Valuation of bundles of ecosystem services associated to the Cévennes landscapes 

Get Complete Project Material File(s) Now! »

Evidence of the mainstreaming of the ecosystem services concept

As pointed out in the introduction, much effort is undertaken to inform decision-making about the management of ES by using economic valuation. Therefore, it is necessary to assess whether the concept of ES is well known and applied among decision-makers and practitioners. This so-called mainstreaming helps to transmit theoretical evidence into concrete action and to consider impacts of policies on ecosystem services ([DPG+09], [MHP+13]). Adoption of the concept is expected to improve understanding for environmental problems and to promote sustainable solutions within local decision-making [PMR16]. In this section we focus on how far the mainstreaming of the ES Concept got and what advantages and caveats exists (i) in policy design and public administration and (ii) in jurisdiction and law.

Use within policy and public administration

The following section presents drawbacks from studies dealing with the implementation of ES within policy and administration by using either content analysis of policy documents or surveys of practitioners. Table 1.1 presents significant articles assessing the integration of the ES concept. These articles were selected from a web search of Google Scholar using the terms « Ecosystem Services » coupled with either « Content Analysis » or « policy documents ». They were complemented by papers appearing in the references of our first sample. The resulting selection covers a wide range of different policy contexts, study areas and administrative scales, ranging from local to international. Integration of ES is mostly measured by the degree of explicitness (using the term « Ecosystem Services » or near substitutes or no explicit term at all) or by the degree of detail used to describe the concept or specific services (e.g. [NHO17]). They provide consistent results on how ES are included and where policy integration of the concept is confronted with barriers. We identified five main issues which are addressed individually.

Explicitness and implicitness

Nearly all papers of table 1.1 report that ES are increasingly mentioned within policy context ([BSP+17]; [RVFGA18]), but often implicitly (referring generally to the role and function without using a specific term). Rarely, these benefits are called explicitly « Ecosystem Services » (or equivalently), or specific ecosystem services (e.g. maintenance of wildlife habitat, flood control, etc.) are mentioned. This lack of explicitness can be seen as an indicator for existing gaps in understanding within administrations and the need for clear definitions in order to diminish ambiguity ([HFM+15]; [MMPK+16]). Accordingly, possible trade-offs among different services are more difficult to address [RVFGA18]. These findings confirm the analysis of Kettunen et al (2014) that a solid conceptual base within policy spheres and administration exists and is growing, but not yet translated into proactive outcomes.

Interdisciplinarity and transdisciplinarity

Regarding the broad range of ways in which human well-being is affected by ES, it becomes important to resolve trade-offs and synergies across different sections and domains [BSP+17]. Two concepts have to be distinguished: interdisciplinarity, where collaboration among several disciplines brings multiple views on the same issue, and transdisciplinarity that aims not only to cooperate, but to create a « common knowledge » among disciplines. This systematic use of the ES concept is still limited, complicating cooperation among different administrative departments and disciplines ([HBC14]; [PBWC18]). This finding is particularly noteworthy, given that interdisciplinary communication, a common ground for dialogue among stakeholders, and awareness-rising are seen as important merits of the ES concept ([HBC14]; [MMPK+16]; [LC18]). Possible solutions to foster this broader view could be to involve more stakeholders and strengthen transdisciplinary capacities within administrations. Research agencies and universities can play a role as knowledge brokers, notably for establishing practicable indicators and common knowledge ([HFM+15]; [DSC+18]; [RVFGA18]).

Differences among ecosystem services

The consideration of ES depends on their nature: most studies examining different types find a higher recognition of regulating services than it is the case for provisioning or nonmaterial services ([MMPK+16]; [RVFGA18]). The latter are mostly considered in the context of tourism and recreation ([BSP+17]; [NHO17]). The same difference is identified by Lam and Conway (2018) but on different scales. At municipal level, recreation is the most frequently mentioned service whereas at regional scale regulating services appear more often. Only Hansen et al (2015) find no disparity among ES types within documents. The limited integration of nonmaterial services other than recreation and tourism is possibly due to the relatively low coverage of content analyses assessing other domains than those already closely related to ES (e.g. flood risk management, land use planning, urban planning, economic affairs, etc.) Interestingly, this retraces the same lack of less tangible nonmaterial services as is found in the ESV literature in general (see [CVLU19]). The frequently observed integration of regulating services corresponds to an adaptation of the concept of ecological function ([PYCL+17]): they represent ecological processes necessary for an ecosystem to be in condition to provide services. For economic valuation, this stimulates the debate about the correct specification of intermediate and final services ([JR11]; [PYCL+17]).

Spatial scales

As seen above, different contexts and spatial scales are important to understand the use of the ES concept within policy documents and possible barriers for their implementation [CMKtB18]. Given the diverse valuation methods, many studies are not easily adaptable to local contexts or to be aggregated or disaggregated in order to fit into administrative borders of practitioners ([HBC14]; [DSC+18]). Moreover, this limits the possibility to link ES to their service providing units (SPU), which would be necessary for environmental-economic accounting ([LC18];[Eur13]). The inclusion of ES within the policy sphere depends on the spatial scale as well. In interviews with practitioners, the latter seem to infer particular importance to regional and national scales [PBWC18]. Content analysis confirms this finding, by acknowledging that regional and national scales are connecting local to global management and help to enforce a top-down application of higher-scaled policy goals ([MMPK+16]; [RVFGA18]). For EU policy, this top-down application depends on the type of policy: regulations are difficult to be reframed and adapted to local and regional environmental contexts [BSP+17] whereas directives depend on the necessary uptake at national and regional level [KtBUS14]. However, regulations from upper-level institutions have been proven to be a possible motivation to adopt the ES concept in practice [Rau18].

Conceptual and operational integration

In order to judge whether the ES concept is represented conceptually in long-term visions or operationally in short-term binding policy, Kettunen et al (2014) suggest distinguishing more precisely between conceptual and operational integration. For Scotland and the EU, policy documents provide a good level of conceptual integration of ES, which is important for general communication and information, notably for NGOs and local officials. Both cases show lacks of operational integration, which is needed to guide decision-makers and administrations ([HBC14]; [BSP+17]; [CMKtB18]). This discrepancy is underlined by Rozas-Vasquez et al (2018) and Nordin et al (2017) who detect no ES completely covered over the whole process from conceptual description to concrete policy measures in environmental planning documents in Chile and Sweden. Hence, the holistic development of alternatives to trade off within ES management is inhibited. Only in Ontario (Canada) it is found that the ES concept is explicitly used to motivate and design policy action [LC18].
The efforts to preserve ES are not only seen as a manner of public decision-making and legislature. Moreover, the enforcement of environmental law is another important step and can help to empower marginalized parts of the population often affected by environmental degradation [KGBZ13]. A general problem is the public good character of ES. Naturally, not having an owner whose rights have been affected, a judgment on their legal distribution, provision or conservation is a source of complications. Furthermore, the intrinsic value attributed to ES cannot be taken into account [Bea18]. This change if nature is defined as a legal subject with its own rights, as it is the case in a few countries e.g. in Bolivia, Ecuador, India or New Zealand [SB18]. Several works reviewed and explained the place of the ES concept and valuation techniques within legal enforcement. First, the implicitness of ES in environmental law is underlined, confirming the analysis for policy documents in section 1.2.1 [Mau17]. The concept is mostly used in so-called soft law (long-term visions, guidelines) than in enforceable law [ML17]. Sharon et al (2018) find 67 cases in common law in English-speaking countries where specific ES are explicitly mentioned, but rather as help to interpret specific cases than as a whole concept. In France, ES are explicitly defined in the National Environmental Law (« Code de l’environnement ») since 2004 in order to account for damages on protected natural resources [Dou18]. Secondly, the use of ES depends on the scale. Given that environmental and urban planning is more often treated by courts at regional or even municipal level, most appearances of the concept are found at this stage rather than in higher-level courts [SFR+18]. This raises the question whether the complexity and interconnectedness of ES, going beyond administrative borders, can be fully assessed. It is proposed to face this problem by shifting the focus on the habitats and ecosystems, as within the legislation on the Natura 2000 Network [Fèv18]. The underlying assumption is that the protection of ES can be achieved by a systemic approach by protecting the environmental « functionality » at the origin of each single ES [Fèv18]. At this point, the discussion on the concept of ecological functions [PHY16] re-emerges. Thirdly, the economically based interpretation of ecosystem services is reflected in most contexts. Several authors report an increasing use of the concepts in combination with questions of valuation [SFR+18] or production function based modeling needed for liability litigation restoration measures in the US [JD18]. In the latter case, authors take the case of using choice experiments to judge the adequacy of restoration to offset losses in other habitats. This expands the use of stated preference methods from purely economic applications such as the determination of liability from the Exxon Valdez oil spill [CMH+03] to other domains. Furthermore, the current focus on economic benefits from ES constitutes a reductionist frame and risks considering less tangible benefits as providing less usefulness for jurisprudence [Dou18]. Here, the inclusion of the expertise of multiple actors such as proposed in most bottom-up implementation frameworks (see section 1.5) might be helpful. Although literature provides evidence for influences and needs for the ES concept in jurisdiction, its concrete use is still limited. Mauerhofer and Laza (2017) show in the analysis of expert questionnaires and interviews that the most advanced use of ES in the European Union is within the Invasive Species Act. Meanwhile, the aim of biodiversity is judged to constitute a more important guideline to enforce conservation efforts than associated ES. The use of the ES concept itself will not necessarily change jurisprudence, but can support actual applications and measurement, and motivate additional advocacy from stakeholders, producers and beneficiaries [Dou18].

Decision-making support by science-policy interfaces

As stated in the introduction, science-policy interfaces (SPI) bring together different actors and scientists from different disciplines and administrative levels. By enhancing effective bidirectional transfer of information between knowledge production and decision-making, they are a key element for ES mainstreaming ([SNT+14], [YWS+14]). Before discussing their role in the process of implementation in section 1.5, we introduce a brief overview of the SPI literature and link it the ES concept. More and more publications present empirical experiences from discussions of peoples involved in these fora (e.g. [HGV+13]; [RMT+15]; [OJT+18]). We will then especially focus on how Economic Valuation can contribute to SPIs by benefit transfer (section 1.3.2). Meanwhile, a close relationship between valuation conducting researchers and decision-making (through SPIs) can provoke suspicion about the validity of their work [BB19]. This advocacy-credibility trade-off is addressed at the end of this section (see section 1.3.3).

Challenges and roles of science-policy interfaces

Most difficulties to include environmental findings in policy-making stem from three kinds of issues [CM00]. Firstly, an institutional fit problem arises if the management in charge does not operate at the geophysical scale of the environmental issue. Secondly, a scale discordance problem exists when the scale of information assessment is not meeting the informational needs of decision-making. Finally, Cash and Moser (2000) identify cross-scale dynamics as spatial or temporal interdependencies demanding collaboration of institutions at several levels. In order to address these problems, a unidirectional « pipeline model » is proposed: Information is transferred to the highest possible scale, expecting that the institution in charge is powerful enough to compel institutions at lower scales to tackle the problem appropriately. A second information transfer model to support a policy goal formation is science-policy interfaces, as defined in the introduction. In this regard, broader intergovernmental platforms such as the MEA (Millennium Ecosystem Assessment), TEEB or the IPBES (Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services) are seen as typical examples of SPIs because they combine different actors from different disciplines and from different administrative levels ([NTW+13], [CM00]). SPIs can serve as a knowledge broker between science, decision-makers and practitioners to solve cross-disciplinary solutions ([TTF05], [LWB+12]). This is especially useful for boundary objects such as ES, if the limits of existing structures and their current functioning can slow down effective solutions ([Tho04]; [Mob10]). Another main objective consists of including decision-making into research agendas to enhance relevance and easy use of research outcomes by practitioners ([PGR16], [OJT+18], [PSVS+18]). Several concrete actions are proposed to strengthen SPIs with most authors emphasizing to focus on processes and projects instead of structures ([NTW+13]; [SNT+14]; [YWS+14]). First, incentives for policy and science have to be created, such as citation measures including gray literature for scientists. This would create appreciation for SPI work in general ([Gus01]; [SNT+14]; [YWS+14]). Secondly, given that decision-making is often relegated from higher-ordered institutions, approaches like compatibility with superior regulations and possible affordance have to be taken into account [Gus01]. This would increase relevance for lower-level decision-makers and support faster implementation at higher scales, as possible problems are anticipated and accounted for in the research stage. Thirdly, if results are accompanied by the presentation of several policy options, greater scope for negotiation within the policy sector is created ([Wat05]; [PGR16]) and decision-makers are forced to demand and apply a more « holistic understanding » in order to discriminate among alternatives. It is argued that these decisions would be more « robust » in the long run (Hirsch and Luzadis, 2013). Others suggest changing publishing mechanisms by more interdisciplinary and inter-domain co-authorship and cross-reviewing between scientists, decision-makers and practitioners ([CK12]; [YWS+14]). However, the effective inclusion of SPIs needs in research agendas also provoke a « quality-feasibility trade-off » [OJT+18] with respect to diverging time horizons between science and decision-making. For example, the demand for quick results by managers limits the use of time-consuming in-depth analyses and participative approaches by researchers [SNT+14]. This contradicts recommendations of the SPI literature which underlines the importance of participation, deliberation and stakeholder integration as a way to include local knowledge in the decision-making and implementation process ([SV06]; [PGR16]; [LMD+19]). Possible long-lasting conflicts harming an effective application could be anticipated and mitigated ([Die13]; [Rog13]). This would favor better outcomes in shorter time horizons. Linked to SPIs, the ES framework serves as an important boundary object that provides a base for collaborative work and creates mutual understanding among working domains and scientific disciplines ([Gus01]; [YWS+14]; [SHE+18]). Distinctions could be made among different types of ES: whereas provisioning and regulating services are relatively standardized among disciplines, nonmaterial services still deliver a flexible framework for different interpretations and discussions ([Ree08]; [SHE+18]). It is argued that standardization is beneficial for successful interdisciplinary collaboration and effective implementation. In the case of non-material benefits, different notions and understandings still exist. Here, pressure for standardization could overweight the majority point of view and marginalize minority positions ([HCD16]; [SHE+18]).

READ  ZnO as a semiconductor

Informational use of ecosystem services valuation: benefit transfer

The aim to use economic valuations to inform decision-makers and to stress the importance of ES for human well-being is the most common reason to conduct valuation studies and referred to as its main purpose ([Sal11]; [CGB+12]; [LRB+13b]; [RKP+14]). However, research gaps in combining findings from decision-making theory with ESV are still highlighted [OJT+18]. Therefore, SPIs are mandatory to connect experts in each of these domains and summarize the most important facts for decision-makers and the general public [YWS+14]. To support the information transfer among different spatial scales and applications, benefit transfer is seen as a potentially practical and useful approach ([JADS17]; [NSM+18]). The problems of transferring benefits arise from the scarcity of data from the initial valuations ([Plu09]; [SDE+11]) but also from the fact that the relationship between the dependent and independent variables may not be the same between the study and policy sites [RLKC15]. Studies aiming to reproduce or confirm these valuations in order to create a more solid, comparable database as a primary source, are not relevant for publication as they cannot – per se – provide methodological innovations ([EWM+12]; [OJT+18]). This also harms the determination of reliance and validity of initial valuations [BB19]. Other caveats are the inclusion of a holistic set of values into benefit transfers ([SV06]; [ABM12]; [CGB+12]; [CAC+16]) and the ecological comparability of spatially different study areas [SS10]. In summary, SPIs are seen as powerful ways to support constructive dialogue and collaboration. Valuations can serve as a way to present economic consequences of several policy options and inform decision-makers. Although several authors mentioned the role of SPI for the implementation stage in general, a methodological classification of implementation contexts is still missing. Implementation research can deliver these theoretical insights.

Researchers and advocacy for ES management

The willingness to mainstream ES in general and to make individual research findings relevant to decision-making in particular, raises the question of how much advocacy for own research interests is necessary or wanted [LM14]. In their key article, [LRB+13b] distinguished among three different ways of how ESV is actually used. First, decision-makers need valuations to trade off different alternatives and finally decide on one of them to be implemented (« decisive »). It retraces how economists aim to deliver relevant cost-benefit analyses. Secondly, in a « technical » setting, valuations are demanded to deliver a specific monetary value which is used for compensations or liabilities (e.g. [JD18]). Finally, « informative » valuations are used to demonstrate value (e.g. [CdDG+97]; [CFC+12]; [GBB13]) and to convince policy-makers to act in favor of the maintenance of ES ([Rau18]; [SP18]). Especially with the last point, researchers are entering a trade-off between credibility and relevance ([SNT+14]; [PGR16]), if they focus solely on communicating results they think of being of most concern to the public [Wat05]. The risk would be to evolve in a direction of either « politicization of science » or « scientification of politics » ([Gus01]; [FR94]; [Lin13]). To sustain credibility, researchers are demanded to provide information on their own involvement in NGOs, which could influence the presentation and communication of their findings [PVES17]. Given the biases introduced by the choice of valuation method [JMLB+18] of associated aggregation tools [MM18] or the way results of ES studies are presented [WEG17], this transparency appears to be important. In fact, further progress by adopting best practice guidelines in the valuation literature (e.g. [JBA+17]) is needed to increase the reliability of results and the validity of approaches [BB19]. Test-retest experiments, open access to (meta-) data and a general willingness to publish reproducibility studies without methodological innovations are possible steps to strengthen credibility ([OJT+18], [BB19]). Figure 1.2 (section 1.5) illustrates our analysis about how a better implementation process can be achieved. It contains the same elements as figure 1.1, with the same relations as dotted black arrows. It is turned by 90 degrees so that the key elements of our analysis are in the middle: The consideration of an independent implementation stage leads us to propose a complementary way to enhance decision-making: by especially accounting for caveats in the implementation phase (1), decision-makers might be encouraged to engage in favor of ambitious environmental policy (2). This consideration of implementation needs is supported by SPIs as pointed out in this section. The following section describes what basic approaches have been proposed in implementation research literature and what consequences this could have on the decisions.

Implementation research

In the previous section, we focused on how the ES concept is mainstreamed in decision-making and how it is supported by SPIs. As discussed in the introduction, these steps have to be complemented by an analysis of how these public decisions can be implemented by responsible practitioners. This section provides a short overview about Implementation Research and its main theories and findings. Implementation research is concerned with finding methods that describe and study policy implementation [Mat95]. The term « implementation » can be defined as the « Development between the establishment of an apparent intention on the part of government to do something, or to stop doing something, and the ultimate impact in the world of action » ([OJ00], p.266). A first review summarizing related literature was provided by Sabatier (1986) and O’Toole (1986). They both divide implementation into two different natures. First, a top-down approach, where a policy goal is defined on a large scale by decision-makers who aim to implement this policy on a more local scale. Secondly, if a policy goal is arising within in a local context and it is tried to create a network to communicate this goal to higher scales, implementation is realized in a bottom-up context. Both approaches will be analyzed in the following two subsections. Historically, after initial theoretical reflections in the 1970s, implementation research became popular in the 1980s with several models dealing with the top-down/bottom-up controversy [OJ00]. Whereas more and more variables influencing policy implementation in one of the two contexts were determined, research focused on the provision of parsimony within the framework (e.g. conflict-ambiguity model in [Mat95]). Actually, two main research topics can be distinguished ([Mat95]; [OJ00]): the analysis of policy goals within a top-down or bottom-up approach, and the proposition of a new inclusive framework. We want to contribute to this last point, showing how transdisciplinary work on ESV relates to the traditional framework.

Table of contents :

I. Theoretical part 
1. Literature review: Implementation context and science-policy interfaces: Implications for the economic valuation of ecosystem services 
1.1. Introduction
1.2. Evidence of the mainstreaming of the ecosystem services concept
1.2.1. Use within policy and public administration
1.2.2. Ecosystem services concept in the legal system
1.3. Decision-making support by science-policy interfaces
1.3.1. Challenges and roles of science-policy interfaces
1.3.2. Informational use of ecosystem services valuation: benefit transfer
1.3.3. Researchers and advocacy for ES management
1.4. Implementation research
1.4.1. The top-down approach
1.4.2. The bottom-up approach
1.4.3. Complementary approaches: transdisciplinary research within sciencepolicy interfaces
1.5. Policy implementation and valuation methods: empirical evidence
1.5.1. The bottom-up case
1.5.2. The top-down case
1.5.3. Complementarity of the valuation approaches for transdisciplinary implementation by science-policy interfaces
1.6. Conclusion
II. Empirical part 
2. Design, preparation and application of a discrete choice experiment – Best practice and methodology for the valuation of the Cévennes landscapes 
2.1. Introduction
2.2. Study area: the Cévennes
2.2.1. Landscape
2.2.2. Demography
2.2.3. Cultural heritage
2.2.4. Impact of the Cévennes National Park
2.2.5. ES and interrelations within the Cévennes
2.2.6. Implications for our study
2.3. Focus groups work
2.3.1. Best practice advices for focus group work
2.3.2. Application of focus groups in our experiment
2.4. Principles of discrete choice experiments and experimental design
2.4.1. Introduction to discrete choice experiments
2.4.2. Experimental design
2.4.3. Implications for our study
2.5. The econometrics of discrete choice experiments
2.5.1. Overview
2.5.2. Logit model
2.5.3. Generalized extreme value models
2.5.4. Mixed logit
2.6. Factorial analysis and cluster analysis
2.6.1. Principal component analysis
2.6.2. Single correspondence analysis
2.6.3. Multiple correspondence analysis
2.6.4. Classification
2.7. The final questionnaire
2.7.1. Introduction and information
2.7.2. DCE and follow-up questions
2.7.3. Indicator statements
2.7.4. Socio-cultural characteristics
2.8. Implementation of the study
2.8.1. Logistics
2.8.2. Selection of participants
2.9. Descriptive statistics and representativeness
2.9.1. Structure of the data
2.9.2. Representativeness
2.10. Conclusion
3. Valuation of bundles of ecosystem services associated to the Cévennes landscapes 
3.1. Introduction
3.2. Landscapes, bundles of ecosystem services and their valuation by discrete choice experiments
3.2.1. Landscapes and their relation to ecosystem services
3.2.2. The concept of ecosystem service bundles
3.2.3. Inferences for the valuation of ecosystem services
3.3. Results of our experiment
3.3.1. General remarks
3.3.2. Analysis of the choice experiment data by multinomial logit
3.3.3. Analysis of the choice experiment data by mixed logit
3.3.4. Factor and cluster analysis
3.4. Discussion
3.4.1. Results of the DCE approach
3.4.2. What kind of landscape is supported and why – insights from the cluster analysis
3.4.3. Analysis of the willingness to pay
3.4.4. Bundles representation
3.5. Conclusion
4. The impact of deliberation on valuation 
4.1. Introduction
4.2. Non-material ES
4.2.1. Complementary value approaches
4.2.2. Non-material ES – or cultural ES…?
4.2.3. Approaches to assess and value non-material ES
4.2.4. Empirical findings in assessments of non-material ES
4.2.5. Role of non-material ES for decision making
4.3. Deliberative approaches to the valuation of ES
4.3.1. Definition and theoretical foundations of deliberations
4.3.2. Complementary approaches and requirements for study design
4.3.3. Expected advantages of deliberative approaches
4.3.4. Occurring biases and other inconveniences
4.3.5. Empirical findings
4.3.6. Importance for decision making
4.4. Implications for valuation
4.4.1. Synergies from deliberations and account for non-material ES
4.4.2. Account for non-material ES in valuation
4.4.3. Implementing deliberative settings in valuations
4.4.4. Positioning of our study
4.5. Analysis
4.5.1. Econometric effects of deliberation
4.5.2. Cluster analysis
4.5.3. Impact of deliberation on consideration of indicator statements
4.5.4. Analysis of transcribed discussions
4.5.5. Impact of deliberation on the quality of valuation
4.6. Discussion
4.6.1. Summary
4.6.2. Deliberation and the effect of expertise
4.6.3. Significant differences and sample size problems
4.6.4. Non-material ES and deliberations
4.6.5. Preference economization and preference moralization
4.6.6. Quality of valuation
4.6.7. What can deliberation bring to the economic valuation of ES?
4.7. Conclusion
A. Appendix of chapter 2
A.1. Ngene Code
A.2. Final questionnaire
A.3. Pictures from the implementation of the study
A.4. Example of a poster distributed around a municipality in the Cévennes
A.5. List of municipalities that have been part of our study area in the Cévennes
A.6. Choice of representative income statistics for the Cévennes sample
B. Appendix of chapter 3
B.1. Output tables
B.1.1. MNL models Cévennes sample
B.1.2. MNL models Montpellier sample
B.1.3. MXL models Cévennes sample
B.1.4. MXL models Montpellier sample
B.2. Factor analysis and cluster analysis
C. Appendix of chapter 4
C.1. Output tables
C.2. Part of a transcribed discussion


Related Posts