 2021 p. 145). 

For accounting to be compliant with the SEEA EA, SUTs have to be filled in. Supply tables show 
which ecosystem types (cropland, grassland, woodland, wetland, etc.) provide a quantifiable 
amount of a service. Use tables show which economic units (agriculture, manufacturing, 
households, etc.) use the service. The ecosystem services that are included in the SUTs represent 
the ‘actual flow’ of services (United Nations, 2021). Supply tables follow the ecosystem type 
classification used in both extent and condition accounts, whereas use tables follow the 
conventional economic unit classification of economic accounts. The use of SUTs is the accounting 
way to effectively conveys the ecological contribution of services in the economic context. 

NBT, as mentioned above, is a cultural ecosystem service offered to visitors and tourists. A visitor is 
a traveller taking a trip to reach a destination outside their usual environment for less than a year; 
a visitor can be classified as a tourist, if their trip includes an overnight stay; or as a same-day 
visitor (or excursionist), if the trip does not include an overnight stay. The SEEA EA identifies the 
‘Number and length (hours) of visits’ as a potential physical metric for recreation-related services 
(United Nations, 2021, Annex 6.1 (p. 157)). In the proposal for a regulation of the European 
Parliament and of the Council amending Regulation (EU) No 691/2011 as regards introducing new 
environmental economic accounts modules (P9_TA(2024)0194 (5)), the number of overnight stays 
in hotels, hostels, camping grounds and other type of accommodations that can be attributed to 
visits to ecosystems was selected as a variable for reporting on NBT-related services in the SUTs. 

In tourism statistics (specifically when analysing accommodation-related data), the term ‘residents’ 
refers to arrivals and nights spent by residents of the country where the establishment is located. 
This also applies to regional data – that is, the concept of a resident is considered at the national 
level and not at the regional level, so arrivals from another region of the same country are 
considered residents. The term ‘non-residents’ refers to arrivals and nights spent by residents of a 
country different from the destination country (Eurostat, 2015). 

With regard to accounting, the ‘number of nights spent for tourism purposes’ was selected to 
quantify the contribution of ecosystems to the tourism sector of the economy, in line with the 
rationale proposed by the SEEA EA. The physical metrics of the service were selected based on the 
recommendations in the SEEA EA (United Nations, 2021, pp. 169–170) and legal proposal 
(Regulation (EU) No 692/2011) (6). 

Nevertheless, four crucial challenges to reporting on NBT still exist when following the SEEA EA. The 
following relate to supply. 

—  How can the number of visits that depend on the biophysical characteristics of ecosystems be 

disentangled from the visits that do not depend on nature? 

—  How can the supply tables be filled in by allocating the NBT flow of service to the different 

ecosystem types? 

(5) 
(6) 

https://www.europarl.europa.eu/doceo/document/TA-9-2024-0194_EN.html 
In accordance with the process of recording cultural services explained in the SEEA EA, ‘recreation-related services are commonly 
quantified using the number of visits to a specific natural location. While these measures are not a direct quantification of the 
ecosystem contribution, they are considered a suitable proxy which can be improved by taking into consideration, as far as possible, 
the number and length of time of interactions with specific features and characteristics of the ecosystems concerned’. In addition, 
for many cultural services, but primarily for recreation-related services, there are businesses involved in facilitating and supporting 
interactions between people and ecosystems. Broadly, the types of businesses that are involved either (i) provide access to the 
ecosystem and/or facilitate activities/experiences within the ecosystem (e.g. covering entry fees, providing guides, operating tours, 
etc.); or (ii) supply goods and services to visitors to support their travel to, and time at, an ecosystem (e.g. hotels, restaurants, 
transport companies and fuel suppliers). 

12 

                                                        
With regard to use, the challenge is determining the primary beneficiary of NBT. 

This report addresses these issues by fine-tuning the methodology developed as part of the 
INCA (7) project. The INCA project was launched by the European Commission in 2015 to test and 
implement the initial version of SEEA EA, SEEA experimental ecosystem accounting (United Nations, 
2014), and produce at the European level pilot applications for ecosystem accounting (La Notte et 
al., 2017, 2021; Vallecillo et al., 2019b; Vysna et al., 2021). While remaining compliant with the 
general framework provided by the SEEA EA standard (United Nations, 2021), INCA has developed 
an operational procedure to assess ecosystem services in a way that is consistent and 
comprehensive with economic accounting (La Notte et al., 2019). 

Figure 2. The INCA approach applied to NBT 

Source: Authors’ own preparation. 

As illustrated in Figure 2, the ecological supply is provided by the ecosystem service potential – 
that is, what ecosystems can provide independently of whether the services are going to be used or 
not. The ecosystem service demand quantifies the qualitative and quantitative requirements for 
human activities. The ecosystem service actual flow is generated by the interaction between 
ecosystem service potential and ecosystem service demand. The ecosystem service actual flow is 
what is reported in SUTs. 

In the case of NBT, the ecosystem service potential is determined by the natural settings that 
enable the conduct of nature-based recreational activities (active or passive). The ecosystem 
service potential data have to be spatially explicit to allow the detailed extrapolation of the actual 
flow. The dataset has to quantify the factors determining supply, meaning a combination of natural 

(7) 

https://ecosystem-accounts.jrc.ec.europa.eu/. 

13 

 
                                                        
features (ranging from the presence of natural parks and lakes to the quality of air and water) and 
human inputs (e.g. infrastructure enabling individuals to reach the locations). The ecosystem service 
demand, in the case of NBT, is linked to the presence of accommodation, whose attendance can be 
(partially or totally) attributed to the presence of opportunities for the direct or indirect enjoyment 
of nature. A physical metric for the ecosystem service must also been selected. For instance, in the 
proposal for a regulation of the European Parliament and of the Council amending Regulation (EU) 
No 691/2011, the metric was identified as the number of overnight stays (8) (European 
Commission, 2022a, 2022b). The tourists who pay for the overnight stays represent the 
‘transactions’ to be filled in in the SUTs (Figure 3). 

Figure 3. The sequence of SUTs for NBT 

(*) 

The proxy used to measure NBT in physical terms may be the number of overnight stays. 

(**)  Non-residents may be from the same country or from other countries. 

SNA, system of national accounts. 

Source: Authors’ own preparation. 

Data to compute the ecosystem service demand may be missing, or their availability may be case 
or scale dependent (e.g. precise data on nature-dependent visits). This makes it extremely difficult 
to evaluate this service, especially at the national or continental level, in a structured manner over 
time. A simplified but consistent approach to estimating the NBT flow may make use of an 
ecosystem service potential map and of the number of overnight stays, collected officially by 
national statistical institutes. Specifically, the ecosystem service potential map can support the 
determination of the number of overnight stays that take place where there are accessible 
opportunities for nature-based experiences (Figure 4). Although this method involves the 
simplification of two very complex dynamics (the motivation for tourism and human–nature 
relations), it has the advantage of using the presence of accessible natural features as conditio 
sine qua non to define ‘tourism’ as NBT. 

(8) 

‘These contributions shall be reported in number of overnight stays in hotels, hostels, camping grounds, etc. that can be attributed to 
visits to ecosystems’ (European Commission, 2022b, Section 3(4)). 

14 

 
                                                        
Figure 4. A simplified approach to NBT 

Source: Authors’ own preparation. 

The development and implementation of a robust, spatially explicit method for determining 
ecosystem service potential is key because: 

—  any approach that is taken provides a starting point for assessing the use of nature-based 

recreation services, both for daily trips by residents and for tourism purposes by non-residents; 

—  for simplified approaches, it provides the quantitative basis for determining a reasonable way 

to distinguish nature-dependent visits from non-nature-dependent visits. 

15 

 
 
3.  Updated version of the ESTIMAP recreation model 

ESTIMAP is a collection of models originally developed for mapping ecosystem service to support 
European policies (Zulian et al., 2013). The ESTIMAP recreation model is an expert-based model 
(EBM) that consists of advanced multilayer lookup tables. EBMs are based on expert opinions 
and/or literature reviews. Each land component is scored and combined according to the capacity to 
provide nature-based recreation opportunities. The complexity of EBM models depends on the 
number of spatial inputs selected and on the spatial techniques applied to combine the inputs. 

In this study, the final output of the ESTIMAP recreation model, the recreation opportunity spectrum 
(ROS) map, is the spatially explicit ecosystem service potential input used to estimate potential 
nature-dependent visits. The concept of the ROS map was originally developed in the 1970s in the 
United States, to provide a repeatable method for identifying, classifying, planning, managing and 
monitoring recreation settings (Clark and Stankey, 1979). It is currently used to evaluate and 
manage recreation-related activities in protected areas (Hill 2019). The original structure of the 
ROS map uses social, biological and managerial attributes to produce a map of opportunity zones 
(Clark and Stankey, 1979; Cerveny et al., 2011). The model has been successively applied at 
different scales and for different purposes (Joyce and Sutton, 2009; Gundersen et al., 2015). 

In 2013, the ROS map was adapted and used as part of the ESTIMAP recreation model to map and 
assess cultural ecosystem services (Zulian et al., 2013; Paracchini et al., 2014). 

3.1.  The basics of the ESTIMAP model 

The ESTIMAP recreation model’s approach (Zulian et al., 2013; Paracchini et al., 2014) is based on 
advanced multilayer lookup tables (Schröter et al., 2015). Advanced lookup tables assign 
ecosystem service scores to land units based on cross-tabulation and the spatial composition 
derived from the overlay of different thematic maps. Ecosystem service scores for each layer of 
input are derived from the literature and from expert opinions (Zulian et al., 2017). The model 
provides a spatially explicit assessment of ecosystems’ potential to provide nature-based outdoor 
recreational and leisure opportunities. It consists of two sections: 

● 

● 

the ecosystem-based potential map, which estimates the potential capacity of 
ecosystems to support nature-based recreation activities; 

the human inputs map, which integrates the concepts of proximity and remoteness, 
dependent on road networks and settlements. 

Both maps are combined to produce the recreation potential (RP) map, also called the ROS map 
(Zulian et al., 2013; Paracchini et al., 2014). The map combines ecosystem-related features, such 
as forests and seminatural areas, high nature value (HNV) farmland, proximity to water and water 
quality and natural protected areas with elements that indicate proximity to users (roads and 
settlements). The map classifies the land using nine categories, from high, through medium to low 
opportunities for nature-based activities, and within these as close, proximal or far from users. 

16 

Figure 5. The ROS map developed for the functional urban area of Varese (Italy) 

UNESCO, United Nations Educational, Scientific and Cultural Organization. 

Source: Modified from Maes et al., 2019. 

Figure 5 shows an example of a ROS map. Areas in dark blue are those with the greatest 
opportunities for nature-based recreation within the boundaries of the city, offering a high 
recreational potential and proximal to roads and settlements. Areas in light yellow / pink are 
locations with low recreational potential; areas in green have medium recreational potential; and 
areas in blue have high recreational potential. For each of these areas, light colours indicate low 
proximity; dark colours represent high proximity; and colours in between those represent a medium 
proximity. Together, the ecosystem-based potential map and the human input map constitute nine 
classes that form the ROS map. Dark blue areas are top areas for nature-based recreation: they 
have a high RP and are in the proximity of roads and residential areas. The data can be 
summarised in a bar graph (or a pie chart) that represents the distribution of each of these nine 
categories in the ROS. The example shows four areas with very specific nature-based recreation 
opportunities, close or proximal to roads and settlements. 

—  Palude Brabbia (A in Figure 5) is a special protection area (SPA) under the European 

Commission directive on the conservation of wild birds, legally designated part of Natura 2000, 
the network of protected areas covering Europe's most valuable and threatened species and 
habitats (site code: IT2010007; Ramsar site No 296 (9)). It is a regional natural reserve. The site 
is situated in a valley of glacial origins and includes areas of marsh, open water and wet 
meadows dissected by channels. It is fed by the Brabbia canal and surrounded by heavily built-
up hills. The marsh vegetation consists of various species of aquatic plants, including a number 

(9) 

https://rsis.ramsar.org/ris/296; https://eunis.eea.europa.eu/sites/IT2010007. 

17 

 
                                                        
of rare species. The area is important for breeding for diverse species of wetland birds. 
Birdwatching and conservation education facilities are available. The wetland depends on an 
elaborate system of water management. 

—  Giardini Estensi (B in Figure 5) is a historic public garden located in the city centre of Varese. 
The garden extends the sumptuous Palazzo Estense, built in the 17th century for Francesco II 
d’Este. The garden has the characteristics of a French garden and a romantic garden. It hosts 
10 historical trees of regional and local importance (10), it is accessible and it provides facilities 
for daily recreation. 

—  Since 2003, Sacro Monte di Varese (C in Figure 5) has been one of the nine United Nations 

Educational, Scientific and Cultural Organization sacred mountains of north Italy. It has multiple 
complexes, each including a number of chapels and other architectural features, created in the 
late 16th and 17th centuries and dedicated to different aspects of Christian belief (11). The site 
is located within the Campo dei Fiori Regional Park. The park hosts a Natura 2000 site (site 
code: IT2010401), and protects 90 species and 17 habitats (12). 

—  Villa Toeplitz is a historic villa located in Varese, Lombardy, Italy. Villa Toeplitz’s gardens span 

8 ha, including two styles (Giardino all’italiana and English landscape garden). The gardens host 
beech trees, oaks and cedar trees, are accessible and provide facilities for daily recreation. 

Figure 6 presents the structure of the ROS model applied at the EU level, adapted for accounting. 
This configuration is slightly simplified compared with the original (Paracchini et al., 2014). To 
produce a time series at the EU level, input data with no time series available were excluded; for 
instance, the hemeroby map, which represents the degree of naturalness in Paracchini et al. (2014), 
was replaced with a ‘suitability of land to support recreation’ map. The original terminology used to 
define the sequential steps of the modelling approach (Paracchini et al., 2014) has been changed 
for consistency with the terminology used in accounting (Vallecillo et al., 2018). The datasets used 
to implement this version of the model are reported in Table 2. The detailed workflow for data 
preparation and modelling is included in Figure 44 in Annex 2. 

(10) 

https://ilregistrodeglialberi.it/cerca?keyword=giardini+estensi&location__region_id=&tree_relevance_id=&tree_accessibility_id=&has
_gps=&has_pictures=&is_alive=). 

(11)  https://whc.unesco.org/en/list/1068. 
(12)  https://eunis.eea.europa.eu/sites/IT2010401. 

18 

                                                        
Figure 6. Workflow of the approach to be implemented to obtain the ROS 

NB: BWQ, bathing water quality; CDDA, Common Database on Designated Areas; CLC, Corine Land Cover; EBP, ecosystem-based 

potential; geom-coast, geomorphology of coast; prox-coast, proximity to coast; RZ, riparian zones; SLSR, suitability of land to 
support recreation. 

Source: Authors’ own preparation. 

The ecosystem-based potential map depends on three components: 

—  Suitability of land to support recreation. This component includes land use data (Corine 

Land Cover (CLC)), HNV farmland and the presence of riparian zones. 

—  Protected areas. This component includes the nationally designated protected areas (from 
the Common Database on Designated Areas (CDDA)) and the Natura 2000 network, including 
land and marine areas. 

—  Water-related elements (‘water elements’ in Figure 6). This component includes sea 
coast and inland elements. The first component the geomorphology of coast and marine 
protected areas. Proximity to coast and bathing water quality, regulated by the EU bathing 
water directive (13), are also considered, for both inland and sea coast locations. 

The human inputs map quantifies proximity and depends on two components. 

—  distance from local roads (‘roads’ in Figure 6); 

—  distance from residential areas (‘residential’ in Figure 6). 

The ROS map depends on: 

—  the ecosystem-based map, reclassified by very high, high, low and very low potential for 

recreation; 

—  the human inputs map, with areas reclassified as near, proximal and far from the settlements 

and road network. 

(13)  The EU bathing water directive requires Member States to identify popular bathing places in fresh and coastal waters and monitor 
them for indicators of microbiological pollution (and other substances) throughout the bathing season, which runs from May to 
September. 

19 

 
                                                        
Table 2. Datasets used to construct the EU-level ROS model 

Variable 

Temporal 
coverage 

Data owner 

Land use (CLC) 

accounting 

HNV farmland 

2000, 
2006, 
2012, 
2018 

2000, 
2012 

Riparian  zone 
delineation 

2011–
2013 

time 

No 
series 
available’ 

European 
Agency 
(https://sdi.eea.europa.eu/catalogue/srv/eng/catalog.search#/metadata/a55d9224-
a326-4cb1-9b9c-3a324520341a) 
(raster  100 m)  version 20 
accounting layer (2019). 

Environment 

(CLC  2018 

European 
(https://sdi.eea.europa.eu/catalogue/srv/eng/catalog.search#/metadata/1bd26e8f-
8ea0-45e0-b6bf-9ed2baff5d28). 

Environment 

Agency 

Data for 2006–2018 were modelled. 

European Environment Agency. 

The  dataset  is  no  longer  accessible  from  the  website  of  the  Copernicus  Land 
Monitoring Service. 

Protected 
areas 

2019–
2021 (*) 

CDDA. 

Nationally designated protected areas: 

 CDDA_2021_v01_public_EuropeEPSG3035.gdb  (https://www.eea.europa.eu/data-
and-maps/data/nationally-designated-areas-national-cdda-16). 

For the United Kingdom:  

CDDA_2019_v01_public_EuropeEPSG3035.gdb  (https://www.eea.europa.eu/data-
and-maps/data/nationally-designated-areas-national-cdda-14#tab-european-
data). 

European Environment Agency (status of bathing water (*)  

(https://www.eea.europa.eu/data-and-maps/data/bathing-water-directive-status-
of-bathing-water-13)). 

European Environment Agency 

(https://sdi.eea.europa.eu/catalogue/srv/eng/catalog.search#/metadata/a55d9224-
a326-4cb1-9b9c-3a324520341a) 

(CLC 2018 (raster 100 m) version 20 accounting layer (2019). 

Eurosion Coastal Erosion Layer (Eurosion, 2005) 

 (https://www.eea.europa.eu/data-and-maps/data/geomorphology-geology-
erosion-trends-and-coastal-defence-works). 

Bathing  water 
quality  

1990–
2022 

to 
Distance 
coast  (sea  and 
inland  water 
bodies) 

2000, 
2006, 
2012, 
2018 

Coastal 
geomorphology 

1990–
2000–
2010 

OpenStreetMap  Fix 

OpenStreetMap  

Residential 
areas 

2000, 
2006, 
2012, 
2018 

(http://download.geofabrik.de/europe.html), retrieved 2021. 

European Environment Agency 

(https://sdi.eea.europa.eu/catalogue/srv/eng/catalog.search#/metadata/a55d9224-
(raster  100 m)  version 20 
a326-4cb1-9b9c-3a324520341a) 
accounting layer (2019)). 

(CLC  2018 

20 

 
variable 

temporal 
coverage 

data owner 

NUTS 

2021 

Eurostat 

(https://ec.europa.eu/eurostat/web/gisco/geodata/statistical-
units/territorial-units-statistics). 

Burnt areas 

2000–2022 

European  Forest  Fire  Information  System  (real-time  updated 
burnt 
database 
areas 
(https://effis.jrc.ec.europa.eu/applications/data-and-services). 

(*) Data for 2006–2018 were modelled based on the date of institution of the areas 
(**)  The maps produced for INCA were created using the old version of the data (https://www.eea.europa.eu/themes/water/status-and-

monitoring/state-of-bathing-water/state/state-of-bathing-water-3). 

Source: Authors’ own preparation. 

3.1.1.  Suitability of land to support recreation 

Land use (14) is the starting point for defining how an area can provide nature-based recreation 
opportunities. Each land use type is scored according to its potential suitability for recreation 
activities. The original model based this component on the hemeroby index, which measures the 
degree of naturalness of a location (Steinhardt et al., 1999; Paracchini and Capitani, 2011). For 
INCA and other applications, this option was not available because historical data for forest 
management, agricultural practices and HNV farmland are not available. Table 15 in Annex 2 
provides the RP scores for CLC land types. 

3.1.1.1.  High nature value farmland 

Farms and agroecosystems can provide several recreational services to communities and society. 
Examples of recreational activities are hunting; the gathering of mushrooms, berries or other wild 
edibles; wildlife observation or nature contemplation; walking or hiking; fishing; horseback riding; 
and camping (Barbieri and Valdivia, 2010). The extent users can benefit from agroecosystems in 
terms of recreation depends not only on several aspects, for instance the presence of didactic and 
tourist farms, special routes (e.g. wine routes), and bike and horse trails, but also on the intensity of 
agriculture practices. A HNV farmland map (Paracchini et al., 2008) that shows the distribution and 
extent of farmland that holds special biodiversity value was used as a proxy to identify areas 
where agroecosystems could provide recreation opportunities. This version of the model is based on 
the HNV farmland 2000 and HNV farmland 2012 datasets, based on CLC accounting layers. A new 
version of the dataset for 2018 exists, but is not comparable to the previous versions. 

Scores for agricultural land in the CLC-based map are increased by 50 % if they overlap with HNV 
farmland. 

(14)  The original model based this component on the degree of hemeroby (naturalness) map (Steinhardt et al., 1999; Paracchini and 

Capitani, 2011). For INCA and following applications, we could not proceed in this way because we were missing historical data for 
forest management, agricultural practices and nature value farmland. 

21 

                                                        
3.1.1.2.  Riparian zones 

Riparian zones represent transitional areas occurring between land and freshwater ecosystems, 
characterised by distinctive hydrology, soil and biotic conditions and strongly influenced by stream 
water. They provide a wide range of riparian functions (e.g. chemical filtration, flood control, bank 
stabilisation, aquatic life and riparian wildlife support) and ecosystem services (15). Rivers and 
riparian areas provide various active and passive opportunities for nature-based recreation 
activities. Examples are boating, camping and hiking, birdwatching and swimming (Manning and 
Ciali, 1981; Kakoyannis and Stankey, 2002; Manning, 2010). 

The map resulting from the combination of CLC and HNV farmland is combined with the riparian 
zones and shows the suitability of land to support recreation. 

3.1.2.  Protected areas 

According to the International Union for Conservation of Nature (IUCN), a protected area is ‘A clearly 
defined geographical space, recognised, dedicated and managed, through legal or other effective 
means, to achieve the long-term conservation of nature with associated ecosystem services and 
cultural values’ (Dudley, N (Ed.) 2008). 

To represent protected areas at the EU level, the Natura 2000 network and the nationally 
designated areas (CDDA) were selected. Natura 2000 sites offer ‘people a unique opportunity to 
discover and enjoy Europe’s rich natural heritage. These recreational activities are compatible with 
the provisions of the Habitats and Birds Directives as long as they do not adversely affect the 
habitats and species present’ (16). The sites are often included in the CDDA database. 

The CDDA database include nationally designated protected areas. Areas are classified following 
the IUCN management categories, which are based on the relevant management objectives. 
Descriptions of each category are provided in Table 3. The system is based on primary and 
secondary objectives and objectives potentially applicable to the area. The categories do not imply a 
simple hierarchy in terms of quality, importance or naturalness, nor are the categories necessarily 
equal in each situation; rather, they should be chosen in order to maximise opportunities for 
conservation and to address threats to conservation (Dudley, N (Ed.) 2008).  

Table 3. Description of the IUCN management categories 

IUCN 
category (*) 

management 

Description 

Category Ia – strict nature 
reserve 

A strict nature reserve is a strictly protected area set aside to protect 
biodiversity and possibly geological/geomorphological features, 
where human visitation, use and impacts are strictly controlled and 
limited to ensure the protection of conservation values. Such 
protected areas can serve as indispensable reference areas for 
scientific research and monitoring. 

These areas are often home to dense native ecosystems where all 
human disturbance except for scientific study, environmental 
monitoring and education is prohibited. Because these areas are so 
strictly protected, they provide ideal, pristine environments that 
enable the measurement of external human influence through 
comparison with other areas. 

(15)  https://land.copernicus.eu/local/riparian-zones/riparian-zones-delineation?tab=metadata. 
(16)  https://ec.europa.eu/environment/nature/natura2000/faq_en.htm. 

22 

                                                        
IUCN management 
category (*) 

Description 

Category Ib – wilderness area 

Category II – national park 

Category III – natural 
monument or feature 

Category IV – habitat or 
species management area 

Category V – protected 
landscape or seascape 

A wilderness area is similar to a strict nature reserve but is generally 
larger and protected slightly less stringently. These areas are 
protected domains in which biodiversity and ecosystem processes 
(including evolution) are allowed to flourish or experience restoration 
if previously disturbed by human activity. These are areas that may 
buffer against the effects of climate change and protect threatened 
species and ecological communities. Human visitation is kept to a 
minimum; often only those who are willing to travel of their own 
devices (by foot, by ski or by boat) are allowed to visit, which offers 
them a unique opportunity to experience wilderness that has not 
been interfered with. 

A national park is similar to a wilderness area in its size and its main 
objective of protecting functioning ecosystems. However, national 
parks tend to be more lenient with human visitation and its 
supporting infrastructure. National parks are managed in a way that 
may contribute to local economies through promoting educational 
and recreational tourism on a scale that does not reduce the 
effectiveness of conservation efforts. 

A natural monument or feature is a small area that is specifically 
allocated to protect a natural monument and its surrounding 
habitats. These monuments can be natural in the fullest sense or 
include elements that have been influenced or introduced by humans. 
These areas should be identified to protect biodiversity or could 
otherwise be classified as a historical or spiritual site, though this 
distinction can be quite difficult to ascertain. 

A habitat or species management area is similar to a natural 
monument or feature, but is more associated with specific areas of 
conservation, for example of an identifiable species or habitat that 
requires continuous protection rather than of a natural feature. Size 
is not necessarily a distinguishing feature. These protected areas are 
sufficiently controlled to ensure the maintenance, conservation and 
restoration of particular species and habitats (possibly through 
traditional means), and educating the public on such areas is widely 
encouraged as part of the management objectives. 

A protected landscape or seascape covers a body of land or ocean 
with an explicit natural conservation plan, but usually also 
accommodates a range of for-profit activities. The main objective is 
to safeguard regions that have built up a distinct and valuable 
ecological, biological, cultural or scenic character. In contrast with the 
previous categories, Category V permits surrounding communities to 
interact more with the area, contributing to the its sustainable 
management and engaging with its natural and cultural heritage. 

23 

 
IUCN 
category (*) 

management 

Description 

Category VI –  protected  area 
with sustainable use of natural 
resources 

Category VI may apply in particular to vast areas that already have a 
low level of human occupation or in which local communities and 
their traditional practices have had little permanent impact on the 
environmental health of the region. The category differs from 
Category V in that it is not the result of long-term human interaction 
that has had a transformative effect on surrounding ecosystems. 

(*) 

https://en.wikipedia.org/wiki/IUCN_protected_area_categories. 

Source: (Dudley, N (Ed.) 2008). 

The matrix of primary, secondary and potentially applicable objectives (Table 4) proposed by Eagles 
et al. (2002) and fully described by Dudley (Ed.) (2008).was used to score the suitability of 
protected areas to support tourism activities. 

Table 4. Cross-tabulation between management objectives and IUCN categories 

Management objective 

Scientific research 

Protection of wilderness 

Preservation of species and genetic diversity (biodiversity) 

Maintenance of environmental services 

Protection of specific natural/cultural features 

Tourism and recreation* 

Education 

Sustainable use of resources from natural ecosystems 

Maintenance of cultural/traditional attributes 

IUCN management category 

Ia 

Ib 

II 

III 

IV 

V 

VI 

1 

2 

1 

2 

— 

— 

— 

— 

— 

3 

1 

2 

1 

— 

2 

— 

3 

2 

2 

1 

1 

2 

1 

2 

3 

— 

— 

2 

3 

1 

— 

1 

1 

2 

— 

— 

2 

3 

1 

1 

3 

3 

2 

2 

— 

2 

3 

—  2 

2 

1 

2 

1 

1 

2 

2 

1 

1 

3 

3 

3 

1 

2 

NB: 1 = primary objective; 2 = secondary objective; 3 = potentially applicable objective; — = not applicable. 

Source: Modified from Eagles et al., 2002. 

The scores applied  to the current  version of the ecosystem-based potential map are  presented in 
Table 5. 

24 

 
 
 
 
Table 5. IUCN categories and associated scores 

IUCN category 

Score 

Ia 

Ib, III, IV 

II, VI 

V 

No category 

Natura 2000 

0.4 

0.6 

0.8 

1 

0.7 

0.7 

Source: Authors’ own preparation. 

The primary objective of strict nature reserves (Ia) is conserving regionally, nationally or globally 
outstanding ecosystems and species. This is achieved by enabling no or relatively low visitation by 
humans and little human disturbance (Dudley, N (Ed.) 2008). The score for NBT is relatively low 
(0.4) because access is not always allowed. 

The primary objective of wilderness areas (Ib) is to protect the long-term ecological integrity of 
natural areas that are undisturbed by significant human activity. Public access is provided, at levels 
and of a type that will maintain the wilderness qualities of the area for present and future 
generations (Dudley, N (Ed.) 2008). The primary objective of natural monuments (III) is to protect 
specific outstanding natural features and their associated biodiversity and habitats. The primary 
objective of habitat/species management areas (IV) is to maintain, conserve and restore species 
and habitats; another objective is to ‘provide a means by which the urban residents may obtain 
regular contact with nature’ (Dudley, N (Ed.) 2008). Categories Ib, III and IV are scored 0.6 because 
their primary objective is nature protection, but they are accessible for recreation purposes. 

The primary objective of national parks (II) is to protect natural biodiversity along with its underlying 
ecological structure and supporting environmental processes, and to promote education and 
recreation, at a level that will not cause significant biological or ecological degradation to the 
natural resources (Dudley, N (Ed.) 2008). The primary objective of protected areas with sustainable 
use of natural resources (VI) is to protect natural ecosystems and use natural resources sustainably, 
when conservation and sustainable use can be mutually beneficial. Categories II and VI are scored 
0.8 because they allow recreation-related activities but the primary objective is not tourism. The 
primary objective of protected landscape/seascape (VI) is to protect and sustain important 
landscapes/seascapes and associated nature conservation, and other value created by interactions 
with humans, through traditional management practices. Among other objectives is to provide 
opportunities for enjoyment, well-being and socioeconomic activity through recreation and tourism. 
Category V receives the maximum score (1) because recreation and tourism are among the 
recognised management objectives. 

In Natura 2000 areas (which do not have IUCN categories) that do not overlap with CDDA or if no IUCN 
category is provided, the protected areas are scored with the average of weights applied to the IUCN 
categories (0.7, as presented in Table 5). 

The time frame of each protected area depends on the legal foundation date (when available) or the date the 
sites were designated under the bird or habitat directive. 

The deployment of a ‘Coherent network of protected areas’ is one of the targets set for 2030 in the 
EU biodiversity strategy (European Commission, 2020). The specific target is to ‘Legally protect a 
minimum of 30 % of the EU’s land area and a minimum of 30 % of the EU’s sea area, and integrate 
ecological corridors, as part of a true Trans-European Nature Network’ (European Commission 
(2020) target EU-BDS.A1). 

25 

To determine the time frame of a site, the legal foundation date is extremely important. This is 
because a site can have been legally protected at the regional or national level and, after a few 
years, become part of the Natura 2000 network. The legal foundation date is available in the CDDA 
database; it corresponds to the attribute ‘date site proposed as eligible for identification as a site of 
community importance’ (17) in the Natura 2000 database. The methodology used to derive the 
foundation year is described in Annex 2. 

3.1.3.  Water-related elements 

The water component represents all water-related elements, marine and inland, that together 
provide opportunities for recreation. 

3.1.3.1.  Inland and sea coast and bathing water quality 

Distance to lakes and coastline (referred to as ‘proximity to coast’ when discussing Figure 6) and 
bathing water quality were evaluated using a proximity model. Figure 7 shows a simplified 
example of the proximity model implemented as a proxy of attractiveness of coastal areas. It is 
assumed that water attractiveness decreases as the distance from the coast increases. Coastlines 
of sea and lakes have been extracted from the CLC 2000 dataset, and an S-shaped impedance 
function has been used to calculate attractiveness (Kwan, 1998; Geurs and van Wee, 2004; Iacono 
et al., 2008). The following inverse logistic function has been applied to all coastlines: 

where d is the distance from coast, α and K are the size and the shape parameters of the function, 
respectively, set at 0.008 and 30 so that attractiveness decreases after 400 m with a maximum 
distance of 1 000 m. Distance to coast is scored uniformly, with a maximum value of 1. 

𝑓𝑓(𝑑𝑑) =

1 + 𝐾𝐾
𝛼𝛼𝑑𝑑
𝐾𝐾 + 𝑒𝑒

Figure 8 shows a graphical example of the approach applied to the distance to sea coast in 
Finland. The value of the area decreases as a function of the distance. 

Figure 7. Example of an impedance function representing sea and lake coast attractiveness 

Source: Authors’ own preparation, based on Paracchini et al. (2014). 

(17)  See  the  definitions  associated  with  the  Natura  2000  dataset  at  Natura2000_end2019_dataset_definitions.xls.  URL: 

https://cdr.eionet.europa.eu/help/natura2000 

26 

 
 
                                                        
Figure 8. Example of a proximity model applied to the distance from the sea coast 

fd, function of the distance 

Source: Authors’ own preparation. 

3.1.3.2.  Bathing water quality 

Bathing water quality at the European level is derived from the data on the ‘status of bathing 
water’. The dataset is compliant with the EU bathing water directive (Directive 76/160/EEC), which 
requires Member States to identify popular bathing places in fresh and coastal waters and monitor 
them for indicators of microbiological pollution. 

The dataset provides 21 968 locations monitored and scored. Quality status varies from excellent to 
poor and has been monitored every year (starting from 1990). Figure 9 shows the distribution of 
bathing water locations by Member State and their share by typology. Coastal bathing water 
dominates, and this explains the high number of monitoring locations in Spain, France and Italy. 

Figure 9. Share of bathing water locations by Member State and typology 

NB: BW, bathing water. 

Source: Authors’ own preparation. 

The quality status was used to score the suitability for recreation, assuming a higher value for 
higher quality. A proximity map was created to represent the impact of bathing water locations 
using the rationale applied for the distance to coast and the function presented in Source: Authors’ 
own preparation. 

Figure 10. Table 6 shows the scores applied for each quality status and the maximum distance 
used for the proximity model. The average over 3 years has been used to represent each time 

27 

 
 
 
frame; for instance, bathing water quality for 2000 is given by the average between the quality 
status reported in 1999, 2000 and 2001. When no information is provided, the point is not 
considered for that time frame. 

Table 6. Suitability scores and proximity (maximum distance) by bathing water quality 

Input 

Quality status 

Score 

Maximum distance 
(m) 

Bathing 
quality 

water 

Excellent quality 

1 

500 

Good quality 

0.8 

Sufficient quality 

0.5 

Sufficient 

Poor quality 

0.4 

–0.5 

No data 

Excluded 

Source: Authors’ own preparation. 

Figure 10. Example of impedance function to represent bathing water quality attractiveness 

Source: Authors’ own preparation. 

3.1.3.3.  Geomorphology of Coast 

The shoreline morphology (rocky coasts, beaches, muddy coasts, etc.) is a crucial factor in 
determining the potential for recreational activities and people’s attraction to the coast. For 
example, the top visitors’ destination will be a long and wide sandy beach (Orams, 2003). In 
ESTIMAP, the Eurosion Coastal Erosion Layer database (Eurosion, 2005) is used to identify the 
different coastal morphological features. Each feature was scored in terms of suitability for 
recreation. Table 17 in Annex 2 shows the scores applied to define potential recreation activities 
and people’s attraction to the coast. 

28 

 
The scores have been developed for and applied to the analyses of ecosystem services in the 
Mediterranean Sea (Liquete et al., 2016). 

The Coastal Erosion Layer database classifies most of the EU shoreline with data that can be 
assumed to be representative of the situation in 2000. The authors estimated the weighted factors 
to be multiplied by the RP for 2000 to represent the situation in 2010. These weighted factors are 
based on the coastal evolutionary trends (erosion, aggradation and stability) of 2000 (Liquete et al., 
2016). No estimate is available for 2012 and 2018, and we have assumed that the situation has 
not changed. The geomorphology of coast categories and scores for recreation activities are 
available in Table 17 in Annex 2. A geographic information system operation was used to model 
the extent of the coastal area. The methodology makes each cell category value a function of the 
category values assigned to the cells around it and stores new cell values in an output raster map 
layer. In this application, we assigned the mode value of the recreation score considering an 11-
pixel neighbourhood area. Only the inland area was retained. 

3.1.3.4.  Marine protected areas 

Coastline in the proximity of marine protected areas has been identified as adding value to 
recreation provision. In this case, the score of the protected area was allocated to 1 km of coastal 
area, using the methodology explained in Section 3.1.2. 

3.1.4.  Human inputs map 

Remoteness and proximity are addressed in the second step of the model, to assess how recreation 
opportunities can be delivered to people. The proxy that has been identified combines information 
on distance from roads and from residential areas calculated using the Euclidean distance on a 
100 m grid and has been mapped by classifying the EU into zones of proximity and remoteness. 

3.1.4.1.  Road network 

Originally, the Tele Atlas multinet network 2009 was used (Paracchini et al., 2014). This dataset is 
classified in nine categories of transportation elements based on their functional importance. In the 
current application, Tele Atlas is replaced by OpenStreetMap (OSM). OSM stores roads with the tag 
‘highway’ and attempts to apply a single classification system to all local conditions. The 
correspondence between Tele Atlas categories and OSM tags for roads are available in Table 18 in 
Annex 2. A Euclidean distance map is generated, and proximity is reclassified in five levels (see 
Figure 11). 

3.1.4.2.  Residential areas 

Residential areas are extracted from the CLC classes ‘continuous urban fabric’ and ‘discontinuous 
urban fabric’. A Euclidean distance map is generated, and proximity is reclassified in five levels.  

Figure 11 shows the combination of the proximity to the road network and the proximity to the 
residential areas map. This cross-tabulation results in the Human input map thereby reclassified as 
near, proximal and far from recreational areas. 

29 

Figure 11. Cross-tabulation matrix for distance from roads and distance from urban areas. 

Source: Authors’ own preparation. 

For the ecosystem-based map resulting from cross-tabulating the human inputs and ecosystem-based 

potential maps, see Figure 12. Cross-tabulation matrix used to derive the ecosystem-based map. 

Figure 12. Cross-tabulation matrix used to derive the ecosystem-based map 

NB: EBP, ecosystem-based potential. 

Source: Authors’ own preparation. 

3.1.5.  Additional parameter: extent of burnt areas in Europe 

An additional ex post parameter used in the ROS map is the extent of burnt areas. In 2021 alone, 
more than 500 000 ha burned in the EU-27, of which more than 98 000 were in Natura 2000 sites 
or other protected areas. Fires mostly affected land types that are extremely important service-
providing areas for NBT. In the EU, a quarter of the total burnt areas in 2021 were in cropland 
(25 %), around 30 % of fires affected woodland and forests and 45 % affected other natural 
ecosystem types (see Figure 13) (San-Miguel-Ayanz et al., 2022). 

30 

 
 
Figure 13. Proportions of land cover types affected in 2021 

Source: Authors’ own preparation, based on San-Miguel-Ayanz et al. (2022) and detailed data at the EU level provided by the author. 

Greece and Italy, heavily dependent on tourism sector, are among the Member States that were 
affected the most by fires in 2021 (San-Miguel-Ayanz et al., 2022). In fact, 2021 was an extreme 
year for fires in Greece and resulted in the highest mapped burnt area since the historically bad 
year of 2007. In Greece, in 2021 the 222 large fires burned a total of 131254 ha (Figure 14). 

Figure 14. Burnt areas in Greece (2021) 

NB: Largest fires are indicated. 

Source: San-Miguel-Ayanz et al., 2022. 

In 2021, Italy was the country second most affected by fires in terms of burnt area (after Türkiye) 
and recorded the highest number of fires. The total burnt area of 159 537 ha mapped from 1 422 
fires was the highest recorded in over a decade. Some 90 % of the damage occurred in July and 
August (Figure 15). 

31 

 
 
 
Figure 15. Burnt areas in southern Italy and Sardinia (2021) 

NB: Largest fires are indicated. 

Source: San-Miguel-Ayanz et al., 2022. 

Previous research estimated a considerable negative impact of burned areas on the number of 
tourist arrivals, both domestic and inbound (Otrachshenko and Nunes, 2022). The authors estimated 
an annual cost to the Portuguese economy due to the impact of burned areas in 2030 of between 
EUR 17.03 million and EUR 24.18 million for domestic tourist arrivals, and between 
EUR 18.26 million and EUR 38.08 million for inbound ones. 

In this analysis the data on fires were derived from the European Forest Fire Information 
System (18), which became operational in 2000 and manages the official data for forest fires in 
Europe. From the system’s dataset, real-time updated burnt areas data were used. Data show the 
burnt area from 2000 to 2023. A time series was derived to cover the time span needed for this 
report which is mainly based on CLC (19). The most important variable to estimate is for how long a 
fire affects recreation activities – that is, before vegetation recovery takes place. In recent studies, 
the normalised burnt ratio (NBR) was used to monitor vegetation or greenness recovery (Yang et al., 
2017; Bright et al., 2019). Post-fire recovery is defined as the ecological processes that take place 
to enable the land to revert to its pre-fire or unburned functional and structural status (Yang et al., 
2017). Post-fire recovery depends on many parameters, including vegetation type, climate, 
topography and seed sources (Bright et al., 2019), and it is extremely complex to estimate a general 
rule that applies at the continental level. The implementation of the ‘burnt areas’ parameter in this 
study is based on the estimate made by Bright et al. (2019). The authors studied the vegetation 
recovery process after fires that took place in eight US states between 2003 and 2007. NBR data 
used for the analysis were calculated for between 1984 and 2016. In their study, they show that 
the percentage NBR recovery differed significantly by burn severity and that most patches had not 
completely recovered to pre-fire NBR levels 9 to 16 years after a fire. In the current study, a 
threshold of 16 years was selected to assume a patch affected from a fire to be at least partially 
restored. Table 7 reports the time spans included in the queries to select the burnt areas, the 
corresponding ROS Map and the computation implemented in this report. The 2018 ROS map at the 
EU level for 2018 is shown in Figure 16. 

(18)  https://effis.jrc.ec.europa.eu/about-effis/effis-network. 
(19)  See the evolution of CLC at https://land.copernicus.eu/en/products/corine-land-cover. 

32 

 
 
 
 
                                                        
Table 7. Burnt area parameters 

Temporal 
coverage 

2000–2001 

2000–2007 

2000–2013 

Reference 
ROS map 

Computation 

2000 

2006 

2012 

EU NUTS 2 computation 

2004–2019 

2018  

2008–2023 

2018  

Source: Authors’ own preparation. 

EU NUTS 2–3 computation and Italian municipalities computation 
(for only 2021) 

Notice that the ESTIMAP recreation model is an EBM, and therefore its configuration (selection of 
input variables, parameterisation and scores) has to be adapted to fit the local characteristics. 

33 

Figure 16. The ROS map for Europe (2018) 

Source: Authors’ own preparation. 

34 

 
4.  An overview of tourism in Europe 

Table 8 shows the data used for NBT accounting. The nights spent in tourist accommodation 
establishments, corresponding to the statistical classification of economic activities in the EU (NACE 
(nomenclature of economic activities) Rev. 2) are, as previously mentioned, used to quantify the 
contribution of the ecosystems to the tourism sector (United Nations, 2021, pp. 169–170; 
Regulation (EU) No 692/2011). 

NACE Rev. 2 (Eurostat, 2008) category I includes accommodation and food service activities, hotels, 
holiday and other short-stay accommodation, camping grounds, recreational vehicle parks and 
trailer parks (Divisions I55.1 (hotels and similar accommodation); I55.2 (holiday and other short-
stay accommodation) and I55.3 (camping grounds, recreational vehicle parks and trailer parks). This 
dataset reports the number of nights spent in each destination. Data are available at the Member 
State level for NUTS 0, NUTS 1 and NUTS 2. They are available at the NUTS 3 level at the EU level 
for only 2020, 2021 and 2022. In Italy, overnight stays are available from the National Institute of 
Statistics at the municipal level. This variable was used to prepare a case study and reflect on the 
impact of scale and data aggregation on the results. 

Table 8. Input data for accounting 

Variable 

Data source 

Temporal 
coverage 

Spatial 
coverage 

spent  at 

Nights 
establishments 
(TOUR_OCC_NIN2__custom_7115881) 

tourist  accommodation 
regions 

NUTS 2 

by 

Eurostat (a) 
August 2023) 

(accessed 

2000–
2022 

DCSC_Occupancy_in_collective_accommodation 

_turistic_areas_by_municipality’ 

Italian National Institute of 
(accessed 
Statistics 
October 2023) 

2014–
2022 

Italian municipal boundaries 

Italian National Institute of 
Statistics 
(accessed 
October 2023) 

2014–
2022 

NUTS 0, 
NUTS 1, 
NUTS 2, 
NUTS 3 (b) 

Municipalities 
and 
aggregated 
municipalities 

Municipalities 

https://ec.europa.eu/eurostat/databrowser/view/TOUR_OCC_NIN2__custom_7115881/default/table?lang=en. 

(a) 
(*)  Data are missing for NUTS 2 for 2000 in Estonia and Malta, NUTS 2 for 2012 in Greece) and NUTS 2 for 2018 in Slovenia. 

Source: Authors’ own preparation. 

35 

Figure 17. Total number of overnight stays (millions), EU-27 (2005–2022) 

Source: Authors’ own preparation, based on Eurostat (2023). 

Figure 17 shows the total number of overnight stays in the EU-27 from 2005 to 2022. Between 
2005 and 2019, overnight stays for tourism increased by 36.05 % – that is, 0.5 % per decade 
(baseline 2010). The trend shows how the tourism sector increased its importance in Europe, 
demonstrating a regular pattern. The pandemic (in 2020–2021) caused a decrease by 50.5 %. The 
sector has so far experienced a slow recovery: overnight stays were down 36 % in 2021 and 4 % in 
2022 compared with 2019. 

36 

 
 
Figure 18. Total overnight stays density (millions) 

NB: The graph shows Member States in descending order by 2019 value. 

Source: Authors’ own preparation, based on Eurostat (2023). 

37 

 
 
 
 
 
Figure 19. A. Tourism density (overnight stays per km2); B. Tourism intensity (overnight stays per inhabitant) 

A 

B 

NB: Countries are sorted in ascending order based on 2019 values. 

Source: Authors’ own preparation, based on Eurostat (2023). 

Considering the total amount of overnight stays (Figure 18), Spain ranks highest, followed by 
France, Germany and Italy. Considering tourism in relation to the size of the country (Figure 19 A), 
Malta ranks highest, followed by The Netherlands. When overnight stays are reported in relation to 
the local population (Figure 19 B), Greece ranks highest (followed by Germany). These indicators 
represent the impact of tourism on the territory and on the local society, respectively (ETC/ULS, 
2018). 

According to Lück and Aquino (2021), the COVID-19 pandemic changed the face of international 
and domestic tourism and focused attention on the importance of tourist health, safety and well-
being like never before. This finding is reinforced by the results of the Flash Eurobarometer (2021) 
survey on the preferences of Europeans for tourism activities. In 2021, 43 % of respondents from 
the EU-27 included the natural environment at the destination among the most important reasons 
for the selection of destinations and 18 % reported the natural environment as the most important 
reason), as shown in Figure 20. 

38 

 
 
Figure 20. The natural environment as a reason for travelling for tourism, EU-27 

Source: Authors’ own preparation, based on Flash Eurobarometer (2021). 

39 

 
5.  EU nature-based tourism accounting 

5.1.  How to compute supply and use tables to account for nature-based tourism 

The ecosystem service flow accounts collect data on the supply of ecosystem services by 
ecosystem assets (in supply tables) and the use of ecosystem services by economic units (in use 
tables), including for households (United Nations, 2021).  Figure 21 shows the accounting 
procedure for two recreation-related services (daily outdoor recreation and NBT), building on the 
procedure implemented as part of the INCA approach (Vallecillo et al., 2019c). 

Figure 21. Schema of the adaptation of the INCA approach to the physical ecosystem service flow accounting 
applied to NBT and recreation-related services. 

NB: AF, actual flow; L, level; M, physical metric; P, potential capacity; SP, service-providing area. 

Source: Zulian and La Notte, 2022. 

To compile the SUTs several pieces of information are needed: 

—  The potential capacity of ecosystems to provide the service, depending on the biophysical model 

(P in Figure 21); 

—  the service-providing area, depending on the biophysical model; 

—  the assets (CLC L1 in Figure 21), depending on the land cover map; 

—  a physical metric for the ecosystem service; 

—  the actual flow of the service; 

—  the users of the service (provided in the use tables in Figure 21). 

Nature-based  daily  recreation  and  NBT  respond  to  different  human  needs  and  require  different 
approaches from an accounting perspective. 

The needs and requirements for nature-based daily recreation are as follows. 

—  Human needs. Recreation, leisure and sports activities play an important role in communities. 
Their many benefits include improving the health and well-being of individuals, contributing to 
the empowerment of individuals and promoting the development of inclusive communities. 
Recreation refers to all those activities that people choose to do to refresh their bodies and 
minds and make their leisure time more interesting and enjoyable. Examples of recreation 
activities are walking, swimming, meditation, reading, playing games and dancing (WHO, 2010). 

40 

 
Many studies focus on the direct and indirect contributions of nature to daily recreation. They 
identify a wide range of related health aspects (including improved cardiovascular and mental 
health) (Ekkel and de Vries, 2017). 

—  Key ecosystem types. The key types are terrestrial and marine ecosystems, but the focus is 

mainly on urban and peri-urban areas, or on ecosystems close to settlements. 

—  Proximity to users. Recreation opportunities must be easily accessible for daily use (Ekkel and 

de Vries, 2017). 

—  Key users. These are broadly identified as inhabitants and households (Vallecillo et al., 2018; 

United Nations, 2021).  

The needs and requirements for NBT are as follows. 

—  Human needs. Previous studies have shown that there are many aspects of tourist motivation, 
which makes it a complex issue. Besides destination choice, tourist motivation is closely linked 
to experiences and activities that tourists seek and share with others. From the various 
theoretical perspectives on tourist motivation, the dominant paradigm has been the push–pull 
approach, addressing factors associated with both the individuals and the tourist destination 
itself (Räikkönen et al., 2023). Although the issue is very complex and has proven critical for 
understanding and explaining tourists’ behaviour, NBT focuses on ‘enjoying and learning about 
natural resources and environments (e.g. landscapes, habitats, water features, and species) and 
biodiversity conservation’ (Räikkönen et al., 2023). This definition includes the term ‘landscapes’, 
for which several definitions exists depending on the context (Simensen et al., 2018). From an 
NBT perspective the interest is on ‘cultural landscapes’ (UNESCO-WHC, 2008), which represent 
the ‘combined works of nature and of man’. In cultural landscapes there is an intrinsic 
relationship between human interventions, cultural aspects and the environment. Examples of 
cultural landscapes that are extremely attractive for touristic purposes are the Alto Douro Wine 
Region (Portugal) (20) and the Vineyard Landscape of Piedmont: Langhe-Roero and Monferrato 
(Italy) (21). Both are described under criterion V in UNESCO (2021), as an ‘outstanding example 
of man’s interaction with his natural environment’. From an ecosystem type perspective, the 
areas fall mainly into the agroecosystems category. 

—  Key ecosystem types. The key types are terrestrial and marine ecosystems. 

—  Proximity to users. The proximity of NBT locations is still important, but the degree of 
accessibility strongly depends on the types of experiences requested by the tourists. For 
instance, some may be interested in easy hiking trails close to their accommodation, while 
others look for remote areas and intensely active experiences. The types of experiences tourists 
seek strongly influence the meaning of proximity in this context. 

—  Key users. The primary users of NBT are the economic units that provide tourism-related 

activities such as accommodation, restoration and guided tours. The secondary users are the 
people who purchase these services. A common proxy for this could be the ‘number of overnight 
stays’, as measured in the case study of Italy (Zulian and La Notte, 2022) 

In INCA, accounting for daily outdoor recreation is based on the potential capacity of ecosystems to 
provide the service modelled using the ESTIMAP RP map; the service-providing areas were defined 
by selecting the areas with high opportunities for recreation close to roads and settlements (only 
one category, called ‘area for daily recreation’) (SP-a in Figure 21); the assets were represented by 
ecosystem type (CLC L1 in Figure 21); the metric for the ecosystem service was ‘potential visits by 
inhabitants’ (M-a in Figure 21); the actual flow of the service was the number of potential visits to 
‘areas for daily recreation’ (AF-a in Figure 21); and the users were identified in households 
(Vallecillo et al., 2018). 

(20)  https://whc.unesco.org/en/list/1046. 
(21)  https://whc.unesco.org/en/list/1390. 

41 

                                                        
In the adaptation to account for NBT, the potential capacity of ecosystems to provide the service is 
still modelled by the ESTIMAP RP map, but the service-providing areas for NBT are defined by 
selecting the areas with medium and high opportunities for recreation close and proximal to roads 
and settlements (four categories) (SP-b in Figure 21); the assets are represented by ecosystem 
type (CLC L1 in Figure 21); the physical metric for the ecosystem service is overnight stays (M-b in 
Figure 21); the actual flow of service is the number of overnight stays in the service-providing 
areas (AF-b in Figure 21); and the users are those of ‘tourism accommodation’, defined as ‘any 
facility that regularly or occasionally provides overnight accommodation for tourists’ (Regulation 
(EU) 692/2011 of the European Parliament and of the Council). 

The biophysical component (which is a key element of the SEEA EA approach) is maintained, but the 
extent of the service-providing area is expanded from one to four RP categories (Zulian and La 
Notte, 2022). 

In summary, to fill in the SUTs a two-step procedure was implemented in each territorial unit 
considered. 

—  The ROS map was combined with CLC level 1 data (raster data are cross-tabulated). 

—  The share of each possible combination was calculated. 

—  Only data related to service-providing areas for NBT were retained. 

—  The fraction of NBT overnight stays was computed by allocating the overnight stays in 
proportion to the proportion of service-providing areas in each CLC level 1 land type. 

—  The total actual flow was then allocated to the tourism/accommodation sector. 

The SUTs are filled in using the actual flow computed considering the total touristic flow. 

Ecosystem types were selected for this study (see Table 9) according to Annex 3 of the guidance 
note on extent accounts (Eurostat, 2023). The classification, mainly based on the work done for the 
European ecosystem assessment (Maes et al., 2020), also integrates the IUCN Global Ecosystem 
Typology (Keith et al., 2022) for the class ‘coastal beaches, dunes and wetlands’. 

Table 9. Ecosystem types used in this exercise 

Ecosystem type 

Settlements 

Cropland 

Grassland 

CLC class(es) 

111, 112, 121, 122, 123, 124, 131, 132, 133, 
141, 142 

211, 212, 213, 221, 222, 223, 241, 242, 243, 
244 

231, 321 

Woodland and forest 

311, 312, 313, 324 

Heathland and shrub 

322, 323 

Sparsely vegetated land 

332, 333, 334, 335 

Wetlands 

Ecosystem type 

Rivers 

411, 412 

CLC class(es) 

511 

42 

Lakes 

512 

Marine transitional land 

423, 521, 522 

Coastal beaches, dunes and wetlands 

331, 421;422 

Sources: Authors’ own preparation; nomenclature derived from (Kosztra et al., 2017). 

5.2.  EU-level actual flow between 2000 and 2018 

The procedure was implemented at the EU level from 2000 to 2018 using the overnight stays data 
aggregated at the NUTS 2 level. 

Not all Member States are equally represented, at the NUTS 2 level, across the 4 years considered 
in this study. As presented in Figure 22, in 2000, data was available for 64.1 % of all NUTS regions. 
The figure gradually increased in 2006 (82 %), 2012 (95 %) and 2018, when 99.5 % of NUTS 
regions has data and only data for Slovenia were missing. Consequently, the trends for NBT 
accounting had to be computed using a subsample of the dataset. Unfortunately, Germany, Greece, 
Spain and Italy are among the countries with missing data for 2000 and 2006; however, they are 
also the countries with the highest number of overnight stays for tourism purposes (see Figure 18 
and Figure 19). 

To compare NBT use over time, only the NUTS 2 regions for which data were provided across all years 
were used. 

Figure 22. Availability of data at the NUTS 2 level 

Source: Authors’ own preparation. 

43 

 
Figure 23. NBT at the NUTS 2 level (2018) expressed in millions. 

Source: Authors’ own preparation. 

44 

 
Figure 24. NBT in relation to the total number of overnight stays (2018) expressed in percentage 

NB: Calculations were based on data gathered at the NUTS 2 level. 

Source: Authors’ own preparation. 

Figure 23 shows NBT use at the NUTS 2 level and Figure 24 presents the spatial distribution of 
NBT use in relation to the total number of overnight stays (NBT share). While in the first map the 
spatial pattern depends mainly on the total amount of overnight stays allocated to nature-based 
opportunities and therefore presents a certain spatial heterogeneity, the second map is relatively 
homogeneous across Member States. This is mostly explained by the level of aggregation of 
tourism data. The biophysical spatial resolution is 100 m; nevertheless, the statistics on tourism are 
available at the NUTS 2 level and this implies a rough generalisation. This issue will be fully 
addressed in the next section. 

One important element to be considered when analysing changes in NBT use is the role of major 
fires, which directly affect the natural areas that attract tourists. The occurrence of fires may, in 
fact, make an area poorer in terms of natural touristic hotspots and therefore result in lower flows. 
Service-providing areas have been increasingly affected by major fires over the years. Burnt areas 
disrupted around 0.50 % of SPA between 2000 and 2006 and 0.77 % between 2018 and 2021 

45 

 
(Figure 25). Major fires result in changes in service use (Figure 26), with an approximately 1.8 % 
decrease in NBT use in 2018. 

Figure 25. Changes across years in the SPA affected by major fires (%) 

Source: Authors’ own preparation. 

Figure 26. Changes in NBT due to major fires (%) 

NB: The chart reports values only for NUTS 2 regions for which data were always available from 2000 to 2018 (58.7 % of NUTS 2 

regions). 

Source: Authors’ own preparation. 

46 

 
 
 
SUTs (Table 10 and Table 11) show a gradual growth in NBT for the accounting years 2000, 2006, 
2012, 2018 (considering the NUTS 2 level, for which data were always available). 

The increase occurs in all ecosystem types and ranges from 24 % (heathland and shrub) to 45 % 
(settlements and marine transitional). On the one hand, the growth depends on an increase in 
overnight stays. On the other hand, the ratio between NBT use and total overnight stays (NBT share) 
between 2000 and 2018 decreased by 0.5 %. 

Figure 27. Changes in NBT because of major fires (2000–2018) (%) 

NB: The graph reports values only for Member States for which data were always available from 2000 to 2018 at the NUTS 2 level 

(58.7 % of NUTS 2 regions). 

Source: Authors’ own preparation. 

Among the Member States with comparable data, Portugal, Croatia, Italy, Cyprus and Spain lost the 
highest amount of NBT due to major fires (Figure 27). The most affected country was Portugal, for 
which the highest loss was estimated in 2012 to be 17.1 %. Croatia, Italy, Cyprus and Spain were 
more affected in 2018 than in 2012, with an average loss of 3.8 %. Among the other 12 Member 
States with comparable data, the greatest loss is reported in Latvia (0.33 % in 2006 and 2012). The 
average loss in these countries was 0.05 % in 2012. Indeed, regional differences within each 
Member State exist, especially in the most affected countries. This will be demonstrated in the next 
section, using Italy as a case study.

47 

 
Table 10. Supply table reporting actual flow (millions) by ecosystem type (2000, 2006, 2012 and 2018) 

Ecosystem type 

Year 

2000 

2006 

2012 

2018 

Settlements  Cropland 

Grassland 

Heathland 
and shrub 

Woodland 
and forest 

Sparsely 
vegetated 
land 

Wetlands 

Rivers 

Lakes 

Marine 
transitional land 

Coastal beaches, 
dunes and 
wetlands 

17.81 

150.73 

123.35 

39.35 

293.47 

11.01 

3.03 

3.20 

12.45 

21.97 

144.89 

136.70 

41.35 

323.55 

11.68 

25.88 

186.58 

139.83 

41.33 

343.12 

12.57 

3.37 

3.54 

3.59 

4.06 

14.42 

15.64 

32.62 

236.78 

174.84 

52.06 

418.84 

14.76 

4.52 

5.25 

20.66 

1.03 

1.20 

1.39 

1.86 

2.46 

2.62 

2.66 

3.48 

NB: The table reports values only for NUTS 2 regions for which data were always available from 2000 to 2018 (58.7 % of NUTS 2 regions). 

Source: Authors’ own preparation. 

48 

 
 
Table 11. Use table reporting actual flow (millions) by sector and households (2000, 2006, 2012 and 2018) 

Sector 

Tertiary 

Primary 

Secondary 

Tourism 

Other 

Accommodation 

Other 

Households 

657.90 

705.34 

776.61 

965.69 

Years 

2000 

2006 

2012 

2018 

NB: The table reports values only for NUTS 2 regions for which data were always available from 2000 to 2018 (58.7 % of NUTS 2 regions). 

Source: Authors’ own preparation. 

49 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 12. Supply table reporting actual flow (millions) by Member State and ecosystem type (2018) 

Ecosystem type 

Member State 

Settlements  Cropland  Grassland 

Heathland 
and shrub 

Woodland 
and 
forest 

Sparsely 
vegetated 
land 

Wetlands  Rivers 

Lakes 

Marine 
transitional 
land 

Coastal 
beaches, 
dunes  and 
wetlands 

AT 

BE 

BG 

CY 

CZ 

DE 

DK 

EE 

EL 

ES 

FI 

FR 

2.69 

1.44 

0.30 

0.41 

1.97 

7.06 

20.43 

2.33 

44.70 

4.34 

5.71 

3.82 

7.18 

3.72 

1.49 

0.46 

4.52 

0.21 

0.00 

2.69 

5.28 

7.43 

3.07 

0.01 

14.69 

9.39 

39.13 

77.25 

0.74 

117.56 

1.04 

0.02 

5.28 

0.48 

0.66 

0.38 

0.24 

0.01 

4.15 

2.41 

2.30 

36.37 

13.32 

24.98 

16.54 

3.95 

75.81 

19.75 

33.11 

72.77 

0.17 

1.37 

0.01 

0.00 

7.99 

7.50 

62.24 

51.18 

11.10 

117.88 

6.24 

0.00 

0.04 

0.22 

0.00 

0.14 

0.00 

0.00 

2.24 

2.77 

0.01 

4.93 

0.24 

0.45 

0.84 

0.06 

0.09 

0.16 

0.02 

0.02 

0.17 

0.01 

0.00 

0.04 

0.05 

0.20 

0.25 

1.44 

1.72 

4.85 

0.38 

0.00 

0.53 

0.14 

0.00 

0.10 

0.12 

0.11 

0.36 

0.07 

0.23 

0.98 

0.10 

0.02 

0.84 

0.66 

1.22 

2.01 

0.00 

0.06 

0.00 

0.00 

0.00 

1.04 

0.03 

0.00 

0.05 

0.29 

0.00 

0.79 

0.00 

0.05 

0.01 

0.11 

0.00 

0.28 

0.19 

0.00 

0.35 

1.13 

0.01 

0.98 

50 

 
 
Member 
State 

Settlements 

Cropland 

Grassland 

Heathland 
and shrub 

Woodland 
and 
forest 

Sparsely 
vegetated 
land 

Wetlands  Rivers 

Lakes 

Marine 
transitional 
land 

Coastal 
beaches, 
dunes and 
wetlands 

Ecosystem type 

HR 

HU 

IE 

IT 

LT 

LU 

LV 

MT 

NL 

PL 

PT 

1.50 

16.54 

8.94 

2.34 

2.85 

1.35 

14.36 

2.81 

34.18 

0.00 

0.31 

6.94 

1.97 

1.19 

0.00 

0.09 

0.10 

0.08 

0.28 

0.22 

0.40 

0.33 

2.10 

0.02 

0.31 

0.57 

0.15 

2.75 

69.75 

14.32 

7.55 

120.19 

10.69 

0.37 

0.88 

2.85 

0.04 

0.06 

0.03 

0.99 

2.97 

0.98 

0.65 

0.43 

4.81 

0.56 

0.46 

0.40 

0.00 

8.51 

29.13 

0.00 

0.00 

0.00 

1.48 

1.06 

2.68 

1.07 

1.96 

0.07 

8.85 

1.81 

16.34 

1.01 

14.37 

7.28 

1.11 

0.01 

29.49 

3.77 

11.47 

0.00 

0.00 

0.00 

0.22 

0.00 

0.02 

0.06 

0.06 

0.02 

0.13 

0.00 

0.00 

0.01 

0.07 

0.01 

0.07 

0.00 

0.00 

0.00 

1.30 

1.62 

6.58 

0.21 

0.28 

1.20 

0.03 

0.12 

0.14 

51 

0.00 

0.00 

0.03 

1.87 

0.00 

0.00 

0.00 

0.00 

0.05 

0.00 

0.23 

0.04 

0.00 

0.04 

1.77 

0.00 

0.00 

0.00 

0.01 

0.51 

0.02 

0.74 

 
 
Ecosystem type 

Member 
State 

RO 

SE 

SI 

SK 

Settlements 

Cropland 

Grassland 

Heathland 
and shrub 

Woodland 
and 
forest 

Sparsely 
vegetated 
land 

Wetlands  Rivers 

Lakes 

Marine 
transitional 
land 

Coastal 
beaches, 
dunes and 
wetlands 

0.21 

1.10 

0.00 

0.12 

3.43 

3.01 

0.00 

2.25 

3.41 

0.76 

0.00 

0.81 

0.00 

6.40 

0.02 

23.94 

0.00 

0.01 

0.00 

6.35 

0.01 

0.14 

0.00 

0.01 

0.17 

0.21 

0.20 

0.48 

0.05 

2.99 

0.00 

0.00 

0.00 

0.01 

0.04 

0.08 

0.02 

0.01 

0.00 

0.00 

0.02 

0.01 

0.00 

0.00 

Source: Authors’ own preparation. 

52 

 
 
Figure 28. NBT by Member State (2018) 

NB: Blue bars show NBT use expressed in millions; the green line shows the NBT share expressed in percentage. The graph shows NBT 

use values in ascendent order. The red line shows the value NBT share calculated at the EU level in 2018. 

Source: Authors’ own preparation. 

Figure 28 shows NBT use at the Member State level in 2018. France, Germany, Italy and Spain 
ranked highest, with more of 200 million nights allocated in areas with opportunities for NBT. The 
NBT share average was 57.4 %, close to the EU-level value (58.1 %). 

In five Member States, NBT use was between 50 million and 100 million, with an average share of 
64.9 %. Among Member States, variability exists, with an NBT share of 52.1 % in the Netherlands 
compared with 73.2 % in Croatia. 

In a third group of Member States, NBT use was under 50 million, with an average NBT share of 
53.9 %. In this group, Ireland, with an NBT use estimated at 20.7 million, had an NBT share of 
59.1 % (above the EU-level value). 

In the other 13 countries, NBT use was under 20 million. Among the 13, the highest NBT shares 
were registered in Luxembourg (76.7 %) and Malta (74.8 %), and the lowest in Belgium (37.2 %). 

In 2018, Member States were severely affected by major fires events, which hampered NBT. Fires 
affected Member States with very diverse orders of magnitude. Figure 29 shows values in Member 
States where changes in NBT use due to fires were particularly relevant. Portugal (16 % loss) and 
Greece (5.5 % loss) were followed by Croatia, Cyprus, Spain, Italy and Bulgaria. These findings are in 
line and complement a study carried on by Neger et al. (2024), which analysed the impact of 
wildfires on European tourism.

53 

 
Figure 29. Changes in NBT use due to fires (%) 

NB: The graph shows Member States in descending order by absolute value, focusing on those with major damages. 

Source: Authors’ own preparation. 

Other European countries are minimally affected by major fires (Figure 30). However, we reaffirm 
that regional differences exist within each Member State; regional vulnerabilities exist, for instance, 
in Ireland, France and Romania, as pointed out by Neger et al. (2024). 

Figure 30. Changes in nature-based tourism use due to fires (%) 

NB: The graph shows Member States in descending order by absolute value. 

Source: Authors’ own preparation.

54 

 
 
 
6.  Nature-based tourism accounting in 2021: demonstrative multiscale 

applications 

For demonstrative purposes, SUTs were computed using data available at different spatial 
aggregations: NUTS 2 and NUTS 3 at the EU level; and NUTS 2, NUTS 3 and at the municipal level in 
Italy. At the municipal level, particular attention was given to the impact of major fires on NBT, and 
an additional case study was prepared for the Veneto region, one of the six most visited Italian 
regions (22). Italy was selected for demonstrative purposes because the Italian National Institute of 
Statistics collects and distribute tourism statistics every year, aggregated at the municipal level (23). 
Data on overnight stays for tourism purposes are available at the European level, aggregated at 
the NUTS 3 only for 2020, 2021 and 2022. The actual flow was computed using data for 2021 at 
the EU level and in Italy. Data from 2020 were not used because in 2020 the tourism sector was 
negatively affected by the pandemic. Data for 2022 were not complete at the EU level. The main 
objective of this exercise was to explore to what extent the spatial aggregation of overnight stay 
data could affect the results. The SUTs were computed using the biophysical data (ROS) available 
for 2018 at the EU level. To account for the SPA loss due to major fires, the ROS map for 2018 was 
corrected, as explained in Section 3.1.5. 

6.1.  Actual flow and use of services at the EU level: comparison of results 

obtained with data gathered at the NUTS 2 and NUTS 3 levels 

The ecosystem types that are affected the most based on data aggregated at different levels are 
heathland and shrub, coastal waters, sparsely vegetated land and marine transitional land, which 
show a loss of actual flow; and forest and grassland, which experience an increase in actual flow 
(Figure 31). Nevertheless, if the relative changes in SPA located in the abovementioned ecosystem 
types exceed +/–40 %, in absolute terms the change differs by several order of magnitude. In 
heathland and shrub, for instance, the level of data aggregation determines a difference of 
38.6 million in overnight stays allocated to NBT, where in coastal areas the difference is 2.4 million. 

Figure 31. Comparison of actual flows computed at the NUTS 3 and NUTS 2 levels 

(*) 

Includes coastal beaches, dunes and wetlands. 

Source: Authors’ own preparation. 

(22)  https://www.istat.it/it/files/2023/04/REPORT_VIAGGIEVACANZE_2022.pdf 
(23)  https://www.istat.it/en/services?data-and-indicators. 

55 

 
                                                        
At the EU level, data aggregation slightly affects the use of services, as reported in Table 13. At 
the NUTS 3 level, service use decreases by 0.89 % (9.22 million) compared with the use computed 
using data aggregated at the NUTS 2 level. 

Table 13. Use table reporting actual flow (millions), computed with data aggregated at the NUTS 2 and 
NUTS 3 levels. 

Sector 

Territorial level 

Primary 

Secondary 

Tourism 

Tertiary 

Households 

Other 

NUTS 3 

NUTS 2 

Source: Authors’ own preparation. 

Accommodation 

Other 

1 027.9 

1 036.9 

The level of aggregation of overnights stays affects service use differently across Member States 
(Figure 32). In 13 Member States, the change is estimated to be less than 2 %. In 6 Member 
States (Denmark, Ireland, Poland, Germany, Italy and France), statistical data aggregated at the 
NUTS 2 level overestimates NBT use. Cyprus and Luxembourg were the Member States least 
affected by data aggregation. In contrast, in 12 Member States data aggregated at the NUTS 2 
level underestimate NBT use, with Belgium and Croatia showing the largest differences. 

Figure 32. Comparison of the NBT use computed at the NUTS 3 and NUTS 2 levels by Member States 

Source: Authors’ own preparation. 

Figure 33 shows the service use share mapped at the NUTS 3 level; the overall pattern allows for 
the identification of the heterogeneity of the distribution. 

56 

 
 
 
 
 
 
 
 
 
 
 
 
Figure 33. NBT share in 2021 at the NUTS 3 level 

Source: Authors’ own preparation. 

57 

 
 
 
6.2.  Case study of Italy: service use evaluated with data gathered across scales 

At the national level, the difference in service use determined using data gathered at different 
levels is not substantial. Compared with data gathered at the NUTS 2 level, those gathered at the 
NUTS 3 level exhibit a 3.8 % lower service use; in contrast, data gathered at the municipal level 
show a 2.6 % higher service use. Differences are more evident when comparing the actual flow 
across ecosystem types, as shown in Figure 34; when using data gathered at the municipal level, 
the actual flow increases in ecosystem types characterised by the presence of water and 
settlements. 

Figure 34. Changes in actual flow computed with data gathered at different levels of aggregation (%) 

NB: coastal water= Coastal beaches, dunes and wetlands 

Source: Authors’ own preparation. 

The differences are not uniform across Italy. Looking at the NBT share, median values range from –
12.2 % (in Emilia Romagna) to 21.4 % (in Valle d’Aosta), as shown in Figure 35 and Figure 36. In 
12 Italian regions, data aggregated at the NUTS 2 level underestimate NBT use (coloured brown in 
the graph). Among them are regions with a significant level of tourism activity linked to nature, 
such as Valle d’Aosta (very important for mountain tourism) and Trentino-Alto Adige (one of the 
three Italian regions where the Dolomites are located). 

58 

 
 
 
Figure 35. Changes in NBT use computed with data gathered at different levels of aggregation in each 
Italian region (%) 

Source: Authors’ own preparation. 

59 

 
Figure 36. Spatial pattern of the difference between NBT share computed with data aggregated at the 
municipal level compared with the NUTS 2 level 

NB: Blue shows the locations where the NUTS 2 evaluation overestimates the share computed with data gathered at the municipal level; 

brown shows the locations where the NUTS 2 evaluation underestimates the share. 

Source: Authors’ own preparation. 

This differences definitively demonstrate the importance of a careful and thoughtful selection of 
aggregation levels for both the statistical data and biophysical data. In this report, we cannot 
discuss in detail the results obtained for each region. Nevertheless, a detailed example focusing on 
Veneto region will be provided in the next section. 

60 

 
Figure 37. Box plot comparing NBT share computed with data aggregated at the municipal level (%) 

Source: Authors’ own preparation. 

Across Italy, the distribution of service use is remarkably diverse, as presented in Figure 37 and 
Figure 38. The average values range from 31.8 % (in the Marche region) to 89.4 % (in the Liguria 
region). As the figures show, the dispersion within each region varies a lot among the different 
zones. Veneto and Lombardy, for instance, present the largest interquartile range. This is probably 
linked to the presence in the same region of areas with a very high potential for NBT (e.g. the 
Dolomites and the Adriatic Sea in Veneto; and the Alps and the west side of Lake Garda in 
Lombardy); in addition, both regions are crossed by the Po Valley, characterised by intensive 
agriculture and industrial development. 

61 

 
Figure 38. NBT share in Italy at the municipal level (2021) 

Source: Authors’ own preparation. 

From a methodological perspective, it is important to quantify to what extent major fires affected 
service use. In 2021, Italy was one of the Member States most affected by fires (San-Miguel-Ayanz 
et al., 2022). In southern Italy (Sicily, Calabria, Basilicata, Campania and Sardegna), service use 
decreased from 5 % to more than 20 %, as shown in Figure 39 and Figure 40. 

62 

 
Figure 39. Impact of major fires on NBT use in Italy (2021) (%) 

Source: Authors’ own preparation. 

63 

 
Figure 40. Impact of major fires on NBT in Italy at the municipal level (%) 

Source: Authors’ own preparation. 

64 

 
 
 
6.3.  The case of the Veneto region explored across scales 

The distribution of the NBT share at the municipal level is remarkably diverse across the seven 
provinces in the Veneto region, as shown in Figure 41 and Figure 42. Rovigo, Venezia and Padova 
show a relatively low NBT share compared with the other four provinces. Rovigo is the province 
with the smallest interquartile range (9.1 %) and low median (9.9 %) and average (16.1 %) values, 
the last being 30.1 percentage points lower than the regional average (46.2 %). Among the outliers 
is Rosolina Mare (1 in Figure 41), one of the top 20 municipalities in terms of tourism arrivals in 
Veneto (Regione del Veneto Elaborazioni dell’ufficio di Statistica della Regione del Veneto su dati 
Istat). In Rosolina, the NBT share is 66.5 %. The small town is located within the Po Delta regional 
park (24) and offers very interesting opportunities for active/passive interaction with nature (25). 

The Venezia province shows a larger interquartile range (14.1 %) than Rovigo and a similar average 
value (16.6 %), which is 29.6 percentage points lower than the regional average. The outlier, with 
the highest NBT share, is Cavallino-Treporti (2 in Figure 41), one of the top 20 municipalities in 
terms of tourism arrivals in Veneto (Figure 43). Cavallino-Treporti is located in the Venetian 
Lagoon and provides various types of opportunities for tourism and recreation, including nature, 
active recreation and sport, art and culture (26). The municipality hosts important nature reserves 
and a Natura 2000 site (27). Its accommodation capacity is mainly provided by camping, and is 
1.8 times higher than Jesolo Lido (3 in Figure 41), another sea location identified among the top 
municipalities in Veneto with regard to tourism but with a lower NBT share (see Figure 43). The 
characteristics described above explain the extremely high NBT use in this small town. 

In addition, the municipality of Venice (4 in Figure 41) has a high NBT share, estimated at 63.7 %. 
This value could be considered higher than expected, but (as explained in Section 4.2) Venice is 
inextricably linked to the Mediterranean area and the Venetian lagoon. The area has outstanding 
universal value worldwide, and the lagoon is considered fundamental. In this regard, the presence 
of ecosystems plays a very important role in making the city and its tourist activity exceptional. 

The Padova province has the lowest median value among the provinces in the Veneto region. The 
average NBT share is 21 % (25 percentage points lower than the regional average). The 
municipalities with the highest share are all part of the Regional Park of Colli Euganei (28) , namely 
Arquà Petrarca, Cinto Euganeo, Baone, Galzignano Terme, Torreglia, Vo’, Teolo, Battaglia Terme (5 
in Figure 41) and Montegrotto Terme. The last is also among the top 20 municipalities in the 
Veneto region (Italy). The reason only Montegrotto Terme (6 in Figure 41) is among the top 20 is 
that together with Abano Terme it hosts one of the most important and traditional thermal stations 
in Europe (29). 

The province of Treviso has a mean NBT share of 41 % and an interquartile range of 45.2 %. 
Overall, the difference from the regional mean is estimated to be –4.9 percentage points. The 
province does not have outliers, or locations with extremely high values compared with the rest of 
the distribution. To be noted is the presence in the area of important Natura 2000 sites (e.g. Fiume 
Sile da Treviso Est a San Michele Vecchio (IT3240031) and Fiume Sile dalle sorgenti a Treviso 
Ovest (IT3240028), both protected under the habitat directive (30)). An extensive area is protected 
by the Regional Park of River Sile (31) (the longest resurgence river in Italy). The area offers 
interesting and attractive opportunities for nature-based recreation activities. 

(24)  https://www.parcodeltapo.org/IT3270023.php. 
(25)  https://www.visitdeltapo.it/rosolina/. 
(26)  https://www.visitcavallino.com/eng/discover-the-parco. 
(27)  https://natura2000.eea.europa.eu/Natura2000/SDF.aspx?site=IT3250003. 
(28)  https://www.parcocollieuganei.com/page.php?id=140. 
(29)  https://it.wikipedia.org/wiki/Bacino_Termale_Euganeo. 
(30)  https://natura2000.eea.europa.eu/?views=Sites_View. 
(31)  https://www.parcosile.it/. 

65 

                                                        
The Vicenza province has a median NBT share of 46.8 % and a mean of 51 % (4.8 percentage 
points higher than the regional mean). The relatively large interquartile range shows the spread of 
the data (outliers are excluded). Among the municipalities with higher values are Foza, Enego, Gallio 
and Asiago, all located in the Altopiano dei Sette Comuni (32) (7 in Figure 41). 

The Verona province has the highest interquartile range, equal to 61.4 %. All tourist locations near 
Lake Garda (8 in Figure 41), namely Bardolino, Malcesine, Garda, Peschiera del Garda, Lazise and 
Castelnuovo del Garda have relatively large NBT shares and are among the top 20 tourist 
destinations in Veneto. 

Finally, the province of Belluno (9 in Figure 41), ranks very high in terms of average and median 
NBT share, which are 82.6 % (36.4 percentage points higher than the regional mean) and 86.6 %, 
respectively. Belluno is the province where part of the Dolomites is located. The whole province is 
characterised by a high NBT share. When values are not high it is because only ROS categories near 
and proximal are considered in this implementation of the approach (see Section 5.1). 

Figure 41. NBT in relation to the total number of overnight stays in the Veneto region: values computed 
using tourism statistics gathered at the (A) NUTS 2, (B) NUTS 3 and (C) municipal level 

Source: Authors’ own preparation. 

(32)  https://www.veneto.eu/EN/Sette_comuni_altopiano/. 

66 

 
                                                        
Figure 42. Box plot comparing NBT share across provinces in the Veneto region (%) 

NB: The values in white are medians. 

Source: Authors’ own preparation. 

Figure 43. Tourism statistics for the top 20 municipalities in the Veneto region 

Source: Authors’ own preparation, based on Regione del Veneto Elaborazioni dell’ufficio di Statistica della Regione del Veneto su dati 
Istat. 

67 

 
 
7.  Conclusions 

NBT is multifaceted and established as an important sector of the economy, with additional 
potential for growth in many countries and regions. Haukeland et al. (2023) identified trends in NBT 
with the largest commercial potential. Among the features providing the most potential, the 
authors report experience of local culture and locally produced products, sustainability and 
responsible travel, authentic nature and culture experiences, physical activities in nature for health 
and fitness, and simple and easily accessible activities (soft adventures). The present study 
reinforces two crucial points: firstly, that all ecosystem types can act as service-providing areas and 
highly modified ecosystems (e.g. agroecosystems or settlements) can attract visitors for 
experiences related to the presence of nature; and, secondly, that there will be an increase in the 
requirement for services related to NBT in the future. 

This study implements an accounting methodology tailored to NBT. The research covers an open 
methodological gap in the INCA project, because NBT was not included in previous work on cultural 
ecosystem services undertaken at the EU level (Vallecillo et al., 2018). The need for an accounting 
methodology for NBT is a pressing and unresolved issue, as NBT is one of the mandatory services 
selected for the national accounts (European Commission, 2022a). 

The present study was conducted with two main aims: to test the implementation of a method to 
account NBT, following the SEEA EA approach (United Nations, 2021), and to explore to what extent 
the proposed method could effectively capture the contribution of ecosystems to tourism. This was 
done using the biophysical ecosystem service potential map computed at the EU level to derive the 
actual flow and use using statistical data gathered at different levels of aggregation. The study 
was carried out at the EU level, comparing data gathered at the NUTS 2 and NUTS 3 levels, and 
using Italy as a case study, comparing data gathered at the NUTS 2, NUTS 3 and municipal levels. 
Finally, an Italian region particularly important for tourism (33) was investigated to show the level of 
detail the method provides. 

The analysis enabled the unpacking and discussion of various methodological aspects that could 
not be identified without a practical testing phase. In addition, the study fostered the meticulous 
consideration of the most important aspects of NBT, which will be useful to structure future 
accounting approaches. 

The methodology presented in this study has strengths and weaknesses. The main strength is that 
it is simple and easy to interpret. Applied at the continental scale, it provides a valuable overview of 
the important role of natural elements in the tourism industry. Nevertheless, several improvements 
are needed. 

One aspect is related to the importance of including all ecosystem types. We state that all 
ecosystem types have the potential to contribute to NBT. Having NBT opportunities in Europe does 
not necessarily imply a remote or wild experience. Instead, it means having: 

—  opportunities to have active experiences (hard or soft activities such as climbing and kayaking or 

walking and birdwatching) in contact with nature; 

—  opportunities  to  visit  unique  locations  characterised  by  cultural  landscapes  that  prove  the 
traditional  close  interrelationship  between  humans  and  the  environment  (e.g.  the  Venetian 
Lagoon and vineyard landscapes); 

—  opportunities to visit outstanding natural habitats (e.g. the Dolomites); 

—  opportunities  to  have  indirect  contact  with  forest,  traditional  agricultural  landscapes  or  other 

ecosystems when visiting sites of cultural interest (e.g. heritage trees in urban parks); 

—  opportunities to enjoy lakes, rivers and coastal areas. 

(33)  https://osservatorioturismoveneto.it/wp-content/uploads/2023/04/IL-TURISMO-IN-VENETO-NEL-2022_rev.pdf. 

68 

                                                        
Nature-based opportunities can be near settlements or main roads. Even urbanised land can offer 
opportunities for NBT. For example, Freiburg in Germany is the gateway to the Black Forest, and 
many Natura 2000 sites are within the boundaries of cities. 

The concepts of proximity and accessibility remain important to disentangle and fully 
contextualise. The relevance of these aspects depends on the visitors’ interests. Some tourists may 
be attracted to remote areas and refuse to spend their free time in crowded locations. Other 
tourists may look for locations that are easily accessible and close to services and facilities. The 
present approach allows for a certain flexibility in this regard, because the parameters of the 
human-based potential map can be adjusted according to the type of visitor, and the SPAs can be 
identified including or not including remote areas. 

The ecological/societal factors determining the supply must also be considered. The aspects 
of the ecosystems’ condition that have an impact on the enjoyment (direct or indirect) of nature 
must be considered. In this study, the extent of burnt areas was included as an experiment to 
demonstrate the importance of embedding condition into the ecosystem service accounting phase. 
The ESTIMAP recreation model for Europe does not include important details on coastal areas / 
marine shoreline, fresh water and wetlands, which are particularly important for nature-based 
activities. Therefore, in accounting, service use can be underestimated in these ecosystems, 
especially if overnight stays are gathered at the NUTS 2 level. 

In addition, in this approach the actual flow and use of services are based on the extent of SPAs. 
The SPAs depend on the quality of the ecosystems, but there is a need to work more on the 
‘cumulative opportunities effect’ that characterises particularly relevant locations. An approach to 
measuring the total number of opportunities available within a defined distance, weighted by the 
importance of the opportunities, was tested in an application implemented in 2021 to analyse daily 
nature-based recreation (Maes et al., 2021)) and should be further explored and implemented to 
deal with NBT. The authors computed the cumulative SPA map, which measures the total amount 
of possible opportunities available within a defined distance. It expresses the co-occurrence of the 
characteristics that contribute to defining an opportunity. With this additional step, the approach 
could provide a higher level of accuracy and spatial heterogeneity for the biophysical map. 

Moreover, this version of the ESTIMAP recreation model does not distinguish between the facilities 
required to enjoy and the facilities required to reach locations with the potential for NBT. This 
aspect was included in an analysis of urban daily recreation carried out in 2021 (see Maes et al., 
2021), in which the opportunity map representing the presence of facilities enabling the enjoyment 
and reaching of areas with potential opportunities replaced the proximity map used in the current 
version of the model. 

The factors determining the use of the services entail other layers of complexity (Pandey and 
Joshi, 2021) because they are strictly connected to the behaviour, cultural backgrounds and stories 
of individuals. One of the main problems with cultural services evaluated using statistical data is 
the extrapolation of the real intentions/interests of tourists who spend nights in areas with multiple 
services (mixed trips). In fact, to fully evaluate the role of nature in tourism activities, it is 
extremely important to identify: 

—  the elements (natural and cultural or facilities) that enable interactions with nature, be they 

active or passive; 

—  the elements (natural and cultural) that interact to create a unique cultural heritage landscape 
(e.g. the Venice area is important from a historical/artistic perspective; nevertheless, many 
characteristics of the city depend on nature (the lagoon, landscape, etc), and many people 
spend time at the Lido of Venice, which is a sea-based location immersed in the lagoon 
(regarding this, NBT does not substitute for other ecosystem services, such as cultural 
heritage). 

69 

In this regard, surveys are particularly important. They help to identify people’s preferences. In 
Europe, Eurobarometer (34) surveys collect information on tourism at the EU level, and many 
countries have national initiatives to collect data on tourism. To ensure these data are informative 
from an accounting perspective, and comparable and effective, the initiatives should be tailored but 
built with a common structure. 

The ESTIMAP recreation model is an expert-based approach; when implemented, experts have to 
be contacted or a detailed literature review carried out in order to adapt the models to different 
circumstances. The conceptual and structural adaptation of the model is crucial to produce reliable 
results. In this regard, one of the most important steps of the modelling approach is identifying a 
group of people with expertise on the topic, covering different perspectives of relevance to the 
research objectives. 

Future developments in nature-based recreation-related services in general (including both daily 
recreation and tourism) will consider (1) adding to the terrestrial ecosystem, including features 
from the marine ecosystem; and (2) fully developing the INCA approach. The first requires the 
expansion of the approach and process indicators able to capture natural hotspots for marine-
based recreational activities (e.g. snorkelling, diving and whale watching). The second requires the 
identification and mapping of the suppliers of tourism services (especially accommodation) and 
ecosystem service demand and eventually the computation of their interaction with the ecosystem 
service potential (provided by the ROS map). Both developments are conceptually well grounded, 
methodologically feasible and able to provide a rich set of data for powerful policy analyses. 

(34)  https://europa.eu/eurobarometer/screen/home. 

70 

                                                        
References 

Barbieri C, Valdivia C (2010) Recreation and agroforestry: Examining new dimensions of 
multifunctionality in family farms. J Rural Stud 26:465–473. 
https://doi.org/10.1016/j.jrurstud.2010.07.001 

Bright BC, Hudak AT, Kennedy RE, et al (2019) Examining post-fire vegetation recovery with 
Landsat time series analysis in three western North American forest types. Fire Ecol 15:. 
https://doi.org/10.1186/s42408-018-0021-9 

Cerveny LK, Blahna DJ, Stern MJ, et al (2011) The use of recreation planning tools in U.S. Forest 
Service NEPA assessments. Environ Manage 48:644–657. https://doi.org/10.1007/s00267-011-
9701-9 

Clark R, Stankey HG (1979) The Recreation Opportunity Spectrum: A Framework for Planning, 
Management, and Research 

Cutler SQ, Carmichael BA (2010) The Tourism and Leisure Experience. In: Morgan M, Lugosi P, 
Ritchie JRB (eds) Consumer and Managerial Perspectives. Channel View Publications, pp 3–26 

Dick J, Andrews C, Orenstein DE, et al (2022) A mixed-methods approach to analyse recreational 
values and implications for management of protected areas: A case study of Cairngorms National 
Park, UK. Ecosyst Serv 56:101460. https://doi.org/10.1016/j.ecoser.2022.101460 

Dudley, N. (Editor) (2008). Guidelines for Applying Protected Area Management Categories. Gland, 
Switzerland: IUCN. 

Eagles PFJ, McCool SF, Haynes CDA (2002) Sustainable Tourism in Protected Areas: Guidelines for 
Planning and Management. Gland, Switzerland and Cambridge, UK. 

European Commission (2020) Bringing nature back into our lives: An EU Biodiversity Strategy for 
2030. Commun. from Comm. to Eur. Parliam. Counc. Econ. Soc. Comm. Comm. Reg. Brussels, 
Belgium 26 

European Commission (2022a) Proposal for a regulating amending Regulation 691/2011 on 
Environmental Economic Accounts Modules. 0210 

European Commission (2022b) ANNEX to the Proposal for a Regulation of the European Parliament 
and of the Council amending Regulation (EU) No 691/2011 as regards introducing new 
environmental economic accounts modules 

Ekkel ED, de Vries S (2017) Nearby green space and human health: Evaluating accessibility metrics. 
Landsc Urban Plan 157:214–220. https://doi.org/https://doi.org/10.1016/j.landurbplan.2016.06.008 

Eurosion (2005) Geomorphology, geology, erosion trends and coastal defence works, Version 2, 
1:100 000. Dataset available at http://www.eea.europa.eu/data-and-maps. More information at 
http://www.eurosion.org/. 

Eurostat (2008) NACE Rev. 2 – Statistical classification of economic activities in the European 
Community 

Eurostat (2015) Methodological manual for tourism statistics. 3.1. Luxembourg: Publications Office 
of the European Union, 201&#21; [In English]. [ISBN 978-92-79-44558-3] 
https://doi.org/10.2785/892233 

Eurostat (2021) Tourism Statistics. URL: https://ec.europa.eu/eurostat/statistics- 
explained/index.php?title=Tourism_statistics#Tourism expenditure: highest_spending_ 
by_German_residents 

Eurostat (2023) Guidance note on ecosystem extent accounts, Technical Note, Version December 
2023. Available at: Eurostat guidance note on ecosystem extent accounts - version December 2023 
(europa.eu) 

71 

ETC/ULS Report 01/2018: Tourism and the Environment - towards a reporting mechanism in Europe 
Technical Report 

European Topic Centre  on Urban Land and Soil Systems (ETC/ULS), (2018). Url: 
https://www.eionet.europa.eu/etcs/etc-uls/products/etc-uls-reports/etc-uls-report-01-2018-tourism-
and-the-environment-towards-a-reporting-mechanism-in-europe 

Flash Eurobarometer 499 – Attitudes of Europeans towards tourism – October 2021 Report EN ET-
01-21-455-EN-N; 

 978-92-76-43736-9 doi:10.2873/035869, Url: 
https://data.europa.eu/data/datasets/s2283_499_eng?locale=en 

Geurs KT, van Wee B (2004) Accessibility evaluation of land-use and transport strategies: review 
and research directions. J Transp Geogr 12:. https://doi.org/10.1016/j.jtrangeo.2003.10.005 

Gundersen V, Tangeland T, Kaltenborn BP (2015) Planning for recreation along the opportunity 
spectrum: The case of Oslo, Norway. Urban For Urban Green 14:210–217. 
https://doi.org/10.1016/j.ufug.2015.01.006 

Haukeland JV, Fredman P, Tyrväinen L, et al (2023) Prospects for nature-based tourism: identifying 
trends with commercial potential. J Ecotourism 1–18. 
https://doi.org/10.1080/14724049.2023.2178444 

Hill NR (2019) National recreation opportunity spectrum (ROS) inventory mapping protocol, version 
1.2. 72 

Iacono M, Krizek KJ, El-Geneidy A (2008) Access to Destinations: How Close is Close Enough? 
Estimating Accurate Distance Decay Functions for Multiple Modes and Different Purposes. 
Minnesota Dep Transp Res Serv Sect 76 

Joyce K, Sutton S (2009) A method for automatic generation of the Recreation Opportunity 
Spectrum in New Zealand. Appl Geogr 29:409–418. 
https://doi.org/https://doi.org/10.1016/j.apgeog.2008.11.006 

Kakoyannis C, Stankey GH (2002) Assessing and evaluating recreational uses of water resources: 
Implications for an integrated management framework. USDA For Serv – Gen Tech Rep PNW 1–59. 
https://doi.org/10.2737/PNW-GTR-536 

Keith DA, Ferrer-Paris JR, Nicholson E, et al (2022) A function-based typology for Earth’s 
ecosystems. Nature 610:513–518. https://doi.org/10.1038/s41586-022-05318-4 

Kosztra B, Büttner G, Hazeu G, Arnold S (2017) Updated CLC illustrated nomenclature guidelines. 
Final Rep by Eur Environ Agency 

Kuenzi C, Mcneely J (2008) Nature-Based Tourism, Global Ris. Springer Netherlands 

Kwan MP (1998) Space-Time and Integral Measures of Individual Accessibility: A Comparative 
Analysis Using a Point-based Framework. Geogr Anal 30:. https://doi.org/10.1111/j.1538-
4632.1998.tb00396.x 

La Notte A, Vallecillo S, Polce C, et al (2017) Implementing an EU system of accounting for 
ecosystems and their services. Initial proposals for the implementation of ecosystem services 
accounts 

La Notte A, Vallecillo S, Marques A, Maes J (2019) Beyond the economic boundaries to account for 
ecosystem services. Ecosyst Serv 35:116–129. https://doi.org/10.1016/j.ecoser.2018.12.007 

La Notte A, Garcia-Bendito E, Ferrini S, et al (2021) Ecosystem Services Accounting – Part III – Pilot 
accounts for habitat and species maintenance, on-site soil retention and water purification. 

72 

Liquete C, Piroddi C, Macías D, et al (2016) Ecosystem services sustainability in the Mediterranean 
Sea: Assessment of status and trends using multiple modelling approaches. Sci Rep 6:. 
https://doi.org/10.1038/srep34162 

Lück M, Aquino RS (2021) Domestic Nature-Based Tourism and Wellbeing: A Roadmap for the New 
Normal? BT – Tourist Health, Safety and Wellbeing in the New Normal. In: Wilks J, Pendergast D, 
Leggat PA, Morgan D (eds). Springer Singapore, Singapore, pp 269–292. 

Maes J, Zulian G, Günther S, Thijssen M, Raynal J, Enhancing Resilience Of Urban Ecosystems 
through Green Infrastructure. Final Report, EUR 29630 EN; Publications Office of the European 
Union, Luxembourg, 2019, doi:10.2760/689989, JRC115375. 

Maes J, Quaglia AP, Guimarães Pereira, Â, Tokarski M, Zulian G, Marando F, Schade S, BiodiverCities: 
A roadmap to enhance the biodiversity and green infrastructure of European cities by 2030, EUR 
30732 EN, Publications Office of the European Union, Luxembourg, 2021, ISBN 978-92-76-38642-
1, doi:10.2760/288633, JRC125047. 

Maes, J., Teller, A., Erhard, M., Condé, S., Vallecillo, S., Barredo, J.I., Paracchini, M.L., Abdul Malak, D., 
Trombetti, M., Vigiak, O., Zulian, G., Addamo, A.M., Grizzetti, B., Somma, F., Hagyo, A., Vogt, P., Polce, 
C., Jones, A., Marin, A.I., Ivits, E., Mauri, A., Rega, C., Czúcz, B., Ceccherini, G., Pisoni, E., Ceglar, A., De 
Palma, P., Cerrani, I., Meroni, M., Caudullo, G., Lugato, E., Vogt, J.V., Spinoni, J., Cammalleri, C., 
Bastrup-Birk, A., San Miguel, J., San Román, S., Kristensen, P., Christiansen, T., Zal, N., de Roo, A., 
Cardoso, A.C., Pistocchi, A., Del Barrio Alvarellos, I., Tsiamis, K., Gervasini, E., Deriu, I., La Notte, A., 
Abad Viñas, R., Vizzarri, M., Camia, A., Robert, N., Kakoulaki, G., Garcia Bendito, E., Panagos, P., 
Ballabio, C., Scarpa, S., Montanarella, L., Orgiazzi, A., Fernandez Ugalde, O., Santos-Martín, F., (2020),  
Mapping and Assessment of Ecosystems and their Services: An EU ecosystem assessment, EUR 
30161 EN, Publications Office of the European Union, Ispra, ISBN 978-92-76-17833- 0, 
doi:10.2760/757183, JRC120383. 

Manning R (2010) Studies in Outdoor Recreation. Oregon State University Press 

Manning RE, Ciali CP (1981) Recreation and river type: Social-environmental relationships. Environ 
Manage 5:109–120. https://doi.org/10.1007/BF01867331 

Neger C, León-Cruz JF, Gössling S (2024) The tourism fire exposure index for the European Union. 
Tour Manag 103: https://doi.org/10.1016/j.tourman.2024.104901 

Orams MB (2003) Sandy Beaches as a Tourism Attraction: A Management Challenge for the 21 st 
Century. J Coast Res 74–84 

Otrachshenko V, Nunes LC (2022) Fire takes no vacation: impact of fires on tourism. Environ Dev 
Econ 27:86–101. https://doi.org/10.1017/S1355770X21000012 

Pandey K, Joshi S (2021) Trends in Destination Choice in Tourism Research: A 25-year Bibliometric 
Review. FIIB Bus Rev 10:371–392. https://doi.org/10.1177/23197145211032430 

Paracchini ML, Capitani C (2011) Implementation of a EU wide indicator for the rural-agrarian 
landscape. In support of COM(2006)508 “Development of agri-environmental indicators for 
monitoring the integration of environmental concerns into the Common Agricultural Policy.” 
Publications Office of the European Union, EUR 25114 EN 

ISBN 978-92-79-22396-9 (PDF) ISBN 978-92-79-22395-2 (print) ISSN 1831-9424 (online) ISSN 
1018-5593 (print), JRC 68008 EUR 

Paracchini ML, Petersen J-E, Hoogeveen Y, et al (2008) High Nature Value Farmland in Europe – An 
Estimate of the Distribution Patterns on the Basis of Land Cover and Biodiversity Data, EUR 23480 
EN, Luxembourg: Office for Official Publications of the European Communities, ISBN 978-92-79-
09568-9 ISSN 1018-5593 DOI 10.2788/8891, JRC 47063. 

73 

Paracchini ML, Zulian G, Kopperoinen L, et al (2014) Mapping cultural ecosystem services: A 
framework to assess the potential for outdoor recreation across the EU. Ecol Indic 45:. 
https://doi.org/10.1016/j.ecolind.2014.04.018 

Raatikainen KJ, Tupala AK, Niemelä R, Laulumaa AM (2023) The intricate diversity of human–nature 
relations: Evidence from Finland. Ambio 181–200. https://doi.org/10.1007/s13280-023-01933-1 

Räikkönen J, Grénman M, Rouhiainen H, et al (2023) Conceptualizing nature-based science tourism: 
a case study of Seili Island, Finland. J Sustain Tour 31:1214–1232. 
https://doi.org/10.1080/09669582.2021.1948553 

Regione del Veneto Elaborazioni dell’ufficio di Statistica della Regione del Veneto su dati Istat 

url: https://statistica.regione.veneto.it/jsp/turismo_focus_altri_comuni.jsp 

Regulation (EU) No 692/2011 OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL 

of 6 July 2011 concerning European statistics on tourism and repealing Council Directive 95/57/EC, 
url: https://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2011:192:0017:0032:EN:PDF 

Rodrigues-Filho JL, Macêdo RL, Sarmento H, et al (2023) From ecological functions to ecosystem 
services: linking coastal lagoons biodiversity with human well-being. Springer International 
Publishing 

San-Miguel-Ayanz J, Durrant T, Boca R, et al (2022) Advance EFFIS report on Forest Fires in Europe, 
Middle East and North Africa 2021 

Schröter M, Remme RP, Sumarga E, et al (2015) Lessons learned for spatial modelling of 
ecosystem services in support of ecosystem accounting. Ecosyst Serv 13:64–69. 
https://doi.org/10.1016/j.ecoser.2014.07.003 

Simensen T, Halvorsen R, Erikstad L (2018) Methods for landscape characterisation and mapping: A 
systematic review. Land use policy 75:557–569. https://doi.org/10.1016/j.landusepol.2018.04.022 

Steinhardt U, Herzog F, Lausch A, et al (1999) Hemeroby index for landscape monitoring and 
evaluation. In: Pykh Hyatt, D. E., Lenz, R. J. (Eds.) YA (ed) Environmental Indices—Systems Analysis 
Approach. EOLSS, Oxford, pp 237–254 

United Nations (2014) System of Environmental Economic Accounting 2012—Central Framework. 1. 
United Nations, New York. [In English]. URL: https: 
https://seea.un.org/sites/seea.un.org/files/seea_cf_final_en.pdf [ISBN 978-92-79-35797-8] 

United Nations (2021) System of Environmental-Economic Accounting - Ecosystem Accounting: 
Final Draft United Nations, New York, USA., 
https://seea.un.org/sites/seea.un.org/files/documents/EA/seea_ea_white_cover_final.pdf 

UNESCO-WHC (2008) Operational Guidelines for the Implementation of the World Heritage 
Convention. Chir. Prax. 74:657–674 

UNESCO (2021) Operational Guidelines for the Implementation of the World Heritage Convention. 
Oper Guidel Implement World Herit Conv 188 

UNWTO (2018) International Tourism Trends 2017. UNWTO Tour Highlights 20 

Vallecillo S, La Notte A, Polce C, Zulian G, Alexandris N, Ferrini S, Maes J. (2018) Ecosystem services 
accounting: Part I - Outdoor recreation and crop pollination, EUR 29024 EN; Publications Office of 
the European Union, Luxembourg, doi:10.2760/619793, JRC110321. 

Vallecillo S, La Notte A, Zulian G, et al (2019a) Ecosystem services accounts: Valuing the actual 
flow of nature-based recreation from ecosystems to people. Ecol Modell 392:196–211. 
https://doi.org/10.1016/j.ecolmodel.2018.09.023 

Vallecillo, S; La Notte, A; Kakoulaki, G; Roberts, N; Kamberaj, J; Dottori, F; Feyen, L; Rega, C; Maes, 
J.(2019b) Ecosystem services accounting. Part II-Pilot accounts for crop and timber provision, global 

74 

climate regulation and flood control, EUR 29731 EN, Publications Office of the European Union, 
Luxembourg, ISBN 978-92-76-02905-2, doi:10.2760/631588, JRC116334. 

Vysna V, Maes J, Petersen J, et al (2021) Accounting for ecosystems and their services in the 
European Union (INCA) 

Weston R, Mihalic T, Guia J, et al (2019) European tourism: recent developments and future 
challenges. Res. TRAN Comm. 104 

WHO(2010) Community-Based Rehabilitation: CBR Guidelines. Geneve 

Yang J, Pan S, Dangal S, et al (2017) Continental-scale quantification of post-fire vegetation 
greenness recovery in temperate and boreal North America. Remote Sens Environ 199:277–290. 
https://doi.org/10.1016/j.rse.2017.07.022 

Zulian G, La Notte A (2022) How to account for nature-based tourism in Europe. An operational 
proposal. One Ecosyst 7: https://doi.org/10.3897/oneeco.7.e89312 

Zulian G, Paracchini M-L, Maes J, Liquete Garcia MDC (2013) ESTIMAP: Ecosystem services mapping 
at European scale. European Commision 

Zulian G, Stange E, Woods H, et al (2017) Practical application of spatial ecosystem service models 
to aid decision support. Ecosyst Serv. https://doi.org/https://doi.org/10.1016/j.ecoser.2017.11.005 

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Glossary 

Ecosystem-based potential map. This map is a spatially explicit model that estimates the 
potential capacity of ecosystems to support nature-based recreation activities. 

Human inputs map. This map is a spatially explicit model that integrates the concepts of 
proximity and remoteness, dependent on road networks and settlements. 

Recreation opportunity spectrum map. This map combines ecosystem-related features, such as 
the presence of forests or seminatural areas, the presence of high nature value farmland, the 
proximity to water and water quality and the presence of protected areas, with elements that 
represent proximity to users (roads and settlements). The map classifies the land using nine 
categories, from high, through medium to low opportunities for nature-based activities, and within 
these as close, proximal or far from users. 

Nature-based tourism (NBT). This is the subsection of tourism in which people travel with the 
purpose of visiting and enjoying destinations characterised by the presence of natural resources 
(Kuenzi and McNeely, 2008). NBT covers different types of activities, ranging from hard adventure 
activities, such as climbing, trekking and mountain biking, to soft adventure activities, such as 
walking and cycling tours, canoeing and camping (Fennell, 2000; Buckley and Coghlan, 2012), or 
any other practice that is complemented by the experience of nature, such as observation. 

Potential physical metrics of ecosystem services. These are physical metrics needed to give a 
clear focus for measurement, recognising that these metrics may be a proxy for ecosystem 
services and will vary depending on data availability (United Nations, 2021). 

Main users and beneficiaries. These are the different economic units that will use the 
ecosystem services; in some cases, the same service may be used by different types of economic 
units. For example, air filtration services will be used by households and businesses (United 
Nations, 2021). 

Service use. This concept incorporates direct physical consumption, passive enjoyment and 
indirect receipt of services. Ecosystem services encompass all forms of interaction between 
ecosystems and people, including both in situ and remote interactions (United Nations, 2021). 

NBT  share.  This  measure  reflects  the  proportion  of  overnight  stays  allocated  to  nature-based 
opportunities. It enables regions with different numbers of overnight stays to be compared and the 
data to be analysed by administrative unit. 

76 

List of figures 

Figure 1. Examples of the landscapes of (A) Tuscany and (B) the Venetian Lagoon .......................................................... 11 

Figure 2. The INCA approach applied to NBT ................................................................................................................................................ 13 

Figure 3. The sequence of SUTs for NBT ......................................................................................................................................................... 14 

Figure 4. A simplified approach to NBT ............................................................................................................................................................ 15 

Figure 5. The ROS map developed for the functional urban area of Varese (Italy) ............................................................. 17 

Figure 6. Workflow of the approach to be implemented to obtain the ROS ............................................................................. 19 

Figure 7. Example of an impedance function representing sea and lake coast attractiveness .................................... 26 

Figure 8. Example of a proximity model applied to the distance from the sea coast ........................................................ 27 

Figure 9. Share of bathing water locations by Member State and typology ............................................................................ 27 

Figure 10. Example of impedance function to represent bathing water quality attractiveness .................................. 28 

Figure 11. Cross-tabulation matrix for distance from roads and distance from urban areas. ..................................... 30 

Figure 12. Cross-tabulation matrix used to derive the ecosystem-based map ...................................................................... 30 

Figure 13. Proportions of land cover types affected in 2021 ............................................................................................................ 31 

Figure 14. Burnt areas in Greece (2021) ......................................................................................................................................................... 31 

Figure 15. Burnt areas in southern Italy and Sardinia (2021) ........................................................................................................... 32 

Figure 16. The ROS map for Europe (2018) .................................................................................................................................................. 34 

Figure 17. Total number of overnight stays (millions), EU-27 (2005–2022) ........................................................................... 36 

Figure 18. Total overnight stays density (millions) ................................................................................................................................... 37 

Figure 19. A. Tourism density (overnight stays per km2); B. Tourism intensity (overnight stays per inhabitant)
.................................................................................................................................................................................................................................................... 38 

Figure 20. The natural environment as a reason for travelling for tourism, EU-27 ............................................................ 39 

Figure 21. Schema of the adaptation of the INCA approach to the physical ecosystem service flow accounting 
applied to NBT and recreation-related services. ......................................................................................................................................... 40 

Figure 22. Availability of data at the NUTS 2 level ................................................................................................................................... 43 

Figure 23. NBT at the NUTS 2 level (2018) expressed in millions. .................................................................................................. 44 

Figure 24. NBT in relation to the total number of overnight stays (2018) expressed in percentage ....................... 45 

Figure 25. Changes across years in the SPA affected by major fires (%) .................................................................................. 46 

Figure 26. Changes in NBT due to major fires (%) .................................................................................................................................... 46 

Figure 27. Changes in NBT because of major fires (2000–2018) (%) ......................................................................................... 47 

Figure 28. NBT by Member State (2018) ........................................................................................................................................................ 53 

Figure 29. Changes in NBT use due to fires (%) ......................................................................................................................................... 54 

Figure 30. Changes in nature-based tourism use due to fires (%) ................................................................................................. 54 

Figure 31. Comparison of actual flows computed at the NUTS 3 and NUTS 2 levels ........................................................ 55 

Figure 32. Comparison of the NBT use computed at the NUTS 3 and NUTS 2 levels by Member States .............. 56 

Figure 33. NBT share in 2021 at the NUTS 3 level ................................................................................................................................... 57 

Figure 34. Changes in actual flow computed with data gathered at different levels of aggregation (%) ............ 58 

Figure 35. Changes in NBT use computed with data gathered at different levels of aggregation in each Italian 
region (%) ............................................................................................................................................................................................................................ 59 

77 

Figure 36. Spatial pattern of the difference between NBT share computed with data aggregated at the 
municipal level compared with the NUTS 2 level ....................................................................................................................................... 60 

Figure 37. Box plot comparing NBT share computed with data aggregated at the municipal level (%) ................ 61 

Figure 38. NBT share in Italy at the municipal level (2021) ............................................................................................................... 62 

Figure 39. Impact of major fires on NBT use in Italy (2021) (%) .................................................................................................... 63 

Figure 40. Impact of major fires on NBT in Italy at the municipal level (%) ............................................................................ 64 

Figure 41. NBT in relation to the total number of overnight stays in the Veneto region: values computed using 
tourism statistics gathered at the (A) NUTS 2, (B) NUTS 3 and (C) municipal level ............................................................. 66 

Figure 42. Box plot comparing NBT share across provinces in the Veneto region (%)....................................................... 67 

Figure 43. Tourism statistics for the top 20 municipalities in the Veneto region ................................................................. 67 

Figure 44. Detailed ROS model workflow ........................................................................................................................................................ 84 

78 

 
 
List of tables 

Table 1. Motivations for tourist experiences and links to cultural ecosystem services ........................................................ 7 

Table 2. Datasets used to construct the EU-level ROS model ........................................................................................................... 20 

Table 3. Description of the IUCN management categories .................................................................................................................. 22 

Table 4. Cross-tabulation between management objectives and IUCN categories .............................................................. 24 

Table 5. IUCN categories and associated scores ........................................................................................................................................ 25 

Table 6. Suitability scores and proximity (maximum distance) by bathing water quality ................................................ 28 

Table 7. Burnt area parameters ............................................................................................................................................................................ 33 

Table 8. Input data for accounting ...................................................................................................................................................................... 35 

Table 9. Ecosystem types used in this exercise........................................................................................................................................... 42 

Table 10. Supply table reporting actual flow (millions) by ecosystem type (2000, 2006, 2012 and 2018) ....... 48 

Table 11. Use table reporting actual flow (millions) by sector and households (2000, 2006, 2012 and 2018)
.................................................................................................................................................................................................................................................... 49 

Table 12. Supply table reporting actual flow (millions) by Member State and ecosystem type (2018) ................. 50 

Table 13. Use table reporting actual flow (millions), computed with data aggregated at the NUTS 2 and 
NUTS 3 levels. ................................................................................................................................................................................................................... 56 

Table 14. Cultural ecosystem service classifications and descriptions (based on the CICES, version 5.2) ............ 80 

Table 15. Scores indicating suitability of land to support recreation based on expert opinions / literature 
reviewed ............................................................................................................................................................................................................................... 85 

Table 16. Number of sites without institution date by Member State and site type .......................................................... 88 

Table 17. Geomorphology of coast categories and scores for suitability of land to support recreation ............... 88 

Table 18. Correspondence between Tele Atlas categories and OpenStreetMap tags for roads .................................. 89 

79 

Annexes 

Annex 1: Cultural ecosystem services, as defined by the Common International Classification of Ecosystem Services 

Table 14. Cultural ecosystem service classifications and descriptions (based on the CICES, version 5.2) 

Class 

Code  Classification 

type 

Simple 
descriptor 

Ecological clause 

Use clause 

Example 
service(s) 

Example  goods 
and benefits 

Example  services  in 
marine 
protected 
areas 

Elements  of 
living 
systems  that  that 
activities 
enable 
health, 
promoting 
recuperation 
or 
through 
enjoyment 
or 
active 
immersive 
interactions 

living 
Elements  of 
systems that enable 
activities  promoting 
health,  recuperation 
enjoyment 
or 
through passive or 
observational 
interactions 

3.1.1.1 By 

type  of 
living  system 
or 
environmental 
setting 

3.1.1.2 By 

type  of 
living  system 
or 
environmental 
setting 

Using 
the 
environment 
for 
sport 
and 
recreation; 
using nature 
to  help  stay 
fit 

Watching 
plants  and 
animals 
where  they 
using 
live; 
nature 
to 
de-stress 

The 
biophysical 
characteristics or qualities 
of  species  or  ecosystems 
cultural 
(settings / 
spaces) … 

… 
are 
that 
engaged  with, 
used  or  enjoyed 
that 
in  ways 
require  physical 
and 
cognitive 
effort 

biophysical 
The 
characteristics or qualities 
of  species  or  ecosystems 
(settings / 
cultural 
spaces) … 

are 
that 
… 
viewed/observed 
by  people  or 
enjoyed  in  other 
passive ways by 
virtue  of  sounds 
and smells etc. 

Ecological 
of 
qualities 
woodland 
that 
make it attractive 
to  hikers;  private 
gardens; 
opportunities  for 
diving 
or 
swimming 

Mix  of  species  in 
a  woodland  of 
interest 
to 
birdwatchers; 
birds, 
whales, 
and 
seals 
reptiles, 
which 
can be enjoyed by 
wildlife watchers 

Sport and leisure 

Recreation; 
fitness; 
stressing; 
mental health 

de-

Observing nature 

de-

Recreation; 
fitness; 
stressing; 
mental  health; 
ecotourism 

Elements  of 
living 
systems that enable 
scientific 
investigation 
creation 
the 

or 
of 

3.2.1.1 By 

type  of 
living  system 
or 

Researching 
nature 

The 
biophysical 
characteristics or qualities 
of  species  or  ecosystems 

…  that  are  the 
subject  matter 
situ 
in 
for 
research 

Site  of  special 
scientific interest; 
Natura 2000 site 

Knowledge 
about 
environment 
and nature 

the 

Researching nature 

80 

traditional 
ecological 
knowledge 

Class 

environmental 
setting 

(settings / 
spaces) … 

cultural 

Code  Classification 

type 

Simple 
descriptor 

Ecological clause 

Use clause 

Example 
service(s) 

Example  goods 
and benefits 

Example  services  in 
marine 
protected 
areas 

living 
Elements  of 
systems  that  enable 
education 
and 
training 

3.2.1.2  By type of living 
system 
or 
environmental 
setting 

Studying 
nature 

biophysical 
The 
characteristics or qualities of 
species 
ecosystems 
(settings / cultural spaces) … 

or 

…  that  are  the 
subject matter for 
in situ teaching or 
skill development 

used 

Site 
voluntary 
conservation 
activities 

Studying nature 

for 

or 
Skills 
knowledge  about 
environmental 
management 

living 
Elements  of 
systems 
are 
resonant  in  terms  of 
culture or heritage 

that 

3.2.1.3  By type of living 
system 
or 
environmental 
setting 

The 
biophysical 
characteristics or qualities of 
ecosystems 
species 
(settings / cultural spaces) … 

or 

…  that  contribute 
cultural 
to 
or 
heritage 
historical 
knowledge  

Sherwood Forest  Tourism; 
identity 

local 

Heritage 

living 
Elements  of 
systems  that  enable 
aesthetic 
experiences 

3.2.1.4  By type of living 
system 
or 
environmental 
setting 

biophysical 
The 
characteristics or qualities of 
species 
ecosystems 
(settings / cultural spaces) … 

or 

that 

… 
appreciated 
their 
beauty 

are 
for 
inherent 

Area 
outstanding 
natural 
panorama site 

beauty; 

of 

Artistic inspiration Realising  the  beauty  of 
nature 

used 

living 
for 
or 

Elements  of 
systems 
entertainment 
representation 
outside  the  setting 
concerned 

3.3.1.1  By type of living 
system 
or 
environmental 
setting 

The  things  in 
nature  used 
to make films 
to  write 
or 
books 

The 
biophysical 
characteristics or qualities of 
ecosystems 
species 
landscapes / 
(settings / 
cultural spaces) … 

or 

that  provide 
… 
material 
or 
subject  matter 
that 
be 
can 
communicated  to 
others 
via 
different  media 

Archive  records  or 
collections 

Nature films 

The  things 
in  nature 
used  to  make  films  or 
write books 

81 

The  things  in 
nature 
that 
help  people 
identify  with 
the  history  or 
of 
culture 
they 
where 
live  or  come 
from 

The beauty of 
nature 

Class 

Code  Classification 

type 

Simple 
descriptor 

for amusement or 
enjoyment 

Ecological clause 

Use clause 

Example 
service(s) 

Example  goods 
and benefits 

Example  services  in 
marine 
protected 
areas 

Bald eagle 

Social  cohesion; 
cultural icon 

Using  nature  as  a 
national 
local 
emblem 

or 

3.4.1.1  By type of living 
system 
or 
environmental 
setting 

living 
Elements  of 
systems 
that  have 
symbolic  meaning,  or 
the 
capture 
distinctiveness 
of 
settings  or 
their 
sense of place 

The 
biophysical 
characteristics or qualities 
of  species  or  ecosystems 
landscapes / 
(settings / 
cultural spaces) … 

Using  nature 
as  a  national 
local 
or 
or 
emblem, 
referencing  a 
particular 
area 
distinctive 
some 
(e.g. 
Yorkshire 
Dales) 

as 
in 
way 
the 

living 
Elements  of 
systems 
that  have 
spiritual or religious 
meaning 

3.4.1.2  By type of living 
system 
or 
environmental 
setting 

The  things  in 
nature 
that 
have  spiritual 
importance 
for people 

The 
biophysical 
characteristics or qualities 
of  species  or  ecosystems 
(settings / 
landscapes / 
cultural spaces) … 

Totemic  species, 
such as the turtle 

Mental 
being 

well-

The  things  in  nature 
that  have  spiritual 
importance to people 

living 

Elements  or  features 
of 
systems 
whose  contemporary 
or 
existence 
conservation 
is 
important to people 

3.4.2.1  By type of living 
system 
or 
environmental 
setting 

The  things  in 
nature 
that 
think 
we 
be 
should 
conserved 

The 
biophysical 
characteristics or qualities 
of  species  or  ecosystems 
landscapes / 
(settings / 
cultural spaces) … 

…  which  people 
seek to preserve 
because  of  their 
non-utilitarian 
qualities 

Areas  designated 
as wilderness 

Mental/moral 
well-being 

Existence 

82 

are 
… 
that 
recognised 
by 
people  for  their 
cultural, 
historical 
or 
iconic  character 
and  which  are 
as 
used 
or 
emblems 
signifiers 
of 
some kind 

… 
are 
that 
deemed to  have 
or 
sacred 
religious 
significance  for 
people 

Class 

Code  Classification 

type 

Simple 
descriptor 

Ecological clause 

Use clause 

Example 
service(s) 

Example  goods 
and benefits 

Example  services  in 
marine 
protected 
areas 

living 

Elements  or  features 
of 
systems 
whose 
intergenerational 
existence 
conservation 
important to people 

or 
is 

3.4.2.2 By 

type 

of 
system 

living 
or 
environmental 
setting 

The  things  in 
nature 
that 
we 
want 
future 
generations 
to  enjoy  or 
use 

The 
biophysical 
characteristics or qualities 
of  species  or  ecosystems 
(settings / 
landscapes / 
cultural spaces) … 

…  which  people 
seek 
to 
for 
preserve 
future 
generations  for 
whatever 
reason 

Endangered 
species or habitat 

Moral well-being Bequest 

NB: The classes considered in this report are in grey. 

Source: Authors’ own preparation.

83 

 
Annex 2: Recreation opportunity spectrum data preparation 

Figure 44. Detailed ROS model workflow 

NB: cross-tab, cross-tabulation; LUT, lookup table. 

Source: Zulian and La Notte, 2022.

84 

 
Table 15. Scores indicating suitability of land to support recreation based on expert opinions / literature reviewed 

CLC  grid 
code 

Label 1 

Label 3 

SLSR score 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

11 

12 

13 

14 

15 

16 

17 

18 

19 

20 

21 

22 

23 

24 

Artificial surfaces 

Continuous urban fabric 

Artificial surfaces 

Discontinuous urban fabric 

Artificial surfaces 

Industrial or commercial units 

Artificial surfaces 

Road and rail networks and associated land 

Artificial surfaces 

Port areas 

Artificial surfaces 

Airports 

Artificial surfaces 

Mineral extraction sites 

Artificial surfaces 

Dump sites 

Artificial surfaces 

Construction sites 

Artificial surfaces 

Green urban areas 

Artificial surfaces 

Sport and leisure facilities 

Agricultural areas 

Non-irrigated arable land 

Agricultural areas 

Permanently irrigated land 

Agricultural areas 

Rice fields 

Agricultural areas 

Vineyards 

Agricultural areas 

Fruit trees and berry plantations 

Agricultural areas 

Olive groves 

Agricultural areas 

Pastures 

Agricultural areas 

Annual crops associated with permanent crops 

Agricultural areas 

Complex cultivation patterns 

Agricultural areas 

Land  principally  occupied  by  agriculture,  with 
significant areas of natural vegetation 

Agricultural areas 

Agroforestry areas 

Forest 
seminatural areas 

and 

Broad-leaved forest 

Forest 
seminatural areas 

and 

Coniferous forest 

85 

0 

0.1 

0 

0 

0 

0 

0 

0 

0 

1 

0.1 

0.3 

0.3 

0.4 

0.5 

0.5 

0.5 

0.6 

0.3 

0.3 

0.6 

0.6 

1 

0.8 

1 

0.8 

0.8 

0.8 

0.8 

1 

0.8 

0.7 

0 

0 

1 

0.8 

1 

0.8 

1 

1 

1 

1 

0.8 

1 

25 

26 

27 

28 

29 

30 

31 

32 

33 

34 

35 

36 

37 

38 

39 

40 

41 

42 

43 

44 

Forest 
seminatural areas 

and 

Mixed forest 

Forest 
seminatural areas 

and 

Natural grasslands 

Forest 
seminatural areas 

and 

Moors and heathland 

Forest 
seminatural areas 

and 

Forest 
seminatural areas 

and 

Forest 
seminatural areas 

and 

Sclerophyllous vegetation 

Transitional woodland/shrub 

Beaches, dunes, sands 

Forest 
seminatural areas 

and 

Bare rocks 

Forest 
seminatural areas 

and 

Sparsely vegetated areas 

Forest 
seminatural areas 

and 

Burnt areas 

Forest 
seminatural areas 

and 

Glaciers and perpetual snow 

Wetlands 

Inland marshes 

Wetlands 

Peat bogs 

Wetlands 

Salt marshes 

Wetlands 

Salines 

Wetlands 

Intertidal flats 

Water bodies 

Water courses 

Water bodies 

Water bodies 

Water bodies 

Coastal lagoons 

Water bodies 

Estuaries 

Water bodies 

Seas and oceans 

NB: SLSR, suitability of land to support recreation. 

Source: Authors’ own preparation. 

86 

 
 
Methodology for determining the year of foundation of protected areas 

In determining the time frame of a site, the legal foundation date is extremely important. This is 
because a site can have been legally protected at the regional or national level and, after a few 
years, become part of the Natura 2000 network. The legal foundation date is available in the CDDA 
database. It corresponds to the date the site was proposed as eligible for identification as a site of 
community importance (DATE PROP SCI) (35) in the Natura 2000 database. Considering that the sites 
may overlap and that the IUCN category is a key attribute in scoring the capacity of an area to 
provide opportunities for recreation, the two datasets were combined. 

Each polygon is classified according to the: 

—  legal foundation date (from the CDDA); 

—  year of reference (from Natura 2000). 

The  oldest  date  is  retained.  Protected  areas  are  then  scored  according  to  IUCN  management 
categories. 

Natura 2000 sites do not have a legal foundation date. Moreover, polygons representing sites protected under 
different directives may overlap. Therefore, (1) each site was labelled with its reference year; and (2) if sites 
overlap, the oldest reference year is considered. 

In Natura 2000, the reference year depends on attributes and a set of defined conditions, as specified below. 

Attributes: 

—  SITETYPE (type of classification for the site): 

●  A: SPAs (sites designated under the birds directive) (3 660), 

●  B:  sites  of  community  importance  (SCIs)  and  special  areas  of  conservation  (SACs) 

(designated under the habitats directive) (22 186), 

●  C: sites where SPAs and SCIs/SACs boundaries are identical (sites designated under both 

directives) (2 006); 

—  DATE_SPA (date site classified as an SPA – designated under the birds directive); 

—  DATE_SAC (date site designated as an SAC (under the habitat directive); 

—  DATE_PROP_SCI (date site proposed as eligible for identification as an SCI); 

—  DATE_CONF_SCI (date site was confirmed as an SCI). 

Conditions: 

—  If SITETYPE = A, year of reference is DATE_SPA; 

—  If SITETYPE = B, year of reference is DATE PROP SCI; 

—  If no DATE PROP SCI, get DATE_SAC; 

—  If SITETYPE = C, year of reference is DATE PROP SCI; 

—  If no DATE PROP SCI, get DATE_SAC; 

There are 47 Natura 2000 sites without a date of institution of any sort (see Table 16). 

(35)  See the definitions associated with the Natura 2000 dataset at Natura2000_end2019_dataset_definitions.xls. 

87 

 
 
 
 
                                                        
Table 16. Number of sites without institution date by Member State and site type 

Member 
State 

Site 
type 

Number 

AT 

DE 

EL 

HR 

LU 

PT 

A 

B 

B 

A 

B 

C 

A 

A 

B 

1 

2 

1 

8 

23 

1 

4 

6 

1 

Source: Authors’ own preparation. 

If the areas intersect with those in the CDDA data, the legal foundation date was identified as the reference 
year; otherwise, the information was gathered from Natura 2000 or the European nature information system. 

Table 17. Geomorphology of coast categories and scores for suitability of land to support recreation 

CEMO (*)  CEMODSEN 

SLSR score 

A 

B 

C 

D 

E 

F 

G 

H 

J 

Rocks and/or cliffs made of hard rocks (little subject to erosion) 

0.6 

Conglomerates  and/or  cliffs  made  of  material  subject  to  erosion:  presence  of  rock 
waste and sediments (sand pebbles on the strand) 

0.5 

Small beaches separated by rocky capes 

Developed beaches (length of beach > 1 km), with strands made of coarse sediments: 
gravel or pebbles 

Developed beaches with sandy strands: fine to coarse sand 

Coastlines made of soft non-cohesive sediments 

Strands made of muddy sediments: ‘waddens’ and intertidal marshes with ‘slikkes and 
schorres’ 

Estuaries 

Harbour areas 

1 

1 

1 

0.8 

0 

0 

0 

88 

 
 
 
K 

L 

M 

N 

P 

R 

S 

X 

Y 

Z 

CEMO (*)  CEMODSEN 

Artificial beaches 

Coastal embankments for construction purposes (e.g. by emplacement of rocks, earth, 
etc.) 

Polders (reclaimed coastal areas) 

Very narrow and vegetated strands (pond or lake shore type) 

Soft strands with rocky ‘platforms’ (rocky flats) on intertidal strands 

Soft strands with ‘beach rock’ on intertidal strands 

Soft strands made of mine-waste sediments 

Soft strands of heterogeneous grain size 

Suitability 
for 
recreation 

1 

0 

0 

0.6 

0.6 

0.8 

0 

0.3 

Artificial  shoreline  or  shoreline  with  longitudinal  protection  works  (dikes)  without 
aerated strands 

0 

Soft strands of unknown grain size 

0.3 

Source: Eurosion, 2005, p. 31. 

Table 18. Correspondence between Tele Atlas categories and OpenStreetMap tags for roads 

Tele Atlas category 

OSM category 

Short 
description 

Functional 
road class 
attribute 
value 

Long description 

Tag(s) 

Description 

0 

1 

2 

Motorways; 
freeways; 
major roads 

All roads that are officially 
classified as motorways. 

—  Highway: 
motorway 

—  Highway: 

motorway_link 

A restricted access major 
divided highway, normally 
with two or more running 
lanes plus an emergency 
hard shoulder, equivalent to 
a freeway 

Major  roads 
less 
important 
than 
motorways 

All roads of high importance, 
but not officially classified as 
motorways, that are part of a 
connection used for 
international and national 
traffic and transport. 

—  Highway: 
trunk 

—  Highway: 
trunk_link 

The most important roads in 
a country’s system that are 
not motorways 

Other  major 
roads 

All roads used to travel 
between neighbouring regions 
of a country. 

—  Highway: 

primary 

—  Highway: 

primary_link 

The next most important 
roads in a country’s system. 
(often linking larger towns) 

89 

Tele Atlas category 

OSM category 

Short 
description 

Functional 
road class 
attribute 
value 

Long description 

Tag(s) 

Description 

3 

4 

5 

6 

7 

Secondary 
roads 

All roads used to travel 
between different parts of the 
same region. 

—  Highway: 
secondary 

—  Highway: 

secondary_link 

The  next  most 
important 
roads  in  a  country’s  system 
(often linking towns) 

Local 
connecting 
roads 

All roads making all 
settlements accessible or 
making parts (north, south, 
east, west and central) of a 
settlement accessible. 

—  Highway: 
tertiary 

—  Highway: 

tertiary_link 

The  next  most 
important 
roads  in  a  country’s  system 
(often  linking  smaller  towns 
and villages) 

Highway: 
unclassified 

The  least  important  through 
roads in a country’s system – 
that is, minor roads of a lower 
classification  than  tertiary, 
but that serve a purpose other 
to 
than  providing  access 
properties 
linking 
villages and hamlets) 

(often 

Local  roads 
of 
high 
importance 

Local roads 

Local  roads 
of  minor 
importance 

All local roads that are the 
main connections in a 
settlement. These are the 
roads on which important 
through traffic is possible, for 
example: 

●  arterial roads within 
suburban areas, 
industrial areas or 
residential areas; 

● 

rural roads with the sole 
function of connecting 
to a national park or 
important tourist 
attraction. 

All roads used to travel within 
a part of a settlement or roads 
of minor connecting 
importance in a rural area. 

All roads that only have a 
destination function, for 
example dead-end roads; 
roads inside living areas; alleys 
(narrow roads between 
buildings, in parks or in 
gardens). 

8 

Other roads  All other roads that are less 

—  Pedestrian 

important for navigation 
systems: paths (roads that 
are too small to be driven 
by a passenger car), 

—  Track 

—  Footway 

90 

 
 
—  Bridleway 

—  Path 

including bicycle paths or 
footpaths (which are 
especially designed for their 
purpose); stairs; pedestrian 
tunnels; pedestrian bridges; 
alleys (that are too small to 
be driven by a passenger 
car). 

Source: OpenStreetMap Wiki, n.d. OSM data tags include motorway, trunk, primary, secondary and tertiary.

91 

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