How much do Europeans value biodiversity?

How much do Europeans value biodiversity? A choice experiment exercise to estimate the “habitat and species maintenance” ecosystem service Alessandra La Notte, Silvia Ferrini, Domenico Pisani, Gaetano Grilli, Ioanna Grammatikopoulou, Sara Vallecillo, Tomas Badura, Kerry Turner, Joachim Maes 2021 EUR 30953 EN This publication is a Technical report by the Joint Research Centre (JRC), the European Commission’s science and knowledge service. It aims to provide evidence-based scientific support to the European policymaking process. The scientific output expressed does not imply a policy position of the European Commission. Neither the European Commission nor any person acting on behalf of the Commission is responsible for the use that might be made of this publication. For information on the methodology and quality underlying the data used in this publication for which the source is neither Eurostat nor other Commission services, users should contact the referenced source. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of the European Union concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. Contact information Name: Alessandra La Notte Email: [email protected] EU Science Hub https://ec.europa.eu/jrc JRC127797 EUR 30953 EN PDF ISBN 978-92-76-46351-1 ISSN 1831-9424 doi:10.2760/927786 Print ISBN 978-92-76-46352-8 ISSN 1018-5593 doi:10.2760/94047 Luxembourg: Publications Office of the European Union, 2021 © European Union, 2021 The reuse policy of the European Commission is implemented by the Commission Decision 2011/833/EU of 12 December 2011 on the reuse of Commission documents (OJ L 330, 14.12.2011, p. 39). Except otherwise noted, the reuse of this document is authorised under the Creative Commons Attribution 4.0 International (CC BY 4.0) licence (https://creativecommons.org/licenses/by/4.0/). This means that reuse is allowed provided appropriate credit is given and any changes are indicated. For any use or reproduction of photos or other material that is not owned by the EU, permission must be sought directly from the copyright holders. All content © European Union, 2021, except cover page: drawing made by Elisabetta C. (9 years old) during the LIPU (Lega Italiana Protezione Uccelli) summer camp How to cite this report: La Notte A., Ferrini S., Pisani D., Grilli G. Grammatikopoulou I., Badura T., Vallecillo S., Turner K., Maes J., How much do Europeans value biodiversity? A choice experiment exercise to estimate the “habitat and species maintenance” ecosystem service, EUR 30953 EN, Publications Office of the European Union, Luxembourg, 2021, ISBN 978-92-76-46351-1, doi:10.2760/927786, JRC127797 Contents Acknowledgements .................................................................................................................................................................................3 Abstract ........................................................................................................................................................................................................4 1. Introduction ........................................................................................................................................................................................5 1.1. Plural values and scale issues related to biodiversity............................................................................................7 2. From the questionnaire preparation to the focus groups and pilot case studies ................................................9 2.1. Definition of reference scenario and literature review ...................................................................................... 10 2.2. The first pilot test of biodiversity questionnaire .................................................................................................. 11 2.2.1. 2.2.2. Section A: Description of the topic ................................................................................................................. 11 Section B: Stated preferences........................................................................................................................... 12 2.2.3. Conclusions .............................................................................................................................................................. 13 2.3.1. Phase 1 ....................................................................................................................................................................... 13 2.3. Focus Groups ......................................................................................................................................................................... 13 2.3.2. Phase 2 ....................................................................................................................................................................... 14 2.3.3. Phase 3 and conclusion ....................................................................................................................................... 15 2.4.1. Sample structure and questionnaire structure ........................................................................................ 15 2.4.3. First CE Pilot study conclusions ...................................................................................................................... 18 2.4. Pilot study of Choice Experiment questionnaire in a city.................................................................................. 15 2.4.2. Results ........................................................................................................................................................................ 17 3. From questionnaire testing to the main survey: major outcomes and their meaning ................................... 19 3.1. Preliminary information on countries and samples ............................................................................................ 19 3.2. Socio-economic analysis based on the respondent profile ............................................................................... 21 3.3. Current knowledge and attitude................................................................................................................................... 22 3.4. Biodiversity management preferences: the Choice cards ................................................................................. 24 3.5. Biodiversity management preferences: perception and attitudes ................................................................ 27 3.6. Conclusions ............................................................................................................................................................................ 30 4. From the survey results to the monetary estimates ...................................................................................................... 31 4.1. The theoretical model underpinning the Choice cards ...................................................................................... 31 4.2. Results from the Choice cards ....................................................................................................................................... 33 4.3. The role of current land use characteristics ............................................................................................................ 34 4.4. From the four sampled countries to the 28 European countries ................................................................... 39 4.5. Estimated values for the maintenance of habitat and species ........................................................................ 41 5. Conclusions ....................................................................................................................................................................................... 43 References................................................................................................................................................................................................ 46 List of abbreviations and definitions ........................................................................................................................................... 49 List of figures .......................................................................................................................................................................................... 50 List of tables ............................................................................................................................................................................................ 51 Annexes ..................................................................................................................................................................................................... 52 1 Annex I. Biodiversity Governance initiatives at global level ...................................................................................... 53 Annex II. The questionnaire ...................................................................................................................................................... 55 SECTION 0 - SCREENING AND QUOTAS ........................................................................................................................................... 56 SECTION A – Description of the topic .............................................................................................................................................. 57 SECTION B – Attitude and perception ............................................................................................................................................. 60 SECTION C –PREFERENCES for AGRICULTURE POLICIES ............................................................................................................ 62 SECTION D – RESPONDENT PROFILE ................................................................................................................................................ 68 Annex III. sampling of countries.............................................................................................................................................. 70 2 Acknowledgements The authors would like to acknowledge support from Laure Ledoux and Jakub Wejchert (DG Env), Veronika Vysna, and Anton Steurer (ESTAT), Jesus Barreiro Hurle (JRC) for guidance and comments on earlier versions of the survey questionnaire, Greti Lucaroni (Italian Ministry of the Environment) for supporting the organisation and implementation of the focus groups, Valentina Di Gennaro (University of Siena) for supporting the field work and questionnaire revisions, Agnese Fanciulli for contributing her art to the questionnaire and report, and SurveyEngine for supporting the technical administration of the online stated preference survey. The initial version of the questionnaire was tested and revised with the support of the MSc students of the University of Siena, degree in Economics of Environment and Sustainability. Authors Alessandra La Notte1 Silvia Ferrini2,3 Domenico Pisani4 Gaetano Grilli3 Ioanna Grammatikopoulou1 Tomas Badura3 Sara Vallecillo1 Kerry Turner3 Joachim Maes6 1 European Commission - Joint Research Centre, Italy 2 University of Siena, Italy 3 Centre for Social and Economic Research on the Global Environment, University of East Anglia, UK 4 University of Foggia, Italy 6 European Commission - DG REGIO, Belgium 3 Abstract Biodiversity is an intangible asset essential for ecosystem function and human wellbeing. The European Union is at the forefront of biodiversity management and policy implementation and has set ambitious strategies to better protect biodiversity and lead achievement of global biodiversity goals. However, biodiversity management entails balancing a range of economic and social trade-offs. A deeper understanding of society’s perception towards biodiversity, the values attached to it, and the heterogeneity around preferences for biodiversity protection and habitat maintenance is key to inform future European strategies. This report provides Europeanlevel spatially explicit estimates of biodiversity non-use value applicable in the decision-making processes and appreciates the hidden contribution of habitat and species maintenance to human wellbeing. A stated preference survey with the choice experiment was conducted in four European countries that were selected to represent a range of diverse environmental and social contexts. A European map of biodiversity values is produced via value transfer techniques. The stated preference survey involved trade-offs between improved, maintained, or deteriorated agricultural practices (from agroforestry to monoculture) farm size interventions, chemical use intensity, biodiversity levels and annual costs. Results reveal heterogeneity in preferences across space and social groups, but biodiversity is persistently a key characteristic affecting public perception of land management practices. The average monetary value attached to habitat and species maintenance estimated as willingness to pay from the stated preference study varies from Euro 28 to 276 per year per family and reflects the current uneven conditions of European natural environment as well as attitudes and policies in support of biodiversity protection. Overall, our results suggest that strengthening habitat and species maintenance policy is considered a necessity by the public. In fact, considering the aggregated amount Europeans are prepared to pay annually (30 billion Euros) for biodiversity, we can anticipate that the Post-2020 Biodiversity policy committed to an annual budget of 20 billion Euros would likely find public support. However, regional diversity need to be fully reflected in effective and fair policy interventions. 4 1. Introduction The first step in protecting biodiversity dates back to 1992. The Convention on Biological Diversity was adopted during the United Nations Conference on Environment and Development held in Rio de Janeiro, together with the Convention on Climate Change and the Convention against desertification. The Convention on Biological Diversity (CBD), adopted in Nairobi (Kenya) in May 1992, now has extensive participation with 196 contributing countries (United States is the big missing player). The signatory countries have committed to protect biodiversity in their territories, to promote adequate measures to access and use biodiversity in developing countries. The follow-up global level initiatives on Biodiversity (ref. Annex I) underpin and promote directives and strategies that eventually took place in Europe. The European Union (EU) has two key instruments to safeguard biodiversity: the Habitats Directive (92/43 / EEC) and the Birds Directive (79/409 / EEC), together with the creation of the European Network "Nature 2000”, the largest collection of protected areas in the world. In May 2011 the EU adopted the Strategy for Biodiversity until 2020. The aim was to strengthen the EU contribution to the mitigation of and/or halting biodiversity loss. The Biodiversity Strategy 2011-2020 included six objectives focused on the root causes of biodiversity loss and aimed at reducing the main pressures on nature and ecosystem services to restore and enhance species and habitats at risk. The objectives of the Biodiversity Strategy were: 1. Full implementation of the Habitats and Birds Directives, to prevent and protect habitats and species losses in order to achieve a significant and quantifiable improvement in their status. 2. Restoration and maintenance of ecosystems and related services, enhancing ecosystems and services through green infrastructure and restoring at least 15% of degraded ecosystems. 3. Increasing the contribution of agriculture and forestry to the maintenance of biodiversity through more sustainable practices and management. 4. Ensuring the sustainable exploitation of fishery resources, through the achievement of a population distribution by age and size indicative of a stock in good condition; through fisheries management that does not have significant negative effects on other stocks, species and ecosystems, with the aim of achieving a satisfactory environmental status by 2020. 5. Combating invasive alien species, identifying and ranking them in order of priority. Then by eliminating them and managing the vectors to prevent the introduction and settlement of new species. 6. Contribute to avoiding the loss of biodiversity worldwide. An initial evaluation of the EU Biodiversity Strategy delivers mixed outcomes. There have been some improvements in the protection and restoration of species and habitats, in the conservation of ecosystems and in efforts to keep Europe's seas healthy (Dickie et al 2017). However, according to the report "State of Nature in the EU", published in October 2020 by the European Environment Agency (EEA), Europe has failed to fully achieve the objectives set by the Biodiversity Strategy. The EEA report concludes that despite the positive efforts made, biodiversity continues to decline , with deteriorating trends being experienced in most European Member States (MSs). Most habitats (81%) and protected species (over 60%) are at risk, or in less-than-ideal condition due to overexploitation and unsustainable management practices. Agriculture and forestry land are recognized as pivot in protecting and restoring biodiversity as together they represent more than two thirds of the entire territorial area of the EU. However, only 8% of agricultural habitats are assessed as improving, while 45% are assessed as deteriorating. 67% of agricultural habitats are completely dependent on agricultural management and 37% of the partially dependent habitats are rated as "bad" quality. The forest areas (60% temperate and 25% Mediterranean) have fared better. The EEA 2020 report reveals that only 31% of them have poor conservation status, although different forest management plans exist in member states, and the Boreal Forest is in a less favourable condition than the Mediterranean. The need to guarantee sustainable agricultural land uses and biodiversity protection, was reinforced by the European Parliament in December 2019 during the launch of the "European Green Deal" (EGD). The EGD promotes an efficient and sustainable growth strategy that aims to combat climate change and protect the environment, with the final aim to improve people's well-being and make Europe climate neutral by 2050. 5 The climate policy ambitions of EU leaders are meant to transform the political climate commitments into a legal obligation system and a set of investment incentives for reducing climate impacts. Along with climate protection, the protection of humans, animals and plants from pollution will be also achieved through the “Farm to Fork” strategy. This aims to reduce drastically the agricultural inputs that cause environmental pollution (fertilizers, pesticides, etc) as these are driver of biodiversity losses and possibly climate changes. Finally, the EGD would support companies to become world leaders in clean products and technologies guided by an inclusive and fair ethos. One of the EGD pillars is focused on biodiversity and in May 2020, the European Commission adopted the new "Biodiversity Strategy for 2030" which represents a long-term, global, systemic plan to safeguard nature and reverse the trend towards ecosystems degradation. The main elements of the EU Biodiversity Strategy for 2030 are:  To establish more binding targets that include stricter forest protection  To enhance the network of protected areas both on land and at sea with rigorous protection for areas with very high biodiversity and climatic value. Specifically, create protected areas for at least: 30% of the land area in Europe; and 30% of the seas;  To restore degraded terrestrial and marine ecosystems, by at least 30%, through a series of concrete commitments and actions and their sustainable management. The key factors of recovery include: - An increase in organic farming; - The arrest and reversal of the decline of pollinators; - The reduction of the use and harmfulness of pesticides by 50%; - The restoration of 25,000 kilometres of rivers; - The planting of at least three billion trees by 2030. The ambition to better protect biodiversity seems well set in the new European strategies, and a number of measures (e.g. improve knowledge, finance and investment, sustainable public and corporate decision making) have been suggested to prompt green growth. The EU has committed to the release of 20 billion euros annually (through EU and national funds) for biodiversity protection with the aim of becoming a world leader for biodiversity enhancement. Furthermore, a significant part (25%) of the EU budget for climate action will be invested in biodiversity and nature-based solutions. The EU seems to stand ready to lead by example, with the adoption of the ambitious global biodiversity goals defined by the Convention on Biological Diversity. However, regulations relating to biodiversity maintenance and sustainability are fragmented. Member states are often faced with contradictory requirements, for example, the promotion of the post 2020 Common Agriculture Policy (Sumrada et al 2020). Montini and Volpe (2017) argue that the current regulatory system is not ecologically focused enough, and the EU sustainability ambitions could not be achieved if the tension between the need to promote functioning private markets and the collective protection of common goods are not fundamentally revised. Beckman et al (2021) are also concerned over the possible impact of the Farm to Fork strategy which could significantly reduce the agriculture production capacity of EU and impose significant economic costs to society. Finally, the protection of biodiversity is highly sensitive to local and social characteristics and a bottom-up approach is desirable to accommodate stakeholder needs and ambitious. This is argued by Armitage et al (2020) who support a community-centred management inspired by the work of Elinor Ostrom, but this, again, supports a systemic switch from global international competitive markets to local participatory and self-organised systems. In this context, it becomes important to investigate (i) what is society’s perception of biodiversity and does this further endorse government actions, and (ii) how much do people value biodiversity and (iii) how heterogeneous are the preferences on biodiversity protection and habitat maintenance. To address the first of these two tasks, the European Commission assigned a series of surveys on “Attitudes towards biodiversity” 1 . The Flash Eurobarometer, i.e. ad hoc thematic telephone interviews, is a trend survey that took place in 2007, 2010 and 2013 in the EU 28 member states (MS). Eurobarometer surveys asked EU citizens to clarify how familiar they are with the term biodiversity and with the concept of biodiversity loss, by investigating: • The level to which EU citizens feel informed about biodiversity issues; • 1 Europeans’ perceptions of the major threats to biodiversity; Ref. https://data.europa.eu/data/datasets/s1103_379?locale=en 6 • Perceptions of the seriousness of biodiversity loss at domestic, European and global levels; • Awareness of the Natura 2000 network and perceptions of the most important roles of nature protection areas; • Views on whether preserving biodiversity is important, and what measures the EU and individuals can take to prevent the loss of biodiversity. The outcomes of the most recent survey (the one undertaken in 2013) show that: • familiarity with the term “biodiversity” has increased in 18 Member States compared with the previous survey in 2010, but slightly more than half of Europeans (54%) feel that they are not well informed about biodiversity loss; • across the EU, some of the most acknowledged threats for biodiversity are: pollution of air and water (96%): intensive farming, deforestation and over-fishing (94%); and conversion of natural areas to other uses (91%); • about 86% of respondents believe that biodiversity loss is a problem in their own country, 88% that it is a problem in Europe, 94% that it is a global problem; • In terms of nature protection areas such as Natura 2000, a significant majority of Europeans (ranging from 99% to 83%) believe that their role in preventing the destruction of valuable areas on land and at sea is important; • 97% of Europeans agree that it is important to halt biodiversity loss because it is a moral obligation and 93% because our well-being and quality of life is based upon biodiversity. Between 87% and 75% of respondents are concerned about the economic impacts of the loss of biodiversity. 92% agree that more financial resources should be allocated for nature protection, and 72% that financial rewards should be allocated to the primary sector to consider biodiversity in their management practices. The availability of financial resources/funding streams does also play an increasing important role in people’s perception. With respect to Eurobarometer surveys, this report expands the range of questions investigating more fully (i) the public’s perception of biodiversity, but also (ii) how much people value biodiversity in monetary terms, to concretely understand the order of financial magnitude that should be foreseen if we are to sustainably manage and protect biodiversity. 1.1. Plural values and scale issues related to biodiversity. So there are clearly a range of underlying human motivations connected to biodiversity conservation/management. A number of different dimensions of nature-based/biodiversity value can therefore in principle be discerned and evaluated in different ways: in monetary terms via accounting prices (market exchange value/simulated exchange values), and via economic welfare measures which have been aggregated into so-called total economic value (TEV). TEV encompasses both use and non-use values based on a number of motivations including among others bequests to the future (Pearce and Turner 1991)); in biophysical and geochemical terms via natural science; and in often more qualitative non-monetary terms via socio-cultural and similar methods. Each of these value dimensions has validity in its own domain (Turner et al 2003). But the key point is the plurality of the values associated with nature/biodiversity. Economists have undertaken so-called welfare valuations based on consumer preferences (either revealed through actions in competitive markets or expressed via in person /online surveys) (Freeman 1993). Non-use values have to be addressed via the expressed preference method which looks to elicit ‘willingness-to-pay’ from survey respondents and includes contingent valuation (CV)/choice experiments (CE) techniques. There are also some hybrid methods, mixing CE/CV with deliberative processes involving stakeholders and related networks, and CE/CV together with Q methodology (Hamson et al 2021). At the other end of the valuation methods spectrum come non-monetary quantitative and qualitative methods which include among others, subjective wellbeing measures, in-depth discussion groups, citizens juries, focus groups and semi-structured surveys, participatory mapping GIS (Kenter 2016). 7 Any investigation of biodiversity value also needs to contain an appreciation of ecosystem-level scales, including landscapes and seascapes, alongside value plurality. Since biodiversity represents all the different components of the system essential for the ecological processes underpinning ecosystem service supply, it can be considered to have infrastructure or ‘glue’ value. Total system value is therefore always greater than total economic value Turner et al (2003). IPBES (2019) seems to have captured, via its Nature’s Contribution to People (NCP), this same concept of value in its ‘habitat creation and maintenance’ heading (Diaz et al 2018). There are two further related notions of value associated with biodiversity, option value and insurance value. Option value is the satisfaction that an individual derives from ensuring that an ecosystem service(s) is available for future use, given that the future availability of the benefit is uncertain. While an ecosystem’s insurance value is related to its ability to continue the supply of ecosystem services even under a degree of stress from forces such as floods/storms and others (Barmgartner and Strunz 2014). In this report, we utilise a stated preference survey with the Choice Experiment approach to address the task of determining the non-use monetary value of biodiversity. The following chapters describe the steps of the Choice Experiment: from the construction of the questionnaire to the pilot case studies (chapter 2), through to the questionnaire testing to the real survey (chapter 3) and on to the survey outcomes and the monetary valuation. 8 2. From the questionnaire preparation to the focus groups and pilot case studies The aim of this study is to deter discern European opinions on biodiversity protection policies, and on biodiversity loss perception. Choice experiments (CE) are survey-based methods of the stated preference family. The CEs rely on the intercept of neoclassical consumer theory (e.g. utility maximization), statistical experimental design theory and econometric discrete choice analysis for data interpretation. The theory of comparative judgement in economics was proposed by Thurstone in 1927 and in nearly 100 years, the application of choice modelling has expanded to multiple sectors and disciplines (Haghani et al 2021). CEs assume that respondents compare alternative policies in each question and select the option that provides the highest utility. From the analysis of these responses the researchers can reveal the trade-offs across features of the alternative policies. CEs require the researcher to design the experiment in advance by assigning levels to the attributes describing the alternatives which respondents are asked to choose from. Attribute levels are assigned to the different alternatives following an experimental design (ED) (Hoyos et al., 2010). Conceptually, EDs may be viewed as the systematic arrangement in matrices of the values that researchers use to describe the attributes representing the alternative projects that will be compared in hypothetical choice sets. In other words, the attributes are matched to build up different possible combinations, with each combination describing a different possible sustainable tourism project. How analysts allocate levels within the different combinations may play a big part in whether or not an independent assessment of each attribute’s contribution to the choices observed can be determined (Rose and Bliemer, 2009). ED theory makes use of various criteria to evaluate the outcome of this level combination process (Ferrini and Scarpa, 2007; Scarpa and Rose, 2008). A CE questionnaire was designed in order to assess the key intervention options valued by respondents and to derive willingness to pay values for supporting biodiversity management strategies. A CE involves the generation of a set of credible valuation scenarios where environmental changes occur. These valuation scenarios are presented to respondents in a survey in which they are guided through the generated set of scenarios and usually asked to choose the one they prefer or value the most. The environmental changes involved in the valuation scenarios need to be described by a combination of a number of characteristics or attributes. These attributes are combined following predefined statistical rules of experimental design and are then organized in so-called choice cards. Respondents are (usually repeatedly) asked to choose from a limited number of possible valuation scenarios relating to how the environmental change might happen. These options vary in the selected attributes in each of the choice cards. Respondents’ choices between alternative valuation scenarios will allow the analyst to uncover individual trade-offs between key attributes of biodiversity protection strategies. It is standard practice to also offer a status quo option of no change. The definition of the change and the questionnaire are critical for the validity of results and as a consequence, a set of steps are necessary before the main survey can be launched. Figure 2.1 reports the steps that should occur when undertaking a CE. Figure 2.1. Steps for the Choice Experiment study 9 The questionnaire is the backbone of the CE, in fact it determines what people value and thus it is crucial to design a process by which both respondents and researchers reach a common understanding of the topic and the methods used. It is important therefore to describe the questionnaire design and its evolution throughout focus group and pilot exercises. This next chapter describes what underpins the final version of the questionnaire. 2.1. Definition of reference scenario and literature review The first step before the creation of any CE questionnaire is the definition of the reference scenario, which is the state of the environment today. In our case study the characterization of the reference scenario posed two main issues. The first concerns the ease of perception, i.e. to find a way to introduce, in a understandable format, a complex theme like biodiversity and its protection. The second issue is to find a proxy of the biodiversity from which to estimate the economic value. The starting point was the study by Vallecillo et al. (2016) “A habitat quality indicator for common birds in Europe based on species distribution models”. The study, in support of the EU 2020 Biodiversity Strategy, describes the biodiversity loss problem using as a proxy the presence of some bird species in Europe. The research developed several indicators of the bird species in the countryside and forest areas. Moreover, it demonstrated, based on an ecological rule that regulates the living being relationships, that the birds’ absence indicates a low level of biodiversity. To overcome the complexity of the problem, the biodiversity concept is explained through the simple notion of a food chain. The food chain concept illustrates a straightforward picture of the relationship between the cause and effect of biodiversity loss. The food chain concept offers an easy way to explain the presence and richness of species. The presence of habitats able to support species is the other element used to simplify the complex notion of biodiversity. The probability that habitats are in a favourable conservation status can depend on positive and negative drivers of biodiversity change. Positive drivers of biodiversity are the location of Natura 2000 sites and the network of green infrastructure. Negative drivers or pressures are the transition of land for development and agriculture, nitrogen enrichment, air pollution, but also management practises such as drainage or abandonment of traditional agricultural practises (Maes, 2013). From the biophysical perspective, these drivers of change become the key to displaying options for choices, and to link each combination of choices with a different scenario of the food chain. When moving from the biophysical perspective to the monetary valuation, we consider a rich literature review on this topic. Among all reviewed papers, we would like to specifically report: - Liekens et al. (2013): “Developing a value function for nature development and land use policy in Flanders, Belgium”. This paper aims to provide a generic monetary value function to assess the public benefits of amenity, recreation and biodiversity values associated with land use changes from agricultural land to different types of nature. In the choice experiment, respondents were asked to choose between different nature development scenarios, described in terms of their ecological quality (nature type, species richness) and a set of spatial characteristics, including, size, accessibility, adjacent land use and distance to the respondents’ residence; - Home et al. (2014): “Public preferences for ecosystem-enhancing elements in agricultural landscapes in the Swiss lowlands”. This study aims to assess the attitudes of the public in Switzerland to ecosystem connectivity measures in rural areas, known as ecological compensation areas, and measuring whether attitudes could be influenced by the provision of ecological information. A choice experiment was conducted using manipulated photographs of typical farmland, with various habitat elements added to an ‘empty’ agricultural landscape; - Badura et al (2020): “Using individualised choice maps to capture the spatial dimensions of value within choice experiments.” This study assesses the willingness to pay of British’ people in supporting low intensity agriculture initiatives either reforestation projects or environmentally friendly agriculture. The paper captures the value of biodiversity using bird indicators as a proxy; - Grilli et al (2021): “The role of choice experiments in natural capital accounting approaches: fast track versus simulated exchange value in the Deben Estuary saltmarshes”. This study aims to assess the use of choice experiments in natural capital approaches at the local scale, focussing on the valuation of a 10 saltmarsh area in England. In the choice experiment, policy interventions restoring the saltmarsh area, were described through attributes including biodiversity protection which was proxied by the number of returning bird species in the area. To build the CE questionnaire several consultations and pilot tests were conducted. At each step, the survey was modified and improved using experts’ feedback, literature review and pilot results. This information is important to explain the work and reasoning behind the final questionnaire and to explain its foundation pillars. in subsequent sub-sections we summarize what people highlighted (during focus groups and pilot tests) as the main issues and misunderstandings related to the previous versions of the questionnaire. Focus groups and pilots are listed in chronological order and described below. 2.2. The first pilot test of biodiversity questionnaire The objectives of this first pilot test were to (i) verify the understanding of various descriptive sections included in the questionnaire and (ii) identify the attributes and levels of the environmental change scenarios. The pilot was conducted face-to-face at the end of January, in Molfetta, a city near Bari, in southern Italy. The sample consisted of 20 people, with different socio-economic characteristics. The average age of the sample was 45.5 years, while the average level of education was a high school diploma. As for the employment status, more than 50% of the sample had a full-time job. The average annual household income level was 10 to 30 thousand Euro. Next we summarise the pilot process section by section. 2.2.1. Section A: Description of the topic At the beginning of this section there is a description of land use change over time in Europe and its related causes. This text helps to introduce the biodiversity concept and its loss and helps interviewees to understand the subject of the questionnaire and to provide some basic knowledge on the main key concepts. At the end of the questionnaire there are some general questions to investigate the perception and knowledge of the respondents’ sample about these topics, such as questions about “biodiversity definition”, “food chain” and “the biodiversity loss’s causes”. Many people expressed curiosity about the theme, and they stated that the description part was clear. Some concern was expressed related to the use of the language in the questions: some words needed to be either changed or better explained. Terms such as “alien species” and “intensive agriculture” needed a better description. This prompted us to revise particular questions by simplifying the language. A specific section of the questionnaire explains the meaning of biodiversity loss and its consequences. To explain and underline the importance of biodiversity the questionnaire uses the food chain concept. Some pictures and short descriptions explain the concept. Figure 2.2 shows a sample of used pictures. Figure 2.2. Sample of the figures reported in the first pilot survey 11 The attention of all the respondents was engaged by these images, and we received positive feedback about the chosen graphics. One general comment is worthwhile reporting: “this section describes a complex concept in an elementary way”. Some questions investigated the biodiversity situation in a respondent’s city. Nobody raised issues in terms of understanding; however, there was some discussion about the terminology. For example, a question asked respondents to express their satisfaction level for current biodiversity levels in their city. One respondent suggested that it is difficult to be satisfied with biodiversity; and the question’s wording was changes to: “how do you think the biodiversity situation is in your city?”. Another example of rewording concerns the question showing a list of possible drivers of biodiversity loss. Respondents who answered: “I don’t know” suggested to add another option “I don’t have enough elements to judge”. 2.2.2. Section B: Stated preferences This section opens by presenting the reference scenario (or status quo) of biodiversity in Europe, and specifically the biodiversity rate loss in Italy and possible future consequences. This description seemed to surprise respondents who were not expecting such negative consequences due to biodiversity loss. Then, the questionnaire proposes a specific task for respondents that is not included in the final version of the CE questionnaire. A list of actions to protect biodiversity is presented to respondents and Table 2.1 reports the most popular ranked options. Subsequently respondents reported their maximum willingness to pay for the first two actions previously chosen. Table 2.1. Ranking of actions and average WTP. Sub-Table 1 Sub-Table 2 Action Score (1=best, 5worst) Average Willingness to Pay (€/y) Action Score (1=best, worst) Waste reduction close to natural habitats (e.g., plastic waste removal). 2.15 23.00 Conversion of intensive agricultural areas in forests 2.10 17.00 Encourage the controls in agricultural and forest area to reduce the illicit agriculture actions such as trees cut. 2.30 20.50 Increase of protected areas (e.g., Parks). 1.60 23.60 Responsible forest management (e.g., plant more than you cut). 2.40 16.05 Encourage the wood firms to reduce the number of trees cut. 2.30 2.50 Conversion of intensive agricultural areas in traditional agricultural areas. 3.15 7.50 Average 16.76 Average Sub-Table 3 Action Encourage checks on the correct use of pesticides and insecticides by farmers. Control on emissions of polluting substances to protect water quality. Average Score (1=best, worst) 1.60 1.40 Average 5- Willingness to Pay (€/y) 14.37 Sub-Table 4 5- Average Willingness to Pay (€/y) Action Score (1=best, worst) 21.50 Reduction in the use of pollutants that are discharged into the water by industries 1.05 12 26.15 Compensation due to the imposition of a reduced use of pesticides and insecticides on farmers 1.95 14.05 23.82 Average 12 Average 5- Willingness to Pay (€/y) 13.02 Results show that in sub-table 1 (Table 2.1) the preferred action was “waste reduction”. A sensible explanation could be that in the last 3 years the mass media has highlighted and debated the danger of plastic waste in the environment. The preferred action in sub-table 2 (Table 2.1) is the increase of protected areas. In sub-table 3 (Table 2.1) there is no clear indication of preferred actions, while in sub-table 4 (Table 2.1) the action concerning the reduction of pesticides was preferred. Sub-table 1 presented the highest WTP compared to the others. This result can probably be justified by people’s perception of the likely effectiveness of actions. In fact, by reading ex-post respondents’ comments, they perceived the actions in the sub-table 1 and 3 to be more effective than the actions in sub-table 2 and 4. A final comment was on biodiversity governance as action in sub-table 2 and 4 needs to be managed national authorities rather than individuals. 2.2.3. Conclusions In summary, the degree of understanding on general topics was satisfactory, 55% of the sample declared that the questionnaire was fully understandable, while 35% of the sample stated it was clear enough, On the other hand, there were some negative comments concerning the use of some “difficult” words, which are taken for granted by experts, but are not clear for non-experts, and some response options were judged to be too vague. This first test clearly shows that there is a need to (i) use concepts that people can readily understand and to (ii) propose actions that people perceive as generating concrete outcomes. 2.3. Focus Groups Focus groups took place at the JRC on the 8th and 9th of March 2018 to define attributes and their respective levels, required for the construction of the Choice Card and to verify the clarity of questionnaire wording. Two separate focus groups were conducted: the adopted procedure was the same but the characteristics of participants were different. In the first focus group day, a group of “non experts” was invited, while the second focus group represented the “expert group”. The definition of expertise is based on participants’ work experience, i.e. biologists and ecologists. The focus groups were led by two moderators who organised the whole exercise into three phases:  in Phase 1 two questions were addressed: the first question lists examples of different drivers of biodiversity loss and asks group members to add other biodiversity loss drivers; the second question asks members to list some effective policies to prevent biodiversity loss;  in Phase 2 participants were asked to describe some actions to apply the policies chosen in the previous phase;  in Phase 3 a draft version of the questionnaire was given to participants, reporting general questions about biodiversity and environmental quality. Focus group protocol requires that respondents are asked to answer questions step by step, and to discuss within group after each phase. During the breakout sessions, respondents exchanged points of view, about the issue and about ideas for possible solutions. To facilitate brainstorming about different thoughts, the moderators tracked the different suggestions on posters (Figure 2.2) and on audio records. The final goal of the group was to design policy options to stop biodiversity loss. Results of the two focus groups could be analysed separately, however during the preliminary analysis many similarities were recorded and are described in the following sub sections. 2.3.1. Phase 1 During this phase, a first question asked for possible drivers of biodiversity loss in addition to those reported in Table 2.2. 13 Table 2.2. Drivers of Biodiversity Loss questioned during the Focus Groups. Drivers Examples Land Use   Proportion of artificial land, arable land, pasture, road density Modification of the hydrogeological cycle, abandonment of pastoral system Pollution  Fertilizers Invasive species  Invasion by alien species Protected areas and Green infrastructures  Proportion of protected areas and green infrastructure The only added drivers were climate change and intensive land use. A first conclusion could be that the drivers already reported in the preliminary questionnaire were generally correct. A second question asked participants to suggest policies able to stop biodiversity loss. Popular policies identified by expert and non-expert respondents were (i) the Common Agricultural Policy (CAP) because it determines land use practices and planning in terms of connectivity, (ii) nature-based solutions to facilitate ecological connectivity and avoid/reduce landscape fragmentation, and (iii) mainstreaming nature conservation policy alongside traditional agriculture practice. Specifically, land use practices refer to the intensity of land exploitation, with particular attention to agricultural areas for controlling and regulating pesticides use; connectivity and green infrastructures that support and eventually harmonize the coexistence of “natural” and “human/artificial” areas.. 2.3.2. Phase 2 Based on the results of Phase 1, I.e. that the policy chosen to avoid biodiversity loss was the “land use” and “land management” option, In Phase 2 respondents were asked for concrete actions to implement the main policy. The suggested policies ranged from long term (i.e. to improve the citizens’ education and awareness) to medium and short term actions (to define and monitor environmental quality targets). In the medium- and shortterm policies, categories, the suggested concrete actions were: pesticide reduction, pollution reduction, monoculture reduction, deforestation reduction, alien species reduction, ecological corridor creation, and higher taxation on pesticide use. Figure 2.3. List of policies and actions suggested throughout the discussion 14 Although all proposed actions can have an effect on biodiversity protection, some of them were weaker in terms of feasibility and effectiveness. Based on the outcomes of the first pilot test and focus groups, the actions actually selected to establish the attributes in the final version of the questionnaire related to pesticide reduction, landscape variety underpinned by ecological connectivity, size, and costs. 2.3.3. Phase 3 and conclusion In Phase 3 the draft version of the questionnaire was proposed to respondents as an additional validation test before the pilot survey was to be further tested with the public. The outcomes generally confirmed the results obtained with the first test pilot. The questionnaire is clear and simple to understand. In the light of the focus groups activities a Choice card with the attributes and their levels was able to be constructed. 2.4. Pilot study of Choice Experiment questionnaire in a city The objective of the CE pilot study was mainly to test the questionnaire used by JRC to collect the willingness to pay for the habitat and species maintenance final survey, and to validate the effectiveness of the choice card attributes and their levels. Therefore, the main elements described in this subsection are: the sample characteristics, the questionnaire structure, and the econometrics results. 2.4.1. Sample structure and questionnaire structure The survey was conducted face-to-face at the end of May 2018, in Molfetta, a city in the South of Italy, with 56.000 residents, of which 86 were the sample in the CE pilot. Table 2.3 provides an overview of the descriptive summary statistics of the sample along with the aggregate characteristics of the Italian population extracted from the 2017 census. Table 2.3. Socio-economic characteristics of respondents. Average Standard Deviation Sex 45.35% M – 54.65% F - Age 49.1 15.71 45 € 36,162.79 18,545.25 € 29,988 5.81% Elementary school; 9.30% Middle school; 38.38% High school diploma; 41.86% University degree; 4.65% Post graduate training. - 18% Elementary school; 32% Middle school; 36% High school diploma; 13% University or Post graduate training. 3.18 1.10 Income Education level Households (HH) size Istat data 2017 The questionnaire is divided into four sections: Section A – Context description: description of biodiversity situation and the causes of loss; Section B – Agricultural policy preferences: CE questions; Section C – Pro environment behaviours: to derive the relationship between the respondent and the environment; Section D – socio-economic characteristics. Table 2.4 reports the attributes and the attribute levels used in Choice card for this second pilot exercise. 15 Table 2.4. Choice card attributes and levels used in the pilot. Levels Attribute Reduction in pesticide use: variation in the pesticide use    No change; 50% reduction; Banned chemicals. Biodiversity level: biodiversity (diversity of wildlife and plants) relative to today’s situation (it is explained in the questionnaire description)    Low; Medium; High. Size: size of farming land where the change can occur    Small (14 hectare as 20 football pitches); Medium (40 hectare as 60 football pitches); Large (100 hectare as 150 football pitches). Distance: represent the distance to your house where the described changes might occur      10 km 20km 50 km 100 km 200 km Cost: in terms of annual contribution (through taxation) towards agri-environmental scheme      € 50 € 75 € 100 € 150 € 200 Land use: the land use type    Woodland Park Organic farming Attribute levels were combined using a D-efficient main-effects experimental design that was derived with the NGENE software. The number of choice cards obtained after combining the attribute levels was 24, which were divided into four blocks: six choice cards were shown to each respondent, with each choice card consisting of two possible biodiversity protection policy options ad a status quo option. Figure 2.4 shows an example of choice card used in this pilot survey. Figure 2.4. First CE pilot survey Choice card example 16 2.4.2. Results Different multinomial/conditional logit (MNL) models were implemented to analyse different variables. However, this section focuses only on MNL model results. Table 2.4 reports model coefficients that can be interpreted only for their signs and significance. This model includes socioeconomic variables as interaction terms with the “sq”, i.e. status quo option (Table 2.5). Table 2.5. First CE pilot MNL model results. Coefficient name Coefficient estimate Z – value Significance Cost -0.0105499 -4.7796 *** Biodiversity 0.2785949 2.3026 * Size 0.2393947 2.1570 * Pesticide reduction 1.3662017 9.5128 *** Distance -0.0063652 -4.1770 *** Woodland 0.3608597 1.5463 Organic farming 0.7638142 3.1725 ** Status quo (sq) option -2.9531425 -2.7700 ** Sq*income_low 1.8566849 2.8308 ** Sq*income_high 0.0974076 0.2578 Sq*education_low 2.3466160 4.1647 Sq*education_mid 0.4084658 1.2012 Sq*nucleo -0.0708314 -0.4608 Sq*HHinc_mid 3.7132817 4.3659 *** Sq*HHinc_high 1.9922605 2.5611 * 0 *** 0.001 ** 0.05 * Significance Log-likelihood *** -350.17 As showed in Table 2.5, the variables “cost” and “distance” have a negative and statistical ly significant effect on the probability to choose to support policies that protect biodiversity. The variables “organic farming”, “biodiversity”, “pesticide reduction” and “size” have a positive and statistically significant sign, implying that organic farming or farming which protects biodiversity or reduces pesticide use and occupies larger plots of land, increase the probability of favouring biodiversity protection policies. The “sq” represents respondents’ willingness to change the current agriculture situation and it is significant and negative. With the interaction of “sq”, and the socio-economic variables: low education “sq*education_low”, medium household income 17 contribution “sq*HHinc_mid” and high household income contribution “sq*HHinc_high”, we can capture the heterogeneity of preferences. Results reveal that education level explains the probability of supporting biodiversity protection policies. A respondent characterized by low education level (middle school diploma) has a higher probability of choosing the “sq” compared to a respondent with a higher level of education (degree or postgraduate training). Respondents with medium income have a higher probability to prefer the current farming system in favour of biodiversity protection policies. Table 2.6. First CE pilot willingness to pay results. WTP estimation (€/person/year) WTP for biodiversity improvement 26.41 WTP for pesticide reduction 129.05 WTP for woodland creation 34.21 WTP for developing organic farming 72.4 2.4.3. First CE Pilot study conclusions Results obtained by the face-to-face pilot study confirm the complexity of communicating biodiversity and ecosystem services conservation policies to the general public. Respondents responded well to the questionnaire but the WTP for biodiversity is lower than the costs of pesticide reduction and organic farming. While pesticide effects are widely known and people are willing to pay to mitigate them, the indirect effects on human wellbeing is less well perceived and the WTP results reflect this. At the same time, respondents did comprehend the different biodiversity protection scenarios and provided valuable insights for the final CE questionnaire. In the final version of the questionnaire (reported in Annex II and described by results in Chapter 3) we choose to use concepts readily understandable by the general public. The main habitat degradation is primarily linked to agriculture practices and pesticides and fertilizer use and biodiversity is captured by birds’ diversity as well as the food chain concept. 18 3. From questionnaire testing to the main survey: major outcomes and their meaning The purpose of this exercise is to assess citizens’ preferences for biodiversity protection using a choice experiment survey in four member states. Focus groups and two pilot studies supported the development of the final survey that was conducted between August-September 2019 with the support of a Survey Engine marketing company. The survey was conducted online with 450 representative respondents in four European countries Czechia, Germany (DE), Ireland (IE) and Italy (IT). The central methodological approach is a stated preference survey in which respondents are asked to make choices about the provision of alternative biodiversity protection strategies. Through their choices respondents reveal their preferences for policies concerning habitat and species maintenance and eventually leading to the determination of a demand curve. Having a handle on the demand curve (i.e. how much habitat and species maintenance is demanded at what price) is the key information used to estimate the value of biodiversity protection. The survey contained four major sections: A. Description of the topic – useful to analyse current knowledge and attitudes toward biodiversity; B. Preferences for agricultural policies – it is the heart of the Choice Experiment, where willingness to pay preferences are stated; C. Attitudes and perceptions – important to delve into environmental perceptions and preferences of the respondents; D. Respondent profile – demographic questions. This chapter summarises the outcome of the survey section by section, starting from a preliminary description of the different country samples and the demographic description of the respondents. 3.1. Preliminary information on countries and samples The aim of the survey is to investigate perception of and the value of support for initiatives concerning habitat and species maintenance in Europe. The 4 countries under consideration are:     Czechia (CZ) Germany (DE) Ireland (IE) Italy (IT) The choice of the countries is driven by expert advice and statistics on habitant maintenance and biodiversity conditions. A set of focus groups and a first in-person pilot were arranged in 2018-19 to discuss the main Drivers and Pressures of changes in habitat and the presence of species (Chapter 2). Intensive farming is commonly considered the main cause of habitat loss that poses challenges to biodiversity in Europe. The countries were thus selected considering their intensity of high impact agriculture as expressed by the average emergy measures for crops (Perez-Soba et al. 2019, Vallecillo et al. 2019). From the EU data on intensive agriculture measures (emergy ratios/yield) we identified two countries with less intensive agriculture practices (CZ, IT) and two with more intensive practises (DE, IE). These countries also represent different EU zones, eastern European, centre Europe, Mediterranean and northern. Details of the sampling are in Appendix III. The 4 countries differ in their agriculture activities and farm sizes (Table 3.1) Table 3.1. Farm size (ha) in the four selected countries based on EU Agriculture statistics. Farm CZ small size DE IR IT 20.4 15.8 14.9 3.2 medium size 131.0 75.4 35.7 12.8 Large 634.5 263.9 103.4 60.0 country (ha) Agriculture (%) 7,886,500 35,738,600 44 8,442,100 47 19 30,133,800 58 42 To maximise survey design and analysis effectiveness for each of the countries, we relied on native speaking researchers available within the team who facilitated the preparation of questionnaire in the 4 languages. In each country the survey was arranged in one pilot (50 respondents) and final survey (400 respondents). In this chapter we report the final survey data. In total, 1596 surveys were collected, of which 1469 were valid. The surveys discarded from the final sample were labelled as protest answers. The protesters are respondents who commented that the survey was useless or of no interest, with a typical protest response “not my fault”. CZ and DE samples have on average 7% protests rate, whereas IE and IT have on average 4%. The impact of protesters on estimates has been tested with econometric tests (Likelihood Ratio) and was significant at 5% significant level, which prompted their removal. The strategy to select online respondents is driven by quota sampling principles that aim to represent the census distribution per country. The classes are defined by age, sex, and regional distribution. The surveys are wellbalanced in terms of socio-economic characteristics (gender, age, income). The average length of the survey is roughly 16 minutes (± 10 minutes), the quickest respondents took 32 minutes the slowest 60 minutes. The geographical distribution across regions in the four countries is reported in Table 3.2. Table 3.2. Regional distribution of responses per Member States. CZ DE IE IT Sample size:361 Sample size:355 Sample size:374 Sample size:379 % % % % Praha 13.3 Baden-Württemberg 13.8 Norther and Western 16.31 Northeast 19 Strední Cechy 13.57 Bayern 16.34 Southern 34.49 Northwest 26.65 Jihozápad 10.53 Berlin 4.23 Eastern and Midlands 49.2 Centre 20.32 Severozápad 10.53 Brandenburg 2.82 Sardinia 1.58 Severovýchod 13.3 Bremen 0.56 South 32.45 Jihovýchod 16.07 Hamburg 1.69 Strední Morava 12.19 Hessen 7.61 Moravskoslezsko 10.53 MecklenburgVorpommern 1.41 Niedersachsen 9.58 Nordrhein-Westfalen 24.79 Rheinland-Pfalz 4.51 Saarland 0.85 Sachsen 5.07 Sachsen-Anhalt 1.69 Schleswig-Holstein 3.1 Thüringen 1.97 20 3.2. Socio-economic analysis based on the respondent profile The 1,469 valid surveys came from the following socio-economic groups (Figure 3.1): 23% from the age group 60-75, 17% from the younger groups (18-29) and 9% from the 75+, the rest is roughly equally split among the remaining age groups. The distribution across age groups is similar to census data as the sampling scheme follows a quota sample. Although younger and older groups are slightly different from the census (Table 3.3). The distribution for professionals as the main income earner is reported in Table 3.4, where differences across countries can be seen. The most representative income groups are the ones in the income brackets Euro 10-50,000 (Figure 3.2). Distinctive differences exist across countries. Figure 3.1. Share of respondents by nationality and age group Table 3.3. Age distribution of responses per Member States. 18-29 30-39 40-49 50-59 60-75 75+ CZ 15.51 18.01 19.94 14.13 28.81 3.6 DE 18.31 13.8 15.49 18.03 22.25 12.11 IE 21.12 20.05 18.98 16.31 19.25 4.28 IT 14.78 13.19 18.47 18.47 21.64 13.46 Total 17.43 16.27 18.24 16.75 22.94 8.37 Table 3.4. Regional distribution of responses per Member States. Countries CZ DE IE IT Total Higher managerial, professional Intermediate managerial, 7.87 14.16 8.66 25.07 14.08 19.38 2.02 29.61 6.67 14.43 Supervisory clerical Skilled manual 19.66 37.28 27.93 22.13 26.62 10.96 4.91 18.16 17.07 12.89 21 Semi skilled 23.31 6.36 6.98 8 11.15 Casual worker, homemaker 18.82 4.05 8.66 21.07 13.31 Others 0 31.21 0 0 7.52 Figure 3.2. Proportion of respondents by nationality and income 3.3. Current knowledge and attitude Citizen biodiversity preferences were calculated for each country after investigating with two questions their current level of knowledge. Notably, citizens lack a solid knowledge of the definition of a food chain, whereas the term of biodiversity is better known (Figure 3.3). At the same time, it seems that citizens in IT and DE present a lower level of knowledge compared to CZ and IE. Figure 3.3. Percentage of respondents who knows about biodiversity and food chain by country In answer to the question “have you heard of the term biodiversity before?” 66% of the sample reports, yes. However, we observed that in DE the proportion of yes answers is 42% and in CZ 56%. This suggests a diverse level of knowledge across MS. Although in the questionnaire quiz on biodiversity, 93% of respondents correctly identified the definition of biodiversity2. Some 86% of the full sample reported that they know the definition of food chain although the DE sample shows the lowest level of knowledge (71%). Looking at the capacity to identify the correct definition of food chain 76% of the full sample reported the correct definition. IT and DE reported the lowest level of correct definitions. In both questions, the definition of biodiversity and food chain was displayed randomly to control for the order effect, and the percentage of correct definition identifications 2 An order effect is detected as the percentage of correct answer decreases when the correct definition was randomly displayed as 2 or 3 option in the list. 22 increased when the correct definition was displayed first. 87% of the sample recognized a link between biodiversity and the food chain. Table 3.5. Definition and knowledge of biodiversity and food chain. Q_2: In your opinion, what could be an appropriate definition of biodiversity: CZ DE IE IT Tot Variety of living organisms, animals and plants; including the diversity among species, across species and places where they live 96% 90% 94% 90% 93% Number of different animals and birds on the earth 1% 7% 2.00% 2.60% 2% Number of different marine and terrestrial habitats 2.25% 2% 3.40% 6.65% 4% 1% 1% 0.30% 0.30% 1% CZ DE IE IT Tot The relationship between ecosystems 6% 5% 6.40% 14% 8.00% The functioning of a biological cycle 12% 25% 10% 15% 15% Dependency of organisms on other organisms as a source of food 80% 70% 83% 71% 76% Other: 2% 0.30% 1% 0.28% 1.00% Other: Q_4: In your opinion, what could be the right definition for a food chain: The current level of national biodiversity is perceived to be satisfactory from most respondents, although the percentage of CZ citizens who reported a low biodiversity quality is evident, see Figure 3.4. Figure 3.4. Percentage of satisfactory level of national biodiversity level by country 23 The questions that aim to assess the respondents’ perception of biodiversity condition in the country and local area are reported in Table 3.6. 23% of Italians consider biodiversity in the nation high or of very high quality, whereas only 6% of respondents in CZ republic consider their national biodiversity quality high. The perception of local conditions differs across countries, the DE sample reports the highest appreciation of high local quality of biodiversity (24%). The biodiversity quality is assessed by respondents using a variety of indicators, but the number of parks/protected areas and the number of plants and animals are the main parameters that drive respondent choices (Table 3.7). Table 3.6. Respondents’ perception of national and local quality of biodiversity. Q6_1: Biodiversity in your country (national) CZ DE IE IT Tot Very low 3.32% 1.41% 2.41% 2.9% 2.52% Low 29.64% 24.51% 24.06% 22.16% 25.05% Normal 52.91% 41.69% 44.12% 47.23% 46.49% High 4.99% 15.21% 17.65% 15.83% 13.48% Very high 0.83% 5.35% 4.81% 7.12% 4.56% I don’t know 8.31% 11.83% 6.95% 4.75% 7.9% Q6_2 Biodiversity in your area (local) CZ IE IT 1.41% 3.74% 4.22% 3.4% 19.72% 28.34% 27.7% 26.21% 42.82% 40.37% 40.37% 43.09% 7.76% 18.31% 15.24% 16.62% 14.5% 1.94% 5.92% 5.35% 6.6% 4.97% 8.31% 11.83% 6.95% 4.49% 7.83% Very low 4.16% Low 28.81% Normal 49.03% High Very high I don’t know DE Tot Table 3.7. Respondents’ perception of factors influencing quality of biodiversity. Q7- when you answered the previous question you were thinking about any of the following Quantity/quality of parks, green areas and/or wooded areas CZ DE IE IT Tot 52% 54% 56% 49% 53% Waste management 42% 26% 39% 42% 37% Water quality 43% 42% 41% 42% 42% Agriculture policies for environmental protection 47% 38% 42% 50% 44% Gut feeling 42% 44% 31% 17% 33% The number of animals and plants 65% 69% 73% 67% 68% Insects 32% 52% 48% 35% 42% Others 10% 30% 30% 20% 20% 3.4. Biodiversity management preferences: the Choice cards Respondents express in the Choice cards their willingness to pay for habitat quality (represented by land use and use of chemical products in agricultural practices) and presence of species (explained through the concept of food chain). The perception of biodiversity might be explained by respondents’ distance from high intensity agriculture sites. The distance is classified into 4 categories and most respondents live within 10 km of such sites (Table 3.8). 24 Member states report differences in distance across categories and the CZ and DE samples reveal the highest proportion of respondents living close to intensive agriculture areas. Table 3.8. Factors influencing respondents’ perception of the quality of biodiversity. q8: How far away is the nearest high intensity farmed area to your house? CZ DE IE IT Tot 0-10km 62.05% 53.24% 34.22% 25.33% 43.36% 11-20km 19% 19.15% 20% 20.58% 19.74% 20-40km 8.03% 9.86% 14.44% 17.94% 12.66% > 40km 2.77% 4.51% 7.49% 14.25% 7.35% Don't know 8.31% 13.24% 23.53% 21.90% 16.88% In answer to the question, how common is high intensity farming in your region, the majority of respondents replied that it is fairly common, although 22% of the sample was unsure on how common this farming system (Table 3.9) was. Table 3.9. Respondents’ perception of how common high intensity farming in their region is. Q9: How common is intensive farming system in your region? CZ DE IE IT Tot Main farming system 19% 24% 22% 15% 20% Fairly used system 50% 42% 44% 46% 46% Uncommon system 10% 12% 12% 20% 14% Don't know 20% 21% 21% 18% 20% The survey then introduces the notion that a switch to less intensive farming systems is possible. The choice experiment enables the modelling of the demand for this switch towards more sustainable agriculture practices. The discrete choice experiment section presents a series of mock land use changes organized in choice tasks (Figure 3.5). The choice task(s) usually involve trade-offs between improved, maintained, or deteriorated levels of provision of habitats and species. In this case, different types of agriculture practices could be enabled at several farm level sizes (from agroforestry to less diverse agriculture land) with consequent costs, chemical use intensity and biodiversity levels. The choices that respondents make reveal their priorities (demand) for the provision of these policies, i.e. what they want and care about most. Where trade-offs with monetary amounts are included (e.g. a price for good/service) respondents’ choices also reveal the value they derive from its provision. Most commonly, this value is measured in terms of the respondent’s willingness to pay (WTP). This measures the benefit that respondents derive from improved or maintained (avoided deterioration) provision of the habitat, in terms of the monetary amount they are prepared to give up securing that level of provision3. Respondents went through a series of six different land management options. Data collected in the choice experiment was used to estimate respondents’ willingness to pay for land use change attributes (Figure 3.6). The latter were designed to match the key variables developed in the biophysical model account. In the final section, respondents were asked a series of questions about their attitudes toward environment and biodiversity and standard socio-economic questions. 3 Willingness to pay (WTP) is a measure of welfare value. It applies (universally) to all types of goods/services and represents the total benefit (accounting value + consumer surplus) derived from that good/service (SEEA EA (A12.4 , pag 242). 25 Figure 3.5. Example of 1 of 36 choice cards presented to respondents (each respondent received 6 cards) The attributes size was country specific and based on the average agriculture farm sizes as documented in the Eurostat Agriculture census data. CZ has small farms which average 20 ha, in DE they average 16 ha, in IE 14 ha and in IT 3 ha. Medium farms are 131 ha in CZ, 75 ha in DE, 36 ha in IE and 13 ha in IT. Large farms cover 634 ha in CZ, 264 ha in DE, 103 ha in IE and 60 ha in IT. The numerical outcomes of the Choice Experiment are reported in Chapter 4 together with the procedure to transfer the monetary estimates for the habitat and species maintenance in the four countries to all countries in Europe. Figure 3.6. Attributes and levels Most respondents engaged in the options offered by choice experiment, and only 4% of the sample systematically choose to opt out (Table 3.10). In the DE sample the proportion of systematic opt out is 7% which is considerably higher than the other samples. Many of these respondents indicated that they cannot afford to pay for these polices. Only a low number of respondents found that the presented polices were not feasible, or that the benefits of habitat maintenance were not significant (14% and 11%). These responses were kept in the dataset and their effect on willingness to pay estimates is negligible as confirmed by statistical tests (not reported here). 26 Table 3.10. Reasons for respondents to opt out. Q10: If you choose “no change”, what was the reason for it? CZ Systematic choice of SQ DE IE IT Tot 4% 7% 4% 2% 4% I cannot afford 50% 41% 71% 50% 51% State is responsible for this 21% 37% 64% 50% 41% Biodiversity is not important 7% 7% 0% 13% 6% Doubt the effectiveness of this policy 29% 11% 14% 0% 14% It was not my fault I do not believe that Agri-schemes support biodiversity 14% 15% 0% 38% 14% 21% 11% 7% 0% 11% Section B of the survey closes with a final question concerning who could be responsible for the management of the biodiversity protection fund. It is interesting to note that respondents trust different organizations, with Czechia in favour of a public research institute, Germany favouring regional government, Ireland and Italy opting for environmental associations (Table 3.11). Table 3.11. Preferences for managing the biodiversity fund (%). Q11: Who should manage the fund collected to protect biodiversity? CZ DE IE IT Total A private research institution 3% 4% 4% 5% 4% A public research institution 23% 17% 16% 12% 17% A private trust or fund 1% 7% 3% 3% 4% A public trust or fund 10% 10% 13% 5% 10% Environmental associations (e.g. WWF) 11% 17% 20% 28% 19% The city council 15% 3% 8% 6% 8% The region 18% 25% 9% 12% 16% Central government 18% 8% 15% 16% 14% The European Union 2% 9% 12% 14% 9% 3.5. Biodiversity management preferences: perception and attitudes The citizens perceived the main causes of habitat losses to be: pollution, industrialization, agriculture, and urbanization (Figure 3.7). 27 Figure 3.7. Respondents’ perception of main causes of biodiversity loss In answer to the question on the best policy to adopt to protect biodiversity and enhance habitant maintenance, the respondents agreed on the actions to be adopted, i.e. to increase areas under protection and implement stringent rules on agricultural activities (Figure 3.8). Figure 3.8. Respondents’ preferences on policy in support of the maintenance of habitat and species Table 3.12 reports the details of the respondent perceptions of the main causes of biodiversity loss. Pollution is considered to be the main cause of biodiversity loss. Industry, urbanization, and intensive agriculture are listed as responsible for biodiversity loss. However, differences exist across countries. These questions were also asked before and after the choice experiment questions, to test for independence of opinions. Most responses remained stable and neutral regardless of which questionnaire information section they were placed in. 28 Table 3.12. Detailed percentages on respondents’ perception on main causes of biodiversity loss (%) Q12: To what extent do you agree or disagree with the following statement about impacts on biodiversity? CZ DE IE IT Tot Urbanization causes biodiversity loss 63% 68% 79% 87% 74% Intensive agriculture causes biodiversity loss 69% 68% 76% 78% 73% Increasing human population causes loss of biodiversity 64% 70% 77% 62% 68% Industrialization causes biodiversity loss 68% 73% 83% 83% 77% Non-native species causes biodiversity loss 62% 55% 60% 61% 60% Pollution causes biodiversity loss 80% 76% 87% 88% 83% Climate change causes biodiversity loss 70% 68% 80% 85% 76% Organic farming prevents biodiversity loss 53% 50% 51% 61% 54% Buying seasonal fruit and vegetables prevents biodiversity loss 34% 44% 55% 71% 51% In order to test for validity response, the same content is proposed with different wording and ordering. When respondents were asked to identify the main cause of biodiversity loss (Table 3.13) they identified pollution, intensive agriculture, urbanization and climate change as the main causes. These results are consistent with Table 3.12. Table 3.13. Further respondents’ perception on main causes of biodiversity loss (qualitative ranking of preferences) Q13: From previous list which one do you consider the most important? And the less important? CZ DE IE IT Most-Least Most-Least Most-Least Urbanization causes biodiversity loss 13 – 5 15 – 8 Intensive agriculture causes biodiversity loss 18 – 5 20 – 7 Increasing human population causes biodiversity loss 13 – 8 Industrialization causes biodiversity loss Non-native species cause biodiversity loss Tot Most-Least Most-Least 17 – 5 13-6 15-6 19 – 6 16 – 8 18-6 15 – 9 12 – 10 7 – 20 12 – 12 9–4 13 – 9 12 – 4 11 – 4 11 – 5 5–8 3 – 16 3 – 10 5 – 14 4 – 12 Pollution causes biodiversity loss 22 – 2 9–7 17 – 4 28 – 4 19 – 4 Climate Change causes biodiversity loss 15 – 7 16 – 6 14 – 4 15 – 5 15 – 5 Organic farming causes biodiversity loss 3 – 15 4 – 12 4 – 18 4 – 12 4 – 14 Buying seasonal fruit and vegetables causes biodiversity loss 2 – 47 5 – 28 2 – 39 1 – 27 2 – 35 A further question was used to identify the most important policy to reduce biodiversity losses. The responses reveal a diversity in priorities (Table 3.14). For CZ and DE samples, the most important policy is stricter rules for farmers, in Ireland and Italy increased area under protection was given priority. 29 Table 3.14. Detailed percentages on respondents’ preferences on policy for biodiversity protection (%) Q14: What is the best policy that could be adopted to protect biodiversity? CZ DE IE IT 25% 24% 24% 27% 25% 9% 9% 17% 15% 12% Introduce stricter rules for agricultural activities 33% 28% 17% 19% 24% Allocate resources for promoting biodiversity protection policies 10% 14% 13% 15% 13% Promote research on the consequences of biodiversity loss 5% 5% 5% 6% 5% Increase citizens’ awareness on biodiversity 13% 12% 16% 13% 13% I do not know 5% 0% 7% 5% 4% None of these 1% 6% 1% 0% 2% Other 1% 2% 1% 1% 1% Increase the areas under protection Set up financial incentives for biodiversity conservation Total Finally, question (3.15) focused on why protect biodiversity at all. The answers reveal a higher environmental sensitivity in the DE and IT samples, with some 14% willing to protect the biodiversity per se (Table 3.15). Overall, all respondents are concerned most about the survival of species for their countries (43%), and to a lesser degree about the benefits of human wellbeing (23%). Table 3.15. Respondents’ opinion on reasons why to protect biodiversity (%) Q15: What do you consider as the main reason for biodiversity protection? For biodiversity per se (for its intrinsic value) To ensure the survival of the different animal and plant species that are typical of our country CZ DE IE IT Tot 9% 14% 9% 15% 11% 43% 37% 44% 47% 43% For the well-being and health of humans 31% 21% 17% 22% 23% For future generation 16% 25% 29% 15% 21% I do not support protection of biodiversity 2% 2% 1% 1% 1% Other 0% 2% 1% 0% 1% In interpreting these outcomes, readers should be careful in not implying a “use” value when the answer is for animal and plant species that are typical of a specific country. This choice does not imply any direct or indirect use (see section 1.1 in this report). Respondents may perceive that they live in a biodiverse country rich in endemic animal and plant species, and these species need to be protected per se. 3.6. Conclusions Overall, the survey results confirm the trend set by Eurobarometer surveys in terms of a growing awareness in civic society of the meaning, importance and causes of biodiversity loss. Growing awareness can be linked to a willingness to pay to maintain habitat and species as a welfare bequest, both across current generational and intergenerational time. Key policy messages seem to be more active management of the territory in terms of reducing emissions of pollutants and the protection of areas and their landscape configuration. 30 4. From the survey results to the monetary estimates The Choice Experiment was undertaken to estimate how much people value biodiversity, interpreted as the maintenance of habitat and species. The survey is the key step of the Choice Experiment but complementary information is needed to convert survey responses into monetary estimates for the surveyed countries and extrapolate these values across the whole EU (Figure 4.1). Figure 4.1. Steps to move from the survey outcomes to a spatially explicit estimate of f monetary values In this chapter, we describe each of these steps, starting from the valuation model underpinning the Choice card used to interview respondents about their preferences. 4.1. The theoretical model underpinning the Choice cards The main survey follows a two-stage approach (following the preparatory meetings and pilots) for the experimental design. The first pilot adopted a non-informative D-efficient design (the design aims to minimize the variance covariance matrix of the model, in other words to split up the attributes in order to maximize the model information). Results from the pilot informed the design of the main survey which was designed with Bayesian D-efficient design with priors. Results in this report just focus on the main final survey but the pilot survey and the preparatory meetings/pilots contributed to final results. Every country is modelled independently as a statistical test of stability of parameters revealed significant differences across countries. Every country model includes the same utility function (Table 4.1). In the example in Table 4.1 we assume that policy option A and B provide a level of utility that can be explained by the land use type, chemical free agriculture, high level of biodiversity, size of average farm targeted to apply the intervention, and the annual cost. The utility of the status quo includes a dummy to take into account that each individual faces different current agriculture conditions. The model, summarized in column 3 of Table 4.1, assumes that the respondent assessed the trade-offs in each choice and selected the best option considering the cons and pros of switching to the alternative A and B scenarios given the current status of land use. From the set of individual choices, it is possible to map respondents’ preferences and willingness to pay. 31 Table 4.1. Specification of utility functions for choice alternative and modelling strategy Example of choice alternative Option A Generic utility level Modelling strategy V_a= Blu*LanduseType a +BCh*Chem a The random utility function: +Bbio*BioHigh a +Bsize*Size a +B*Cost a 𝑈 =𝑓 𝑥 ,𝛽 + 𝜀 where 𝑥 is a vector of k(5 in our case) attributes describing the option j, 𝛽 is a vector of parameters to be estimated and 𝜀 is a residual unobserved component. 𝐿 Option B V_b= Blu*LanduseType b +BCh*Chem b +Bbio*BioHigh b +Bsize*Size b +B*Costb Vsq=SQ+ Blu*LanduseType +BCh*Chem sq + Bbio*BioHigh +Bsize*Size sq sq (𝛽)𝑔(𝛽|𝜃)𝑑𝛽 where P (β) is usually assumed to be a logit choice probability conditional on β . The 𝑦 is the vector of choices. Considering panel data the log-likelihood function is: = No change or Status quo (SQ) = ∫𝑃 , , 𝑦 𝐿𝐿(𝜃) 𝑙𝑛 ∫ 𝑃 (𝑦 |𝛽)𝑔(𝛽|𝜃)𝑑𝛽 Simulated maximum likelihood is adopted to get the model parameters. Estimates as conducted in STATA 16, using 1000 random draws. sq 32 4.2. Results from the Choice cards Table 4.2 presents the model estimates for individual attributes across each member state. Respondents hold heterogeneous preferences for nearly all of the attributes, as can be seen from the second part of Table 4.2. Table 4.2. Coefficient estimates for single country panel mixed logit models CZ DE IE IT Mean coefficient parameters Cost -0.005 Land use2 (light changes) -0.172 Land use3 (large changes) 0.220 * 0.333 ** 0.050 Species diversity High 0.765 *** 0.776 *** 1.163 *** 0.868 *** Size 0.001 *** 0.001 *** 0.004 *** 0.007 *** Chemical Low 0.523 *** 0.381 *** 0.323 *** 0.383 *** Chemical low + species diversity high -0.156 Status quo -4.549 *** -0.004 *** 0.091 0.014 *** -0.007 *** -0.004 *** -0.313 *** -0.219 *** 0.050 -0.184 -4.246 *** -5.461 0.044 *** -4.423 *** Standard deviation coefficient parameter Land use2 (light changes) -0.021 Land use3 (large changes) 0.645 Species diversity High -0.008 0.011 -0.004 *** 0.007 -0.321 -0.026 1.379 *** 1.357 *** 1.167 *** 1.206 Size -0.001 *** -0.004 *** -0.006 *** 0.006 Chemical Low -0.776 *** -0.572 *** 0.696 *** -0.642 Chemical low + species diversity high -0.029 Status quo 3.558 *** 3.199 PseudoR2 0.240 *** 0.240 0.230 0.250 SampleSize 361 355 374 379 0.024 -0.096 *** 2.928 *** *** 0.028 *** 3.031 *** *** significance level at 0.1 ** significance level at 0.05 * significance level at 0.01 The main observations for the first part of Table 4.2 are: 33  Cost attribute: the coefficient estimates have the expected negative sign and are statistically significant (at the 1% level). This means that respondents’ choices were constrained by budget considerations and, all else being equal, respondents preferred lower cost options, compared to higher cost options.  Land use 2: the coefficients are rarely significant or present negative values, this suggests that compared, to current land use types respondents do not have strong preferences for this type of land use configuration; or that in IE and IT respondents would not support a switch to more diverse agriculture land.  Land use 3, the coefficient estimates are significant at 10% and 5% in Czechia and Germany, respectively, and in these countries, respondents are prepared to financially support the switch to a more agriculturally diverse land use.  Biodiversity high attribute: all coefficient estimates show the expected positive sign and are statistically significant (at the 1% level). This means that, all else being equal, respondents preferred options that offered higher levels of biodiversity, compared to options that offered lower levels. The size of the coefficient is the highest of all (apart from sq, see below) which means that high biodiversity matters most to the respondents, and this holds true for all countries.  Size attribute: all coefficient estimates show the expected positive sign and are statistically significant (at the 1% level). This means that, all else being equal, respondents preferred larger amounts of land converted to low intensity agriculture practices  Chemical Low attribute: all coefficient estimates show the expected positive sign and are statistically significant (at 1% level). This means that, all else being equal, respondents preferred agriculture practice which allowed a switch to low use or chemical free agricultural practices.  Chemical low and Biodiversity high attribute: this variable represents the join effect of high biodiversity and low chemical attributes. This variable captures the possible substitution or complementary effect of chemical use and biodiversity. The coefficient is not statistically significant in all cases.  Status quo attribute: all coefficient estimates are significant (1% level) and with a negative sign. This suggests that respondents are not satisfied with the current land use management, and they will face a welfare loss if better management of agriculture is not implemented. The model estimates are employed to derive WTPs estimates. However, the next step before proceeding to the estimates is to clarify how to deal with current land use characteristics. In fact, they strongly affect respondents’ choices which are conditioned by the landscape context in which respondents live. 4.3. The role of current land use characteristics Since individual choices are influenced by the current land uses, portrayed in the questionnaire by the national maps indicating high intensity agriculture zones, it is possible that spatial variation bias exists. Two alternative model specifications are possible: (i) one where the status quo is on average assumed as the worst quality (intensive agriculture, low biodiversity quality, intense use of chemicals and extensive farm sizes) ; or (ii) one which spatially differentiates the current status quo using respondent’s landscape characteristics (in this study we use NUTS3 regions to define the landscape context). This second specification formally acknowledges the role of space in deriving willingness to pay measures. The role of spatial factors in survey design and empirical analyses of stated preference (SP) studies has grown in popularity and Glenk et al (2019) and De Valck and Rolfe (2018) formally review the role of space in environmental policy analysis. They identify that multiple spatial factors can influence SP responses: distance from respondent’s home to the improved sites (e.g. Sutherland and Walsh, 1985, Bateman et al. 2006; Schaafsma et al. 2012, 2013; Liekens et al. 2013), the administrative boundaries of the site (e.g. Dallimer et al. 2014; Rogers and Burton 2017, Bakhtiari et al. 2018, Badura et al. 2020) and the quality of substitutes and complement sites in the geographical location of the site under investigation (e.g. Schaafsma et al. 2012, 2013, Meyerhoff, 2013, De Valck et al. 2017, Logar and Brouwer 2018). 34 In this study, we included the geo-locations of proposed changes as for example in Johnston et al. (2002) and Holland and Johnston (2017) and this is achieved by the current land use maps included in the questionnaire. However, we could not detail characteristics of a specific location as in Badura et al (2020) since the biodiversity benefits could occur everywhere without a specific geographical location. At the same time, we can anticipate that the respondent’s ability to trade-off and value biodiversity is influenced by the current land use near to where respondent lives. By using geographical information related to respondents’ NUTS3 location, CORINE Land cover maps, nitrates in the soil (as proxy of agriculture intensity) and other land use characteristics, a current land use configuration for each respondent was differentiated. This modelling strategy produces more realistic valuation as the estimates are not inflated by the generic assumption that the respondents’ status quo land conditions are the worst levels, and increases spatial heterogeneity in the estimates. Specifically, respondents report their residential postcode and by using the Geo-names postcode location webpage we located the majority of respondents in the relevant NUTS34. Then, by using the CORINE land cover data plus other relevant landscape indicators, we were able to characterise the EU member states: their current proportion of agriculture land, Shannon index diversity, share of natural ecosystem in the area and in the surrounding areas and Nitrates levels. Table 4.3 reports the average for each indicator. Table 4.3. Average value for each land use indicator % cropland Shannon index % natural area % natural area in % of nitrates surroundings MS AT 0.248249 0.447559 67.45272 68.40569 25.12979 BE 0.499767 0.33896 27.32889 26.33874 81.24659 BG 0.491109 0.395305 45.62836 45.19295 34.18718 CY 0.479585 0.514508 43.21492 47.35868 16.9731 CZ 0.439879 0.417834 46.15367 49.58467 62.22419 DE 0.342282 0.409125 48.12624 17.57219 73.37769 DK 0.630776 0.286103 16.64341 73.3055 54.47707 EE 0.215684 0.4784 75.3736 57.66328 26.56356 EL 0.393648 0.544624 57.77762 53.92734 26.90201 ES 0.409136 0.511627 55.12493 83.55152 30.49476 FI 0.146974 0.348076 82.99167 48.88049 27.4088 FR 0.414905 0.431316 48.93912 57.81192 53.08572 HR 0.392078 0.428961 56.34131 36.77316 0.763715 HU 0.55867 0.403275 35.02279 82.80026 47.63885 IE 0.149734 0.500638 77.84559 41.81368 58.4897 IT 0.523836 0.374268 40.86427 46.15028 32.12558 LT 0.546671 0.41924 42.11338 64.81657 39.27635 4 In Ireland 20 observation could not be matched with none nuts3, 2 observations in DE and 1 CZ whereas in Central EU the mismatch is due to respondents located in borders and the postcode non clearly belonging to the study areas, in IE we are investigating whether the reclassification of some postcodes and NUTS3 in the last few years can have cause the mismatch. 35 LU 0.390907 0.4489 50.89163 60.25022 85.68702 LV 0.260715 0.467438 62.1112 46.9063 19.49201 MT 0.54537 0.296551 17.37127 41.82672 79.03595 NL 0.356726 0.406986 46.13932 54.64664 85.7433 PL 0.468394 0.411366 41.08871 45.68921 56.45185 PT 0.396001 0.428797 53.41 82.68501 19.90323 RO 0.4759 0.415057 45.16452 64.7231 27.07526 SE 0.162566 0.391179 81.04688 45.60556 32.09294 SI 0.29229 0.390611 67.7373 46.9674 29.52048 SK 0.447619 0.36965 48.48953 NA 39.96225 UK 0.266589 0.368548 42.17006 NA 53.00531 EU 0.379882 0.410639 48.0756 49.2646 53.11092 NA: Not Available The surveyed member states differ in terms of their land characteristics, and IT and CZ have the greater proportion of agriculture land compared to IE and DE. The country with the highest Shannon index is IE, whereas IT report the lowest among the four states. IE and IT also differ in terms of their proportion of natural ecosystem, with IT at 41% and IE at over 77%. In terms of the wider proportion of natural areas, DE has the lowest proportion and CZ the highest. Considering the proportion of Nitrates CZ, DE and IE are very similar whereas IT report the lowest level. These variables have been used to specify for each respondent the land use typology for the area oaround a given resident. The statistics used to classify respondents land use conditions are percentiles as shown in Table 4.4. Table 4.4. Summary statistics for land use indicators 25' percentile 50' percentile 75' percentile mean st.dev Shannon index 0.35 0.41 0.46 0.41 0.1 Share of natural area 29.5 47.97 65.59 48.1 23.31 37.3 47.88 61.7 49.46 18.05 26.73 45.8 73.17 53.11 36.16 0.21 0.38 0.55 0.38 0.22 Share natural area surrounding areas Average Nitrate % cropland in The effect of including individualised land use indicators in the models is measured through the willingness to pay (WTP) measure for biodiversity and chemical levels reported at the bottom of Table 4.6. The individualised status quo was achieved by using the indicators of tab 4.5. When the percentage of cropland of the NUTS3 area was above 40% and the share of natural area in the zone and in the surrounding areas was 66 and 62+ respectively, the land use of the status quo was set up as “intensive agriculture”. When both shares of areas were between 50 and the 75th percentile the status quo land use was listed as traditional agriculture. When they were lower than the 50th percentile, the status quo agriculture was listed as diverse agriculture. The attribute ‘chemicals ‘for the status quo is high if the indicator was 73 or higher, it was set to medium for values between 46 and 73 and low for indicators below 46. The attribute ‘biodiversity’ for the status quo was considered low when the Shannon index was lower than 0.41, medium for values between 0.41 and 0.46 and high for indicators higher than 0.46. 36 Considering the overall results reported in Table 4.5 we can see that the specification of respondent’s land use conditions marginally reduces the WTP amounts. This reflects more realistic valuations that capture the variability of land use in the four countries. Since spatially explicit estimates, with individualised status quo conditions, produce more conservative estimates we prefer to use these models in the report. Table 4.5. Summary statistics for land use indicators Status quo spatially differentiated No Status quo differentiation CZ CZ DE IR IT DE IR IT WTP_species diversity 161.03 191.24 178.43 216.11 171.93 191.52 187.26 219.37 WTP_low chemicals 110.10 93.86 49.61 95.43 112.54 95.70 45.21 90.23 Based on the spatially differentiated coefficient estimates, we can compute the WTP for each of NUTS1 of the four countries where the survey took place (Table 4.6). Based on these estimates, Figure 4.2 reports the overall WTP for biodiversity, low chemical use and size. Results at the regional level (Tab 4.6) confirm the importance of tailored habitat maintenance polices since estimates are quite heterogeneous. WTP per size varies from Euro 0.03 in Hamburg to Euro 1.66 in the Centre of Italy where prestigious crops (e.g. wine) and protected landscape exist (e.g. Unesco Val d’Orcia). On the contrary , the value of biodiversity is generally higher in Germany with an annual value of Euro 276 or 270, while at the same time in the Saarland region the WTP for biodiversity is statistically null. Irish estimates are quite homogenous across space but the value of biodiversity is generally lower than other states. Respondents might have already adapted their preferences to a high intensity agriculture landscape while still being satisfied overall with the current level of biodiversity (Fig 3.4 and tab 3.6). German and Irish results report on average a lower WTP for lower chemical supported agricultural systems than respondents in CZ Republic and Italy (Fig. 4.2). Table 4.6. WTP per regions and habitat maintenance key features (Euro/household/year) MS Region name NUTS WTP biodiversity WTP low chemicals WTP_sizeHa (95%CI) CZ Praha CZ1 133 (123-145) 106 (101-111) 0.15 (0.146-0.16) CZ Strední Cechy CZ2 162 (149-175) 123 (117-128) 0.13 (0.126-0.14) CZ Jihozápad CZ3 154 (138-110) 93 (87-99) 0.14 (0.135-0.15) CZ Severozápad CZ4 177 (160-193) 112 (107-118) 0.14 (0.135-0.15) CZ Severovýchod CZ5 150 (138-163) 104 (97-109) 0.13 (0.12-0.131) CZ Jihovýchod CZ6 168 (156-178) 113 (107-119) 0.13 (0.122-0.131) CZ Strední Morava CZ7 160 (147-172) 111 (105-117) 0.15 (0.142-0.152) CZ Moravskoslezsko CZ8 180 (166-193) 114 (107-121) 0.14 (0.133-0.145) DE BadenWürttemberg DE1 204 (190-218) 103 (99-108) 0.28 (0.25-0.31) 37 DE Bayern DE2 161 (148-174) 90 (86-94) 0.37 (0.35-0.40) DE Berlin DE3 261 (240-282) 100 (92-108) 0.22 (0.16-0.29) DE Brandenburg DE4 163 (132-194) 79 (73-84) 0.39 (0.33-0.44) DE Bremen DE5 28 (7-48) 57 (48-66) 0.09 (0.08-0.092) DE Hamburg DE6 211 (177-245) 51 (39-63) 0.03 (-0.002-0.07) DE Hessen DE7 243 (222-264) 111 (106-116) 0.26 (0.22-0.31) DE MecklenburgVorpommern DE8 70 (21-119) 72 (65-79) 0.25 (0.21-0.28) DE Niedersachsen DE9 242 (226-259) 95 (92-99) 0.44 (0.41-0.48) DE NordrheinWestfalen DE10 185 (173-196) 93 (90-95) 0.28 (0.26-0.31) DE Rhein land-Pfalz DE11 270 (248-291) 101 (96-105) 0.10 (0.04-0.15) DE Saarland DE12 -11 (-36-14) 60 (55-64) 0.62 (0.48-0.76) DE Sachsen DE13 126 (102-151) 106 (97-114) 0.33 (0.28-0.38) DE Sachsen-Anhalt DE14 276 (241-311) 92 (83-102) 0.37 (0.27-0.47) DE Schleswig-Holstein DE15 48 (24-71) 82 (74-89) 0.24 (0.17-0.30) DE Thüringen DE16 255 (208-302) 65 (60-70) 0.40 (0.34-0.45) IE Norther and Western IE1 176 (169-183) 52 (48-55) 0.63 (0.62-0.65) IE Southern IE2 176 (169-183) 50 (48-55) 0.67 (0.65-0.68) IE Eastern and Midlands IE3 180 (172-180) 49 (48-52) 0.63 (0.62-0.64) IT North East IT1 253 (242-264) 104 (100-108) 1.67 (1.65-1.68) IT North-west IT2 191 (182-199) 90 (86-94) 1.64 (1.63-1.65) IT Centre IT3 199 (189-208) 84 (80-89) 1.66 (1.61-1.69) IT Sardinia IT4 251 (226-277) 150 (139-162) 1.65 (1.61-1.69) IT South IT5 222 (214-231) 99 (96-102) 1.58 (1.57-1.59) 38 Figure 4.2. WTPs for high species diversity, low chemicals and per hectare land use maintenance Given the spatial differentiation effect on the WTP results, any upscaling of these results to other European countries needs to be anchored to regional biophysical, social, and economic conditions. 4.4. From the four sampled countries to the 28 European countries The Choice Experiment was undertaken in four countries. To populate all Member State (MS) estimates we need a consistent and transferable approach to assign the estimates of the four member states to all EU states. Following the key steps of the benefit transfer approach, each MS is initially characterised by their biophysical and socio-economic characteristics at NUTS1 level. The biophysical characteristics are represented by the variables used to assign a land use type (ref. section 4.3) and represent the current state of land use in Europe. The socio-economic data is taken from the European Commission’s data service, Eurostat. Population per NUTS1 is the value for 1 January 2019 (series ‘demorpjangrp3’). GDP per capita in euros is the value for 2017 (series ‘nama10r3gdp’). Given this information an all MSs, a cluster analysis is employed to identify the similarity across NUTS. The cluster analysis is a classification method which, in a transparent and reproducible procedure, identifies groups of observations that are similar to each other considering their characteristics. This method is particularly popular in social science when solid theory to classify observations is lacking and this statistical tool identifies patterns in the data and groups them using their characteristics. A two-step process, is followed, applying firstly a hierarchical clustering and then a partitioning clustering. A tree diagram (dendrogram) is produced and we can identify the sites with similar characteristics (Figure 4.3). This analysis assesses the similarity of surveyed regions with other European regions in oder to progress with benefit transfer measures. Sampled regions spread nicely across cluster groups confirming the good statistical representativeness of our four surveyed sites. North Sweden is the only exception, as this NUTS (SE3) does not conform to any of the others. In this case, we assume that SE3 takes the value of the other Swedish NUTSs. European countries are clustered in 7 groups, in each group there are one or more NUTS1 of the surveyed member states, except group 7 where only SE3 exists. This seems plausible as SE3 is the north of Sweden and our sample sites represent more central, east, and Mediterranean sites. 39 Table 4.7. Clustering European countries Groups 1 and 2 Group NUTS1_ID Groups 3 and 4 Groups 5, 6 and 7 Group NUTS1_ID Group NUTS1_ID 1 AT1 3 BG3 5 AT2 1 BE3 3 CZ0 5 AT3 1 DE5 3 FR2 5 BG4 1 DE7 3 FR3 5 DE4 1 DE8 3 FR4 5 EE0 1 DE9 3 FR5 5 EL1 1 DEB 3 HU1 5 EL2 1 DEC 3 HU2 5 EL4 1 DED 3 HU3 5 ES1 1 DEE 3 LT0 5 ES2 1 DEF 3 PL1 5 FI1 1 DEG 3 PL2 5 FR6 1 LU0 3 PL3 5 FR7 1 NL1 3 PL4 5 FR8 1 NL2 3 PL5 5 HR0 1 NL3 3 PL6 5 ITG 1 NL4 3 RO2 5 LV0 1 UKC 3 RO3 5 RO1 1 UKD 3 RO4 5 SE1 1 UKE 3 SK0 5 SE2 1 UKJ 4 DE1 5 SI0 1 UKK 4 DE2 5 UKL 2 BE1 4 DEA 5 UKM 2 BE2 4 ES5 5 UKN 2 DE3 4 ITC 6 EL3 2 DE6 4 ITF 6 ES3 2 DK0 4 ITH 6 ES4 2 FR1 4 ITI 6 ES6 2 UKF 4 PT1 6 IE0 7 SE3 2 UKG, UKH, UKI 40 Figure 4.3. Dendrogram: clustering of NUTS1 using land use biophysical and socio-economic information 4.5. Estimated values for the maintenance of habitat and species Estimates of the surveyed regions are then applied to other regions following a mean benefit transfer approach. We are ultimately able to provide a reference table for the monetary values attributed to the maintenance of Habitat and Species at NUTS1 level, as reported in Table 4.8. The values refer to the WTP per households (HH). To have an overall estimate of how much this WTP is in Europe, we can refer to the calculation undertaken for the ecosystem service “habitat and species maintenance” within the Integrated system for Natural Capital Accounting (INCA). In fact, chapter 3 of La Notte et al. (2021) specifically refers to these monetary estimates (Table 4.8) to assess the annual flow of “habitat and species maintenance”. The multiplication per number of households in the INCA application depends on the biophysical mapping of habitat (reference to the low chemical attribute) and species (reference to the species diversity): only where habitat and species presence are assessed (through appropriate indicators) can the value be attributed. This approach is undertaken to assure that a higher value is attributed to areas in which the presence of habitat and species is also high. By inflating the 2012 values reported in the INCA report to 2019 (the year when the survey was undertaken), the overall value is 35,660 million EUR including UK, and 30,018 million EUR without UK. The INCA calculation of the overall estimate attributed to the maintenance of habitat and species is only one of the many calculation types that could possibly be undertaken. However, a careful interpretation of what these numbers mean and how to interpret them is key to avoid misleading policy messages, and to make sure that a tool that is built to support environmental action is not used to downgrade the significance of the value of nature. 41 Table 4.8. Willingness to Pay estimates: average per households per year (reference year 2019) Countries WTP_biodiversity WTP_low chemicals WTP_size (per hectare) AT 105.74 189.41 0.80 BE 80.81 179.58 0.21 BG 111.60 180.50 0.52 CZ 109.47 160.42 0.14 DE 88.83 175.43 0.39 DK 75.46 236.16 0.13 EE 114.41 206.84 1.02 EL 113.47 206.42 1.01 ES 72.07 188.12 0.80 FI 114.41 206.84 1.02 FR 110.82 185.47 0.57 HR 114.41 206.84 1.02 HU 109.47 160.42 0.14 IE 50.13 177.36 0.64 IT 97.60 205.13 1.18 LT 109.47 160.42 0.14 LU 84.01 145.71 0.26 LV 114.41 206.84 1.02 NL 84.01 145.71 0.26 PL 109.47 160.42 0.14 PT 95.07 204.87 1.20 RO 111.37 178.30 0.48 SE 114.41 206.84 1.02 SI 114.41 206.84 1.02 SK 109.47 160.42 0.14 UK 95.83 193.13 0.57 42 5. Conclusions A resilient biodiversity can underpin the sustainable supply of a diverse list of ecosystem services humans’ use: from the provision of food, genetic materials and medicines to outdoor recreation, air and water filtering, pollination, carbon balance and cultural services. However, people’s perception of the fundamental role that habitats/species long run existence plays for the food chain and the importance of the food chain for the longterm survival of human beings on this planet reveals that a value is attributed to biodiversity beyond its material and immaterial “usage”. Biodiversity has a non-use value that can be represented by the habitat and species maintenance service, so-called infrastructure or glue value (see section 1.1 in this report). This non-use maintenance service is perceived and valued by the people who are willing to pay for it, in addition to provisioning, regulation and cultural use value services. The 2030 Biodiversity Strategy is a core part of the European Green Deal. The purpose of the Strategy is to put Europe’s biodiversity on the path to recovery by 2030 and to enable the EU to take a leading role in negotiating a new global framework to halt biodiversity loss. Among all actions listed in Chapter 1, the new EU-wide Biodiversity Strategy will5:   Restore degraded ecosystems at land and sea across the whole of Europe by: o Increasing organic farming and biodiversity rich landscape features on agricultural land o Restoring at least 25 000 km of EU rivers to a free-flowing state o Reducing the use and risk of pesticides by 50% by 2030 o Planting 3 billion trees by 2030 o Halting and reversing the decline of pollinators Unlock 20 billion €/year for biodiversity through various sources, including EU funds, national and private funding. The first main actions listed here directly concerns the use and management of terrestrial land, and specifically agricultural practices expressed in terms of organic farming, use of pesticide and species diversity (e.g.. reverse the decline of pollinators). As highlighted by focus groups and pilot studies underpinning the construction of the questionnaire (Ch. 2), people’s perception of drivers of biodiversity loss are perfectly in tune with the 2030 Biodiversity Strategy. To capture people’s willingness to support habitant maintenance polices, the ecological meaning of biodiversity and its changes were represented by a set of scenarios where land use was managed from monoculture to agroforestry with the use/ban of chemicals and presence/absence of species that can guarantee the existence of the food chain. Chapter 3 summarises these scenarios and the key perceptions of the policy drivers for biodiversity protection. The last action listed here concerns the funding for biodiversity. It would be also interesting to find out whether 20 billion €/year is an appropriate minimum estimate of the resources needed to be allocated to the Biodiversity Strategy. From the estimates based on the Choice Experiment for the EU27, the yearly monetary flow people are willing to allocate to biodiversity is more than 30 billion €/year. Europeans would like to see biodiversity actions allocated 1/3 more than the forecasted financial flow. However the estimates confirm that Europeans also see the benefits of allocating 20 billion €/year for protecting biodiversity. The availability of this additional dataset of monetary estimates could serve several other purposes in addition to the double checking the order of magnitude of financial funding allocated to biodiversity protection. Before exploring the other benefits of providing monetary estimates for biodiversity protection policies, we need to distinguish between the use values of ecosystem services, supported by biodiversity as an intermediate service, and the non use value (glue value) of habitat and species maintenance that represents a final service per se. The aggregate economic measure total economic value (TEV) is always less than the total system value (TSV) because of the glue value service (Turner et al 2003). 5 Summary reported from EC official documentation available at https://ec.europa.eu/environment/strategy/biodiversity-strategy2030_en 43 Multiple goods and services traded in the market are provisioning services supported by biodiversity (e.g. fruits, fibre, timber, etc) and they provide a flow of resources essential for our economies and wellbeing. The willingness to pay for habitat and species maintenance is an additional stream of resources provided by the society, not directly visible from the market perspective, but nevertheless important when taking policy decisions that affect the future of countries and their inhabitants. Many challenges have to be met when attempting to measure this ‘invisible’ non-use value. First, a simple and intuitive format is needed to communicate and illuminate this value. To explain biodiversity we separated it into habitat and species and relate their possible changes to concepts that people can easily understand. Any ecological terminology was avoided and we refer to land use changes that people can relate to: the basic notion used to explain the importance of species is the food chain, and the transformation in relation to habitat degradation that is commonly perceived concerned the use of chemicals in agriculture and the landscape transformation. Second, to find a methodology to attribute a value to the infrastructure/glue value of biodiversity is not straightforward. No exchange value technique could possibly serve this purpose. We need to adopt state-based social preferences to estimate non-use values. A Choice Experiment is a quantitatively strong technique able to provide not only the monetary estimates but also a social portrait of the situation in which respondents live. To build a survey is neither quick nor easy. Social science is as complex as natural science. Several focus group exercises, and several pilots had to be undertaken before the final survey could be undertaken. Third, when moving from respondents’ preferences in the survey to monetary estimates many features have to be addressed in terms of reference contexts and geographical distribution. The procedure to up-scaling the results from 4 regions to the EU level is feasible, but the quality of the results depends on how much spatial and environmental characteristics matter in the quantitative assessment. Biophysical and social contexts play in fact an important role when you need to move from the point data to the areal allocation. Each chapter of this report is meant to address these three aspects, with detailed descriptions. The development of the survey and monetary estimation of willingness to pay results represents only the starting point of supporting biodiversity protection policies. In fact, once we have the monetary valuation of habitat and species maintenance, what could we do with it? From a macroeconomic perspective, the valuation of habitat and species maintenance can be used as an ecosystem service flow to be reported in Supply and Use tables in natural capital accounting (La Notte et al., 2021). The benefit of reporting this ‘hidden’ flow of value is to fully acknowledge the role of visible and invisible services by aggregated ecosystem type (Figure 5.1). For example, biodiversity rich sites (for example protected areas) may provide higher flows of services like habitat and species maintenance and outdoor recreation/amenity and cultural experiences, rather than crop and timber provision (that are contributing to output that are already part of the System of National Accounts, SNA, but not fully attributed to the natural capital and biodiversity). Trade-off analysis would show this counterbalancing generated by biodiversity. While natural capital accounting is an ex-post assessment, planning and impact assessment analysis are preinvestment analysis that can be enhanced by biodiversity estimates. Critical biodiversity losses can impinge on welfare benefits and pre-assessment analysis, and complemented with biodiversity estimates and hotspots zones, can reduce the risk of environmental, economic and social irreversible losses (Schoukens, H., & Cliquet, A. 2016). For example, if a region presents high biodiversity values and a mining activity is authorised even with the possibility to compensate biodiversity losses off-site, it is quite possible that local communities would strongly oppose the mining activity and the overall wellbeing of the area might be compromised. Figure 5.1. Visual simplification of a macroeconomic perspective use of biodiversity value estimates 44 From a microeconomic perspective, Cost-Benefit Analysis of ecological restoration projects (e.g. Török et al 2011, Faivre et al 2017) might be enhanced by habitat and species maintenance values. The decision about whether to finance a project is traditionally based on financial figures referring to costs and revenues. Any environmental project aiming at enhancing the ecological status of for example soil and water through restoration actions would likely focus primarily on implementation costs since biodiversity estimates are costly and time consuming although their values can counterbalance the costs (Figure 5.2). Figure 5.2. Visual simplification of a microeconomic perspective use of biodiversity value estimates All national and regional agriculture programs link directly to habitat and species maintenance, and any policy instrument (e.g. subsidies, taxes, exclusion zones) directed to farmers should fully account for the welfare benefits of these actions, but the lack of knowledge of people preferences could produce suboptimal results. However, preliminary analysis of habitant and species maintenance benefits vs farmers’ biodiversity implementation costs can support the transition to sustainable agriculture initiatives. Valuation studies based on stated preferences (SP) methods have been important information sources particularly for revealing the value of those ecosystem services that lack the direct and hence apparent connection to the socio-economic system. This value is mainly represented by the non-use value component of the total economic value concept, and this is why welfare estimates elicited through SP methods may be the only possible representation. The outcomes of the CE study on the welfare value of habitat and species maintenance enhance the current knowledge on the value of biodiversity. Moreover, this enhancement is spatially specific because the CE outcome outlines the spatial distribution of this value under the EU-27 regional context. Values of empirical studies as the current one, offer the primary material for the compilation of valuation databases (e.g. the Ecosystem Service Valuation Database-ESVD or the Environmental Valuation Reference Inventory-EVRI database). These valuation databases can facilitate and accelerate ecosystem accounting applications; they provide the summary statistics of the valuation methods used and the value estimates for a great range of ecosystem services and ecosystems. This may very quickly demonstrate the average value as well as the spectrum that this value may take. The database can facilitate ecosystem accounting applications through the employment of the Value Transfer method. We are already aware of the use of this method for ecosystem accounting purposes. Even though this method is promising for future applications mainly because it can accommodate the need for regular (periodic) and consistent (modelling of values) ecosystem accounts, further work is needed towards structuring valuation databases that would be compatible and suitable for the objective of ecosystem accounting. Finally, circular bio-economy strategies, based on promoting bio-resources and local assets (Bugge et al 2016), would benefit from a preliminary analysis of habitat and species maintenance values to tailor interventions with regional environmental and economic conditions and needs. 45 References Armitage, D., Mbatha, P., Muhl, E.-K., Rice, W., & Sowman, M. (2020). Governance principles for communitycentered conservation in the post-2020 global biodiversity framework. Conservation Science and Practice, 2(2), e160. https://doi.org/https://doi.org/10.1111/csp2.160 Badura, T., Ferrini, S., Burton, M., Binner, A., & Bateman, I. J. (2020). 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Ecological Indicators, 69, 488–499. https://doi.org/https://doi.org/10.1016/j.ecolind.2016.05.008 48 List of abbreviations and definitions CBD Convention on Biological Diversity CE Choice Experiment EEA European Environment Agency EGD European Green Deal EU European Union INCA Integrated system for Natural Capital Accounting MNL MultiNomial/conditional Logit (models) WTP Willingness to PayABΓ Alpha Beta Gamma 49 List of figures Chapter 2 Figure 2.1. Steps for the Choice Experiment study Figure 2.2. Sample of the figures reported in the first pilot survey Figure 2.3. List of policies and actions suggested throughout the discussion Figure 2.4. First CE pilot survey Choice card example 9 11 14 16 Chapter 3 Figure 3.1. Share of respondents by nationality and age group Figure 3.2. Proportion of respondents by nationality and income Figure 3.3. Percentage of respondents who knows about biodiversity and food chain by country Figure 3.4. Percentage of satisfactory level of national biodiversity level by country Figure 3.5. Example of 1 of 36 choice cards presented to respondents Figure 3.6. Attributes and levels Figure 3.7. Respondents’ preferences on policy in support of the maintenance of habitat and Species Figure 3.8. Respondents’ preferences on policy in support of the maintenance of habitat and species 21 22 22 23 26 26 28 28 Chapter 4 Figure 4.1. Steps to move from the survey outcomes to a spatially explicit estimate of monetary Values Figure 4.2. WTPs for high species diversity, low chemicals and per hectare land use maintenance Figure 4.3. Dendrogram: clustering of NUTS1 using land use biophysical and socio-economic information 31 39 41 Chapter 5 Figure 5.1. Visual simplification of a macroeconomic perspective use of biodiversity value estimates Figure 5.2. Visual simplification of a microeconomic perspective use of biodiversity value estimates 50 44 45 List of tables Chapter 2 Table 2.1. Ranking of actions and average WTP Table 2.2. Drivers of Biodiversity Loss questioned during the Focus Groups Table 2.3. Socio-economic characteristics of respondents Table 2.4. Choice card attributes and levels used in the pilot Table 2.5. First CE pilot MNL model results Table 2.6. First CE pilot willingness to pay results 12 14 15 16 17 18 Chapter 3 Table 3.1. Farm size (ha) in the four selected countries based on EU Agriculture statistics Table 3.2. Regional distribution of responses per Member States Table 3.3. Age distribution of responses per Member States Table 3.4. Regional distribution of responses per Member States Table 3.5. Definition and knowledge of biodiversity and food chain Table 3.6. Respondents’ perception of national and local quality of biodiversity Table 3.7. Respondents’ perception of factors influencing quality of biodiversity Table 3.8. Factors influencing respondents’ perception of the quality of biodiversity Table 3.9. Respondents’ perception of how common high intensity farming in their region is Table 3.10. Reasons for respondents to opt out Table 3.11. Preferences for managing the biodiversity fund (%) Table 3.12. Detailed percentages on respondents’ perception on main causes of biodiversity loss (%) Table 3.13. Further respondents’ perception on main causes of biodiversity loss (qualitative ranking of preferences) Table 3.14. Detailed percentages on respondents’ preferences on policy for biodiversity protection (%) Table 3.15. Respondents’ opinion on reasons why to protect biodiversity (%) 19 20 21 21 23 24 24 25 25 27 27 29 29 30 30 Chapter 4 Table 4.1. Specification of utility functions for choice alternative and modelling strategy Table 4.2. Coefficient estimates for single country panel mixed logit models Table 4.3. Average value for each land use indicator Table 4.4. Summary statistics for land use indicators Table 4.5. Summary statistics for land use indicators Table 4.6. WTP per regions and habitat maintenance key features (Euro/household/year) Table 4.7. Clustering European countries Table 4.8. Willingness to Pay estimates per households per year (reference year 2019) 51 32 33 35 36 37 37 40 42 Annexes 52 Annex I. Biodiversity Governance initiatives at global level The Convention on Biological Diversity (CBD) was adopted in Nairobi (Kenya) in May 1992. The Convention is considered all-encompassing as it provides the protection of all living organisms but leaves countries free to manage their internal goals and actions. The Convention on Biological Diversity has no list of species to protect or sites to manage, but provides three primary overarching goals: 1. The conservation of biological diversity; 2. The sustainable use of biodiversity; 3. The fair and equitable sharing of the benefits deriving from the use of biodiversity. The Convention envisages a link between these three objectives, but this should be mediated through a fair and equitable sharing of the benefits derived from biodiversity. Biodiverse Developing economies should be able to afford to protect biodiversity within a sustainable development strategy. Industrialized countries on the other hand often benefit enormously from the use of biological resources from developing countries, but the benefit accrued by latter are often much less (Marden et al 2020). For this reason, conditions for the exploitation of resources have been included in the Convention and are summarized in the procedure called "Access and Benefit Sharing” (Carrizosa, 2004). In the last decade, these conditions had been comprehensively revised through the adoption of the "FAO Treaty on Plant Genetic Resources" in 2001 and the "Nagoya Protocol" in 2014. These agreements are fundamental provisions for a legislative framework to regulate the protection and benefit sharing of biodiversity resources. Although, a combination of loose definitions, lack of coordination and complex regulations have so far failed to adequately protect biodiversity and promote a bio-economic development path (Maunder ref in here). Ten years after the CDB creation, the United Nations held a Conference on Environment and Development in Johannesburg (South Africa) to discuss the state of biodiversity protection and promote actions. The CBD signatory countries pledged to significantly reduce the rate of biodiversity loss by 2010 through a formal agreement called the “2010 Biodiversity Goal”. The agreement, although not legally binding, committed participating countries to its achievement. Unfortunately, the 2010 Goal was not achieved. The main obstacle was the lack of consensus in the scientific debate grappling with the problem of accurately measuring the rate of biodiversity loss. There is still scientific debate on how to measure the loss of biodiversity. In 2010, to overcome this lack of measurement and accountability of biodiversity loss the "Aichi Biodiversity Targets" were set up. During the Conference of the Parties the "Strategic Plan for Biodiversity 2011-2020" held in Nagoya, Aichi prefecture (Japan), 20 objectives, divided into five groups of strategic goals were defined. They were: • Strategic objectives A: address the underlying causes of biodiversity loss by integrating the issue of biodiversity into social and government policies; • Strategic objectives B: reduce direct pressures on biodiversity and promote sustainable use; • Strategic objectives C: improve the state of biodiversity by safeguarding ecosystems, species and genetic diversity; • Strategic objectives D: improve the benefits deriving from biodiversity from ecosystem services; • Strategic objectives E: improve implementation through participatory planning, knowledge management and strengthening development skills. Each macro objective unfolded into specific actions. A few examples are reported below: 1. At the very least by 2020 the population should be made aware of the value of biodiversity and the necessary steps to be able to conserve and use it in a sustainable way; 2. The value of biodiversity should be integrated into national and local development strategies and planning processes, and incorporated into accounting and reporting systems; 3. Incentives, including subsidies, that are harmful to biodiversity should be eliminated, or reformed, in order to minimize or avoid negative impacts. Instead, positive incentives for the conservation and sustainable use of biodiversity should be developed and applied in harmony with the Convention and other international obligations. Always taking into consideration the socio-economic conditions of the countries; 53 14. Ecosystems that provide essential services, including water-related services, and contribute to health, livelihoods and well-being, should be restored and safeguarded ,taking into account the needs of women, indigenous and local communities, the poor and the vulnerable ; 20. The mobilization of financial resources to effectively implement the Strategic Plan for Biodiversity 2011-2020 from all sources, and in accordance with the process established and agreed in the Strategy for Mobilization of Resources, should increase substantially from current levels. This objective will be subject to changes depending on resource needs assessments to be developed and reported by the Parties. The Aichi's targets were overall clear and very ambitious but, in September 2020, the United Nations announced that none of the twenty objectives were fully achieved. This was a very disappointing announcement (implying inefficient and ineffective use of public funding) if we consider the huge investment of time and resources. The situation is serious, but not irreversible. Lately, the advisory body of the United Nations Convention has called for the development of a new Global Framework for Biodiversity after 2020 (Global Biodiversity Framework - GBF). A Working Group was set up to develop the Post-2020 Global Framework for Biodiversity. The Working group met twice. The first meeting was held in Nairobi from 27 to 30 August 2019, marking the official start of the negotiations. The working group has drafted some objectives to be achieved by 2030, and in line with the three overarching primary objectives of the CBD. The conclusion of this consultation process was the conference in Kunming (China) scheduled for October 2020 where the new commitments to preserve biodiversity loss were supposed to be discussed and agreed. Due to the ongoing pandemic, the conference is now scheduled for October 2021. A "zero draft" of the new Global Framework for Biodiversity (GBF) has been prepared and initially discussed in Rome (24 to 29 February 2020) by the working group and country representatives. The "draft zero" of the Post-2020 Global Framework for Biodiversity contains five key long-term goals to be achieved by 2050 , each of which can be concretely defined and measured. The five fundamental objectives, to be negotiated in October 2021 during the COP 15 [ X is the value to be negotiated], are: 1. No net ecosystem loss (net positive balance between acquired and lost ecosystems) by 2030 ; and through the extension and integrity maintenance of freshwater, marine and terrestrial ecosystems, increases of at least 20% by 2050, thus ensuring the resilience of ecosystems. 2. The percentage of endangered species must be reduced by [X%] and the abundance of species must be increased on average by [X%] by 2030 and by [X%] by 2050; 3. Genetic diversity must be maintained or enhanced on average by 2030 and for [90%] of species by 2050; 4. Contribute to: - An improvement in nutrition for at least [X million] people by 2030 and [Y million] by 2050; - An improvement in sustainable access to safe and safe drinking water for at least [X million] people by 2030 and [Y million] by 2050; - An improvement in resilience to natural disasters for at least [X million] of people by 2030 and [Y million] by 2050; - Achieving at least [30%] of the mitigation commitments to achieve the Paris Agreement targets in 2030 and 2050. 5. The benefits, shared equally and justly, from the use of genetic resources and associated traditional knowledge should have increased by [X] by 2030 and reached [X] by 2050. The adoption of the new Global Framework for Biodiversity (GBF) post-2020 will play an important role as it represents a springboard towards the 2050 Vision of "Living in harmony with Nature" which aims that in 30 years biodiversity can be valued, conserved, restored and used wisely, maintaining ecosystem services, supporting a healthy planet and providing essential benefits for all people. The targets are clear but the tools and processes to reach these Specific, Measurable, Ambitious, Realistic and Time-bound (SMART) goals are less consolidated and developed (Primmer, & Furman, E. 2012, Laurila-Pant, et al 2015, Maxwell et al 2015 Feger, C., & Mermet, L. 2017). An OECD 2019 report called “The Post-2020 Biodiversity Framework” reviewed the past biodiversity conservation ambitions and the lack of progress and provides a set of recommendations for the coming global consultation. The report is transparent on the challenges to measuring the progress toward the Aichi targets, as well as the problem of comparability across states. This highlights the complexities surrounding the enabling processes required to reach the 2050 living in harmony goal. 54 Annex II. The questionnaire Thank you for taking the time to complete this survey. We are carrying out research about what is important to people when policies are designed the conservation of biodiversity. The survey will last between 20 – 25 minutes. There are no right or wrong answers and no specific knowledge about biodiversity is required to participate. Who is running the research? The research is run by Joint Research Centre in collaboration with the University of East Anglia. Why is the survey being done? The survey is part of a wider research project which aims to study biodiversity in Europe. What will data be used for? If you agree to take part to the survey you will be asked about yourself, your opinion on land management and environmental protection. The data will be used to understand the current land use management to design appropriate land use changes for supporting and enhancing biodiversity protection. Your response will be anonymized and will be available only to researchers with both scientific and ethical approval. A report will be also made available publicly at the end of the project by 2020. No personal information which could be used to identify you will be published or shared. You can withdraw from the survey at any time and do not need to specify the reason. However, once the data will be anonymized there is no way researchers can identify and remove your record. For further information you can contact Alessandra La Notte, ([email protected]). How is the survey conducted? If you agree to participate in the survey, your participation is voluntary and you are a free to refuse to answer questions. Consent to participate in this study is implied by ticking the box found at the bottom of this page. I am aged 18 years or more and hereby voluntarily consent to my involvement in the research project. I am aged 18 years or more and hereby voluntarily consent to my involvement in the research project I confirm I do not confirm 55 SECTION 0 - SCREENING AND QUOTAS S1. Please can you provide your partial home postcode postcode? This information will be treated as confidential and will only be used for research purposes o RECORD First 3-digit home POSTCODE o Prefer not say (this ends the survey) THANK & CLOSE S2. Please enter your postcode: Please only enter capital letters and numbers, don't include any other characters. Enter text below _________ S3. Please indicate your gender o Male o Female o Other / Prefer not to say S4. Please indicate your age o 18-29 o 30-39 o 40-49 o 50-59 o 60-74 o 75 or more S5. Are you the main income earner in your household? o No ASK Q5 o Yes ASK Q5 o No income earners SKIP TO Q7 S6. What is the main income earner’s occupation in your household? If the main income earner is retired, please select their occupation before retirement o Higher managerial, administrative or professional o Intermediate managerial, administrative or professional o Supervisory or clerical and junior managerial, administrative or professional o Skilled manual worker o Semi or unskilled manual worker o Casual worker, dependent on state pension only, or dependant on state welfare 56 SECTION A – Description of the topic D1. The European natural environment faces intense pressures from land use and climate change, urban expansion, and intensive agriculture. The number of organisms (plants and animals) living in the natural environment (e.g. natural forest, grasslands or wetlands) is decreasing. This lowers the variety of plants and animals left in nature. Governments can implement policies that might maintain, improve or reduce this variety. 1. Have you heard before of the term biodiversity? o o Yes No [IF Q1= NO, go directly to Q3] 2. In your opinion, what could be an appropriate definition of biodiversity: o Variety of living organisms, animals and plants; including the diversity among species, across species and places where they live o Number of different animals and birds on the earth o Number of different marine and terrestrial habitats o Other____________ 3. Do you know the concept of “food chain” in nature? o Yes o No [IF Q3= NO, go directly to D2] 4. In your opinion, what could be the right definition for a food chain: o the relationship between ecosystems o the functioning of a biological cycle o dependency of organisms on other organisms as a source of food o Other 5. Do you think that biodiversity and food chain are affecting each other? o o o Yes No I don’t know 57 D2. Biodiversity means the variety of all living organisms, animals and plants, present on the planet. In a diverse environment different organisms exist and their relationship, with the "eating and being eaten" rule, describes a food chain which underpins a healthy environment. In reality, each environment presents unique characteristics and for example an insect can belong to more than one food chain and the environment is formed of many food chains together that represent a food web as in the picture. The more biodiverse an environment is the more complete is the food web. Example of a food web: As the environment is degraded (e.g. due to diversity of the landscape as in picture on the left-hand side) some species disappear [or their quantity reduces substantially] which leads to loss of biodiversity. Environmental degradation lowers the number of species (animals and plants) in the landscape, as shown in the Figure below. Particular forms of landscape management can lead to a loss of biodiversity; governments can prevent this outcome. 58 6. If you were asked to assess the level of biodiversity in your local area and nationally, what would it be your opinion? Very low Low Normal High Very high I don’t know Biodiversity in your country (National) Biodiversity in your county (Local) 7. When you answered to the last question, were you thinking about any of the following? ❑ quantity/quality of parks, green areas and / or wooded areas ❑ waste management ❑ water quality ❑ agriculture policies for environmental protection ❑ Gut feeling ❑ The number of animals and plants ❑ Insects ❑ Others (specify) 59 SECTION B – Attitude and perception 8. To what extent do you agree or disagree with the following statements about impacts on biodiversity: (Please tick one, and only one box on each row) I totally agree I agree rather than disagree Urbanization causes biodiversity loss Intensive agriculture causes biodiversity loss Increasing human population causes loss of biodiversity Industrialization causes biodiversity loss The introduction of nonnative species causes biodiversity loss Pollution causes biodiversity loss Climate change causes biodiversity loss Organic farming causes biodiversity loss Buying seasonal fruit and vegetables causes biodiversity loss 60 I neither agree or disagree I disagree rather than agree I totally disagree 9. From the list above which one do you consider to be the most important? And the least important? Most Least Urbanization causes biodiversity loss Intensive agriculture causes biodiversity loss Increasing human population causes loss of biodiversity Industrialization causes biodiversity loss The introduction of nonnative species causes biodiversity loss Pollution causes biodiversity loss Climate change causes biodiversity loss Organic farming causes biodiversity loss Buying seasonal fruit and vegetables causes biodiversity loss 10. In your opinion, what is the best policy that could be adopted by a state to protect biodiversity? (please select one and only one answer) o Increase the areas under protection (e.g. nature reserve, natural park) o Set up financial incentives for biodiversity conservation that everyone can access o Introduce stricter rules for agriculture activities that impact biodiversity o Allocate more financial resources for promoting policies that support biodiversity protection o Promote research on the consequences of biodiversity loss o Increase citizens' information on the importance of biodiversity o I don’t know o None of these o Other (specify) 11. What do you consider as the main reason for biodiversity protection? (Please select only one answer). o o o o o o For biodiversity per se (for its intrinsic value) To ensure the survival of the different animal and plant species that are typical of our country For the well-being and health of humans For future generations I do not support protection of biodiversity Other (please specify): 61 SECTION C –PREFERENCES for AGRICULTURE POLICIES High intensity farming has led to a significant increase in food production through more productive animal rearing and mono cultivation of crops. However, it has also been the biggest driver of changes in the condition of the natural environment and has produced a less varied landscape with negative consequences for biodiversity. The following pictures and table summarize high intensity farming: High intensity farming Amount of food grown High levels of food produced per hectare Number of farm animals High numbers of animals per hectare Levels of inputs used High levels of fertilisers, pesticides and machinery used Effects on wildlife Main cause of declines in biodiversity (e.g. farmland bird species; pollinators) 12. How far away is the nearest high intensity farmed area to your house? o o o o o 0-10km 11-20km 20-40km More than 40km Do not know 13. How common is this farming system in your region? o Main farming system o Fairly used system o Uncommon system o Don't know A switch to less intense farming systems is possible. These include for example:      planting new trees, bushes and grasses along with crops known as agroforestry or with livestock rearing known as agro-silvo pastoral setting aside land for harbouring pollinating insects and wildlife or for planting wildflowers, planting a variety of crops converting agricultural land into woodland organic farming or decreased use of chemicals such as fertilisers or pesticides. 62 These systems have the following features comparing to high intensity farming: High intense farming less intense farming systems Amount of food grown High levels of food per hectare Reduced Number animals Reduced of farm High numbers of animals Levels of inputs used High level of fertilisers, pesticides Reduced and machinery used Effects on wildlife Reduced biodiversity Increased biodiversity Governments can design specific agricultural policies to improve the state of biodiversity in agricultural lands. This survey aims to explore public preferences for these policies. Please take a moment to look at your country map where red represents zone with high intense farming. [DISPLAY MAP of country] You will be asked to choose from three policy alternatives. Two alternatives would lead to an improvement in biodiversity and one alternative that keeps things as they are. The changes in biodiversity in agricultural lands might not be perceived by general population as it might impact less visible plants or animals. For each alternative you will be asked to consider the following characteristics that can vary as explained below: o The reduction in chemicals compared to today ▪ Banned chemicals (i.e. organic farming) ▪ Reduce by 25% ▪ Reduce by 50% 63 ▪ Reduce by 75% Change in biodiversity (diversity of wildlife and plants) relative to today’s situation. This change might not be visible to humans or accessible for their enjoyment. However, as mentioned previously in the survey the high level of biodiversity is crucial for healthy environments. ▪ Small change ▪ Medium ▪ Large change The size of farming land where the change can occur. · Small (14 hectare as 20 football pitches) · Medium (40hecatre as 60 football pitches) · large (100hectare as 150football pitches) 64 Costs in term of ANNUAL contribution towards agri-environmental scheme. Households like yours will pay a specific TAX that will generate a FUND to support biodiversity protection. We will now present you with six questions in which you are asked to compare the three alternatives. All you need to do is decide which of the three alternatives you prefer in each situation. Please treat each question separately. There is no right or wrong answer, but it is important that your responses reflect your true opinion as your responses might impact future agri-environmental policies. Also remember that any changes will cost the amount shown as a new TAX which reduces your ability to make other purchases, such as daily house expenditures. 65 6 choice cards QNEW: “To what degree do you believe that your responses will be taken into account in policy and administration of habitat maintenance for biodiversity protection in your country?”. definitely considered rather considered rather not considered 66 definitely not considered I do not know The 6 choices that you made presented hypothetical changes in agricultural lands that might occur in the future. Previous studies show that people tend to make decisions in hypothetical situations (as you did before) that differ when the situation became real. For example, a recent study asked people whether they would purchase a new food product. This purchase was hypothetical as no one actually had to pay money when they indicated a willingness to purchase. In the study, 80% of people said they would buy the new product, but when a grocery store stocked the product in reality, only 43% of people actually bought the new product when they had to pay for it. This difference (43% of people buying the product vs. 80% of people stating they would) is an example of people overestimating their hypothetical choices. Think about what you are deciding on. Your choice implicates consequences (e.g. have to pay for biodiversity improvements that are good for the environment but maybe not directly visible to you!). Please keep this in mind while we offer you the possibility to revise three of the previous choices. Randomly allocation of 3 choice cards with previous choice. Let people revise their choices. 14. If you chose the option “No change” in all of the sets, what was the reason for it? (Choose only one the most important reason - from the following). ❑ I can’t afford to pay. ❑ I am in favour of this initiative, but it is the responsibility of the state to support it. ❑ I do not believe that biodiversity protection is important. ❑ I doubt that this initiative can be effective for the protection of biodiversity ❑ I was not the cause of the problem and therefore I do not understand why I should support this initiative. ❑ I think biodiversity protection is not guaranteed through an agri-environmental scheme. ❑ Other reasons (specify) 15. Who should manage the fund collected to protect biodiversity? o o o o o o o o o A private research institution A public research institution A private trust or fund A public trust or fund An environmental association like WWF The city council The region Central government The European Union 67 SECTION D – RESPONDENT PROFILE D1. What is your education level? Elementary school Middle school High school degree Degree or other university degree Post graduate training No educational qualifications o o o o o o D2. How many people live in your household, including yourself? Please include every adult and child: 1 person 2 people 3 people 4 people 5 people 6 people More than 6 I would rather not say o o o o o o o o D3. How many people under 18 years are living in your household? o o o o o o None 1 person 2 people 3 people 5 people 5 or more people Now we would like you to consider your weekday engagement with nature and ask you question about the time you spent last month outdoors. By outdoors we mean open spaces in and around villages, towns and cities, including parks, canals and nature areas; the coast and beaches; and the countryside including farmland, woodland, hills and rivers. This does not include: - routine shopping trips or; - time spent in your own garden 19. How many outdoor visits did you make last month in total? (This could be anything from a few minutes to all day.) _______________ 20. Which of the following best describes where you spent most of your total outdoor time? o o o o o in a town or city In a seaside town or place Other seaside coastline (including beaches and cliffs) In the countryside (including areas around towns and cities) I don't spend any time outdoors 68 Q22. How many days of the total did you spend in your favourite location? Q23. How far is the this location from your home? o o o o o o o Less than 1km 1-3km 3-5km 5-10km 10-20km 20-40km More than 40 km Q23. How do you get to your most favorable location? o o o o Walking Cycling Car driving Other Q24. Are you or anyone in your family a farmer? Yes, No Q25. What is your annual net family income (after taxes)? Please include all sources of income, including benefits, stipends, pension etc. (please indicate the income of YOUR family, not your individual income) o o o o o o o Up to 10.000 € from 10.000 € to 30.000 € from 30.000 € to 50.000 € from 50.000 € to 70.000 € from 70.000 € to 90.000 € over 90.000 € I don’t remember Q26. What is your opinion on the questionnaire and its instructions? o o o o Everything was all clear and understandable The questionnaire was mostly clear and understandable, although some parts should be specified better The questionnaire was unclear and difficult to understand Other(specify)_____________________________________________________________ Thank you very much for your time and collaboration. submit answers and finish 69 Annex III. sampling of countries As the agriculture land use change is the selected policy to protect biodiversity the selection countries is driven by intensity of high intense agriculture in EU. The intensity of agriculture is proxied by the emergy of cropland as provided by JRC. The table below reports the distribution of emergy value in EU. The mean value is 1,793,228 and the 25th percentile is at 476,598 and the 75th at 3,169,611. Table 1A. Crops Emergy stats for EU Average crops emergy Percentiles Smallest 1% 101255 101255 5% 287859 287859 10% 25% 327725 476598 327725 329902 50% 909997 Largest 75% 3169611 3928360 90% 95% 99% 4306819 4433934 5752171 4306819 4433934 5752171 Member States: 24 Mean 1793228 Std. Dev. 1674682 Variance Skewness Kurtosis 2.80E+12 0.85591 2.428442 The countries were selected to represented low and high emergy cropland areas. The sampled countries are CZ, DE, IE and IT. The statistics of the selected countries are as follow: Table 2A. Emergy crops for the sampled MSs Average Crops emergy Low emergy Similar states CZ 423630.3 Yes PT,BG,RO, EL,SI, LT DE 2014560 No EE,AT,FI,UK,LV IE 3928360 No SE,DK,NL IT 646876.1 Yes HU,PL,SK,ES,BL,FR In these countries we then followed a quota sampling scheme to select respondents who represent countries census data on gender and age. A further geographical sampling was adopted to capture the intense agriculture distribution as in Fig. 2. The emergy indicator is reported as red when the man-made inputs are intense. 70 Figure 1A. Geographical distribution of intense agriculture in the main macro-regions The distribution of respondents reflects the countries census data and the quote are distributes as follow: 71 Table 3A. Sampling scheme for socio-economic characteristics in selected MSs CZ DE IE IT M_18_29 8.19% 8.76% 9.70% 7.56% M_30_39 9.18% 7.70% 9.75% 7.15% M_40_49 9.93% 7.81% 9.66% 9.24% M_50_59 7.67% 9.70% 7.94% 8.89% M_60_75 10.84% 9.97% 9.02% 10.27% M_75_plus 2.92% 4.98% 2.94% 4.99% F_18_29 7.82% 8.01% 9.53% 7.05% F_30_39 8.63% 7.40% 10.44% 7.07% F_40_49 9.42% 7.68% 9.76% 9.36% F_50_59 7.55% 9.61% 8.09% 9.29% F_60_75 12.69% 10.89% 9.22% 11.40% 5.18% 7.50% 3.95% 7.72% F_75_plus Table 4A. Sampling scheme for regions in selected MSs DE CZ IE IT Praha 12.31% Baden-Württemberg 13.23% Norther and Western 17.64% North East 19.25% Strední Cechy 12.46% Bayern 15.69% Southern 33.29% NorthWest 26.70% Jihozápad 11.54% Berlin 4.37% Eastern and Middland 49.07% Centre 20.04% Severozápad 10.50% Brandenburg 3.06% Sardinia Severovýchod 14.23% Bremen 0.83% South Jihovýchod 15.99% Hamburg 2.21% Strední Morava 11.53% Hessen 7.51% Moravskoslezsko 11.44% MecklenburgVorpommern 1.98% Niedersachsen Nordrhein-Westfalen 9.58% 21.55% Rheinland-Pfalz 4.93% Saarland 1.22% Sachsen 4.98% Sachsen-Anhalt 2.75% Schleswig-Holstein 3.49% Thüringen 2.64% 72 2.80% 31.21% GETTING IN TOUCH WITH THE EU In person All over the European Union there are hundreds of Europe Direct information centres. You can find the address of the centre nearest you at: https://europa.eu/european-union/contact_en On the phone or by email Europe Direct is a service that answers your questions about the European Union. 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